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.

Read more

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

Feedback

Thank you for reading this week’s edition of Recent Academic Research. Remember to fill out the poll to let me know which paper was your favorite and like the post if you enjoyed it.

Feel free to follow up with any questions, comments, or ideas for the future!

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.

Emerging Market Returns and EPS

National GDP growth has zero correlation with equity returns in emerging markets because corporate earnings growth is the only metric that reaches shareholders.

Investors frequently allocate to emerging markets under the assumption that rapid economic expansion leads to superior stock performance. However, this analysis of 120 years of data across 36 countries proves that GDP growth is an unreliable predictor of returns.

The disconnect occurs because economic growth often benefits labor and consumers, or leads to capital dilution through the issuance of new shares, which prevents existing shareholders from capturing the gains. In contrast, earnings per share growth is the fundamental driver of equity value.

While a country might experience high GDP growth, if that growth does not translate into bottom line profits for listed companies, investors will see poor results.

Chasing high growth economies without considering dilution or profitability is a common mistake that leads to underperformance.

Wool, Phillip and Hsu, Jason C. and Hsu, Jason C. and Ritter, Jay R. and Zhao, Yanxiang, What Matters More for Emerging Markets Investors: Economic Growth or EPS Growth? (May 05, 2022). Available at SSRN: https://ssrn.com/abstract=6523858 or http://dx.doi.org/10.2139/ssrn.6523858

Diplomatic Sentiment and Capital Flows

War related diplomatic rhetoric serves as a quantitative leading indicator for international capital flows and anticipates shifts in risk before traditional indices.

This research uses large language models to analyze diplomatic transcripts and quantify sentiment related to potential conflict. The study finds a direct relationship between aggressive rhetoric and capital flight, particularly in the context of U.S. and China relations.

Specifically, a one unit decline in the Chinese diplomatic sentiment index corresponds to a daily net outflow of approximately 35.6 million dollars. Unlike traditional geopolitical risk indices which often lag behind events, this sentiment measures the verbal temperature of international relations in real time.

It shows that professional capital is highly sensitive to the tone of official statements long before physical conflicts or policy changes occur. Monitoring the nuances of diplomatic language allows for a more proactive approach to managing exposure in sensitive regions.

Yang, Cunyi and Zhang, Shuchi and Kyriakou, Ioannis and Papapostolou, Nikos C., When Words Move Money: Diplomatic Sentiment and International Capital Flows (March 18, 2026). Available at SSRN: https://ssrn.com/abstract=6437669 or http://dx.doi.org/10.2139/ssrn.6437669

Leading Indicators

The predictive power of financial indicators for market drawdowns is highly dependent on the specific forecast horizon being used.

Predicting S&P 500 drawdowns requires a framework that matches specific indicators to the desired timeframe, as no single variable excels across all horizons.

This empirical study tests 30 indicators and finds that short term signals, such as the VIX and the National Financial Conditions Index (which shows an AUC of 0.725 at one month), lose their efficacy as the window expands.

Conversely, variables like M2 money supply growth only become significant predictors at the twelve month mark. The results indicate that the most reliable windows for macro informed risk management are the six and twelve month horizons.

Successful risk management requires rotating indicators based on whether the goal is to manage immediate volatility or long term portfolio drawdowns. Using the wrong tool for the timeframe leads to missed signals and inefficient hedging.

Uyar, Alexander, Leading Indicators of S&P 500 Drawdowns: A Horizon-Specific Macro-Financial Framework (March 20, 2026). Available at SSRN: https://ssrn.com/abstract=6448158 or http://dx.doi.org/10.2139/ssrn.6448158

Return Seasonality

Return seasonality in anomaly portfolios is a mechanical byproduct of individual stock behavior rather than an independent risk premium.

This study examines whether the seasonal patterns observed in factor portfolios, such as momentum or value, represent distinct market risks. By analyzing 125 anomaly portfolios, the researcher demonstrates that these patterns are inherited from the underlying stocks.

Individual stocks possess inherent calendar month cycles, and when these stocks are grouped into portfolios, the resulting seasonality is a simple mathematical projection. The research confirms this by showing that a seasonality factor built from individual stocks fully accounts for the returns of anomaly based strategies (whereas the reverse is not true).

