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 how the Boltzmann Framework (a 150-year-old equation originally designed to describe gas particles) gives us something Black-Scholes never did: the full probability distribution of what your option is worth at expiry, not just its expected value.

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 you the expected value of an option’s payoff under a specific probability model. That is it. One number. It tells you what the option is worth on average, in expectation, discounted back to today.

For pricing, that is exactly what you need. When you buy or sell an option, you want to know its fair value. The expected payoff, discounted, is that value.

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?

Black-Scholes does not answer these questions. It gives you the mean of a distribution it never shows you. What follows is a derivation of that full distribution, a backtest of how well it calibrates in practice, and an honest assessment of where it holds and where it breaks down.

Figure 1: Black-Scholes gives you a single expected value. The full distribution tells you the probability of expiring worthless and the range of possible payoffs if it does not.

Deriving the Distribution

We model the log-price of the underlying stock as:

xₙ | x₀ ~ N( x₀ + (N–n)μ, (N–n)σ² )

where x = log(S) is the log stock price, μ is the daily drift, σ is the daily volatility, and N–n is the number of days remaining to expiry.

This is the assumption that underlies the Black-Scholes model — geometric Brownian motion. We will test this assumption rigorously later. For now, we work within it.

A European call with strike k pays at expiry:

c = (Sₙ – k)⁺ = [exp(xₙ) – k] · H[exp(xₙ) – k]

where H is the Heaviside step function (1 if the argument is positive, 0 otherwise). At any date before expiry, c is itself a random variable — its value is unknown. What does its probability distribution look like?

The key formula

Using a generalized change-of-variables formula based on the Dirac delta function, we can write the probability density function (PDF) of the payoff c as:

Ψ(c, N) = α · δ(c) + (1–α) · γ(c, N)

This is the central result. The distribution splits into exactly two parts.

Figure 2: The full payoff distribution Ψ decomposes into a spike at zero (the option expires worthless with probability α) and a continuous distribution γ(c,N) for the payoff if it does exercise. The Black-Scholes price is the expected value of this entire distribution.

Alpha: the default probability

The first component is a spike at c = 0. This represents the probability that the option expires worthless (the stock ends below the strike and you lose the premium). The size of this spike is:

α = ½ · erfc ( (x₀ + (N–n)μ – log k) / (σ√(2(N–n))) )

In other terms: α is determined by how far the expected future log-price is from the log-strike, scaled by the uncertainty in that log-price. When the stock is deep below the strike, α is close to 1. When it is well above, α is close to 0. You can compute it daily as the stock moves.

Think of α like a probability of rain. When the forecast is for a 70% chance of rain, it doesn’t mean it will definitely rain. It means that in 100 similar situations, it rains 70 times. Alpha works the same way: α = 0.72 means that in similar market conditions with similar time to expiry and similar distance from the strike, the option expires worthless 72% of the time.

Gamma: the exercise-conditional distribution

The second component is γ(c, N), which is a log-normal distribution describing what the payoff looks like conditional on the option actually being exercised. This is a shifted log-normal in (k + c):

γ(c, N) ∝ (1/(k+c)) · exp(–[log(k+c) – (N–n)μ – x₀]² / (2(N–n)σ²))

The key properties of γ: it is right-skewed (large payoffs are rarer but possible), it spreads out with longer time to expiry, and it shifts right as the stock price rises above the strike. The 1/(k+c) factor means payoff density decays faster than the log-price density, meaning the deep in-the-money gains are less probable than the underlying stock movement alone would suggest.

Figure 3: Left — γ(c,N) for an at-the-money option at three horizons. Longer expiry spreads the distribution and shifts the mode right. Right — γ(c,N) for three strike levels at a fixed 2-month horizon. An ITM option has a distribution shifted further right.

Recovering Black-Scholes as a special case

A useful sanity check: if you take the expected value of Ψ(c, N) over all c ≥ 0, you recover exactly the Black-Scholes price integral. Numerically, the difference is less than $0.0001 for all tested horizons.

This confirms that Black-Scholes and the full distribution are consistent. Black-Scholes is not wrong, but it is simply incomplete. It gives you the mean of Ψ without telling you the shape.

Tracking Alpha Over a Position's Life

One of the most practical applications of α is as a live risk monitor. You re-estimate μ and σ from recent price history and recompute α each day. As the stock rises above the strike, α falls. As it falls below, α rises. As expiry approaches, α converges to either 0 or 1 depending on where the stock is relative to the strike.

Figure 4: Top — a simulated stock path relative to the strike k = $104. Bottom — the corresponding α tracked daily. When the stock falls into the OTM zone, α rises. When it climbs above the strike, α drops. Near expiry, α converges rapidly.

This is more information than a simple delta or moneyness metric. Delta is a sensitivity measure (how much the option price moves per dollar of stock movement). Alpha is a probability (what the market-implied chance is that you walk away with nothing). They measure different things.

Practical use case

If α rises above 0.80 and you hold a long call position, the distribution is telling you there is an 80% chance of expiring worthless. That may or may not be actionable depending on your thesis, but it is a clean, calibrated number to use in position-sizing and stop-loss decisions.

Does It Work? A Backtest

The setup

We run a systematic backtest to answer the most important question about any risk metric: does the predicted probability match the realized frequency?

We simulate 3,500 trading days of price data (~14 years), construct synthetic call options at five different strike levels and three expiry horizons on every entry date, compute α using rolling estimates of μ and σ, then check how often options in each predicted-α bucket actually expire worthless. This produces roughly 47,800 option observations.

We run this across four different price-generating processes, each representing a different assumption about how markets behave:

The calibration result under GBM

The reliability diagram below is the key output. The x-axis is the predicted α; the y-axis is the fraction of options in that bucket that actually expired worthless. A perfect forecast sits on the 45° dashed line (if α = 0.70, exactly 70% of options should expire worthless).

Figure 5: Reliability diagram for α under Pure GBM. Each point is a bucket of predictions. Dot size scales with number of observations. The formula calibrates well across the full range of α values, with a Brier score of 0.17 (vs 0.25 for a no-skill baseline).

The calibration is reasonably good. The Brier score (lower is better, 0.25 is no skill) of 0.17 reflects meaningful predictive content. The slight deviations from the 45° line are mostly due to parameter estimation noise as we are estimating μ and σ from rolling windows rather than knowing the true values. When we run the backtest with oracle (true) parameters, the Brier score drops significantly, confirming that estimation noise is the dominant source of error, not the formula itself.

Where it breaks down

The more important result is what happens when we deviate from GBM. The jump-diffusion process is the most relevant case for practitioners, since equity markets do make sudden large moves.

Figure 6: Left — calibration under Pure GBM. Right — calibration under GBM + Jumps. The jump process introduces systematic bias: α underestimates the true OTM probability in the middle range, because the formula has no way to anticipate large negative jumps.

The pattern in the right panel is specific and interpretable. For options that α predicts will expire worthless around 40–60% of the time (near-ATM options), the actual OTM frequency is higher than predicted. This is exactly what you would expect from fat tails and negative skew: there are more scenarios where the stock drops suddenly below the strike than the Gaussian model predicts.

The honest summary

The formula works well when the stock moves smoothly. It systematically underestimates downside risk when markets jump. For equity options specifically, treating α as a lower bound on the true default probability is a reasonable conservative adjustment.

Error decomposition

We decompose the backtest error into two components: parameter estimation noise (from rolling μ and σ estimation) and model misspecification (from using the wrong DGP).

The takeaway: if you improve your volatility forecast, you improve the calibration of α more than if you change the functional form of the formula. Implied volatility (backed out from market option prices) is likely a better σ input than historical realized vol for this application.

Here is a link to download the data and Python backtest for this post: File

Key Takeaways

Risk management of options positions

The most direct application. For any call option position, you can compute α daily and use it as a probability-weighted risk signal:

• Long call: α directly measures your at-risk capital. If α = 0.75 and you paid $500 in premium, your expected loss from expiry is $375.

• Short call: α measures the probability you walk away with the premium. 1–α is the probability you have to pay out.

• Spreads: compute α for each leg separately and combine to get a distribution of net payoffs across the spread.

VaR and expected shortfall on options

Once you have the full distribution Ψ(c, N), VaR and ES calculations follow directly. Rather than using delta-normal approximations (which assume options behave like linear instruments — they don’t), you integrate over Ψ directly.

