Let’s get into it.

Alpha from Mandelbrotian Prices

The most accurate option pricing model turned out not to be the most profitable one.

The authors ran five pricing models against S&P 500 option chains, then turned each into a simple trading rule: buy when the model says the market price is too low, sell when it says the market price is too high. On raw accuracy, classical Black-Scholes held up remarkably well, landing closest to actual prices across most medium and long dated contracts, which is a little annoying given how many of its assumptions are known to be false. But when those same models became strategies, Black-Scholes finished fifth out of nine. The winner was Mandelbrot's multifractal model, which treats prices as rough and self-similar rather than smoothly random, and which more than tripled starting capital in the backtest.

Figure 1: Every model-based strategy roughly tripled its starting capital. Buy-and-hold, momentum, and mean reversion finished essentially flat. Note that Black-Scholes, the most accurate pricer in most categories, sits mid-pack here.

The paper says it plainly, noting that “accuracy in option price calculation does not always translate” into trading results. The catch is that data limits confined the backtest to a single date in 2023, so read this as proof of concept, not verified edge. The broader lesson still holds, that the model that best explains yesterday's prices is not automatically the one that finds tomorrow's mispricings.

Bhattacharyya, Ritabrata and Goh, Zhi Hwee and Korovedzai, Rudairo Orpah and Chen, Jun-Han, Alpha Generation using Option Trading Strategies based on Modeling Options Prices considering Mandelbrotian Movement of Prices (July 21, 2026). Available at SSRN: https://ssrn.com/abstract=7154800 or http://dx.doi.org/10.2139/ssrn.7154800

ETF (Mis)pricing: Blame the Inventory

ETF prices drift from the value of their underlying holdings largely because the firms responsible for keeping the two aligned run into inventory limits, not because the underlying assets are broken.

Authorized Participants (the large trading firms permitted to create and redeem ETF shares) are supposed to arbitrage away any gap between an ETF's market price and the value of its basket. Using FCA regulatory data covering 128 UK listed ETFs from 2018 to 2022, the authors observe each firm's daily inventory directly for the first time, and the pattern is consistent. When an AP holds more ETF shares than it wants, it quotes cheaper prices to offload them, and the fund slips to a discount. The adjustment shows up almost entirely in the ETF price, while NAV barely responds.

Figure 2: Corporate bond ETFs, February to May 2020. Median price to NAV gap (black), spread across funds (bands), Fed and BoE interventions (vertical lines). The gap closes as policy eases dealer funding pressure.

The effect also splits by asset class, with equity ETFs more sensitive to inventory and bond ETFs more sensitive to unexpected order flow. APs sometimes take directional positions instead of correcting gaps immediately, which undercuts the assumption that arbitrage is instantaneous. For investors, this reframes premiums and discounts (bond ETFs moved over 5% from NAV in March 2020) as a read on dealer capacity rather than fund quality, since inventory practices “can exacerbate mispricing, particularly during periods of stress.”

Kraus, Wladimir and Kirilenko, Andrei A. and Linton, Oliver B. and Xiao, Mingmei, ETF (Mis)Pricing (May 25, 2025). Available at SSRN: https://ssrn.com/abstract=7143458 or http://dx.doi.org/10.2139/ssrn.7143458

Climate Attention and the Bond Market

Public curiosity about global warming, measured by Google searches, predicts higher returns on U.S. Treasury bonds over the following year.

The authors built a monthly index of worldwide searches for “global warming” and test whether it forecasts the excess return Treasuries earn over cash. It does, and not marginally. Higher search interest reliably precedes higher bond returns across maturities from two years all the way out to twenty four, and the relationship survives controls for the yield curve, the standard bond forecasting factors, and five separate uncertainty measures.

More impressively, the signal holds up out of sample, where most predictors quietly die, cutting forecast errors by roughly 15% against the historical average benchmark. The mechanism is less exotic than it sounds. Rising climate attention travels with expectations of a softer economy (weaker production, higher unemployment) and a tilt toward safer assets, so investors mark down the expected path of policy rates. The effect sits almost entirely in expected short rates rather than term premia, and it barely existed before the Paris Agreement. For investors, that suggests attention data can proxy for shifts in macro expectations before conventional indicators register them.

Yu, Deshui and Tang, Jiachen and Li, Luyang and Zhou, Mingtao, Climate Attention and Treasury Bond Risk Premia. Available at SSRN: https://ssrn.com/abstract=7203448 or http://dx.doi.org/10.2139/ssrn.7203448

Trading on Language Models Under Market Frictions

Large language models beat word-counting sentiment on financial news, but their edge lives almost entirely in the sentences where meaning depends on structure rather than vocabulary.

The authors push nearly a million firm specific news stories through six sentiment engines, from the classic Loughran McDonald word list to an 8 billion parameter LLaMA-3, then force every signal through the frictions an actual desk faces (trades happen only after the news was genuinely observable, positions pay spreads and costs, and no trade can exceed a tenth of a stock's daily volume). The word list barely beats a coin flip and loses money once turnover is paid for. The decoder models survive, with LLaMA-3 posting the strongest net long short performance in a test window chosen to sit after its own training cutoff.

On plainly worded news, a 1980s word list trails an 8 billion parameter model by 17 points. On contrastive clauses (revenue beat, guidance cut) the gap more than doubles to 37, and the word list lands below a coin flip. Sample of 1,200 test articles, coded by two annotators. Chart: Alpha in Academia. Data: Table 13, MFAST working paper (SSRN 7213792).

The more useful result is where that edge comes from. LLaMA-3's advantage over the dictionary is smallest on plainly worded headlines and roughly doubles on negation, contrastive clauses, and forward looking guidance, exactly the sentences where a revenue beat sits next to a guidance cut. It also widens in less liquid names. The lesson for investors is that reading comprehension, not model size, is the thing being sold, and that a signal only counts once costs and capacity are in the room.

Kirtac, Kemal, Financial Language Models as Applied Artificial Intelligence Systems for News-Based Trading under Market Frictions. Available at SSRN: https://ssrn.com/abstract=7213792 or http://dx.doi.org/10.2139/ssrn.7213792

This week for paid subscribers

Paid subscribers are watching the equity correlation matrix collapse onto a single factor across the 2008 and 2020 crashes, then testing whether that same fragility measure warns of a crash in advance. It forecasts the crisis that built up endogenously and is blind to the exogenous one, with the first sustained signal crossing fifteen months before Lehman. Python backtest code included.

Alpha in Academia

When correlations concentrate

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4 days ago · 10 likes · Alpha in Academia

Disclaimer: The content provided in this newsletter, "Alpha in Academia," is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

Today we will take a look at how when markets crash, the correlation matrix of the equity cross section collapses. The market factor absorbs a rising share of total variance and the effective number of independent factors falls sharply. This spectral concentration replicates cleanly across both the 2007-08 financial crisis and the 2020 Covid crash. But the same signal, run as a causal early-warning indicator, only forecasts the crisis that built up endogenously, and the exogenous shock is invisible to it in advance.

Let’s dive right in.

Introduction

Igor Halperin’s July 2026 paper introduces an approach called Observable Matrix Dynamics that tracks the equity cross section through the trajectory of a fixed-size distance matrix and its spectrum. We run two fixed-size matrix observables across the crisis decades using the current S&P 500 universe with daily adjusted closes, and the central empirical finding is that the correlation spectrum collapses onto the market factor at the 2008 and 2020 onsets.

A separate section tests whether these fragility signals actually forecast the crash they concentrate on, and finds that only the endogenously building 2008 crisis is predictable in advance.

“An endogenous fragility measure can forecast a crisis that grows out of the correlation structure itself, and cannot forecast an exogenous shock such as a pandemic, or a dispersed decorrelated unwind.”

Data & Methodology

The setup is a rolling Pearson correlation matrix on the S&P 500 cross section over a 504-day window, from which we extract the eigenvalues and read two summary statistics. The first is the market-factor share, the largest eigenvalue divided by the sum of all eigenvalues, which measures how much of total return variance is absorbed by the single largest common factor. The second is the participation ratio, defined as the square of the sum of eigenvalues divided by the sum of squared eigenvalues, which counts the effective number of independent factors. Both are standard diagnostics from the random matrix theory of correlation matrices, and both are known to move sharply at crisis onsets.

We compute these statistics across two crisis periods and average them over the calm year before onset and the first two months of the crash. The universe is current S&P 500 constituents with full history back to each period’s two-year pre-roll, filtered to 349 names for the 2007-08 window and 353 names for the Covid window. This universe is survivorship-biased by construction, which means 2008-era failures like Lehman, Bear Stearns, and Washington Mutual are absent from our sample. If anything this should understate the correlation stress at the 2008 onset, since the survivors are disproportionately the firms that made it through the crisis intact.

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If you have been reading Alpha in Academia for a while, you will know that the Thursday posts tend to go a little further than the weekly paper summaries. Sometimes I implement a strategy. Sometimes I replicate a result, work through a model, or find that an interesting idea does not quite survive contact with the data.

This page is where all of that work lives. I have also included the research companions I have available. Depending on the investigation, that may mean code, a notebook, data, tests, or reference results. They are there if you want to rerun the work, change an assumption, or take the analysis in another direction.

If you find something I missed, get a different result, or have an idea you would like me to look at next, please let me know.

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Let’s get into it.

The Stock Market Never Learns

Halperin borrows a tool from AI research to watch how the entire stock market’s correlation structure moves over time, and finds that unlike a neural network, the market never settles into a stable pattern, even decades after a crisis passes.

The core idea here is clever: treat the whole S&P 500 like a machine learning model in training, and watch whether its “internal representation” (the correlation structure between all 500 stocks) ever stabilizes the way a trained neural network’s does. It never does. Across three crises (the 2001 dot-com bust, 2008, and 2020), the market’s correlation geometry collapses in dimension during a crash, as everyone starts moving together, then partially unwinds afterward, but never settles into the kind of clean, learned structure you’d see in a model that’s finished training. Separately, Halperin ranks stocks by relative performance and volatility each day and tracks those rankings as their own systems. Performance rankings shuffle fast (a week), while volatility rankings are sticky (a month or more), and only the volatility ranking shows a real “arrow of time,” meaning it behaves differently forwards than backwards, flaring hardest at the 2002 and 2008 lows. For investors, the practical takeaway is: the correlation-based fragility signals only gave advance warning for 2008, the slow-building crisis. They were blind to Covid.

As Halperin puts it, the market is best read as an object that a crisis “drives further from equilibrium rather than toward a new one.”

Halperin, Igor, Observable Matrix Dynamics of Stocks (July 20, 2026). Available at SSRN: https://ssrn.com/abstract=7149898 or http://dx.doi.org/10.2139/ssrn.7149898

Why a Yen Funding Rate Half a World Away Moves the Price of U.S. Corporate Debt

Japanese banks fund roughly a quarter of America’s CLO market through yen-dollar swaps, and the cost of that swap, not U.S. credit conditions, is what actually drives pricing at the top of the capital structure.

Here’s the setup that makes this paper fun: Japanese banks, led by the agricultural cooperative bank Norinchukin (nicknamed “the CLO whale”), hold about a quarter of all AAA-rated U.S. collateralized loan obligations. But they don’t fund those purchases with dollars, they fund them by swapping yen into dollars through FX derivatives. That swap has a price, called the cross-currency basis, and it turns out to explain over 60% of the quarterly variation in CLO issuance, more than VIX, credit spreads, or any domestic macro variable the authors threw at it.