This suggests that seasonality is not a unique premium for which investors are compensated. This finding implies that complex seasonal factor strategies do not offer superior risk adjusted returns compared to simpler stock level approaches.

Wang, Jiaqi, Are Return seasonality Due to Risk or Mispricing? Evidence from Anomaly seasonality (January 26, 2026). Available at SSRN: https://ssrn.com/abstract=6441120 or http://dx.doi.org/10.2139/ssrn.6441120

Feedback

Thank you for reading this week’s edition of Recent Academic Research. Remember to fill out the poll to let me know which paper was your favorite and like the post if you enjoyed it.

Feel free to follow up with any questions, comments, or ideas for the future!

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 the second post in this series. Last week, we covered the structure of digital and digital range options and their associated greeks.

Today, we’ll dive deeper into modeling these digital range options with historical data from a Kalshi Market. I have sent the code and data to your email.

Let’s get into it.

The 5-Strike Model and Implied Spot

By looking at the different between contracts on Kalshi, we can effectively back out the market’s expected volatility. If the price for a narrow range like 90 to 91°F is very high, the market is pricing in low volatility, and high certainty that the true temperature will be in this bucket.

If that price drops while other ranges like >93°F rise, the market is pricing in a fatter tail event. Pin risk remains the primary danger in these markets. If the temperature is hovering exactly at 90.0°F as the market expires, your position value will oscillate violently between $0 and $1. This causes your Gamma to approach infinity, making the position nearly impossible to manage or hedge as you approach the cliff (but you can’t delta hedge in many prediction markets, as you can’t trade the underlying temperature!).

The mathematical foundation of this model rests on the observation that Kalshi’s exhaustive probability buckets implicitly define a cumulative distribution function. While these between contracts are displayed as a one degree difference, such as 78 to 79°F, they actually represent two degree differences in reality. This is because each between contract is bounded by the half degree midpoints between adjacent buckets. For example, a contract labeled 78 to 79°F is effectively a bet that the temperature lands between 77.5 and 79.5. By summing these bucket probabilities from the bottom up, we can construct five synthetic digital call prices at the true cliff boundaries of 75.5, 77.5, 79.5, 81.5, and 83.5.

We avoid the classical log-normal Black-Scholes framework because temperature does not grow multiplicatively like a stock price. Instead, we utilize an additive normal distribution model where the settlement temperature is modeled with a market-implied mean and a market-implied standard deviation in degrees Fahrenheit.

This approach is appropriate because using % moves do not make sense, as a forecast of 80°F does not necessarily mean greater degree moves than a 50°F forecast. Applying a log-normal model to weather would create artificial skews that do not exist in physical reality.

Rather than manually inputting a temperature forecast from an external source, the dashboard solves for the market’s implied mean directly from the bucket prices.

We compute a discrete mean as a probability-weighted average of bucket midpoints to provide a fast and intuitive estimate of the consensus. However, the model primarily relies on a continuous mean derived by fitting a normal distribution to our five synthetic strike data points using the Nelder-Mead optimization algorithm.

This fitted mean is significantly more robust than a simple discrete average because it utilizes the full distributional shape and naturally handles inconsistencies where tail contracts might be priced poorly relative to the center of the book. This continuous mean serves as our spot price for every subsequent pricing and Greek calculation.

Below, I’ll walk through the dashboard (which you all have access to), and I’ll show how we can use our own implied vol calculations to determine how contracts are mispriced.

Read more

Let’s get into it.

Company Disclosures and Returns

Subtle linguistic shifts in regulatory filings reliably predict future stock returns.

Researchers developed a disclosure drift signal that measures how much a company alters its vocabulary regarding risks and uncertainties from one regulatory document to the next. They discovered that these shifts in language consistently forecast abnormal equity returns over the following two months.

The predictive power becomes significantly stronger when a firm exhibits this linguistic drift while simultaneously releasing a formal explanation of material events. The authors call this phenomenon disclosure coherence. When a company changes its narrative but stays silent about the underlying reasons, the subsequent returns are considerably weaker.