From the backtest, the γ distribution understates tail payoffs, especially in jump regimes. For conservative VaR calculations, we recommend widening the γ tail by one volatility regime (equivalent to computing γ with σ × 1.2 to 1.5), depending on how jump-prone the underlying is.

Structural credit models

In Merton-style credit models, a firm’s equity is a call option on its assets with strike equal to the face value of its debt. The default probability in this framework is directly α, computed using the firm’s asset value, asset volatility, and debt structure. This framework gives you the full distribution of equity value at debt maturity, not just the expected value.

What Comes Next

The backtest used historical realized volatility to estimate σ. For listed options, implied volatility (backed out from market prices using the inverse Black-Scholes formula) is a better forward-looking estimate. Using implied vol as the σ input would likely tighten the calibration of α noticeably, especially for shorter-dated options. Additionally, the backtest here used synthetic price paths. Running the same calibration test on real historical options data (for example, using OptionMetrics or CBOE data for SPX or liquid single names) would give a cleaner picture of real-world performance. The synthetic test is informative but not a substitute.

The Black-Scholes model implies a flat volatility surface, a single σ for all strikes and expiries. In reality, implied volatility varies by strike (the volatility smile) and by expiry (the term structure). Incorporating a strike-dependent σ into the formula would make α and γ more accurate for options away from the money.

Given that the biggest calibration failure is in jump regimes, a practically useful extension is to add a jump-adjustment to α for equity options. This could be as simple as a correction factor calibrated to the VIX level, or as involved as fitting a Merton jump-diffusion model to implied vol data and computing α under that richer model.

The derivation works for any payoff structure that can be expressed as a function of the underlying log-price. Puts, straddles, and barrier options all have computable payoff distributions under this framework. The math is analogous; the Dirac delta sifting approach generalizes.

Conclusion

Thank you for supporting the newsletter!

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

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.

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.

Today we will take a look at how a rigid, non-economic corporate settlement tradition in Japan creates a structural cash imbalance that can be systematically exploited for intraday alpha in the foreign exchange market.

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 introduces structural non-economic flow into the market. When these massive, price-insensitive market participants are forced by regulatory or cultural habits to transact at the exact same times, they leave footprints.

Nowhere is this dynamic clearer than in the Asian financial hubs, particularly Tokyo. Because of rigid corporate traditions deeply embedded in Japanese business culture, a specific calendar anomaly has quietly persisted for decades. It is called the Gotobi Anomaly.

The Core Microstructure Mechanic

The word Gotobi (五十日) translates literally to “days ending in five or zero.” In Japan, corporate financial operations are traditionally tied to these specific calendar dates: the 5th, 10th, 15th, 20th, 25th, and the final business day of the month. If you are a Japanese company doing business globally, these are the days your international invoices come due, your payroll settles, and your trade financing balances clear.

This creates a massive, one-sided liquidity mismatch. Japan is a heavily export-reliant economy, but its massive importing firms (think energy conglomerates buying oil or electronics distributors importing components) have a structural problem: they earn revenue in Japanese Yen (JPY), but their invoices are denominated in US Dollars (USD). On Gotobi days, these firms must aggressively sell JPY and buy USD to pay their bills.

The Fix

Crucially, Japanese corporate treasuries do not trade iteratively throughout the day like portfolio managers. Instead, they hand their massive orders to domestic commercial mega-banks (like MUFG, SMBC, or Mizuho) to be executed at an official, standardized daily benchmark rate.

This benchmark is known as the Telegraphic Transfer Middle Rate (TTM). Every single trading day, the official Tokyo TTM Fix is calculated and locked in at exactly 09:55 AM Tokyo Time (JST).

This sets off a highly predictable chain reaction, starting with the bank’s risk, as commercial banks take on massive corporate orders to sell billions of Yen and buy billions of Dollars at whatever rate the market prints at exactly 09:55 AM. Then, if a bank knows it has a massive structural order to buy USD at 09:55 AM, it cannot wait until 09:55 AM to buy those Dollars, or it will move the market against itself and suffer catastrophic slippage. Thus, to hedge their risk, bank treasury desks begin aggressively accumulating USD/JPY hours ahead of the deadline, driving the exchange rate systematically higher. Once the 09:55 AM fixing window passes, the corporate demand is instantly fulfilled, the buying pressure vanishes, and the market typically experiences a sharp, mean-reverting correction.

Our core trading strategy is beautifully simple: We buy USD/JPY early in the Tokyo session (03:00 AM JST) to ride the coattails of institutional pre-hedging accumulator flows, and we flip the position flat to the market the exact minute the fixing window closes (09:55 AM JST).

Read more

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.

Today we will take a look at whether prediction markets can forecast macroeconomic data more accurately than professional surveys, and how statistical tests show Kalshi’s ability to predict inflation, unemployment, and Fed rate decisions.

Let’s dive right in.

The Idea

Every month, professional economists produce forecasts for three numbers that move markets: CPI inflation, the unemployment rate, and what the Federal Reserve will do with interest rates. These forecasts drive bond positioning, mortgage pricing, and portfolio strategy.

Prediction markets offer a different approach. On Kalshi, you can buy a contract that pays $1 if CPI comes in above 3% next month. The price of that contract reflects the market’s collective probability estimate, updated continuously as new information arrives. No survey lag. No wait time period for the next Bloomberg consensus.

We ran statistical tests to evaluate how good these markets actually are. We used publicly available data from Kalshi’s research repository, FRED, the Philadelphia Fed’s Survey of Professional Forecasters (SPF), and the San Francisco Fed’s Monetary Policy database.

How the Contracts Work

For a given CPI release, there are contracts at multiple thresholds: ‘above 2.0%’, ‘above 2.5%’, ‘above 3.0%’, and so on. The prices of these contracts together imply a full probability distribution, not just a point estimate.

If the ‘above 3.0%’ contract trades at $0.40 and the ‘above 3.5%’ contract at $0.22, the market is saying there is roughly an 18% chance CPI falls between 3.0% and 3.5%. Apply this logic across all thresholds and you have a complete, daily-updating forecast distribution.

Kalshi covers CPI (headline and core), unemployment, payrolls, GDP growth, and Federal Reserve rate decisions. For GDP and core CPI especially, Kalshi is one of the only sources of a real-time, market-based probability distribution. Everything else either provides a point estimate or requires a few weeks wait for a survey.

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Media sentiment is highly asymmetric across topics, meaning bad news decays far more slowly than good news, and this “stickiness” is surprisingly most severe in data-rich areas like corporate strategy and financial stability.

We all know the financial media loves a crisis, but it turns out the “stickiness” of bad news depends entirely on what the journalists are writing about. By digging into decades of Wall Street Journal archives, researchers discovered that unexpected negative news acts like a stubborn stain, lingering in media narratives much longer than positive developments of the exact same magnitude. While you might assume that data-heavy topics would correct themselves quickly as fresh information arrives, the opposite happens. In fields like corporate strategy, real estate, and financial stability, bad news frequently opens up a cascading narrative of uncertainty, triggering follow-up coverage, earnings revisions, and secondary anxieties that feed on themselves for up to a year.

Figure 1: Within-topic asymmetry in cumulative sentiment persistence

For investors, this topic-level narrative inertia is a critical blind spot because aggregate sentiment indices completely mask these dynamics. A sudden drop in market mood might not mean macroeconomic fundamentals are decaying, but rather that media attention has rotated into a specific topic where negative narratives are naturally harder to dislodge. Ultimately, understanding this asymmetry matters because where bad news lands matters as much as how bad it is.

Agarwal, Isha and Chen, Wentong and Prasad, Eswar S., When Does Bad News Stick? Topic-Level Asymmetry in Narrative Dynamics (May 21, 2026). Available at SSRN: https://ssrn.com/abstract=6812261 or http://dx.doi.org/10.2139/ssrn.6812261

How Execution Discipline Rescues Crypto Machine Learning

The core finding of this paper is that hourly machine-learning models can generate highly profitable trading signals for Bitcoin, but these gains completely disappear under realistic transaction costs unless the trading strategy explicitly filters out weak, low-conviction forecasts to suppress excessive turnover.

Financial machine learning often suffers from a frustrating gap between statistical accuracy and actual trading profitability, a reality that becomes glaringly obvious at short horizons. When testing standard machine learning models on hourly Bitcoin data, the raw predictive signals look promising on paper. However, if you naively execute every single signal flip, the strategy quickly self-destructs. Because hourly forecasts constantly hover around zero, a simple sign-based rule triggers non-stop trading, allowing standard transaction costs to quietly bleed the account dry. To save the strategy from death by a thousand cuts, the authors introduce a cost-aware filter that blocks any trade where the forecast magnitude fails to outweigh the cost of execution.