The real hook, though, is that this sensitivity isn’t stable. After Japan tightened bank capital rules in 2019, the pass-through from funding costs to AAA spreads roughly tripled, because the shock pushed the price-insensitive whale out and let more rate-sensitive buyers set the margin. That’s a good reminder that “foreign ownership share” is a lazy proxy for exposure. What matters is who’s marginal, and how easily they can walk away.

Huber, Amy and Kundu, Shohini, When Funding Markets Move Credit Markets: Foreign Investors and U.S. CLOs (May 15, 2026). Available at SSRN: https://ssrn.com/abstract=7086758 or http://dx.doi.org/10.2139/ssrn.7086758

Deep Hedging Finds Free Money

A neural network trained to hedge derivatives will, if left alone, quietly convert itself into a leveraged bet on the market’s historical drift rather than an actual hedge.

Deep hedging replaces the classic quant approach of assuming a price model (Black-Scholes, Heston, etc) and deriving a formula, with a network that just learns the best trading policy directly from simulated or historical paths, optimizing for a risk-adjusted objective rather than a textbook Greek. The elegant idea, borrowed from Buehler and coauthors, is that “the price of a derivative is the cost of its hedge,” so if you can learn the optimal hedge, you’ve also learned the fair price. The catch shows up fast: when the author trained this framework on S&P 500 data from 2015 to 2025, the network didn’t learn to hedge options responsibly, it learned to go long the index, sell puts, and mostly ignore the derivative it was supposed to be hedging, because that combination was simply the highest-Sharpe trade over that specific decade.

Figure 2: shows the distribution of trading gains “Under P” (raw historical drift) versus “Under Q” (drift-adjusted, risk-neutral measure), which visually demonstrates how removing the statistical arbitrage collapses the fat right tail into a distribution centered near zero.

The fix involves reweighting the training paths into a new probability measure that strips out the drift, forcing the model to actually hedge instead of quietly rediscovering “stocks go up.” It’s a clean reminder that any backtested strategy, human or machine, can mistake a decade’s tailwind for genuine skill.

Buehler, Hans, (Deep) Learning to Trade II - Deep Hedging, Model Uncertainty, Deep Bellman Hedging (March 18, 2026). Available at SSRN: https://ssrn.com/abstract=7086438

A Machine That Draws Arbitrage-Free Options Markets, and Then Learns to Simulate Them

A weighted sum of Black-Scholes prices, fit with plain linear programming, can capture an entire options market’s shape without ever creating a mathematical arbitrage, and once encoded into just twenty numbers, that shape can be simulated forward in time under a risk-neutral measure.

Building a model of the options market has always meant a tradeoff. Flexible models fit reality poorly, while accurate ones (like local volatility) are painfully hard to compute and calibrate without human babysitting. This paper’s answer, called SANOS, sidesteps the mess entirely by expressing every option price as a weighted mix of simple Black-Scholes prices across a grid of strikes. Because that structure is mathematically guaranteed to avoid static arbitrage, fitting it becomes a fast, off-the-shelf optimization problem rather than an art project, and in testing it priced 91.4% of real SPX options within the bid-ask spread, in well under a second.

Note: Fitted 1000 options within 2 ATM implied volatility standard deviations which had a Vega/sqrtT of at least 0.1% on 2025-05-06 across all 48 expiries from 1D to 657 business days. Options were chosen by closeness to ATM. The model fitted 91.4% of all options within bid/ask. Of those options not fitted the median error is just 21% of half spread.

The authors then compress five years of these fitted surfaces into a 20-number daily state, run PCA to find just five real drivers of surface dynamics, and use that to simulate future markets. As they put it, once set up, “it is fast.” For traders, this means a genuinely tractable way to stress-test strategies against markets that behave like the real thing, without hand-tuning a volatility surface every morning.

Buehler, Hans and Horvath, Blanka and Kratsios, Anastasis, DYSANOS - Generative Dynamic Smooth Arbitrage-free Non-parametric Option Surfaces (Presentation) (July 01, 2026). Available at SSRN: https://ssrn.com/abstract=7047878 or http://dx.doi.org/10.2139/ssrn.7047878

This week for paid subscribers

This week, paid subscribers get full access to a special two-part deep dive on pricing spread options and forecasting correlation when traditional models fail. Part 1 covers the pricing machinery and where standard approximations break down, while Part 2 tackles the hidden risk of correlation instability and how to actually select your parameters. Both posts include complete Python backtest code and historical datasets so you can run the entire pipeline yourself.

Part 1:

Part 2:

Disclaimer: The content provided in this newsletter, “Alpha in Academia,” is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

Yesterday we built three ways to price a spread option and found that the pricing machinery is not the problem. Kirk’s approximation is accurate to a few basis points in the region where it actually gets used.

We ended on the thing that is the problem. A spread option’s price depends on the correlation between its two legs about as much as it depends on either leg’s volatility. But unlike volatility, correlation has no market, no implied surface, and no vocabulary for expressing how unsure you are. It’s a number someone types into a box.

Today: which number, and what the choice costs.

Let’s dive right in.

How Much Does It Actually Move?

The case for treating correlation as a fixed parameter is that it does not wander much. Volatility clusters and spikes, but two things that are economically linked (crude oil and the fuels made from crude oil) ought to stay linked.

Over our sample, the sixty-day rolling correlation between crude and the 2:1 product basket has a full-sample value of 0.677. That single number is the one most likely to end up in the box.

Here is what the rolling estimate actually did.

Figure 1: Sixty-day and 250-day rolling correlation between crude and the product basket, 1986–2026. The dashed line is the full-sample value a model would typically use.

The sixty-day estimate ranges from 0.22 to 0.98, nearly the entire theoretical span. And the departures are not brief excursions around an otherwise stable mean. The rolling correlation sits more than 0.15 away from the full-sample value on 33.7% of all trading days.

Put differently, for a third of the past forty years, the number in the box was wrong by an amount we are about to put a price on.

What That Range Is Worth

A parameter wandering between 0.22 and 0.98 only matters if the price cares. So take our three-month at-the-money crack spread option, hold both volatilities fixed at their forty-year values, and change nothing at all except the correlation.

A 5.6x range. Same strike, same expiry, same volatilities, same underlying. The entire difference is an assumption.

And that is not a point made with implausible extremes. Restricting to the middle of the distribution (5th to 95th percentile of what correlation has actually done), the same option still spans $3.30 to $7.78, a factor of 2.4.

So, which number do you type in? Before answering that, it is worth understanding one property of this correlation, because it turns out to explain more than it looks like it should.

Does Correlation Really Spike in a Crisis?

There is a well-documented result in equity markets that correlations rise in a downturn. Diversification thins out precisely when it is wanted, because in a sell-off individual names stop trading on their own news and start trading on the market’s.

The question is whether that transfers here. A crack spread is not a portfolio of equities. It is one commodity against the fuels refined out of it, and the link between them is a physical production process rather than a shared risk appetite. So it is genuinely unclear, before looking, whether the same pattern should hold.

We can check directly. Split every day in the sample into five buckets by the prevailing crude volatility, from calmest to most stressed, and see what correlation was doing in each.

Correlation rises from calm into the middle of the distribution, which is the equity pattern working as advertised. And then, in the most stressed quintile, where crude volatility averages 61% annualized, three times the calm regime, it falls back to 0.696, below the mid-regime level. The dispersion widens too: the standard deviation of ρ is at its highest, 0.148, exactly where you would want it lowest.

So the shape is a hump, not a ramp. The equity intuition holds through ordinary stress and then inverts in genuine crisis.

The mechanism is not mysterious once you look at the individual events. In a real dislocation crude stops trading on the same information as the products. April 2020 is the cleanest illustration in the sample: crude collapsed on a storage constraint, a mechanical problem about where to physically put barrels, while gasoline and diesel kept tracking actual fuel demand. On the day WTI printed −$36.98, gasoline was still trading comfortably above zero. The legs had decoupled entirely.

Hold onto this finding. It looks like a piece of trivia about crisis behavior, and it turns out to be the reason the standard way of estimating correlation is biased.

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On 20 April 2020, West Texas Intermediate settled at minus $36.98 a barrel as storage at Cushing filled to the brim and long contract holders paid buyers to take physical oil off their hands.

It was reported as a curiosity of storage economics, and it was. But it also quietly broke something. If you were pricing an option on a refining margin that week using the standard toolkit, the model did not give you a bad number. It gave you no number at all.

Today we will look at what you use instead. There are three approaches: one that is exact but narrow, one that is approximate and lives on every commodities desk in the world, and one brute-force method that makes no assumptions at all. Along the way we will find something sharper than any of them.

Let’s dive right in.

The Spread That Runs a Refinery

A refinery is, financially speaking, a machine that converts one commodity into two others. It buys crude oil and sells gasoline and distillate. What it earns is not the price of any of those three things, but the gap between what it sells and what it buys.

The industry has a shorthand for this: the 3:2:1 crack spread. For every three barrels of crude a typical US refinery processes, it yields roughly two barrels of gasoline and one of distillate. So the margin per barrel of crude run is

with one bookkeeping detail. Crude is quoted in dollars per barrel; refined products are quoted in dollars per gallon. Nothing means anything until the products are multiplied by 42.

That number is what a refiner actually earns, and therefore what a refiner actually wants to hedge. Which is why options on it exist, and why we need something to price them with.

All three legs are published daily by the EIA and are free to pull back to 1986: WTI at Cushing for crude, New York Harbor gasoline and heating oil for the products. Requiring all three to print on the same day gives us 10,080 trading days spanning forty years.

Figure 1: The legs and the margin, 1986–2026. Crude and the product basket track each other closely. The gap between them is what the option is written on.

Over the full sample the margin averages $12.11 per barrel, with a median of $7.86, ranging from −$3.72 to $71.74. It has been negative on 5 days out of 10,080, about 0.05% of the time. Which makes sense, since refiners shut down when processing crude loses money.

But notice that it can go negative, and the entire reason why there is a problem with standard pricing methods.

Why Black-Scholes Cannot Price This

The instinct is to treat the crack spread as an asset like any other: Feed it into Black-Scholes, and get on with your day.

However, Black-Scholes assumes the underlying asset is lognormal, which makes the mathematics tractable, but rests on the assumption that prices compound multiplicatively and cannot fall below zero.

A spread is the difference of two lognormals. And the difference of two lognormals is sadly not lognormal. There is no change of variables that makes it so. Thus, no lognormal variable can go below zero. But history shows that our spread has been negative before. Because you can’t take the logarithm of a negative number, Black-Scholes would actually return nothing.

So we need machinery built for two assets from the beginning.

Method One: Margrabe (1978)

William Margrabe solved a specific version of this problem in 1978, and the trick is worth understanding even if you never use the formula, because it explains why these options behave the way they do.

Consider an option to exchange one asset for another: the right to give up asset 2 and receive asset 1. Its payoff is max⁡(S1−S2, 0), a spread option with a strike of exactly zero.

Margrabe’s move is to stop pricing in dollars and price the option in units of S2 instead. The payoff becomes:

which is an ordinary call option on the ratio struck at 1.

And here is the point. The difference of two lognormals is not lognormal, but the ratio of two lognormals is. Black-Scholes applies exactly, with an effective volatility of

The correlation between the two legs enters the price directly, and it enters with a negative sign. Higher correlation means lower effective volatility, which means a cheaper option.