This suggests that investors place a premium on transparent communication and that “the market effectively distinguishes between companies that transparently communicate evolving risks” to their shareholders.

Rigel, Sebastian, Disclosure Drift as a Predictive Signal for Equity Returns (April 01, 2026). Available at SSRN: https://ssrn.com/abstract=6444659 or http://dx.doi.org/10.2139/ssrn.6444659

Improved Trend Following

Trend following strategies generate substantially higher returns when investors optimize their risk exposure based on the current market environment.

Traditional trend following strategies operate on a simple binary rule of taking a maximum long position in rising markets and a maximum short position in falling markets. The author proposes a sophisticated new framework that calculates the exact optimal portfolio weight for any given market condition by maximizing the expected risk adjusted return.

This optimized approach reveals that taking a full short position during a bear market is almost always a mistake. Instead, the math dictates that investors should drastically reduce their exposure or sit in cash during severe downturns.

The dynamically optimized strategy comfortably beats both classic momentum models and newer adaptive models across United States and international equities. The author notes that “the performance gains are economically meaningful” and remarkably uniform across different markets.

Zakamulin, Valeriy, Rethinking Trend Following: Optimal Regime-Dependent Allocation (March 09, 2026). Available at SSRN: https://ssrn.com/abstract=6376479 or http://dx.doi.org/10.2139/ssrn.6376479

Prediction Markets and Crypto Volatility

Daily price changes in regulated macroeconomic prediction markets reliably forecast the future volatility of major cryptocurrencies.

Platforms like Kalshi allow participants to place binary bets on economic outcomes like Federal Reserve rate decisions or inflation data. The researchers discovered that the daily shifting probabilities in these event contracts are a leading indicator for cryptocurrency volatility.

Interestingly, different digital assets react to completely different macroeconomic channels. Bitcoin volatility is highly sensitive to shifting expectations about monetary policy, particularly during active rate cutting cycles. Conversely, smaller alternative tokens respond much more strongly to shifting inflation expectations.

The brilliance of this approach is that it provides a continuous daily read on macroeconomic uncertainty, unlike traditional economic data releases that only happen on fixed calendar dates. The authors conclude that these event contracts contain “information about future cryptocurrency realized volatility that is not captured” by conventional financial instruments.

Mohanty, Hardhik and Bhaskar Krishnamachari. “Do Prediction Markets Forecast Cryptocurrency Volatility? Evidence from Kalshi Macro Contracts.” (2026). https://arxiv.org/abs/2604.01431

Mispricing from Regulations

Statutory diversification rules force mutual funds to prematurely sell their winning mega cap stocks, which temporarily depresses the prices of these dominant companies and creates predictable buying opportunities.

United States tax laws require regulated investment companies to keep their largest individual stock positions below 5 percent of their total portfolio. As the broad stock market has become incredibly concentrated at the top in recent years, successful large cap growth funds are increasingly hitting this regulatory ceiling.

To remain legally compliant, these portfolio managers are forced to blindly trim their largest and most volatile holdings, completely regardless of their actual investment conviction. This mechanical forced selling exerts artificial downward pressure on the prices of these massive companies.

The researchers found that these specific equities go on to experience a significant rebound as the temporary underpricing eventually corrects itself. The authors note that “stocks held disproportionately in large positions by constrained funds earn abnormally high future returns” over the following months. This is a great example of how a structural inefficiency can create a lucrative mispricing anomaly in the largest and most liquid stocks in the world.

Lubos Pastor, Taisiya Sikorskaya, and Jinrui Wang, “The Hidden Cost of Stock Market Concentration: When Funds Hit Regulatory Limits,” NBER Working Paper 35007 (2026), https://doi.org/10.3386/w35007.

Feedback

Thank you for reading this week’s edition of Recent Academic Research. Remember to fill out the poll to let me know which paper was your favorite and like the post if you enjoyed it.

Feel free to follow up with any questions, comments, or ideas for the future!

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. This will be the start of a two-part series. A few weeks ago, we talked about how you can view prediction markets (like Kalshi and Polymarket) as derivative markets.

Specifically, these prediction markets resemble exotic options, like digital options, digital range options, and one-touch options.