Figure 3: Impact of model architecture on cost-aware strategy performance

This simple addition changes everything, drastically cutting down unnecessary trades and unlocking an annualized return above 65% for the top-performing model configuration. Interestingly, the tree-based XGBoost architecture proved descriptively tougher to beat than its complex neural network alternatives. Ultimately, this research matters because it shifts the spotlight away from complex model tuning and back to raw execution discipline, proving that market alpha depends less on finding a perfect model and more on whether your trading system has the patience to sit on its hands. As the authors aptly note in their conclusion, “short-horizon predictive signals are fragile, transaction costs are powerful, and the path from forecast to trade matters as much as the forecasting model itself”.

Bysik, Andrei and Ślepaczuk, Robert, Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting (May 19, 2026). Available at SSRN: https://ssrn.com/abstract=6795938 or http://dx.doi.org/10.2139/ssrn.6795938

Why Rules Beat Raw Math Out of Sample

Constrained portfolio optimization and Bayesian models like Black-Litterman significantly outperform standard mean-variance strategies in real-world market conditions by curbing extreme asset concentration and reducing sensitivity to input errors.

When you let a standard computer algorithm build an optimal stock portfolio based purely on historical data, it tends to behave like a reckless gambler, aggressively piling nearly eighty percent of your money into just three large companies. This structural quirk, known as the corner portfolio problem, happens because the math chases low volatility on paper while ignoring real-world concentration risk. To see how we can fix this, we examined a research paper that tracked ten major United States stocks through recent market cycles. By forcing the model to obey simple diversification rules, such as capping any single stock position at fifteen percent, the portfolio surprisingly beat the unconstrained strategy in out-of-sample testing, delivering a smoother ride and higher risk-adjusted returns.

Figure 3: Constrained vs Unconstrained Efficient Frontier

The researchers also integrated the Black-Litterman model, a clever framework that anchors your portfolio to the broader market and only deviates when you have high-confidence, specific trading views. For investors, this paper proves that raw mathematical efficiency in a backtest is often a mirage, and that trading constraints are not annoying roadblocks, but rather essential tools that enhance actual portfolio robustness when market regimes inevitably shift. As the authors note in their closing remarks, institutional asset management requires frameworks that provide a “more stable and economically realistic foundation for long-term portfolio allocation decisions.”

Verma, A. K., & Barkam, S. (2026). From Classical Optimization to Bayesian Integration: A Comprehensive Analysis of Systematic Portfolio Management (arXiv:2605.29413v1). arXiv. https://doi.org/10.48550/arXiv.2605.29413

The Trump Factor Moves the Markets

Elections trigger positive short-term stock index returns alongside sharp spikes in market volatility, while political communication via social media moves trading volume and specific sectors in a transient fashion.

No other politician has stood in the crosshairs of financial analyses as frequently as Donald Trump, and the accumulated data reveals that his words and victories consistently move markets. When looking at aggregate index reactions, his election victories historically trigger an immediate wave of market optimism that drives stock prices up, though this is preceded by an uncertainty shock that sends option-implied volatility soaring before it plummets post-election. Beneath the surface, a fascinating sector-specific risk rotation occurs. Capital routinely flows out of defensive, safe-haven assets like utilities and health care, rushing instead into growth-sensitive cyclical industries, financials, and heavily regulated “brown” assets anticipating domestic deregulation.

Interestingly, the data shows that while fossil fuel equities enjoy an immediate post-election bump, these gains are remarkably short-lived and segment back to zero within weeks, suggesting that long-term market forces tend to outlast symbolic policy shifts. For the modern investor, this comprehensive synthesis highlights that political rhetoric and electoral surprises create robust, highly tradable pockets of short-term volatility, but underlying economic realities ultimately dictate long-term asset pricing.

Batt, Elias-Noah and Munkow, Jan and Schiereck, Dirk, The Finance Researcher’s Favorite – President Donald Trump and the Stock Market. Available at SSRN: https://ssrn.com/abstract=6813771 or http://dx.doi.org/10.2139/ssrn.6813771

This week for paid subscribers

Paid subscribers are getting a look at how out-of-sample physical grid signals can expose predictable volatility corridors, and how an expanding multi-factor regression can harvest those imbalances before real-time delivery. 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 how out-of-sample physical grid signals can expose predictable volatility corridors, and how an expanding multi-factor regression can harvest those imbalances before real-time delivery.

Let’s dive right in.

Recall

Let’s review our baseline model from our first look at the Texas power grid from two weeks ago. By simply selling power in the Day-Ahead Market and buying it back in Real-Time every single hour of the year, we uncovered a persistent, structural average spread of $0.93 per megawatt-hour. It was a respectable baseline, but trading every single hour of the year regardless of external signals is a heavy-handed tactic. In the real world, the friction of execution can quickly erode those theoretical gains. To evolve this baseline into something truly institutional, we have to look at how sophisticated desks trade the physical shape of the grid rather than just reading a static clock.

This transition from a naive clock to a selective, signal-driven framework is present in energy economics. In their seminal paper analyzing wholesale electricity markets, researchers Akshaya Jha and Frank Wolak discussed how financial virtual bids act as an essential arbitrage mechanism to align forward and spot prices. They examined the boundaries of market efficiency and concluded that convergence bidding profitability is structurally bound by transaction costs. Specifically, they noted in their conclusion that purely financial forward market trading can improve the operating efficiency of short-term commodity markets by optimizing thermal generation dispatch and reducing total variable production costs.

We can take this foundational thesis and extend it to modern intermittent grids, arguing that this spot price risk is driven by the real-time velocity of physical forecast errors. When the system operator’s day-ahead expectations decouple from physical reality, a violent real-time price correction occurs, and that is exactly where our new strategy hunts for alpha.

Jha, A., & Wolak, F. A. (2013). Testing for market efficiency with transactions costs: An application to convergence bidding in wholesale electricity markets. Stanford University. https://arefiles.ucdavis.edu/uploads/filer_public/2014/03/27/caiso_vb_draft_v8.pdf

Data Architecture

Before constructing a predictive engine, we must address the data required to blend financial prices with the physical state variables of the grid. Our asset universe consists of the same Day-Ahead Market (DAM) and Real-Time Market (RTM) as part one’s, using 2025’s data and HB_North prices.

We then will overlay system-wide load parameters and intermittent wind forecasting matrices. While the DAM closes at 10:00 AM, the physical inputs and resource planning schedules are locked in by grid operators much earlier in the morning. To reflect real-world desk execution and mitigate look-ahead bias, we discard any wind forecasts posted after 8:00 AM on the day prior to delivery. This way, we guarantee that our feature space matches the exact information pool available to a systematic participant before capital is committed.

Feature Engineering and Grid Physics

To capture conditional alpha, we must move away from static chronological filters and design features that directly map the physical stress states of the transmission network. The ERCOT power grid operates as a balancing act between fixed consumer demand and variable, intermittent renewable generation. Because electrical energy cannot be stored efficiently at a massive grid scale, any sudden divergence between the system operator’s day-ahead forecasts and the real-time physical dispatch forces the market to clear through violent price corrections. Our strategy targets this structural forecast decoupling by tracking the physical velocity of wind and load.

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Let’s get into it.

Social Media Is Quietly Costing Retail Investors

Financial recommendation algorithms on social media platforms systematically shift retail attention toward speculative, hype-driven stocks, inducing uninformed trading that ultimately reduces investor welfare and erodes market quality.

Digital platforms typically are not passive conduits of user opinions as they actively filter, rank, and personalize information to maximize user engagement opposed to content accuracy. By analyzing granular user feeds on StockTwits, this study demonstrates how algorithmic curation shapes investor behavior. When a platform algorithmically boosts specific contributors, it creates a localized attention shock. Investors predictably crowd into these promoted tickers, significantly boosting retail trading volumes while completely tuning out non-curated alternatives.

Crucially, this customized content appears to actively harm retail portfolios. As the paper warns, “investor behavior and market outcomes will reflect platform-level design choices,” meaning retail order flows become significantly less informed under heavy algorithmic exposure. Individuals are frequently lured into trading in the wrong direction, buying stocks right before they decline and selling right before they appreciate. This effect is particularly severe around chaotic earnings announcements when cognitive constraints are high.