Two assets that move together produce a spread that barely moves, and an option on something that barely moves is not worth much. Push correlation toward 1 and the spread flatlines. Push it toward −1 and the spread whips around violently. The option price follows.

Which raises an uncomfortable question we will return to at the end: if correlation is baked this deeply into the price, how confident are you in the number you are using for it?

But first, there is a catch, and it is a serious one. Margrabe’s trick works only at a strike of exactly zero. Put a real strike K on the option and the payoff in units of S2 becomes

and K/S2 is random. The strike stops being a constant, the ratio is no longer a clean call option, and the closed form collapses.

Margrabe is exact. It is also, for most real options, unusable.

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Let’s get into it.

Flexible Forwards in Time-Dependent Models

A widely used shortcut in pricing currency hedges, assuming the optimal exercise trigger moves in a straight line with volatility, turns out to be wrong, and fixing it is now fast enough to do on a trading desk.

Flexible forwards let a company lock in an exchange rate but choose when to actually settle inside a window, which quietly makes them American style options on timing rather than plain forwards. Andersen, Itkin and Kazbek price them under a Heston model whose parameters drift with time, letting the skew stay steep at longer maturities instead of flattening out. The interesting result is not the speed (though pricing a contract in roughly a second, about ten times faster than a fine grid solver, is nice), it is the shape of the exercise surface. Earlier work assumed the trigger price rises linearly with variance. It does not. The curve bends, sometimes sharply, and the bend changes through time.

Figure 1: The early exercise surface for an American put: the full surface (left) and slices at fixed dates (right). The curves bend with variance rather than running straight.

Their localized basis method (DSINC) stays accurate where the standard cosine expansion wobbles, roughly twelve times better on median error. In one test the timing option was worth 271 pips versus a typical 50 to 100 pips quoted, which is the kind of gap that eats a sales margin whole.

Andersen, Leif B.G. and Itkin, Andrey and Kazbek, Rakhymzhan, Valuing American options and Flexible Forwards contracts in time-dependent models (June 24, 2026). Available at SSRN: https://ssrn.com/abstract=6991498 or http://dx.doi.org/10.2139/ssrn.6991498

Corporate Bond Dealers Aren't Just Plumbing

Corporate bond dealers quietly telegraph where prices are heading, and almost nobody outside the market is watching.

Dealers in corporate bonds broadcast “axes,” non-binding signals telling clients which bonds they would like to buy or sell. The conventional story says these are housekeeping, dealers nudging inventory back toward comfortable levels, nothing more. This paper, using a huge dataset of axes from the Neptune platform (over eight billion of them, covering most of the US investment grade and high yield indices), finds something more interesting. Bonds dealers signal interest in buying go on to outperform those they want to sell, by roughly 25 basis points over the following month. The telling detail is the shape of the move. Inventory-driven price pressure reverses, because temporary imbalances clear. Dealer interest does not reverse, it keeps drifting in the same direction, which is what information looks like as it seeps into prices.

Figure 2: Top and bottom quintiles, tracked 60 days either side of portfolio formation. Sorting on dealer inventory (left) produces the familiar snap back. Sorting on dealer interest (right) produces a drift that keeps going.

The effect is strongest in high yield, where fewer analysts look and information travels slowly, and axes even foreshadow rating upgrades and downgrades. The author concludes that “pre-trade dealer quoting activity may contribute to price discovery.” For investors, that reframes dealers as participants, not plumbing, and suggests the quotes arriving in your inbox carry a signal worth reading.

Geilen, Max, Corporate Bond Dealers and Price Discovery (June 24, 2026). Available at SSRN: https://ssrn.com/abstract=6990060 or http://dx.doi.org/10.2139/ssrn.6990060

Steadier Factors: Anchoring Rolling PCA With Prior Exposures

Nudging a rolling factor model toward an economically sensible starting point makes it far more stable, at little cost to accuracy.

When analysts break multi-asset returns into a few underlying factors, they usually re-run the model each month on a moving window of data. The trouble is that these factors keep drifting: the “growth” factor this month may quietly turn into something else next month, forcing constant rebalancing and muddying any story about what is actually driving returns. The authors add a gentle pull toward a prior set of exposures grounded in economic intuition, then let the data decide how hard to pull. At a moderate setting, year-ahead factor drift shrinks by roughly a quarter and the turnover of factor-mimicking portfolios falls noticeably, while the model's ability to reconstruct next month's returns barely moves.

Figure 2: Each point is a regularization strength. Left means steadier factors, down means better fit. The moderate setting (λ = 0.3) wins on both.

Push too hard and the model just parrots the prior, so the sweet spot is deliberately mild. Tested on nearly a century of US stock portfolios and dropped into a minimum-variance strategy, the stabilized version preserved risk performance while trimming both trading and drawdown. For investors, that means lower turnover costs and factor attributions you can trust from one month to the next.

Nakagawa, Kei and Kato, Masahiro and Imamura, Mitsuyoshi, Subspace Regularized Principal Component Analysis Using Prior Exposure Information (June 25, 2026). Available at SSRN: https://ssrn.com/abstract=6993538

Risky Oil: Betting on the Tails

A machine learning model built to bend around extreme events forecasts oil price tails better than the linear workhorses most analysts still rely on.

Forecasting the average future oil price is one thing; forecasting the ugly surprises at either end of the distribution is what actually keeps producers, airlines, and central bankers awake. This paper pits three approaches against each other and finds that a flexible machine learning model (which lets relationships between oil and its drivers stay linear when that suffices, but bends into nonlinear shapes when markets go haywire) consistently produces the sharpest tail forecasts.

A stochastic-volatility Bayesian VAR comes a close second, while the popular quantile regression approach barely beats a naive no-change forecast. The edge widens as the forecast horizon lengthens, and it holds even against a benchmark that already accounts for shifting volatility, so the gains come from capturing genuine nonlinearity rather than just noisier noise.

Demand factors drive the downside, supply factors drive the upside, and the authors show these tail signals even help predict Fed rate moves. For anyone hedging oil exposure or pricing energy risk, the takeaway is that allowing for nonlinearities when forecasting oil prices matters most precisely when it is hardest, during the turbulent episodes that break simpler models.

Baumeister, Christiane and Huber, Florian and Marcellino, Massimiliano, Risky Oil: It's All in the Tails. Available at SSRN: https://ssrn.com/abstract=7118662 or http://dx.doi.org/10.2139/ssrn.7118662

This week for paid subscribers

Paid subscribers are getting a from-scratch DCC-GARCH check on whether gold, silver, wheat, and corn actually hedged four US equity sectors around COVID, going beyond theoretical hedge ratios to measure realized volatility reduction. We find gold's hedge against financials didn't just weaken but briefly flipped to increasing portfolio risk, silver grew more entangled with materials, and corn quietly outperformed its weak-safe-haven billing as the most consistent volatility reducer in the study. Python backtest code and data included.

Alpha in Academia

Did Commodities Actually Hedge Sector Risk During COVID?

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19 days ago · 9 likes · Alpha in Academia

Disclaimer: The content provided in this newsletter, "Alpha in Academia," is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

Today we will take a look at how Gold’s reputation as a universal hedge doesn’t hold up (and briefly inverted), while corn quietly outperformed its “weak safe haven” reputation as a consistent volatility reducer.

Let’s dive right in.

Introduction

A 2025 working paper by Grant, Moodliar, Rissik and Huang raised the question whether hard commodities (gold, silver, platinum) and soft commodities (corn, soybeans, wheat, livestock) behaved as hedges or safe havens for US equity sectors between 2014 and 2024. Using wavelet coherence and DCC-GARCH models across eleven GICS-classified sectors, we find that gold’s traditional role as a strong hedge deteriorated after the COVID-19 pandemic, that silver and platinum grew more positively correlated with cyclical sectors, and that soft commodities such as corn and wheat became weak but improving safe havens, with corn and wheat increasingly favored in optimal portfolio construction during the pandemic period.

We isolate four commodities (gold, silver, wheat, corn) against four sectors (Financials, Energy, Utilities, Materials), rebuild the DCC-GARCH pipeline from scratch using daily ETF proxy data, and ask a narrower and more falsifiable question: did the correlation structure between these assets actually shift around COVID, and if an investor had mechanically applied the resulting hedge ratios, would their portfolio have actually been less volatile as a result? The second half of that question, real realized volatility reduction rather than a theoretical hedge ratio, is not something the original paper focuses on, and is the main addition here.

Data and Methodology

Daily adjusted closing prices were pulled from Tiingo for 1 January 2014 through 31 December 2024 (2,768 trading days after return calculation). Four sector SPDR ETFs stand in for the Bloomberg GICS sector indices: XLF (Financials), XLE (Energy), XLU (Utilities), and XLB (Materials). Four commodity ETFs stand in for continuous futures: GLD (gold), SLV (silver), WEAT (wheat), and CORN (corn). This substitution is a real limitation worth flagging up front: ETFs carry expense ratios and can drift from spot or front-month futures pricing, and standard EOD data providers such as Tiingo do not carry continuous futures series. The substitution is reasonable for a correlation and hedging study, since ETF returns track the underlying commodity closely at daily frequency.

Read more

Let’s get into it.

Gold's Hedge Is Cracking

Gold’s decades-old reputation as the market’s default safe haven quietly broke down after COVID, while unglamorous soft commodities like corn and wheat became meaningfully better portfolio diversifiers.

Researchers at the University of Cape Town and Stellenbosch tracked how seven commodities, three “hard” (gold, silver, platinum) and four “soft” (corn, soybeans, wheat, livestock), moved alongside eleven US equity sectors from 2014 to 2024, using wavelet coherence and DCC-GARCH models to capture how correlations shift across both time and investment horizon. The headline result: gold, long treated as the default flight-to-safety trade, went from negatively correlated with sectors like financials and industrials before the pandemic to increasingly positively correlated after it, stripping away the protection investors assumed was there.

Silver and platinum told a similar story, drifting toward stronger positive co-movement with cyclical sectors like materials and energy. Soft commodities never became strong safe havens either, but corn and wheat held up better, with optimal portfolio weights rising across every sector during COVID. As the authors put it, gold’s “traditional role as a strong safe haven and hedge asset deteriorated after the COVID-19 pandemic.” Anyone still parking risk in gold out of habit might want to check whether that hedge is actually still there.

Grant, Mark and Moodliar, Bhavaniya and Rissik, Luke and Huang, Chun-Sung, On the Hedge and Safe Haven Properties of Soft and Hard Commodities: Evidence from the United States GICS-Sectors (March 01, 2025). Available at SSRN: https://ssrn.com/abstract=7046078 or http://dx.doi.org/10.2139/ssrn.7046078

Bond Futures Spreads That Trade Like Their Own Market

European government bond futures calendar spreads aren’t just a synthetic byproduct of two expiring contracts, they behave like an independent liquidity venue precisely when rollover pressure peaks.

Using 127 million order book updates from nine EUREX bond futures (Bund, Bobl, Schatz, BTP, OAT, and others), this paper tracks what happens in the ten days before contract expiry across three linked markets: the expiring contract, the new contract, and the calendar spread connecting them. The expected story plays out late, trading activity and tighter quotes migrate from the old contract to the new one mostly in the final two or three days. But the spread book gets interesting earlier. It builds up meaningful resting depth well before that late migration, and for eight of nine products, buying the spread directly is consistently cheaper than manually legging into both contracts separately, sometimes dramatically so in less liquid names like the Spanish BONO future.