In this series, I will create an application (through Streamlit) which you can run to model these markets as exotic options, and the option Greek exposures that you would have from a position in these markets.

In addition, I will show how can you trade these markets based on the difference between the market implied price and our implied price. The code for this post has been sent to your email.

Let’s get into it.

Choosing a Market

First, we have to choose a specific market to model. While this idea can be applied to almost all prediction markets (except for markets like mentions or politics)

We will focus on the Daily High Temperature markets on Kalshi. To be specific, we will be focusing on the Los Angeles market, but this will only be pertinent in the next post. I chose this market over others as we get to model both digital options and digital range options. We unfortunately don’t get to model one-touch options, but those are more complex, and likely better suited for a future post if there is enough interest.

In this framework, we treat the Forecasted High Temperature as the price of the underlying asset (the “Spot” price). The True High Temperature realized at the end of the day represents the settlement value.

Temperature markets possess a fundamental difference from equity markets that directly impacts option pricing: Mean Reversion vs. Geometric Brownian Motion.

Stocks follow a random walk with no theoretical upper bound. This creates “fat tails” in one direction. However, temperature is physically bounded and mean-reverting. If the average high for July in NYC is 85°F, the probability of hitting 115°F is pretty much impossible, while a stock hitting a 10x multiple of its current value is not.

Because temperature cannot move to infinity, the “tails” of these digital options behave differently than standard equity binaries. In a typical Black-Scholes model, the probability of an extreme outlier remains a factor in the price. In temperature markets, the probability density function collapses much faster as you move away from the mean. This means out-of-the-money (OTM) digital options in weather markets often decay toward zero much faster than their equity counterparts as the limits are bounded.

To apply our model, we define the variables as follows:

• Underlying (Spot): The current consensus forecast, calculated from the current digital option and digital range option prices (we will go more into this next post).

• Strike (K): The temperature threshold defined by the Kalshi contract (ex. >90°F or 88°F - 89°F).

• Payout: A fixed $1.00 if the condition is met, and $0.00 otherwise.

By framing the market this way, we can move away from “betting” and toward and volatility / price arbitrage.

Digital and Digital Range Options

To value these prediction markets, we use the Black-Scholes model for Digital Options. While standard “vanilla” options have a payoff that increases the further the stock moves past the strike, digital options have a fixed, “all-or-nothing” payoff.

The Pricing Model

The pricing for a digital call is significantly simpler than a vanilla call. Because the payoff is a fixed $1.00 if the event occurs, the price of the option is simply the discounted probability that the underlying (S) will be above the strike (K) at expiration (T).

Mathematically, assuming interest rates (r) are 0%, the price is:

Where N(d2) is the cumulative distribution function of the standard normal distribution, and d2 is calculated as

In this context, the "Price" you see on Kalshi (e.g., $0.65) is literally the market-implied N(d2), or a 65% probability of the temperature threshold being met.

Digital Call Payoff

A Digital Call (or “Threshold” contract) pays out if the temperature exceeds a specific strike.

At Expiry: The payoff is a “Step Function.” It is worth $0.00 if the spot is 99.9 and $1.00 if it hits 100. There is no middle ground.

1 Year to Expiry: With one year to go, the “cliff” is smoothed out into an S-curve (Sigmoid). This represents the uncertainty; even if the current spot price is 80, there is still a statistical chance it could reach 100 in a year.

Digital Range Payoff

A Digital Range option (or “Between” contract on High Temperature markets) pays out only if the temperature finishes inside a specific window (e.g., between 90°F and 110°F). You can think of this as a Digital Bull Spread: you are Long a Digital Call at 90 and Short a Digital Call at 110.

At Expiry: The payoff is a “Rectangle” or “Box.” If the spot price is 89 or 111, the contract is worthless.

1 Year to Expiry: The curve looks like a Bell Curve (Probability Density). The highest value is centered between the two strikes, as that is the “safest” place for the option to be to ensure it stays within the range as time passes.

Pin risk is the primary danger in these markets. If the temperature is hovering exactly at 99.9°F as the market is about to expire (and you own the >100 digital), your position value will oscillate violently between $0 and $1. As we will see in the next section, this causes your Gamma to approach infinity, making the position nearly impossible to manage or hedge as you approach the “cliff.”