For market participants, the takeaway is clear in that relying on customized social media feeds as an informational shorthand is an easy way to get trapped in short-term return reversals, proving that engagement-maximizing design choices carry substantial hidden economic costs.

Drake, Michael S. and Koenraadt, Jeroen and Thornock, Jacob and Twedt, Brady J., Social Media Algorithms as Information Intermediaries (May 07, 2026). Available at SSRN: https://ssrn.com/abstract=6731603 or http://dx.doi.org/10.2139/ssrn.6731603

The Real Cost of U.S. Fiscal Confusion

Erratic and confusing communication from the U.S. Treasury acts as a direct driver of sovereign bond market instability and inflation, with its economic penalty shifting radically depending on the nation’s debt burden.

Macroeconomic researchers traditionally treat Treasury press releases as mere political noise, but analyzing three decades of official communication reveals that institutional signaling has a powerful, tangible footprint on asset pricing. During periods of low inflation, policy confusion primarily acted as a real-economy drag, depressing industrial production as businesses paused hiring. However, in our current high-debt era, this transmission mechanism has fundamentally fractured. Treasury erraticism now bypasses the real economy entirely, transmitting directly into severe price inflation and violent shifts in the sovereign bond market.

When high-frequency messaging oscillates unpredictably, anxious bond investors instantly demand a premium to absorb long-term American debt, heavily distorting the long end of the yield curve. For investors, these findings are a wake-up call that the forward guidance paradigm is no longer the exclusive sandbox of the Federal Reserve. In a structurally leveraged economic regime, a chaotic fiscal authority compromises bond market liquidity and unanchors long-run inflation pricing, proving that “the forward guidance paradigm is no longer the exclusive domain of monetary policy.”

Guo, Yiting and Zhu, Zeju, The Price of Fiscal Confusion: Evidence from U.S. Treasury Communication. Available at SSRN: https://ssrn.com/abstract=6804466 or http://dx.doi.org/10.2139/ssrn.6804466

The Global Attention Saturation Threshold

When global investor attention concentrates past a critical threshold, the market mechanism abruptly switches from aggregating diverse private insights to synchronizing around a single public narrative, triggering severe liquidity drops and explosive systemic volatility.

Markets are highly efficient information processors until everyone starts looking at the exact same thing. This study uncovers a distinct behavioral boundary, known as the Attention Saturation Threshold, across 22 global equity indices. Below this point, standard linear asset pricing models work beautifully, but crossing it flips a switch.

Interestingly, as the authors observe, “the most dangerous regime transition arrives when standard indicators are quiet,” meaning the hours immediately preceding a breach are characterized by an eerie, suppressed calm as investors quietly abandon private data to anchor on a single shared storyline.

Once the threshold is breached, market makers face a highly synchronized, one-sided stampede of order flow. Fearing inventory risks, they swiftly pull back their quote depth, causing transaction costs to skyrocket and forcing returns to consolidate into a single global factor.

Balaji, Aditya and Goyani, Priyam and Malkan, Vevaan and Panchal, Aarnav and Balaji, Krishna, The Attention Saturation Threshold: A Real-Time Coordination Stress Monitor for Global Equity Markets. Available at SSRN: https://ssrn.com/abstract=6806933 or http://dx.doi.org/10.2139/ssrn.6806933

The Look-Ahead Trap in LLM Market Forecasts

Pretrained large language models exhibit a statistically and economically meaningful look-ahead bias in financial forecasting because their training data contains historical market outcomes, artificially inflating their pre-cutoff performance.

When evaluating how well artificial intelligence forecasts financial markets, it is easy to mistake a model's memory for actual predictive skill. By comparing predictions made before and after a major large language model's training cutoff date, this research uncovers a significant hidden advantage.

In the pre-cutoff window, the model's daily index forecast errors are roughly 18% lower, a performance boost that is heavily concentrated in the highly covered S&P 500. A similar patterns emerges for individual stock prices and quarterly earnings, proving that the network routinely supplements sparse prompt information with realized market facts embedded deep within its parameters.

Crucially, this look-ahead contamination drastically compresses the accuracy gap between the artificial intelligence and professional Wall Street analysts, particularly during highly volatile quarters when genuine forward-looking forecasting is at its absolute hardest. For investors and researchers, these findings matter because they show that checking an AI's performance using data from within its training window can be “misleading in the environments where accurate earnings expectations matter most.”

Liang, Chuan, Look-Ahead Bias in Financial Forecasts Generated by Large Language Models (September 01, 2024). Available at SSRN: https://ssrn.com/abstract=6772819 or http://dx.doi.org/10.2139/ssrn.6772819

This week for paid subscribers

Paid subscribers are getting a look into optimizing trade execution by dismantling the textbook U-shaped volume curve to eliminate slippage. This post explores the SPAR model to treat time-of-day periodicity as a fixed effect, benchmarks it against state-space models, and updates VWAP weights to slash tracking error. Python backtest code included.

Alpha in Academia

Why Knowing When the Market Trades Matters More Than You Think

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a month 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 intraday trading volume follows predictable daily patterns, and how a simple statistical trick can use those patterns to execute trades more efficiently.

Let’s dive right in.

The Problem with Volume Forecasting

Intraday equity volume follows a predictable, U-shaped pattern, yet most forecasting models either ignore this curve or hardcode a functional form. If you want to execute a large equity order with minimal market impact, you need to know when the market is liquid. Executing 500,000 shares of INTC at 11am looks very different from the same order at 3:55pm. The first hits a thin market; the second catches the closing auction at peak liquidity. Getting this timing wrong costs real money in slippage and market impact.

The tool of choice for managing this is Volume-Weighted Average Price (VWAP) execution (breaking a large order into smaller clips timed to align with predicted volume). VWAP strategies require volume forecasts at the 5-minute interval level, throughout the trading day.

The core challenge is that intraday volume is not stationary. It has a strong deterministic periodic component that repeats every trading day. The U-shaped curve (high volume at the open and close, a trough around midday) is the textbook pattern, first documented by Admati and Pfleiderer (1988) and Jain and Joh (1988). But empirical reality is messier. Some stocks show J-shapes, where morning activity is subdued relative to the close. Others have W-shapes, multimodal distributions, or significant variation across regimes.

Figure 1. Stylized intraday volume periodicity. Left: classic U-shape (high-volume liquid stocks). Right: J-shape (back-loaded, common in less actively traded names). Both are normalized to a mean of 1.0 across the trading day.

The standard approach, exemplified by the Component Multiplicative Error Model (CMEM) of Brownlees, Cipollini & Gallo (2011), decomposes volume into daily, intraday, and periodic components, but it requires specifying the functional form of the periodic curve in advance. If you assume a U-shape and the stock trades in a J-shape, your forecast is structurally misspecified from the start.

The SPAR Approach: Demean and Regress

The SPAR model reframes periodicity not as something to model, but as something to remove. The key insight is that intraday trading volume, viewed across multiple days at the same time of day, looks like a panel dataset. Each 5-minute bar index k plays the role of an individual in a panel regression; each trading day t plays the role of an observation.

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Let’s get into it.

How Interest Rate Swaps Flatten the Yield Curve

Access to efficient interest rate swaps flattens the government bond yield curve by lowering the cost of managing interest rate risk over time.

When a major economy opens its financial borders, it gives us a clean look at how derivative markets interact with cash assets. By evaluating China’s recent integration of its onshore swap market, this research reveals that giving investors access to high-quality hedging tools compressed the government bond term spread by about nine basis points. Interestingly, this flattening effect is not driven by investors stripping out risk when a trade is initiated, but rather by their ability to dynamically manage duration over the entire holding period. When managing active exposure becomes cheaper and less plagued by basis risk, investors are naturally more comfortable holding longer-dated bonds.

This increased willingness to hold duration means that financial intermediaries absorb less risk on their balance sheets, requiring less compensation. Ultimately, the data confirms that “better-functioning interest rate derivative markets can flatten the government bond yield curve”. This highlights a powerful complementarity: a healthy derivatives ecosystem does not cannibalize cash trading, it strengthens it, lowering long-term financing costs for issuers and creating a more stable environment for macro portfolios.