The authors put it plainly: calendar spreads play an active and economically meaningful role within the rollover process. For anyone rolling futures positions near expiry, that’s a real, quantifiable execution cost sitting on the table if you’re not checking the spread book first.

Uzun, Illia and Stenfors, Alexis, Calendar Spreads as Autonomous Liquidity Pools: Evidence from Triangular Rollover Dynamics in Bond Futures Markets. Available at SSRN: https://ssrn.com/abstract=6999365 or http://dx.doi.org/10.2139/ssrn.6999365

A Number Theory Trick Just Flagged Something Weird About This Bull Market

Since 2018, S&P 500 stock prices have started obeying Benford’s Law (the odd rule that natural datasets favor digit 1 over digit 9) more strongly than at any point in 64 years, and historically that pattern only ever showed up during a crisis.

Here’s the setup. Benford compliance in stock prices, it turns out, isn’t some mysterious market-efficiency signal, it’s almost entirely explained by how spread out prices are across the S&P 500 (one variable alone explains 82% of it). For 60 years, the only thing that stretched prices out enough to trigger strong compliance was a recession, and specifically a recession that crushed cheap stocks while expensive ones barely moved. Every time that happened (1970s oil crisis, 2008 crash), the pattern reverted within months once the economy recovered. But since 2018, the same statistical fingerprint has shown up for seven straight years with no recession attached, and it’s happening for the opposite reason: expensive stocks are rising fast while cheap stocks barely budge, not falling.

The paper ties this directly to M2 growth, and the authors frame it plainly, calling it the statistical configuration of crisis without its macroeconomic expression. If the plumbing that normally corrects stretched valuations (bankruptcies, recessions, deleveraging) has been sitting dormant this whole time, that’s worth knowing before you assume “no recession” means “no fragility.”

Mateos Sanchez, Carlos and Alcaraz Carrillo de Albornoz, Vicente and Farkas, Walter, The Signal beneath the Calm: Benford's Law and the Latent Fragility of Liquidity-Driven Financial Markets (June 17, 2026). Available at SSRN: https://ssrn.com/abstract=6962319

Sanctions Didn't Kill the Gold Arbitrage, They Just Slowed It Down (And Flipped Brent Entirely)

After Russia’s 2022 clearing and settlement infrastructure was severed from the West, gold and silver prices on the Moscow Exchange kept tracking their Chicago counterparts almost perfectly in the long run, but the market lost its ability to correct short-term price gaps quickly, while Brent crude oddly became more important to price discovery, not less.

Researchers compared Moscow Exchange futures on gold, silver, and Brent crude to their Chicago and ICE equivalents from 2019 through early 2026, using the kind of statistical toolkit normally applied to cross-listed stocks or ETFs tracking the same asset. The long-run one-to-one price relationship never broke, even after sanctions cut off Russian banks and brokers from global clearing. What changed was speed: it used to take one or two trading days for a price gap between Moscow and Chicago gold to close, and now it takes up to five.

Gold’s informational role on the Moscow exchange nearly vanished, but Brent’s grew, likely because the contract increasingly reflects discounted Russian crude rather than the global benchmark. As the authors put it, “the relation survives, but the mechanism that enforces it is impaired.” For anyone modeling cross-border arbitrage or basis risk, that’s a useful, quantifiable lesson in how sanctions actually degrade a market.

Belanov, Aleksandr and Mischhenko, Viatcheslav, Price Discovery and Arbitrage Efficiency under Infrastructure Severance: Evidence from MOEX-CME Derivative Pairs, 2019-2026 (June 07, 2026). Available at SSRN: https://ssrn.com/abstract=6915859

Disclaimer: The content provided in this newsletter, “Alpha in Academia,” is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

Let’s get into it.

Finding a Private Company's Public Twin

Private credit has exploded into a multi-trillion dollar asset class, but there is a basic problem, many private borrowers do not publish the kind of financial statements analysts need to estimate default risk, or the numbers arrive stale, months out of date. Researchers at BlackRock built a workaround using machine learning. They trained a random forest model (an algorithm that grows hundreds of decision trees) on thousands of publicly traded corporate bonds, using visible market signals like yield, spread, and bond structure to predict credit ratings. Then, instead of only using the model to make predictions, they mined its internal structure to measure “similarity” and find each private issuer’s closest public lookalikes, a small handful of public bonds trading like true peers. The private company’s implied rating becomes a weighted average of its public twins’ actual agency ratings.

Tested across a ten-year period that includes the 2020 crash, the model landed on the exact rating band or within one notch of it, achieving “more than 95% accuracy for both NA and EMEA regions,” and consistently beat a simpler sector-average benchmark. For investors underwriting private credit deals with limited disclosure, this offers a transparent, data-driven second opinion on where a borrower’s true credit quality sits.

Yadav, Ravi and Saha, Anubhab and Singh, Saurabh and Turmuhambetov, Gauhar Akylbekovna and Mehta, Dhagash, A Machine Learning-based Public Market Equivalent Framework for Estimating Default Risk in Private Credit (April 15, 2026). Available at SSRN: https://ssrn.com/abstract=6916138 or http://dx.doi.org/10.2139/ssrn.6916138

Why Long-Term Rates Overreact

Uncertainty about how persistent a shock will be is, by itself, enough to make even a perfectly rational forecaster overreact to distant news and under react to recent news, at the same time.

Ask any forecaster how long a shock to inflation or interest rates will actually last, and they will admit they are not fully sure. This paper shows that this honest uncertainty, once you write it into the math, forces long-horizon forecasts to become steadily more persistent and to eventually overreact the further out you look, no matter what the true underlying process is or how carefully the forecaster reasons.

The authors trace this one mechanism through six long-standing puzzles in finance: why 15-year Treasury forward rates move almost in lockstep with short-term rates, why the long end of the yield curve gets explained away by a vague “term premium” instead of actual rate expectations, why bond returns look predictable after the fact, and why long-horizon asset prices are excessively volatile. Fitting the model to the real Treasury curve, modest uncertainty (nothing exotic, just not being sure whether short rate persistence is 0.85 or 0.95) reproduces these patterns closely. As the authors put it, forecasts are “necessarily as persistent as is believable and over-react.” For investors, this means the long end of the curve may be less about bias or mispricing than about honest uncertainty doing exactly what the math says it must.

Greg Kaplan and Ken Miyahara, “How Does Monetary and Fiscal Policy Affect the Economy in the Face of Large Shocks?,” NBER Working Paper 35400 (2026), https://doi.org/10.3386/w35400.

What the VIX Doesn’t Tell You

The VIX, the “fear gauge” quoted everywhere from CNBC to options desks, is built from a formula that implicitly assumes you can observe option prices at every possible strike, from zero to infinity. In practice, exchanges only trade a limited band of strikes, and nobody prices the extreme tails. This paper shows that gap is not a minor technicality. For the standard VIX formula, and for nearly every popular measure of skewness and kurtosis used in academic finance, the missing tail information means there is no upper limit on what the true value could be. The number everyone reports is just one arbitrary point plucked from an unbounded range of values, all equally consistent with the option prices actually observed.

Predictive regressions and cyclicality studies built on these measures can be engineered to show almost anything, “rendering their empirical conclusions largely uninformative.” The authors also propose a fix, a family of alternative variance and skew measures that stay tightly bounded using the same visible data. For investors leaning on the VIX or similar option-implied signals, the point is blunt: the number is standing in for a far wider range of possibilities than it lets on.

Bondarenko, Oleg and Dillschneider, Yannick and Schneider, Paul Georg and Trojani, Fabio, What can you Really Tell from Option Prices? (June 24, 2026). Swiss Finance Institute Research Paper No. 26-49, Available at SSRN: https://ssrn.com/abstract=7017219

The Billion-Dollar Minimum for Bond Indexing

A bond portfolio can be too small to track its own benchmark, no matter how skilled the optimizer, because minimum trade sizes set a hard floor on how close it can get.

Bond portfolio optimization has always felt like equity optimization’s neglected cousin, and a new paper from an Amundi research team explains why, then builds the missing framework from scratch. The real payoff isn’t the math, it’s what happens when you try to actually trade the thing.

Using real ICE BofA corporate bond indices with anywhere from 4,663 to over 20,000 securities, the authors show that minimum trade sizes and lot constraints impose a hard floor on how closely a portfolio can hug its benchmark. At 50 million dollars, the best achievable active share (a measure of how much a portfolio diverges from its index) sits around 80% for the euro index and 85% for the global one, and these numbers only fall into reasonable territory once a portfolio crosses roughly a billion dollars. The paper notes this problem is “particularly acute for smaller portfolio sizes.” For anyone comparing bond ETFs or sizing up a smaller fixed income mandate, this is a reminder that tracking error isn’t just a skill problem, it’s a scale problem, and size alone can decide whether a strategy is even implementable.

Ben Slimane, Mohamed and Cherief, Amina and Roncalli, Thierry and Xu, Jiali, Bond Portfolio Optimization (July 03, 2026). Available at SSRN: https://ssrn.com/abstract=7064358

This week for paid subscribers

Paid subscribers are getting a look at whether crowded hedge fund positioning can actually be traded against. The post digs into why fading the crowd produces a clean, out-of-sample-stable edge in silver, why that same edge in gold is quietly decaying as the trade becomes more widely known, and why copper inverts the relationship entirely, exposing the fact that positioning was never the real signal, mean reversion was. Python backtest code included.

Disclaimer: The content provided in this newsletter, “Alpha in Academia,” is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

Reviewed and updated 28 July 2026

Hello and welcome back to another paid post!

Today we are going to take one of the most repeated headlines in all of commodity markets and ask whether there is any money in doing the exact opposite. The idea is old and intuitively appealing: The fast money crowds into a trade, the trade gets stretched, and the crowd, as crowds tend to, eventually gets carried out. If that story is true, then the crowd’s own positioning should tell you when to fade it.

Let’s dive right in.

The Cheat Sheet the Government Publishes Every Friday

Every week, the Commodity Futures Trading Commission (CFTC) releases something called the Commitments of Traders (COT) report, and it is one of the closest things retail traders have to seeing the other side of their own hand. The report takes every major futures market and sorts the people holding positions into buckets, based on who they actually are and why they are there. Two of those buckets matter for us, and the whole strategy lives in the tension between them.

The first is Managed Money. In the CFTC’s disaggregated report, this category includes registered commodity trading advisers and commodity pool operators, along with unregistered funds identified by the Commission. It is a reporting category, not proof that every position follows the same trend strategy. Here I test its aggregate net position without assigning a motive to each trader.

The second bucket is Producer/Merchant/Processor/User. These are entities predominantly engaged in producing, processing, packing, or handling the physical commodity and using futures to manage commercial risk. Their aggregate position often sits across from speculative demand, but the report does not identify the motive for each trade.

The distinction still gives us a useful empirical question. Managed Money is a reportable speculative category; Producer/Merchant/Processor/User is tied to commercial activity. Their aggregate net positions can lean against each other, but the labels do not tell us which side is informed, patient, early, or late. The test below asks only whether an extreme Managed Money position was followed by a profitable contrarian portfolio in these three metals.

The hypothesis in one line: an extreme speculative position might coincide with an exhausted trend. That is the proposition under test, not something the category labels establish.