Exotic Greek Exposures

Managing risk in prediction markets requires a departure from vanilla options intuition. Because these contracts have a fixed payout, their Greeks are characterized by “spikes” and “sign flips” rather than the relatively smooth curves seen in standard equity options.

Note on Scaling: In the following graphs, the y-axis is scaled to the 1-month-to-expiry lines. As time (T) approaches zero, digital Greeks are mathematically explosive. For example, Delta and Gamma are proportional to 1/sqrt(T) and 1/T respectively. On the 1-day lines, these values often shoot off the chart; I have truncated them here to ensure the 1-month and 1-year profiles remain visible.

Digital Option Delta: The Probability Density

The price of a digital option is the market-implied probability, as we discussed above. Delta is the first derivative of that price with respect to the spot.

Because the price is a cumulative distribution, its derivative is the probability density function.

Delta peaks At-the-Money (ATM) because that is where the probability of finishing ITM is most sensitive to a $1 move. Deep ITM or OTM, the probability is “sticky,” so the derivative (Delta) is near zero.

At expiration, the price becomes a discontinuous step function. The derivative of a step function is the Dirac delta function, which is an infinitely high, infinitely thin spike at the strike. This represents the impossible hedge ratio as the contract expires “on the pin.”

Digital Option Gamma, Vega, and Theta

Unlike vanilla options, where Greeks often maintain the same sign, digital Greeks flip as you cross the strike price (K).

Gamma: Gamma is the derivative of Delta (with respect to Spot). Because Delta increases as you approach the strike from below and decreases once you pass it, Gamma is positive OTM and negative ITM.

As you can see, the 1-day gamma goes off the chart, like delta does in the prior graph. This is because of how delta is very large when the digital option is near the spot price.

Vega: Volatility is a “double-edged sword” for digitals. If you are OTM, higher volatility increases the odds of a jump into the money (Positive Vega). If you are already ITM, higher volatility only increases the risk of falling back below the strike (Negative Vega). This relationship becomes sharper as you get closer to the expiration.

Theta: Time decay follows the same logic. If you are ITM, the passage of time “locks in” your win (Positive Theta). If you are OTM, time is your enemy as you run out of runway to hit the strike (Negative Theta).

Read more

Sentiment and Investor Attention

Trading strategies based on retail investor attention generate strong short term profits, but this effect is heavily dependent on the underlying news sentiment.

Retail investors often rely on media buzz to make trading decisions, creating temporary pricing inefficiencies that quantitative strategies can exploit. This study tests a text mining strategy that buys stocks experiencing high search volume and media attention.

The authors discover that merely tracking attention is not enough to guarantee positive returns. The success of an attention driven trade relies on the emotional tone of the surrounding news.

When market attention spikes alongside positive sentiment, the trading strategy captures significant excess returns because retail buyers aggressively bid up the stock. Conversely, when the news is highly visible but negative, the strategy fails to produce the same short term gains. The authors summarize that "increased investor attention can improve the predictability of stock returns" when properly contextualized.

CHEN, Yao-Tsung and Yu, Cheng-Yen, Investigating the Interaction Effect between Sentiment and Attention on the United States Stock Market via a Trading Strategy Using Attention Words. Available at SSRN: https://ssrn.com/abstract=6437648 or http://dx.doi.org/10.2139/ssrn.6437648

Forecasting Returns with Cash Flows

By separating permanent growth trends from temporary cycles, the predictive power of valuation metrics improves.

Asset pricing models often struggle because common metrics like the price to dividend ratio are notoriously poor and inconsistent at predicting future returns. This paper resolves the issue by proving that cash flows contain both a permanent growth trend and a short term cycle.

Researchers from Harvard and NYU demonstrate that adjusting for these underlying trends drastically improves out of sample forecasting for both short and long horizons, as the authors discovered an out-of-sample R^2 of 9% at one-year and 22% at five-year horizons.

This means the secular rise in valuation ratios over the past century is actually driven by higher trend growth rather than permanently lower discount rates.