Institute for Monetary and Financial Research, Hong Kong, How Interest Rate Swaps Reshape the Yield Curve (May 13, 2026). Hong Kong Institute for Monetary and Financial Research (HKIMR) Research Paper No. 03/2026, Available at SSRN: https://ssrn.com/abstract=6756880

The Dollar as an FX Liquidity Superspreader

A new study reveals that while the US dollar acts as an efficient, passive intermediary for global currency trading during normal times, it transforms into a dangerous systemic “superspreader” of illiquidity during financial crises.

The foreign exchange market is the largest financial market in the world, but its plumbing relies on a hidden geometry where most minor currency pairs cannot be traded directly and must instead be synthesized through the US dollar. In tranquil periods, this triangular setup works beautifully, allowing market makers to quietly disperse local shocks across the network. However, when market stress binds dealer balance sheets, this efficient intermediary mechanism abruptly breaks down. Instead of acting as a passive vehicle, liquidity shocks now originate directly within core dollar pairs and rapidly radiate outward, synchronizing a market-wide evaporation of liquidity across seemingly unrelated currency crosses.

Figure 1: Illiquidity level correlations. Panel (a) displays the correlation matrix for 60 currency pairs from 1999–2007, showing a mix of positive (blue) and negative (red) correlations, indicating varied liquidity dynamics during this period. Panel (b) presents the same matrix for 2007–2013, revealing a significant shift toward stronger, widespread positive correlations (dominantly blue), suggesting increased systemic liquidity risk and market integration during the crisis era.

This regime shift causes traditional risk models to fail precisely when accuracy is most critical, because they mistakenly assume currency correlations remain static during a panic. For investors and traders, this finding matters because it uncovers the hidden structural costs of execution risk. Ignoring this triangular geometry means systematically underestimating how fast transaction costs can spike during a crisis, proving that the dollar is an efficient bridge in calm waters but a major vector for contagion when the wind blows. As the author notes in the paper’s conclusion, “ignoring network topology leads to a systematic mispricing of liquidity risk.”

Institute for Monetary and Financial Research, Hong Kong, FX Illiquidity Networks and Vehicle Currencies (May 13, 2026). Hong Kong Institute for Monetary and Financial Research (HKIMR) Research Paper No. 04/2026, Available at SSRN: https://ssrn.com/abstract=6756859

De-Risking Momentum with Adaptive Execution

By pairing traditional trend strength strategies with an adaptive machine learning layer, investors can capture steady market gains while actively dodging catastrophic momentum crashes.

Traditional trend following strategies are notoriously difficult to manage because they are highly vulnerable to sudden, violent market reversals. When market regimes abruptly shift, yesterday’s winning stocks can instantly plummet, causing devastating portfolio drawdowns. This paper introduces an elegant fix by separating asset selection from execution timing, utilizing a smart reinforcement learning model that treats trading as a series of dynamic, daily choices rather than rigid monthly actions. Tested across both the United States and Chinese equity markets, this framework consistently outpaced traditional passive indexes and static trend strategies.

Figure 2: Monthly cumulative return net (RN) trajectories of DMDQN and benchmark strategies in the China market (HP signal), 2019–2023. ∗ indicates stop-loss liquidation triggered at RN < 0.94.

The real magic happens during market panics, where the automated agent learns to step aside, lock in profits, or trigger disciplined liquidations. By shifting exposure away from toxic environments, the system managed to cap historical drawdowns significantly while keeping equity curves remarkably smooth. For investors, this demonstrates that execution timing matters just as much as stock picking. Ultimately, this adaptive framework proves that intelligent timing “effectively reduces the crash risk associated with traditional momentum investing,” turning a notoriously brittle anomaly into a resilient asset management tool.

Deng, dongya and Xu, Donghai and Li, Yuanshun and Ji, Xiaodong and Xu, Wei, Dynamic Momentum Trading via Deep Q-Networks: An Intelligent Execution Framework for Portfolio Management. Available at SSRN: https://ssrn.com/abstract=6765671 or http://dx.doi.org/10.2139/ssrn.6765671

How Human Cognition Shapes Market Prices

Standard financial market behaviors, such as volatility clustering and price unpredictability, can emerge naturally from the basic ways the human brain processes information and makes decisions.

This paper introduces a simulated market model showing that complex financial behaviors are fundamentally a reflection of human psychology. By applying a prominent cognitive model of binary choice to simple buy and sell transactions, the researchers demonstrate how independent market participants accumulate noisy evidence before executing their trades. When these individual psychological processes are combined with a basic price impact mechanism, the model naturally replicates major market phenomena like volatile trend changes and long memory in trading patterns without needing complex institutional rules.

Intriguingly, because the model relies entirely on how individual traders respond to shifting price loops, it also spontaneously generates realistic market cycles and asset bubbles. This close alignment tells us that “asset prices may indeed be essentially traced back to the way human cognition” operates. This pedagogical exercise implies that deeply ingrained human biology, rather than intricate market architecture, may be the primary driver behind the persistent irregularities, trading risks, and structural breaks we observe in daily market data.

Mazzon, Andrea and Patacca, Marco and Torricelli, Lorenzo, Cognitive Models for Market Price Formation (May 12, 2026). Available at SSRN: https://ssrn.com/abstract=6753698 or http://dx.doi.org/10.2139/ssrn.6753698

This week for paid subscribers

Paid subscribers are diving into the structural mechanics of ERCOT power markets to exploit the Volatility Risk Premium. This walkthrough explores a Virtual INC strategy, maps out the "sunset anomaly," and includes a sensitivity analysis on transaction friction. 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 structural mechanics of the Texas power grid by diving into a fundamental spread.

Let’s dive right in.

The Volatility Risk Premium

Power plants are massive, slow-turning gears. Turning them on takes hours, sometimes days, and once they are spinning, shutting them down is costly and inefficient. On the other side of the plug, millions of people are flipping light switches and cranking air conditioners based on the whims of the weather and the time of day.

Because we haven’t yet mastered large-scale electricity storage, the grid must be perfectly balanced in real-time. To manage this, markets like the Electric Reliability Council of Texas (ERCOT) and other Independent System Operators (ISOs) and Regional Transmission Organizations (RTOs) operate in two distinct stages: the Day-Ahead Market (DAM) and the Real-Time Market (RTM).

As the names suggest, the Day-Ahead market is essentially a financial commitment, while the Real-Time market is the physical reality. In a perfectly efficient world, these two prices should converge. But in the real world, plans often diverge from reality, leaving a gap that traders call the Day Ahead/Real-Time (DART) Spread.

This post explores a fundamental convergence bidding strategy: a Virtual Increment Bid (Virtual INC). By selling power in the Day-Ahead market and buying it back in Real-Time, we are effectively betting that the market’s fear of tomorrow is greater than the actual cost of the grid’s physical reality.

Read more

Let’s get into it.

Volatility Dynamics Around Earnings

Short-volatility strategies consistently outperform long-volatility approaches when trading earnings announcements, as the post-earnings “volatility crush” typically provides a more reliable edge than betting on a pre-earnings implied volatility hike.

Trading earnings is often seen as a directional gamble, but this research reframes it as a sophisticated dance with implied volatility. The core discovery is that the market tends to overprice the uncertainty leading up to a report, making short-volatility strategies like strangles the dominant performers across total profit and win rate.

While many retail traders flock to long-volatility bets hoping for a massive price swing, the study highlights that these long-vol plays have become increasingly difficult to monetize due to market regime shifts and rising efficiency. In fact, the lucrative returns once found in longing volatility during the pandemic era have largely evaporated as the market has adapted.

This research matters for investors because it suggests that the real alpha isn't in predicting the stock's direction, but in harvesting the premium from those who overpay for protection.

As the paper concludes, “shorting volatility that captures post earnings vol-crash is lucrative based on our results”.

Lu, Minda, Navigating IV: Options Trading Strategy in Earnings Season (May 01, 2026). Available at SSRN: https://ssrn.com/abstract=6710818 or http://dx.doi.org/10.2139/ssrn.6710818

Brazil’s Sustainable Bond Market

The data shows that Brazil’s green bond issuances tend to trigger a significant negative market reaction over an extended horizon, completely defying the positive trends typically observed in more developed financial hubs

While sustainable bonds are often treated as a win-win in the US and Europe, the Brazilian market seems to view them through a far more skeptical lens. After tracking 62 issuances over nearly a decade, the data reveals a systematic slide in stock prices, culminating in an average cumulative abnormal return of -1.71% over a 21-day window. This suggests that rather than celebrating a commitment to the environment, local investors may be pricing in the heavy costs of certification and reporting, which can feel like a burden in a high-interest-rate environment.