That gives us a clean question. When Managed Money’s net position is unusually high, fade it short; when it is unusually low, fade it long. Then ask whether the resulting portfolio was positive after the report could conservatively have become actionable. The outcome can support or reject the rule in this sample, but it cannot by itself identify why prices moved.

Read more

Let’s get into it.

Adverse Selection Eats the Spread

On a diverse basket of CME futures, the spread a market maker earns is almost perfectly cancelled out by the losses it pays to better-informed traders, leaving profit indistinguishable from zero.

When a market maker posts a passive quote, it collects a small spread but pays a hidden cost every time a smarter trader picks off that quote just before the price moves. Using rare data that carries the true buyer-or-seller sign on every trade across 13 CME futures (from crude oil to the S&P 500), Gatto measures both sides of that trade fill by fill and finds they essentially offset: the maker captures a sliver of spread and gives back almost exactly the same amount as prices drift against it, on every single contract.

Figure 1: Left: the half-spread a maker captures is consumed almost entirely by adverse selection, netting near zero. Right: the same near-cancellation holds on all 13 contracts.

In the author's words, “adverse selection consumes essentially the whole captured half-spread,” leaving profit statistically indistinguishable from zero. But under stress (the SVB scare, a hot FOMC week, the COVID crash) the losses actually exceed the spread, and the maker bleeds more as volatility rises. Standard clever quoting tricks and order-flow signals fail to fix it once real trading costs are charged. For investors, the takeaway is sobering, knowing that in these venues, passively supplying liquidity is structurally a break-even-at-best business, and the edge everyone chases mostly is not there.

Gatto, Daniel, Adverse Selection Consumes the Touch: A True-Aggressor-Signed Maker-P&L Decomposition Across 13 CME Futures (June 20, 2026). Available at SSRN: https://ssrn.com/abstract=7022599

What Investors Disagree About

When investors argue about a company's actual fundamentals, that disagreement predicts lower future returns, but the same fights over noise and chatter predict nothing.

Most research treats investor disagreement as a single number, a measure of how much people argue without asking what they argue about. This paper ran a large language model over 220 million posts on China's biggest stock forum and split the arguing into categories. The result is that content is everything. Disagreement about fundamentals (earnings, valuation, prospects) predicts lower returns of roughly 0.7% per month, the classic pattern where optimists dominate prices when shorting is hard and overvaluation later unwinds. Disagreement classified as noise predicts nothing, and the popular aggregate measure actually points the wrong way because it is really just tracking sentiment (the two move together at 0.81).

Figure 2: Risk-adjusted monthly returns by disagreement type. Recreated from Figure 1 of the original paper.

Tellingly, the effect shows up right when earnings get announced and the arguing gets resolved, and it survives once you account for whether earnings actually surprised. The authors put it plainly, that investor disagreement “is not one thing.” For anyone using crowd sentiment or forum buzz as a signal, the lesson is that the crude aggregate can quietly reverse the sign of what you think you're measuring.

Yi, Kefu and Wu, Feng, What Investors Disagree About: LLM-Decomposed Retail Disagreement and the Cross-Section of Stock Returns. Available at SSRN: https://ssrn.com/abstract=7027983 or http://dx.doi.org/10.2139/ssrn.7027983

Instant Liquidity, Latent Risk: When Bond ETFs Break

Bond ETFs break not because investors flee, but because the plumbing that keeps their prices honest quietly seizes up.

Melin and Rouxelin build a continuous-time model of two connected markets, a slow over-the-counter world where the actual bonds trade and a fast exchange where the ETF shares trade, linked by the authorized participants who shuttle assets between them.

The surprising result is that ETF fragility runs through prices, not redemptions. During March 2020, bond ETFs saw smaller outflows than mutual funds yet dislocated far more sharply from their net asset value, with the deepest discounts landing in the safer, more liquid segments rather than the riskiest ones. Their model explains this safety inversion, that when redemption baskets are costly for intermediaries to absorb, those middlemen demand a wider discount to play along, and once a single redemption looks doubtful, every share reprices as if none can be redeemed.

Figure 3: Premium or discount to NAV across four bond ETF categories, 2020 to 2024, with 90-day SOFR overlaid. Dislocations cluster in March 2020, deepest for municipals (near 8 percent), showing how stress hits some segments far harder than others.

For investors, the lesson is that a calm-looking ETF can mask a redemption mechanism that fails precisely when you need liquidity most, and those crisis discounts are structural signals, not free money.

Rouxelin, Florent and Melin, Lionel, Instant Liquidity, Latent Risk (July 01, 2026). Available at SSRN: https://ssrn.com/abstract=5304325 or http://dx.doi.org/10.2139/ssrn.5304325

The Enduring Influence of Openness on Risk-Taking

Cities forced open to foreign trade in 19th-century China still breed bolder investors today, 170 years later.

When the Qing dynasty was compelled to open a set of “Treaty Ports” to Western trade after 1842, those cities absorbed more than goods and factories. They picked up a taste for risk that never left. Lin and Tang trace this through three very different windows: commercial newspaper ads from 1850 to 1950 (Treaty Ports ran far more, especially in volatile industries like finance and real estate), state-controlled economic news from 1949 to 1988, and modern mutual fund accounts from Alipay.

The through-line is striking, because the formal institutions that created the advantage (foreign consulates, concessions, customs houses) were all dismantled after 1949, yet the behavior survived. Tellingly, the effect shows up only among long-rooted locals, not immigrants, pointing to culture passed down through families rather than current economics. For investors, it is a reminder that regional risk appetite is partly inherited, and that where money comes from can shape how boldly it gets deployed.

Lin, Tse-Chun and Tang, He, Enduring Influences of Openness on Risk-Taking Behaviors: Evidence from 170 Years of Ads, News, and Retail Investments. Available at SSRN: https://ssrn.com/abstract=7030024 or http://dx.doi.org/10.2139/ssrn.7030024

This week for paid subscribers

Paid subscribers are getting a look at whether tomorrow's stock direction is actually predictable, using a nonparametric sign-prediction rule that strips out the mechanical edge created by a stock's own upward drift before testing what real forecasting power remains. The post digs into why small-caps show a genuine 2.8 percentage point accuracy edge while large-caps mostly do not, why the drift adjustment quietly separates real signal from data artifact, and why mean-reverting names like a certain Arkansas community bank can turn a buy-and-hold loser into a tradable winner. Python backtest code included.

Disclaimer: The content provided in this newsletter, "Alpha in Academia," is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

Reviewed and updated July 28, 2026

Hello and welcome back to another paid post!

Today I will take a look at a simple nonparametric sign-prediction rule and what it would take to test it reliably against a drift-adjusted random walk benchmark.

Let’s dive right in.

Introduction

Every retail trader has asked the same question at some point: if a stock just had a big day up, is tomorrow more likely to be up too, or is a reversal coming? The answer turns out to depend enormously on which stock you are asking about, what the broader drift of that stock looks like, and how you define a “big” day. Getting any one of those three things wrong produces results that look like forecasting ability when they are really just artifacts of the data construction.

The analysis is built around a nonparametric statistic called the Probability Difference (PD), designed to measure the sign predictability of daily equity returns after stripping out the mechanical component that comes from a positive expected return. The logic is elegant: if a stock earns 5 percent per year on average, then same-direction sequences will be slightly more common than reversals even if daily returns are purely random. The PD statistic accounts for this, then asks whether the residual predictability is statistically significant.

There are 3 questions at the center of the analysis. First, do smaller stocks show meaningful out-of-sample directional predictability? Second, do larger stocks show the same pattern? Third, does predictability differ after positive and negative extreme-return days? Those questions are worth asking, but the implementation has to identify company size correctly and control its statistical search before the answers can be trusted.

The Methodology: What the PD Statistic Actually Measures

Standard momentum and reversal research measures whether returns are correlated across time. The PD statistic takes a different approach: it only cares about the sign of the return, not its magnitude. Define Dt as plus one if today’s return is positive and minus one if it is negative. The PD statistic for a given estimation window of W trading days is:

Read more

Let’s get into it.

Decoding the Smart Money Trail

Institutional investors leave detectable footprints across options markets, dark pools, and the order book, and a systematic four-layer framework can help retail traders read them.

When a hedge fund takes a large directional position, it rarely announces the fact. Instead, it routes block trades through dark pools to avoid tipping its hand, sweeps call options across multiple exchanges to build leveraged exposure efficiently, and leaves a quiet but readable trail in the market’s plumbing. This paper by practitioner Vishal Chopra argues that four publicly accessible data streams, interpreted in sequence, can reconstruct much of that institutional intent: unusual options activity (particularly call or put sweeps that cross 50% of existing open interest), off-exchange volume spikes from FINRA’s weekly dark pool transparency data, large-lot patterns in the real-time tape, and price positioning relative to anchored VWAP (a volume-weighted average price calculated from a meaningful prior event rather than the daily open).

The author formalizes this into a weighted conviction score, where options flow carries the most predictive weight, followed by dark pool confirmation, tape patterns, and price structure. The framework stops well short of claiming to close the gap between retail and institutional participants entirely, but the evidence from market microstructure research it draws on is real: options order flow genuinely leads equity prices, and dark pool prints carry information about future returns. The practical question is whether the synthesis adds more than the sum of its parts.

Chopra, Vishal, Institutional Order Flow Analytics: Decoding Smart Money Signals in U.S. Equity and Options Markets: A Practitioner Framework for Identifying Informed Trading Activity Across Lit and Dark Market Venues. (June 06, 2026). Available at SSRN: https://ssrn.com/abstract=6889358

Catching Small Fish Before the Market Does

A simple, nonparametric measure of return sign dependence predicts next-day direction for the majority of small-cap stocks, while generating no edge at all in large caps.

The core intuition here is behavioral and almost disarmingly simple: if investors systematically under-react to news, positive returns should tend to follow positive returns, and negative should follow negative. The Probability Difference (PD) statistic formalizes this by measuring how often same-sign return sequences occur relative to reversals.

The clever part is what the author had to fix to make this work in equities specifically. Stocks have a positive long-run drift, which creates a false impression of momentum, and they suffer from microstructure noise (the random bounce between bid and ask prices) that obscures real patterns. The paper addresses these with a drift-adjusted null hypothesis and a threshold filter that only fires after a prior return is extreme enough to cut through the noise. Tested across 25 years of U.S. data, the adapted PD correctly predicted next-day direction for 84% of small-cap stocks, and PD-guided strategies delivered positive Sharpe ratios net of transaction costs.

Large caps showed essentially zero edge, which is exactly what you’d want to see: a model that goes quiet in efficient markets isn’t broken, it’s honest. The most striking finding is an asymmetry in where predictability lives: the signal after large positive returns is substantially stronger than after large negative ones, consistent with the disposition effect (investors hold losers too long and sell winners too soon), a pattern that notably disappears in large caps.

Semenov, Andrei, Nonparametric Directional Forecasting in Equity Markets: Size, Conditional Patterns, and Economic Payoffs. Available at SSRN: https://ssrn.com/abstract=6997954 or http://dx.doi.org/10.2139/ssrn.6997954

When Reddit Talks, Markets Move Together

Synchronized spikes in retail attention on WallStreetBets coincide with simultaneous price bubbles across NVIDIA, Bitcoin, and Ethereum, even though these assets share no fundamental economic linkage.