The authors emphasize that "the apparent weakness of return predictability is an artifact of measurement error" in classic models.

Hillenbrand, Sebastian and McCarthy, Odhrain, Expected Returns with Cash Flow Trends and Cycles (March 02, 2026). Available at SSRN: https://ssrn.com/abstract=6332619 or http://dx.doi.org/10.2139/ssrn.6332619

AI’s Impact on Employment

While businesses predict artificial intelligence will significantly boost future productivity, current adoption has yielded almost zero impact on employment or output so far.

There is a massive gap between the hype surrounding artificial intelligence and its measurable economic reality. Surveying thousands of executives across four major economies, researchers from various central banks (the Fed, BoE, etc.) found that while seventy percent of firms actively use these tools, the immediate economic effects are negligible.

The vast majority of business leaders report absolutely no changes to their hiring needs or labor productivity over the last three years.

Despite this muted recent past, the forward looking expectations remain incredibly optimistic. Executives forecast that these technologies will soon drive a noticeable surge in productivity and a slight reduction in overall headcount.

Interestingly, everyday employees hold a conflicting view, expecting the technology to actually create jobs rather than eliminate them. The researchers note a "sizable gap in expectations, with senior executives predicting reductions in employment" compared to their workers.

Yotzov, Ivan and Barrero, Jose Maria and Bloom, Nicholas and Bunn, Philip and Davis, Steven and Foster, Kevin and Jalca, Aaron and Meyer, Brent and Mizen, Paul and Navarrete, Michael A. and Smietanka, Pawel and Thwaites, Greg and Wang, Ben Zhe, Firm Data on AI (March, 2026). FRB Atlanta Working Paper No. 2026-3, Available at SSRN: https://ssrn.com/abstract=6466706 or http://dx.doi.org/10.29338/wp2026-03

A Better Target for Central Banks

Central banks achieve significantly better economic stability when they target a broad financial conditions index because it encourages market arbitrageurs to suppress financial noise.

This research explores how monetary policy should react to noisy financial markets in an open economy. Typically, central banks adjust interest rates to manage output gaps, which inadvertently spills volatility into connected markets like equities and foreign exchange.

The authors argue that explicitly targeting a broader financial conditions index (FCI) yields far superior macroeconomic results. When a central bank commits to this broad target, it signals to market arbitrageurs that the institution will forcefully counter non fundamental noise.

This shared commitment triggers a recruitment effect where private traders step in to stabilize prices alongside the central bank, which dampens volatility across all asset classes.

The authors find that "some degree of FCI targeting is always optimal" to smooth out market turbulence.

Ricardo J. Caballero and Alp Simsek, “Financial Conditions Targeting in a Multi-Asset Open Economy,” NBER Working Paper 34974 (2026), https://doi.org/10.3386/w34974.

Feedback

Thank you for reading this week’s edition of Recent Academic Research. Remember to fill out the poll to let me know which paper was your favorite and like the post if you enjoyed it.

Feel free to follow up with any questions, comments, or ideas for the future!

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.

Bias in Prediction Markets

Prediction markets are frequently distorted by wealthy, ideological “whales” who overpay for their preferred outcomes, creating a profitable edge for smaller, more objective traders.

While many view prediction markets as great forecasting tools, this research shows they are often just mirrors of wishful thinking.

The study reveals that the biggest players in these markets, the “whales” who place the largest bets, are frequently the least sophisticated participants. Instead of trading on secret information, these high-stakes actors are typically driven by ideological conviction and a “Yes Bias,” which is the tendency to overpay for an event simply because they want it to happen.

This behavior creates a structural opportunity for smaller, disciplined traders to profit by taking the opposite side of these emotionally charged bets (as I currently am - more on that in this week’s paid post).

Furthermore, the data shows that the loudest participants in the comments are often the least informed, proving that social buzz is usually just distracting noise.

Deleep, Avaneesh and Lee, John and Bai, Jenny and Suresh, Dhruv and Dhawan, Harsh, How Wise is the Crowd? Bias and Edge in Prediction Markets (February 28, 2026). Available at SSRN: https://ssrn.com/abstract=6322678 or http://dx.doi.org/10.2139/ssrn.6322678

AI Productivity and Markets

Stock market gains from AI productivity breakthroughs are effectively “canceled out” by investors when political and economic uncertainty is high.