Interestingly, the market doesn’t seem to care what the competition is doing, as no significant spillover effects were found among rival firms. This tells us that these issuances are treated as idiosyncratic, firm-specific decisions rather than industry-wide shifts.

For the modern investor, this is a stark reminder that ESG signals are not universal, and in emerging markets, “institutional development constitutes a prerequisite for effective functioning of sustainable bond markets”. Without robust local regulation, these green initiatives may just be considered expensive marketing.

Farias, Camila and Almeida, Vinicio and Felipe, Israel, Corporate Sustainable Bonds in Brazil: Market Reactions and Spillover Effects (May 01, 2026). Available at SSRN: https://ssrn.com/abstract=6692059 or http://dx.doi.org/10.2139/ssrn.6692059

On-Chain AI Forecasting Performance

Large language models currently struggle to meaningfully outperform the collective intelligence of prediction markets, often failing when they simply track market prices with added noise.

The race to see if artificial intelligence can actually outsmart human markets has reached a new milestone with Foresight Arena, a decentralized benchmark that tests agents against real-world prediction markets like Polymarket. While we often hear about AI’s “superhuman” capabilities, this study reveals a more grounded reality where models like Claude and GPT-5 currently cluster right around the market consensus.

Interestingly, the research shows that simply tracking the market is a losing strategy for an AI, as models that relied too heavily on current market prices actually underperformed due to the noise they introduced. The most successful agents found a thin edge in high-frequency environments like crypto, where they could integrate breaking news faster than the market mid-price could adjust.

This suggests that while AI is becoming a formidable participant, the “wisdom of the crowd” remains a remarkably high bar to clear. For investors, this highlights the efficiency of prediction markets as a baseline and suggests that AI’s true value may currently lie in its speed and news integration rather than some superior, inherent logic that humans lack. As the authors conclude, these on-chain records provide an environment that “inherits the integrity properties of the underlying blockchain”.

Nechepurenko, Maksym and Shuvalov, Pavel, Foresight Arena: An On-Chain Benchmark for Evaluating AI Forecasting Agents (April 29, 2026). Available at SSRN: https://ssrn.com/abstract=6674059

Cross-Border Credit Risk Transmission

Evergrande’s 2021 offshore default acted as a systemic catalyst, causing a sharp increase in onshore credit spreads for Chinese developers that previously relied on U.S. dollar financing.

When China’s property giant Evergrande defaulted on its U.S. dollar debt in late 2021, the shock waves did not stop at the border. This research explores how that single offshore event triggered a “cross-border credit risk spillover” into China’s domestic bond market. Essentially, if a developer had issued U.S. dollar bonds in the past, investors suddenly viewed their local, yuan-denominated debt as significantly riskier.

This contagion was not just psychological, but rather driven by a tangible breakdown in credit ratings and a sudden evaporation of secondary market liquidity. Before the crisis, risk largely flowed from domestic fundamentals to offshore prices, but the default flipped this relationship entirely.

The offshore market effectively seized the steering wheel, becoming the primary driver of domestic risk pricing as a core-periphery network structure emerged. For investors, this finding is a stark reminder that in an integrated financial system, “offshore markets can become dominant sources of systemic risk spillovers”. It suggests that domestic credit health is often at the mercy of global funding channels, meaning a crisis in New York or Singapore can rapidly inflate borrowing costs in a local market thousands of miles away.

Su, Saier and Shi, Yining and Tai, Liyu, Cross-Border Credit Risk Spillover: A Mechanism Investigation Based on Dynamic High-Dimensional Network Analysis. Available at SSRN: https://ssrn.com/abstract=6721430 or http://dx.doi.org/10.2139/ssrn.6721430

This week for paid subscribers

Paid subscribers are testing the January Barometer by pairing seasonal signals with yield curve data, measuring why a logical "OR" rule effectively filters market noise while strict "AND" requirements fail to catch dual-signal stress.

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 January Barometer and whether that seasonal signal becomes meaningfully stronger when paired with a macroeconomic lens.

Let’s dive right in.

January Barometer

Imagine being handed a single data point on January 31st each year: the stock market’s return for that month. Would you use it to make a decision about how to invest for the next eleven months? It sounds almost too simple. And yet, that is precisely the premise of one of the most discussed calendar based market signals in finance: the January Barometer.

The January Barometer, popularized by Yale Hirsch in his Stock Trader’s Almanac (1972) says that when January’s equity return is positive, the stock market tends to finish the year with strong gains through December. When January is negative, the next eleven months are, on average, considerably weaker. The Journal of Investment Management demonstrates that the optimal implementation is straightforward. You hold equities in years following positive Januarys and retreat to Treasury bills following negative ones. This strict rule prevents any look-ahead bias as the signal at the end of month t determines the position for month t + 1. It is a blunt instrument, but it is one that the data has been reluctant to dismiss.

This post is motivated by a natural follow up question: if one signal drawn from investor sentiment can predict the rest of the year to some extent, does adding a second signal sharpen that prediction? The signal we have in mind is the slope of the U.S. Treasury yield curve (the spread between long term and short term Treasury yields). If the yield curve is telling us something about the economic environment that the January Barometer cannot, then combining the two might produce a richer and more reliable composite signal.

Read more

Inevitable Stock Market Bubbles

In simple growth models where production has decreasing returns to scale, stock price bubbles are not just possible but often inevitable when wage income and dividend income grow at different rates.

Financial bubbles are usually discussed as psychological phenomena or the result of market irrationality, but this research demonstrates that they can emerge as a necessity in perfectly rational economies.

The core driver is a mismatch in growth speeds: when total wages grow faster than corporate profits, the high demand for assets from workers pushes stock prices above their fundamental value. This happens most frequently when standard economic assumptions, specifically the Inada conditions for labor, are violated. Whether an economy is rapidly expanding or even steadily contracting, a bubble can form if dividends lag behind the broader income available for investment.

This suggests that the “fundamental value” of a stock might be a poor guide for price action in economies where capital and labor income are drifting apart. It highlights that market prices can remain “irrationally” high forever.

Sorger, Gerhard, Inevitability of stock price bubbles in simple growth models. Available at SSRN: https://ssrn.com/abstract=6661356 or http://dx.doi.org/10.2139/ssrn.6661356

Sector Performance of News Sentiment

AI-driven sentiment analysis of news headlines can predict short-term stock price movements, but its effectiveness varies significantly by industry, with high-coverage sectors like technology showing the strongest risk-adjusted returns.

While the stock market is often moved by public sentiment, this research demonstrates that “news-driven tradability” is not uniform across all sectors.

By applying a logistic regression model to over 11,000 headlines, the author found that news sentiment acts as a reliable predictive signal primarily for sectors with heavy media coverage, such as technology and financials. In contrast, sectors like healthcare and real estate often exhibit negative Sharpe ratios when traded on news alone, likely due to sparse headlines and a higher ratio of market noise.

Interestingly, even in sectors with high trade volume, outperformance is not guaranteed; the study found that the quality and clarity of the sentiment signal matter more than the sheer number of articles.

This suggests that while AI can extract “alpha” from the media cycle, it is most effective when applied as a sector-specific tool rather than a blanket market strategy. “The sentiment signal is greatly affected by noise, but can still predict certain sectors.”

Prakash, Harshavardhan, Predicting U.S. Stock Sectors Using AI-Driven News Sentiment Analysis (April 26, 2026). Available at SSRN: https://ssrn.com/abstract=6653318 or http://dx.doi.org/10.2139/ssrn.6653318

Crypto Risk Factors

Excess returns in the cryptocurrency market are driven by systematic risk factors beyond simple price trends, creating significant mispricing that “smart money” can exploit through disciplined factor tilting.

While the crypto market is often viewed as a lawless frontier of sentiment and speculation, this research identifies a clear underlying structure of systematic risks that dictate price movements.

By analyzing 29 major cryptocurrencies, the authors found that standard models like the CAPM fail to explain why coins with similar risk profiles deliver vastly different returns. Instead, return dispersion is driven by eight core factors, including global financial stress, DeFi risk-taking, and macroeconomic confidence. The study demonstrates that investors can harvest “alpha” by using a factor-tilting strategy, by essentially overweighting coins aligned with factors that are currently being rewarded, such as global volatility during market distress.

This approach moves beyond the “buy and hold” mentality, suggesting that the most efficient path to profits lies in navigating an “Efficient Factorial Frontier” where risks are actively managed rather than just accepted.