The GameStop saga of 2021 made it clear that retail investors coordinating on social media could move individual stocks. This paper asks a harder question: can that same coordination produce bubbles across entirely unrelated asset classes at the same time? Using hourly price and Reddit mention data from 2023 to 2024, the authors track explosive price episodes across AI stocks and cryptocurrencies using a statistical bubble-detection procedure (essentially a rolling test for whether prices are growing faster than any rational fundamental could justify). They find that NVIDIA alone registered twelve distinct bubble episodes over the sample period, with some lasting over 59 consecutive hours, and that its bubble windows overlap meaningfully with those of Bitcoin and Ethereum. The smoking gun is the attention data: during the March 2024 co-bubble episode, WallStreetBets mentions of NVIDIA ran more than four times their normal hourly rate, with Bitcoin and Ethereum mentions showing the same pattern simultaneously.

Critically, the paper distinguishes this from ordinary market connectedness: Tesla, which is economically linked to NVIDIA through AI exposure, shows return spillovers with it but no co-explosivity, suggesting that synchronized bubbles are a behavioral phenomenon driven by shared retail narratives, not just correlated fundamentals. For investors, the implication is interesting: Reddit is now a cross-asset contagion channel.

Aloosh, Arash and Choi, Hyung-Eun and Ouzan, Samuel and Shahzad, Syed Jawad Hussain, Social Media Co-Attention and Investment Co-Bubbles. Available at SSRN: https://ssrn.com/abstract=6985533 or http://dx.doi.org/10.2139/ssrn.6985533

The Hidden Third Market in Futures Rollovers

Calendar spread books in European bond futures don’t just reflect liquidity from their two underlying contracts: they generate their own, and often offer cheaper execution than trading the legs separately.

Every quarter, traders holding European government bond futures face a familiar chore: roll their position from the expiring contract to the next one. The conventional picture is a two-party handoff, with liquidity migrating from March to June. But a new paper using 127 million limit order book updates from EUREX argues there’s a third player worth watching: the calendar spread book itself. The rollover, it turns out, is better understood as a triangle than a baton pass.

As expiry approaches, the spread book quietly accumulates a large share of displayed (resting) liquidity even while raw order activity still dominates the outright contracts, and in eight of nine futures studied, executing the roll directly through the spread book was cheaper than legging into it through March and June separately. The execution advantage was especially striking in less liquid markets. For investors executing rollovers at scale, ignoring the spread book isn’t just leaving money on the table, it’s misreading the market’s actual structure.

Uzun, Illia and Stenfors, Alexis, Calendar Spreads as Autonomous Liquidity Pools: Evidence from Triangular Rollover Dynamics in Bond Futures Markets. Available at SSRN: https://ssrn.com/abstract=6999365 or http://dx.doi.org/10.2139/ssrn.6999365

This week for paid subscribers

Paid subscribers are getting a complete seasonal-trend decomposition of the legendary “glamour market” (pork bellies), derived from a 69-year-old USDA cold-storage dataset that reveals what price action alone never could: not just when a market dies, but the exact structural decay that killed it. Python decomposition code included.

Disclaimer: The content provided in this newsletter, “Alpha in Academia,” is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

Reviewed and updated 28 July 2026

Hello and welcome back to another paid post!

Today I will take a look at what a monthly USDA inventory series can—and cannot—tell us about one of America’s most famous agricultural futures contracts.

Let’s dive right in.

The Glamour Market

For about thirty years, if you wanted to gesture at the casino of commodity speculation without actually explaining anything, you reached for two words: pork bellies. It was the punchline. In Trading Places, pork bellies appear in the commodities monologue; frozen concentrated orange juice futures ruin the Duke brothers. Traders called it “the glamour market,” half in love with it and half embarrassed to be. Bellies were volatile, theatrical, and a little ridiculous, and everyone knew the name even if almost nobody could tell you what was actually in the contract.

And then, in 2011, it stopped. On 15 July, CME announced that frozen pork belly futures and options would be delisted after “a prolonged lack of trading volume” and “significant discussion with industry participants.” The infamous ticker went dark the following Monday.

The cold-storage record offers one clue. It can describe how frozen inventories changed, but it cannot establish what caused the market to disappear.

The Bet in the Freezer

Let’s start with the thing itself. A pork belly is the slab of layered fat and muscle that runs along the underside of the hog — the cut that, cured and sliced, becomes bacon. One hog, two bellies, and for most of the 20th century a very specific scheduling headache.

The historical rationale was a seasonal inventory problem: bellies could be stored for later sale, leaving packers and buyers exposed to the price at which that inventory would eventually change hands.

Freezing does not remove that price risk; it converts a perishable product into inventory whose future value is uncertain.

That is a textbook hedging problem, the kind that can summon a futures market into existence. CME’s own history dates the first frozen pork belly futures trade to 18 September 1961. The date is fixed; the exact mix of hedging motives is a separate historical question.

The storage hypothesis goes like this: buy a belly cheap in the off-season, pay to keep it frozen, sell it dear when demand peaks. On that account, the futures curve was, in effect, the market quoting you the price of that carry. The speculators who made bellies famous would have been renting exposure to a seasonal storage cycle, dressed up in enough volatility to make it thrilling.

This explanation depends on two historical questions: whether demand and storage were seasonal, and whether firms actually used the contract to hedge that risk.

The analysis below can examine the storage series. It cannot tell us, on its own, whether a change in that series caused traders to leave the contract.

Read more

Let’s get into it.

The Limited Reach of the World Cup Distraction Effect

The famous “World Cup distraction” that halves trading on national exchanges doesn't reach the global, round-the-clock markets where most money now moves, and two common ways of measuring it conjure the effect out of thin air.

The original finding was clean: when a country's team plays, trades on that country's stock exchange drop sharply because the local trader is also the local viewer. The question now becomes whether that distraction survives in the venues that now carry most of the world's volume (crypto, index, commodity, and currency futures), where no single nation's fans matter to the price. Short answer: No. Across eleven instruments measured minute by minute, during-match trading sits flat on zero even as Wikipedia attention spikes to roughly six to eight times its baseline, and Gatto can statistically rule out declines beyond about 6 to 9 percent in the deepest markets, real nulls rather than weak tests.

Figure 1: World Cup attention jumps several-fold, but trading volume across global markets doesn't budge.

More useful for any data-driven investor is the warning underneath: two natural-looking measurements (a drop in cross-market correlation and an all-hours volume jump) reproduce the famous effect from no underlying response at all, driven purely by which instruments happen to be open. The lesson travels far beyond football: a “signal” can be an artifact of what your sample includes, and globally traded markets don't blink when any one country looks away.

Gatto, Daniel, The Reach of the World Cup Distraction Effect: Evidence from Global Trading Venues (June 16, 2026). Available at SSRN: https://ssrn.com/abstract=6955879

Measuring How Much of a Backtest's Edge Persists

A new diagnostic reveals how much of a trading strategy's “edge” actually survives once you stop fitting it to the past.

Most backtest-overfitting tools ask whether a strategy got lucky across many trials. This paper asks something different and arguably more useful: when a strategy uses market conditions (past returns, volatility, macro signals) to time its bets, how much of that conditioning information still works out-of-sample? Dominguez builds the information-survival ratio, a clean score from zero to one, where one means the in-sample edge fully carries over and zero means it collapses back to a plain unconditional benchmark.

Figure 2: Information-survival ratios across asset classes (rows) and signal types (columns). Greener cells mean more of the in-sample edge survives out-of-sample. Hedge funds retain the most, single stocks the least.

Tested across equities, industries, momentum portfolios, hedge funds, and single stocks, survival turns out to be highly uneven. Hedge fund indices retain the most conditioning power (often above 0.90), while individual stocks retain the least, drowned out by idiosyncratic noise. Crucially, no single type of signal wins everywhere: volatility works best for equities, while past returns dominate elsewhere.

For investors, the lesson is sobering and practical, a strong backtest means little if its underlying logic doesn't replicate forward, and this ratio puts a number on that risk.

Rodriguez Dominguez, Alejandro, The Information-Survival Ratio: A Within-Strategy Diagnostic of Conditioning Overfit in Walk-Forward Backtests (June 02, 2026). Available at SSRN: https://ssrn.com/abstract=6905139

How China’s Financing Shift Is Repricing Municipal Debt

When Chinese local governments swap land deals for proper bonds, the hidden safety net under their financing vehicles’ debt quietly frays, and investors notice.

For years, municipal corporate bonds (MCBs) in China have been priced cheaply not because the issuers were sound, but because everyone assumed the local government would quietly bail them out. This study tracks what happens when that assumption erodes. As provinces shift from land-sale-funded financing toward formal revenue bonds (debt explicitly backed by government credit), the implicit guarantee propping up MCBs weakens, and spreads widen.

The effect is real but not dramatic: a one-standard-deviation move toward this new model lifts issuing spreads by about 0.218 percentage points, roughly 11% of the average. Two forces drive it. Governments, leaning less on their financing vehicles, cut the subsidies and equity injections that signaled support, and the safer revenue bonds crowd out investor demand for riskier MCBs.

The pattern bites hardest where it should, on lower-rated bonds and land-dependent regions. For investors, it is a reminder that government backing, once treated as free, is being repriced in real time.

Du, Junying and Liu, Xinyang, From Land to Bonds: Local Government Financing Transformation and MCB Pricing in China. Available at SSRN: https://ssrn.com/abstract=6934485 or http://dx.doi.org/10.2139/ssrn.6934485

How Markets Learn from Sequential Geopolitical Escalations

Markets price geopolitical risk most aggressively the first time, then learn to shrug off later, broader shocks.

This study uses three escalations of the Israel conflict (a local attack in 2023, a regional fight with Iran in 2025, and a global episode involving the U.S. in 2026) as a natural experiment on how investors react to repeated bad news. Tracking 56 airline stocks, Kaplanski finds that the first shock was the one investors took most personally. Airlines with direct operational exposure to Israel, especially Israeli carriers and exposed low-cost airlines, got hammered hardest, while the rest of the industry was treated as relatively safe.

Figure 3: Cumulative abnormal returns for airline stocks, September 2023 to April 2026, grouped by exposure to Israel: Israeli carriers (ISR), airlines operating in Israel before the conflict (ISR-Op), and those without (NonISR-Op). Shaded bands mark the three escalation episodes. Lower panels show jet fuel prices and the geopolitical risk index.

But that exposure-based pricing didn't stick. By the regional escalation, the market barely flinched (the response was muted and short-lived despite the wider geographic scope), and by the global episode the selloff was broad and uniform, hitting everyone roughly equally rather than punishing the obviously exposed names. The paper shows that investors shifted toward “a broader reassessment of industry-wide risk.” For investors, the lesson is that the crowd's first reaction to a crisis tends to overshoot on the names that look most exposed, and that gap often reverses once the market recalibrates.

Kaplanski, Guy, Market Learning from Sequential Geopolitical Escalations: Evidence from Airline Stocks (March 05, 2026). Available at SSRN: https://ssrn.com/abstract=6847121

This week for paid subscribers

Paid subscribers are getting the full probability distribution of a European call option at expiry, derived from a 150-year-old physics equation (the Boltzmann framework) that delivers what Black-Scholes never did: not just the expected value, but the odds your option expires worthless and the range of payoffs if it doesn't. This post tracks that default probability daily as a live risk monitor, backtests its calibration across smooth and jump-prone markets (where it works, and where it systematically understates downside), and extends the result to VaR, credit models, and position sizing. Python backtest code included.