The authors summarize the potential of this disciplined approach by noting that “disciplined exposure to risk premia may offer a promising avenue in emotionally driven cryptomarkets”.

Jamhamed, Fayssal and Martin, Franck and Rondeau, Fabien and Thélissaint, Josué and Tufféry, Stéphane, On Risk Pricing and Arbitrage in the Cryptomarket: Anomalies and Factor Tilts (October 29, 2025). Available at SSRN: https://ssrn.com/abstract=6690098

The Narrative Factor

The “Narrative Factor” serves as a powerful cross-sectional return predictor that significantly outperforms traditional momentum strategies while maintaining a remarkably low correlation to established style factors.

Equity markets are often dictated by viral stories (from pandemic fears to AI booms), yet most systematic models remain anchored to backward-looking financial metrics. This research introduces a way to quantify these public discourse shifts by measuring how much a topic is being discussed and mapping that attention to specific company exposures.

The narrative factor produces risk-adjusted returns that are roughly three times stronger than a standard momentum strategy. Even when stripped of any overlap with common traits like value or quality, the signal remains a robust predictor of where prices are headed next. This suggests that public narratives are a quantifiable force that shapes market expectations before they fully reflect in stock prices.

As the author notes in the conclusion, “public discourse, systematically measured, contains meaningful cross-sectional pricing information not captured by traditional equity factors”.

Reese, Charlie, The Narrative Factor: A Systematic Approach to Capturing Narrative Alpha from Public Discourse (April 30, 2026). Available at SSRN: https://ssrn.com/abstract=6685058 or http://dx.doi.org/10.2139/ssrn.6685058

This week for paid subscribers

Our second paid post of the week focuses on examining the mean-reverting and trending properties of butterfly trades in the Treasury market.

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.

Welcome back to another paid post. Today, we will be diving deeper into the U.S. Treasury market, by examining Butterfly trade structures.

As I’ll show, there are remarkable statistically significant patterns in this market.

Let’s get into it.

Bond Investor Expectations

Before we go into my own analysis, AQR has an interesting paper on bond investor expectations. Data shows that bond investors are naturally contrarian. They usually expect mean reversion, meaning if rates have been falling, they bet on them eventually rising back to a “normal” level. Because bonds are quoted in yields (which are forward-looking), investors tend to focus on where rates should be rather than just following the recent price trend.

However, this contrarian mindset hasn’t actually matched the data over the last two centuries. Historically, bonds have actually shown positive decadal autocorrelation, with a correlation of +0.5 since 1800 and +0.6 since 1900.

This means that bond returns have tended to continue in the same direction over long periods rather than reversing. You can see the forecasts of bond investors during various historical periods below.

Despite this historical trend of continuation, experts and the market have stuck to mean-reverting expectations. This led to the “folly of forecasting” seen from 1981 to 2021, where economists repeatedly predicted rate hikes that never happened as yields drifted lower for 40 years.

While these expectations of a reversal made sense at the time, they were consistently beaten by a secular downtrend that most observers now believe has finally reached its limit.

As we will show below, bond yields do not always exhibit mean reversion (even on short time periods!).

Read more

Welcome back to the second post in this series. Last post, we explored how the success of a treasury auctions (as measured through the bid-to-cover ratio) impacts returns across Treasuries.

Part 2 extends the same event-study framework to two cross-asset benchmarks: the U.S. dollar (via UUP) and U.S. equities (via SPY).

The data, sample, and bucketing are identical to Part 1: 574 auctions across the 2-, 5-, 10-, and 30-year maturities from January 2015 to present, with auctions ranked as “weak” (bottom 20% BTC within their own maturity) or “strong” (top 20%). For each auction we measure the forward percentage return of UUP and SPY at the 1-day, 1-week, and 1-month horizons.

I have sent the updated code to your emails. Let’s get into it.

Read more

Redefining Returns

Defining target returns as relative ranks rather than raw values can nearly triple the predictive accuracy of machine learning models.

While most quant researchers focus on cleaning and scaling stock characteristics, this study reveals that how you define your target is actually the primary driver of performance.

By switching from raw returns to rank-based or standardized targets, models can filter out common market noise and focus on what actually matters: which stocks outperform their peers within a specific month. This shift is particularly powerful for nonlinear models (like random forests) which can fail entirely when presented with raw, heavy-tailed data but thrive once targets are stabilized.

However, ranking is not a universal solution because it discards information about the actual magnitude of price moves. This loss is costly in volatile segments like micro-cap stocks or emerging markets where extreme outcomes contain genuine economic signals rather than just noise.

Ultimately, the best transformation depends on the return distribution of the specific market (investors who ignore the shape of their data risk optimizing for the wrong objective).

Cakici, Nusret and Zaremba, Adam, Getting the Target Right in Return Prediction (April 20, 2026). Available at SSRN: https://ssrn.com/abstract=6615698 or http://dx.doi.org/10.2139/ssrn.6615698

Safe Haven Assets

The historical relationship between gold and Treasury yields is not a constant market law but a regime dependent one that only activates when real interest rates are exceptionally low or negative.

While gold is often viewed as a simple inverse play on interest rates, this study reveals that the two assets only behave as rivals under specific conditions. By examining global data, the researchers identified three distinct eras where the link between gold and bonds fundamentally shifted.

During the era of near zero interest rates following the global financial crisis, gold and Treasuries acted as close substitutes because a scarcity of safe options forced investors to choose between them based on small yield differences. However, before that crisis, the two assets actually moved in the same direction because they both responded to broader market stress.

Most striking is that this relationship has decoupled in recent years. Despite the return of positive real yields, gold prices have surged due to central bank demand and geopolitical risks rather than interest rate moves.

This breakdown means that relying on old correlations to hedge a portfolio can be dangerous. Investors must recognize that gold’s role in a portfolio is not static (it adapts to the broader supply and demand for safety).

Batten, Jonathan A. and Lončarski, Igor and Szilagyi, Peter G. and Zhou, Han-Xian, Gold and U.S. Treasuries as Competing Safe Assets. Available at SSRN: https://ssrn.com/abstract=6627301 or http://dx.doi.org/10.2139/ssrn.6627301

S&P 500 Convexity

The S&P 500 systematically outperforms active managers not because of superior stock selection, but because its passive structure preserves the outsized, “convex” returns of winning stocks that professional managers are forced to trim.

The persistent failure of active managers to beat the benchmark is usually blamed on fees, but this research suggests even the most gifted stock pickers are being sabotaged by their own structural constraints.

The authors introduce the “convexity gap,” a phenomenon where professional fund rules (such as five percent position limits and style mandates) force managers to sell their biggest winners prematurely.

While the S&P 500 naturally allows a single high performer to compound from a tiny fraction of the index into a massive driver of returns, active funds are structurally required to “cut their flowers” and redistribute that capital into laggards. It is a bit humbling to realize that the index often wins simply because it is too passive to interfere with its own success.

Data shows that nearly 70 percent of institutional accounts underperform even before fees are taken into account. This suggests that the primary engine of market wealth is the outsized growth of a few extreme winners, which active management is designed to prune away.

Milligan, Nina and Milligan, Michael, The Convexity Gap: How the S&P 500 Preserves Convexity and How Active Managers Can Too (April 03, 2026). Available at SSRN: https://ssrn.com/abstract=6543859 or http://dx.doi.org/10.2139/ssrn.6543859

Bitcoin ETF Flows

An inflow of one hundred million dollars into Bitcoin ETFs typically triggers a fifty-three basis point price jump, but this immediate impact is actually a temporary surge masked by a relentless cycle of new money.

The launch of spot Bitcoin ETFs created a direct bridge between traditional brokerage accounts and digital asset prices, establishing a measurable link where investor demand dictates market movement.

This research quantifies that connection, finding that every hundred million dollars in net flows explains roughly twenty-one percent of daily price variation.

More interestingly, the study identifies a “flow-persistence illusion” where price jumps appear permanent only because new money arrives in waves, hitting the market before previous shocks have a chance to reverse.

While individual trades move the needle temporarily, the consistency of institutional flows creates a cumulative drift that nearly doubles the initial impact over ten days. This discovery suggests that ETFs act as a momentum machine, where initial price gains attract further inflows in a self-reinforcing feedback loop.

Lim, Boon Chuan, The Price Impact of Spot Bitcoin ETF Flows. Available at SSRN: https://ssrn.com/abstract=6592830 or http://dx.doi.org/10.2139/ssrn.6592830

This week for paid subscribers

Paid subscribers are working through a two-part event study on Treasury auction bid-to-cover ratios — measuring the impact on USD and SPY across 574 auctions from 2015 to present. 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.

Welcome back to another post. Today, we will be exploring how the success of a treasury auction impacts market returns across Treasuries. In the next post, we will cover the effects on other markets, like U.S. equities and the dollar.

Specifically, we will be looking at a common metric, the bid-to-cover ratio, and its impact. Spoiler: the bid-to-cover ratio has statistically significant effects on future market performance!

This topic has been investigated across various academic papers, and we will showcase a few of the findings from these papers as well. Let’s get into it.

Treasury Auctions and Bid-To-Cover Ratio

Before we get into the analysis, I want to explain how Treasury Auctions are conducted and what Bid-to-Cover ratios are.

The U.S. Treasury funds government operations by selling debt securities (Bills, Notes, and Bonds) to the public through a Dutch Auction system. In this format, bidders submit the lowest yield they are willing to accept. The Treasury starts at the lowest yield and works its way up until the entire offering is sold. The highest yield accepted is called the “high yield” or “stop,” and it is the rate that all successful bidders receive.

Primary Dealers (large financial institutions like Goldman Sachs, J.P. Morgan, and Citigroup) are legally obligated to participate in every auction to ensure the debt is sold. Therefore, the real signal comes from the “marginal” demand for these auctions. This demand is driven by investors like foreign central banks, pension funds, and asset managers who choose to bid only when the price is right.

To measure the strength of this demand, we look at the Bid-to-Cover (BTC) Ratio. The BTC ratio is a simple calculation: the total dollar volume of bids received divided by the dollar volume of securities sold.

• A high BTC indicates strong demand. The Treasury had plenty of bidders to choose from, suggesting a high appetite for U.S. debt.

• A low BTC indicates weak demand. This suggests the Treasury struggled to find buyers, forcing it to “dig deep” into the bid book and accept higher yields to fill the offering.

As the data below shows, “normal” demand varies significantly across the yield curve. The 2-year note, for example, is highly liquid and frequently used as a cash proxy, leading to a much higher average BTC (2.82x) compared to the 30-year bond (2.33x).

Because these benchmarks differ, we categorize an auction as “weak” if it falls into the bottom 20th percentile of its specific maturity’s history. These weak points are where we typically see the largest spillover effects into equities and the dollar.

The following charts reflect the historical distributions of the BTC ratio over the last decade (2015 to present).

Now, let’s see the impact that BTC ratios have on Treasuries.

The Increasing Importance of the BTC Ratio

There is academic research that backs up the importance of the BTC ratio. Two recent papers explain why this metric is (increasingly) a critical signal.

A 2025 study from Harvard Business School (What Treasury Auctions Reveal About Investor Demand)1 found that the Treasury market has become significantly more inelastic since 2010.

The market’s price sensitivity to auction results has increased by approximately five times over the last decade. A “weak” auction (low BTC) now triggers a much larger yield spike than it would have historically. The researchers argue that because the market is more brittle, auctions have become the primary venue for investors to gauge global demand.

Research from the European Central Bank (ECB Working Paper No. 2056)2 analyzes the specific information contained in the BTC ratio. The authors argue that BTC is a strategic signal from primary dealers regarding the conviction of “informed” investors, such as pension funds and foreign central banks.

The paper highlights two main findings:

• A high BTC leads to lower yields in the secondary market because it reveals that institutional players are aggressive buyers.

• The predictive power of the BTC ratio is strongest during periods of high market volatility.

Together, these papers show that Treasury auctions are important data releases. The HBS paper establishes that the market is increasingly sensitive to these events, while the ECB paper identifies the BTC ratio as the mechanism that transmits demand information to the broader market.

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Forecasts for AI’s Impact

Most experts anticipate significant breakthroughs in artificial intelligence by 2030, yet they expect historical adoption lags and structural bottlenecks to keep the resulting economic growth within historical bounds.

This week, I found an NBER paper that surveys the “brain trust” (everyone from academic economists to the engineers building frontier models) and the results offer a new perspective on how AI will shape markets.

While almost everyone expects systems to surpass human ability in most tasks by 2030, the median forecast for economic growth stays remarkably close to historical trends at roughly 2.5 percent. The real friction, according to these experts, isn’t the code itself but the messy reality of how slowly businesses actually retool their operations and how aging populations might drag on productivity gains.

Interestingly, the industry insiders are far more bullish on growth than the academics, who worry more about a permanent dip in labor force participation.

This gap suggests that while the technology is moving quickly, the markets will likely not exhibit a rapid change, but rather an uneven and slow change. Investors should keep an eye on the transition period, as the authors argue that “policymakers cannot simply plan for the median outcome” given the high uncertainty involved.

Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell, Matt Reynolds, Dan Mayland, Ria Viswanathan, Ananaya Mittal, Rebecca Ceppas de Castro, Josh Rosenberg, and Philip Tetlock, “Forecasting the Economic Effects of AI,” NBER Working Paper 35046 (2026), https://doi.org/10.3386/w35046.

Prediction Markets and Earnings

A small minority of prediction market traders consistently outperforms analyst consensus by identifying earnings misses that trigger significant, multi-day stock price declines.

A new research paper explores how traders on the Polymarket platform are outperforming traditional analyst consensus by betting on corporate earnings results.

While sell-side analysts often provide a "walk-down" estimate, which is a systematically depressed figure that companies can easily beat to signal competence, the prediction market crowd provides a more objective reality check.

The study demonstrates that when these markets assign a low probability of a beat, a short-only strategy earns 5.90 percent over the following ten days. This accuracy is not driven by the general public but by a small "contrarian minority" of 22 sophisticated wallets that specialize in specific market domains.

This bearish signal is particularly effective because institutional constraints, such as borrowing costs or fiduciary rules, often prevent equity markets from pricing in bad news immediately. The paper concludes that "the crowd's advantage is concentrated in pricing operating fundamentals".

Feng, Chloe, Minority Report: Contrarian Traders, Prediction Markets, and the Return of Post-Earnings Drift (March 18, 2026). Available at SSRN: https://ssrn.com/abstract=6477080 or http://dx.doi.org/10.2139/ssrn.6477080

Integrating Climate Risk

Integrating local temperature anomalies into portfolio optimization can nearly double annual growth rates by systematically reducing exposure to climate driven volatility.

A new research paper introduces two sophisticated metrics (Climate Risk Exposure and Climate Exposure Volatility) to help investors navigate the growing financial threats of extreme weather. While many traders rely on static country level indices to guess environmental risk, these new tools track how the changing frequency of heatwaves and temperature spikes actually interacts with a company’s physical assets.

By analyzing global equity portfolios between 2020 and 2025, the study found that portfolios optimized for climate resilience significantly outperformed traditional benchmarks. For example, a balanced strategy that weighed climate risk alongside market returns achieved a 33.5 percent annual growth rate, more than doubling the 15.8 percent return of a standard market cap weighted approach.

The authors argue that “extreme temperature events exert a negative effect on most sectors,” making climate exposure a critical factor in managing future drawdowns. Identifying firms with high asset intensity in volatile regions is becoming a fundamental way to generate returns in an increasingly unstable environment.

Azzone, Michele et al. “Temperature Anomalies and Climate Physical Risk in Portfolio Construction.” (2026).

Improving Bond Models

Compressing daily news, market data, and macro releases into a single vector reveals hidden economic risks that traditional yield curve models fail to capture.

A new research paper proposes a novel way to quantify the aggregate economic state by treating each trading day like a word in a sentence, mapped into a dense vector through machine learning. This “world embedding” fuses everything from news narratives and geopolitical risk to raw financial data and central bank communications into a unified, daily metric.

For decades, fixed income traders have struggled with the “spanning puzzle,” which is the observation that the yield curve shape often fails to account for all relevant macroeconomic information. By using this multimodal representation, the author finds that “economic similarity is encoded as distance in the learned space,” allowing the model to uncover unspanned risks that standard models ignore.

In practice, adding these embedding factors increases the predictive power for bond excess returns by 10 to 34 percentage points beyond traditional yield curve factors.

Tabatabaei, Elham, World Embedding: The Daily Economic State and Bond Risk Premia (March 31, 2026). Available at SSRN: https://ssrn.com/abstract=6503446

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