Disclaimer: The content provided in this newsletter, "Alpha in Academia," is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

Today we will take a look at the full expiry-payoff distribution of a European call option: the probability of zero payoff, the range of positive payoffs, and what changes when the assumed price process changes.

Let’s dive right in.

What Black-Scholes Is and Is Not

The Black-Scholes formula is one of the most used equations in all of finance. But it is worth being precise about what it actually gives you, because there is a common confusion about this.

Black–Scholes gives the discounted risk-neutral expected payoff of a European option under its stated assumptions. That is one number: a price today, not a physical forecast of every possible expiry outcome.

For pricing under those assumptions, that is the object you need. For risk analysis, the rest of the assumed distribution can still matter.

But for risk management, one number is not enough. If you hold a call option on a stock that is currently trading just below the strike with two months to expiry, what you actually want to know is:

• What is the probability this expires worthless?

• If it does pay off, what range of outcomes should I plan for?

• How does that probability change as the stock moves day by day?

The familiar Black–Scholes price does not display those answers by itself, although the model assumptions imply a distribution. What follows is a derivation of that distribution, a controlled synthetic stress test, and a narrower account of what the result can and cannot support.

The payoff distribution depends on the probability measure and process you choose. A physical-measure distribution describes assumed real-world outcomes; a risk-neutral distribution is the object used for no-arbitrage pricing.

Those expectations coincide only in a special parameterization. An undiscounted physical expected payoff should not be labelled a Black–Scholes price.

Read more

Let’s get into it.

The Boltzmann Equation in Finance

The probability distribution of a European call option’s payoff can be derived in closed form, something the literature had left unresolved for over fifty years of options research.

Textbook options pricing tells you the expected value of a call at expiration, which is what Black-Scholes gives you. But what does the full probability distribution of that payoff look like? This paper answers that question by repurposing the Boltzmann equation, originally a tool from 19th-century statistical mechanics for modeling gas particles, as a framework for financial probability distributions. The key result is a closed-form expression that decomposes the call payoff distribution into two pieces: a spike at zero (the probability the option expires worthless) and a log-normal-shaped distribution over positive payoffs (the probability it gets exercised).

Figure 1: Default PDF Coefficient and Closing Price of Alphabet as a function of the entry time.

The authors track this distribution over the 138-day life of an Alphabet call option and show it behaves exactly as intuition demands, widening when the stock has room to move and collapsing to near certainty as expiration approaches. The expected value of this distribution recovers the standard Black-Scholes price, so the new result is consistent with existing theory while being strictly more informative. For risk managers and traders, knowing the full distribution, not just the mean, is what actually lets you size positions and calculate tail risk properly.

Bogliardi, Michele and Charif Khalifi, Zoubida and Kitapbayev, Yerkin and Noguer I Alonso, Miquel and Occhionero, Giulio and Zubelli, Jorge, The Boltzmann Equation in Finance (October 01, 2024). Available at SSRN: https://ssrn.com/abstract=4972632 or http://dx.doi.org/10.2139/ssrn.4972632

Cross-market risk spillovers in Belt and Road Initiative economies

In Belt and Road Initiative financial markets, stock markets are tightly integrated while bond markets remain surprisingly segmented, and Singapore along with Eastern European economies consistently export risk while China persistently absorbs it.

Across 17 economies spanning East Asia, Southeast Asia, South Asia, and Central and Eastern Europe, financial shocks don’t travel equally. This paper maps risk spillovers across stock, bond, and foreign exchange markets simultaneously, a more complete picture than the usual single-market studies. The stock market is the most interconnected (averaging 67% spillover intensity), bonds the most segmented (44%), and FX sits in between.

The pattern of who sends and who receives risk is strikingly consistent across all three markets: Poland, the Czech Republic, and Singapore are persistent exporters of volatility, largely because of their deep ties to European financial infrastructure, while China absorbs external shocks across equities, bonds, and currency alike despite being the world’s second-largest economy. COVID-19 caused a synchronized spike across all three markets, but the Russia-Ukraine war only disrupted currency markets, leaving stocks and bonds relatively unaffected. One counterintuitive driver: rising global policy uncertainty actually reduces cross-border spillovers, because institutions pull capital home rather than rebalancing across markets. For anyone building emerging-market or BRI-focused portfolios, the key takeaway is that China’s financial markets behave more like shock absorbers than shock originators.

Zhang, Kaige and Yao-Peng, Li and Wu, Xin, Cross-market risk spillovers in Belt and Road Initiative economies ​. Available at SSRN: https://ssrn.com/abstract=6922454 or http://dx.doi.org/10.2139/ssrn.6922454

Strategies for Dynamic Bond Ladder Portfolios

Traditional bond ladders passively roll to maturity regardless of yield curve conditions, but framing each rung as an optimal stopping decision, sell now or preserve the option to sell later, lets institutional portfolios adapt dynamically without abandoning the ladder structure entirely.

Bond ladders are a staple of insurance and pension fund investing: buy bonds with staggered maturities, collect coupons, reinvest when bonds mature. Simple, predictable, widely used. The problem is that passive ladders can’t respond when the yield curve shifts regime, say from normal to inverted, and holding legacy low-coupon bonds while short-term rates surge is a loss you could have avoided. This paper proposes treating each ladder rung as an American-style option, where the manager solves a sequential decision problem: at each monthly interval, should you sell a bond outright, strip its coupons to extract near-term liquidity while keeping the principal, or just hold?

The framework computes the value of acting now against the value of preserving the right to act later, the core insight of optimal stopping theory applied here to fixed income. In a simulated 20-year experiment spanning a normal-to-inverted yield curve transition, the adaptive ladder reduced drawdown and tail risk relative to passive buy-and-hold, without converting the portfolio into an unconstrained trading strategy. The remaining obstacle for real-world adoption is computational cost, since solvency models at insurers and pension funds run across thousands of scenarios repeatedly.

Figure 2: Computational time (seconds) comparison between the analytic Gram–Charlier approximation and Monte Carlo value-function evaluation.

The analytic approximation developed here runs substantially faster than brute-force simulation while closely matching its results, making the framework feasible for the production models institutions actually rely on.

Chudtong, Mantana and Peters, Gareth and Anderson, Avery and Yan, Haoran, Optimal Multiple-stopping Strategies for Dynamic Bond Ladder Portfolios (May 27, 2026). Available at SSRN: https://ssrn.com/abstract=6835362

Generative AI and Corporate Credit Spreads

Firms with more AI-exposed workforces saw rising stock prices and rising borrowing costs at the same time, a split that standard finance theory struggles to explain.

When ChatGPT launched in November 2022, something strange happened in corporate bond markets. Companies whose employees were most exposed to AI-driven automation got more expensive to borrow from, not cheaper. Researchers document that credit spreads widened by roughly 6 to 8 basis points per standard deviation of AI workforce exposure, a larger shift than what typical policy uncertainty shocks produce. The puzzle is that these same firms saw their stock prices rise, meaning equity and debt investors looked at the same shock and reached opposite conclusions about firm risk. The explanation lies in what economists call parameter uncertainty: in late 2022, nobody knew the true cost, success rate, or competitive fallout of actually deploying generative AI at scale. Equity, as a claim that benefits from upside optionality, priced in the opportunity. Debt, as a claim sensitive to downside risk, priced in the uncertainty.

The authors confirm this pattern holds across five successive AI model launches, not just ChatGPT, and is strongest for financially fragile firms and those with weak governance. For bond investors in particular, AI exposure is not a free lunch.

Gandhi, Priyank and Lu, Juntai and Pan, Jasper and Plazzi, Alberto and Wei, Jia, Equity Prices the Opportunity, Debt Prices the Risk: Generative AI and Corporate Credit Spreads (March 31, 2026). Available at SSRN: https://ssrn.com/abstract=6503440 or http://dx.doi.org/10.2139/ssrn.6503440

This week for paid subscribers

Paid subscribers are getting a look at whether equity and debt markets price AI exposure differently and why they should. We embed a Merton structural credit model with parameter uncertainty to explain why the same generative AI shock simultaneously lifted stock valuations and widened corporate bond spreads, then test five cross-sectional predictions against 20 years of TRACE bond data. Python replication code included.

Disclaimer: The content provided in this newsletter, “Alpha in Academia,” is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

A paid post, open to everyone

Many of you ask what the paid edition includes.

So I decided to make one of the more popular recent paid research posts available to everyone so you can see whether a paid subscription is for you.

If you find it valuable, the paid edition is $18 a month or $180 a year.

Become a paid subscriber

With a new investigation every Thursday, the annual plan works out to about $3.50 per investigation. Either plan also includes the complete archive of 50+ investigations and all available research companions.

I hope you find the article useful. At the end, you can download the research companion, including the Python code, method notes, tests, and reference results.

Hello and welcome back to another paid post!

Today we will take a look at whether a Japanese corporate settlement tradition is associated with a recurring intraday pattern in USD/JPY.

Let’s dive right in.

The Invisible Clock of the FX Markets

Unlike equity markets, where almost every participant is driven by a singular goal, maximizing investment return, the $7.5 trillion-a-day global currency market is filled with massive players who do not care about alpha. Central banks trade to stabilize local inflation; multinational conglomerates trade to clear supply chains; international shipping firms trade simply to pay their overseas staff.

This can introduce structural non-economic flow into the market. When large, price-insensitive participants transact at similar times, they may leave footprints.

One proposed calendar effect in Tokyo is called the Gotobi Anomaly. The historical test below asks whether USD/JPY tends to rise before the stated fixing window on those dates.

The Core Microstructure Mechanic

The word Gotobi (五十日) translates literally to “days ending in five or zero.” The article’s stated calendar rule focuses on the 5th, 10th, 15th, 20th, 25th, and the final business day of the month.

The proposed mechanism is that Japanese firms with US Dollar-denominated liabilities may need to sell Japanese Yen and buy US Dollars around these dates. If that demand is concentrated, it could create a recurring rise in USD/JPY before the fixing window.

The Fix

The hypothesis centers on the Telegraphic Transfer Middle Rate (TTM), a customer exchange-rate benchmark used by Japanese banks.

For this test, the relevant fixing boundary is assumed to be 09:55 AM Tokyo Time (JST).

The proposed sequence is simple: if banks expect customer demand for US Dollars near that boundary, they may hedge beforehand, pushing USD/JPY higher. Once the window passes, that pressure may fade. The backtest examines the price pattern; it does not observe the customer orders or bank hedges directly.

Our core trading rule is beautifully simple: We buy USD/JPY at 03:00 AM JST and exit at the close of the timestamp-labeled 09:55 AM JST one-minute bar.

Data and Methodology

For this study, we gathered 20 years of intraday 1-minute (M1) USD/JPY OHLC data spanning from January 2006 through December 2025 from HistData.com. Under the Japanese bank-business-day calendar and boundary rules in the companion, this gives us 1,175 complete execution windows.

But here is the catch: reliable, institutional-grade historical FX data repositories typically save their historical files using fixed US Eastern Standard Time (EST) all year round, completely ignoring Daylight Savings Time (DST) adjustments. This is an absolute minefield for a strategy that relies on a precise local Tokyo clock.

Japan does not observe Daylight Savings Time; Tokyo stays locked in UTC+9 every day of the year. The underlying data source treats its timestamps as a static EST baseline, so treating them as DST-aware US/Eastern timestamps would shift the summer bars by an hour. The notebook instead localizes the source timestamps to fixed UTC-5 before converting them to Asia/Tokyo time.

Results

Over the 20-year sample span, the calendar rule yields 1,434 unique scheduled dates after rollbacks and deduplication; 1,175 contain both required boundary bars. The remaining 259 are recorded as incomplete rather than filled or inferred. This is a Japanese bank-business-day sample, with weekends, national holidays, 1–3 January, and 31 December treated as closures.

Across those 1,175 gross windows, the test generates 5,331.7 pips, 4.54 pips per trade, a profit factor of 1.62, and an event-annualized Sharpe ratio of 1.27. These figures exclude spread, commission, slippage, and financing.

Using the 415-minute timestamp difference between 03:00 and 09:55, the 1,175 completed positions amount to about 4.6% of the continuous 20-year sample. The compounded gross-return path has a maximum drawdown of −3.85% under the stated rules.

The gross pattern wins on 61.62% of the retained windows. That consistency is compatible with the microstructural thesis, but performance from one rule and one data feed does not by itself validate the causal explanation or establish a live trading edge.

Practical Implementation & Execution Risks

While a gross event-annualized Sharpe ratio of 1.27 looks attractive on a research screen, translating these backtest metrics into a real brokerage account requires an explicit model of spread, commission, slippage, and financing.

The gross average is 4.54 pips per trade. In a simple sensitivity that subtracts a fixed round-trip cost, 0.4 pips lowers the average to 4.14 pips, the profit factor to 1.55, and the event-annualized Sharpe ratio to 1.17. At 1.0 pip, those figures fall to 3.54, 1.46, and 1.01; at 1.5 pips, to 3.04, 1.38, and 0.88. This is only a cost sensitivity: the test still does not model actual commissions, slippage, or financing.

The code exits at the close of the timestamp-labeled 09:55 AM bar. The one-minute data alone do not establish that this close is the fixing instant or the last print before 10:00. The boundary choice matters: the gross average is 5.08 pips at the 09:55 bar open, 4.54 pips at its close, and 4.20 pips at the close of the 09:59 bar.

Furthermore, the entry boundary is 03:00 AM JST, six hours before 09:00 and nearly seven hours before the stated fixing boundary. Feed continuity and spreads at that hour matter, but the test does not model them.

What I’m trying to get at is this: This simple strategy produces an elegant gross historical pattern under an explicit business-day rule. But like everything else in quantitative finance, there is no such thing as a free lunch. A pattern in one backtest is not proof that the edge is live or executable; that requires careful bar conventions and realistic trading costs.

Core Takeaways

The 20-year journey of the Gotobi anomaly suggests a useful direction for the modern market researcher: some potential edges may come from structural, mandatory, non-economic buying or selling pressure. But this result remains specific to one data feed and one explicit calendar rule. It is a starting point for further cost-aware testing, not a robust foundation for live trading.

Conclusion

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As always, this is for educational purposes, and should not be implemented in any live trading or taken as investment advice

Research companion

The companion includes the tested implementation, method notes, fixed dependencies, synthetic tests, and reference outputs. Supply your own HistData files; the package contains no market data.

Download the research companion

Version 1.0.3 · Tested with Python 3.12 · 8 GB RAM recommended

Revision note — 27 July 2026: I implemented the calendar as Japanese bank business days, corrected the historical results and chart, clarified the exposure measure and bar timing, and added the research companion. The evidence still supports a positive gross pattern, but not the proposed cause or a live trading edge.

Sources

Source: HistData.com, USD/JPY one-minute historical data, 2006–2025.

Disclaimer

The content provided in this newsletter, “Alpha in Academia,” is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.

Let’s get into it.

Credit Alpha and Hit-Ratio Targeting

Standard corporate bond market-making targets can severely hurt profitability because winning an order from a highly informed trader costs significantly more than winning one from a retail investor trading for simple rebalancing reasons.

Electronic bond market makers typically use a “hit ratio,” the percentage of client requests they win, as their primary metric for success. However, treating all client flow equally forces dealers to aggressively quote and inadvertently subsidize highly informed, toxic order flow. By replacing raw targets with a residual-quality-adjusted metric, we can mathematically strip out public credit factors, carry, and index trends from post-trade performance, isolating true client toxicity. When tested in a multi-bond framework, this quality-adjusted pricing naturally tightens quotes for low-risk, inventory-recycling clients while widening out against toxic counter-parties.

Shifting focus from raw volume to residual quality allows a desk to achieve its client service mandates while dramatically cutting adverse-selection costs, turning a rigid institutional metric into an optimized, risk-aware profit driver. Niang concludes that a uniform raw target is bad especially when the economic quality of fills is heterogeneous and suggests that the optimal strategy would be to “subsidize service selectively for low-residual-toxicity, recyclable, and forecastable flow” opposed to blindly chasing every hit.

Niang, Bouna, Residual-Quality-Adjusted Hit-Ratio Targeting in Corporate Bond RFQ Market Making Credit Alpha, Client Flow Quality, and Style-Aware Warehousing (May 19, 2026). Available at SSRN: https://ssrn.com/abstract=6815279 or http://dx.doi.org/10.2139/ssrn.6815279

Liquidity Provision in Korean Retail Markets

In retail-dominated markets like South Korea, foreign institutional investors act as primary liquidity providers in illiquid stocks, earning significant short-term premiums for absorbing local order imbalances.

In the US, retail traders typically act as the marginal liquidity providers, stepping in to absorb order flow from big institutions and earning a small premium when the market overextends. This paper flips that dynamic on its head by analyzing eleven years of weekly trading data from the Korean equity market, which is structurally dominated by domestic retail volume.

The researchers found that when foreign investors aggressively buy less liquid Korean stocks, those same equities experience positive abnormal returns over the following week. This isn't necessarily a sign of superior fundamental intuition, but is actually classic, risk-averse liquidity provision. Foreigners are stepping up to absorb order flow pressure in corners of the market where immediacy is expensive, trading against contemporaneous price moves to pocket a temporary concession.

This structural behavior becomes starkly apparent when tracking the holding horizons, as the abnormal per-week alpha decays eighteenfold from the first week to the twelfth week. This highlights that “the foreign abnormal return reflects compensation for risk-averse liquidity provision” rather than a slow, structural repricing of fundamental information. For global investors navigating emerging or retail-heavy cross-sections, it serves as a reminder that the identity of the stabilizing marginal trader is highly dependent on local market architecture.

Sujin, Pyo and Lee, Woojin, Who Provides Liquidity in Retail-Dominated Markets? Evidence from Korea. Available at SSRN: https://ssrn.com/abstract=6885262 or http://dx.doi.org/10.2139/ssrn.6885262

Hedge Funds in Debt

The Federal Reserve’s monetary policy stances dictate hedge fund exposures to corporate and sovereign debt, transforming these funds into central transmitters of market shocks during modern tightening cycles.

The Federal Reserve's post-2015 money market operations have fundamentally tightened the link between monetary policy and hedge fund behavior. When the central bank adopts a hawkish stance, rising short-term rates systematically drive expansionary bond market exposures across nearly all major fund strategies. This relationship shifts the focus away from traditional alpha generation toward a highly policy-sensitive setup.

This shift is particularly acute in corporate debt markets, where higher rates coincide with increased fund sensitivity to credit factors. However, this dynamic changes drastically during market stress. During crises, funding liquidity from prime brokers dries up or becomes prohibitively restricted, causing funds to aggressively shed debt holdings, withdraw leverage, and shift capital into cash.

Consequently, structural shifts in the post-pandemic era have transformed hedge funds from net absorbers of market noise into prominent net transmitters of idiosyncratic volatility to the broader financial ecosystem. Noori specifically emphasizes that modern debt markets exhibit a “stronger and more policy-sensitive HF role in bond markets.” This dynamic footprint matters deeply for market participants, as modern fixed-income stability is now intrinsically linked to the regulatory and speculative leverage cycles of non-bank financial intermediaries.

Noori, Mohammad, Hedge Funds in Debt (May 30, 2026). Available at SSRN: https://ssrn.com/abstract=6858498 or http://dx.doi.org/10.2139/ssrn.6858498

Volatility Drag and the Admissible Financing Boundary

A disciplined, rule-based borrowing policy implemented after market drawdowns can systematically recover the wealth lost to compounding volatility drag without increasing the long-term risk of ruin.

Modern Portfolio Theory beautifully explains what assets to hold, but it leaves us completely stranded when the path goes sideways. Every risky portfolio suffers from a hidden tax known as volatility drag, or Itô's Zeta, which automatically shaves a chunk off your compounded returns over time. For instance, a diversified portfolio with 15% volatility faces a predictable math drag of exactly 1.125% per year, turning a theoretical million-dollar nest egg into a significantly smaller realized sum over a thirty-year horizon.

While traditional finance treats this drag as an unalterable cost of doing business, this paper demonstrates that we can fight back using a mechanical, second-mover financing rule. By establishing an arithmetic benchmark of where the portfolio should be and borrowing small, strictly bounded amounts to maintain equity exposure only after the market drops, investors can generate “McKean's Alpha” to offset the drag.

The raw model is annoyingly difficult to beat, provided you follow a precise deviation rule rather than just blindly buying dips. In extensive simulations across thirty-year horizons, a precise rebalancing rule recovered virtually 100% of the volatility tax even under steep 9% borrowing costs. However, the math has a hard ceiling, meaning that “disciplined financing restores the path, and excessive financing ends in ruin.” For investors, this changes the entire game, as volatility shouldn't just be feared as a risk metric, because it can actually be utilized as the raw material for path optimization, proving that a structured financing policy is just as vital to lifetime wealth as asset allocation.

Anderson, Thomas, Volatility Drag and the Perpetual Borrowing Option: The Admissible Financing Boundary (May 18, 2026). Available at SSRN: https://ssrn.com/abstract=6795058 or http://dx.doi.org/10.2139/ssrn.6795058

This week for paid subscribers

Paid subscribers are getting a look at whether prediction markets can forecast macroeconomic data more accurately than professional economists. We run Diebold-Mariano and probability calibration tests on Kalshi distributions to evaluate their predictive edge over the Survey of Professional Forecasters across inflation, unemployment, and Fed rate decisions. Python backtest code included.

Disclaimer: The content provided in this newsletter, "Alpha in Academia," is for informational and educational purposes only. It should not be construed as financial advice, investment recommendations, or an offer or solicitation to buy or sell any securities or financial instruments. Past performance is not indicative of future results. The financial markets involve risks, and readers should conduct their own research and consult with qualified financial advisors before making any investment decisions.

The interpretations, opinions, and analyses presented herein are those of the author and do not necessarily reflect the views of the original researchers, their institutions, or the full implications of the cited academic papers. While every effort is made to accurately represent the research discussed, readers should be aware that the summaries and interpretations may not capture the full scope or nuances of the original studies. The information contained in this newsletter is believed to be accurate and reliable at the time of publication, but accuracy and completeness cannot be guaranteed. The author and publisher accept no liability for any loss or damage resulting from reliance on the information provided.

This newsletter may contain links to external websites or resources. The author is not responsible for the content, accuracy, or reliability of these external sources.

By subscribing to or reading this newsletter, you acknowledge that you have read and understood this disclaimer and agree to hold the author and publisher harmless from any liability that may arise from your use of the information contained herein.