We often expect a major technological leap to drive stock prices up instantly, but this paper shows that the broader political climate acts as a gatekeeper. Since 2018, productivity gains in AI-intensive firms have triggered significant valuation jumps, but only when economic policy uncertainty was low.

When the news cycle is dominated by trade wars or election instability, these same productivity gains have almost no impact on stock prices. This happens because high uncertainty causes “risk premia” to spike, meaning investors demand a much higher discount to hold onto growth stocks, which masks the value of the actual technological progress.

For the market, this means that even the most revolutionary AI tools won’t move the needle if the macro environment is too volatile.

Oka, Arsene, Economic Policy Uncertainty and the Pricing of Productivity in AI Equities (February 21, 2026). Available at SSRN: https://ssrn.com/abstract=6283479 or http://dx.doi.org/10.2139/ssrn.6283479

Momentum Strategy in Crypto

The cryptocurrency market is governed by a powerful momentum effect, where past winners consistently outperform laggards over a two-to-four-month horizon.

In the chaotic world of digital assets, many investors struggle to find a signal in the noise, but a simple momentum strategy proves remarkably robust.

By analyzing the 50 most active cryptocurrencies, this research demonstrates that coins with the strongest performance over the last 50 to 130 days tend to maintain their lead, while underperformers continue to slide.

This isn’t just a side effect of Bitcoin’s price movements; a market-neutral approach (buying the top winners and shorting the bottom losers) delivered double the risk-adjusted returns (Sharpe Ratio of 1.98) of a simple Bitcoin buy-and-hold strategy.

For investors, this finding proves that “systematic cross-sectional strategies offer a viable and risk-aware alternative to the directional speculation that dominates retail crypto trading.”

Chen, Kaston and Chen, Jaslyn, A Forecast Model For Crypto Currencies (February 24, 2026). Available at SSRN: https://ssrn.com/abstract=6300843 or http://dx.doi.org/10.2139/ssrn.6300843

Volatility as an Asset Class

Volatility should be treated as a standalone asset class that offers a consistent “insurance premium” to those willing to provide liquidity during market stress.

Most investors view volatility simply as a measure of risk, but successful funds treat it as a source of steady income, similar to collecting insurance premiums. This research highlights the “Volatility Risk Premium,” a structural reality where the market consistently overestimates how much stocks will actually swing, allowing option sellers to pocket the difference.

While this strategy provides steady “carry” or income, it comes with the risk of sudden, sharp losses during market crashes. To manage this, the paper suggests monitoring the “term structure” (the difference between short-term and long-term volatility expectations) as a warning sign to reduce exposure before a blow-up.

For a diversified portfolio, treating volatility as an asset is essential because “volatility exposure, managed with discipline, is one of the most powerful tools for navigating regime transitions.”

Verma, Divyanshu, Volatility as an Asset Class: Carry, Convexity, Crash Risk, and Hedge Fund Implementation (November 11, 2025). Available at SSRN: https://ssrn.com/abstract=6301718 or http://dx.doi.org/10.2139/ssrn.6301718

Feedback

Thank you for reading this week’s edition of Recent Academic Research. Remember to fill out the poll to let me know which paper was your favorite and like the post if you enjoyed it.

Feel free to follow up with any questions, comments, or ideas for the future!

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 to the second and last post in this series. Last post, we showed how LTCM failed due to an extreme tail event, how kurtosis shows up in the markets, and we introduced the option greek Volga.

We also covered some research papers on the topic that show opportunities that are based on tail risks.

Today, we will show exactly where you are exposured to Volga in the options chain, how to create an option structure to trade Volga, and more academic papers.

Let’s get into it.

The Change in Vega for a Change in Implied Vol

From the last post, we know that Volga (also known as Vomma, Vega Convexity, or DvegaDvol) is the option Greek that represents how much Vega will change for a change in implied volatility.

To understand the intuition behind this concept, let’s look at how the Vega profile changes across strike prices as implied volatility (IV) rises or falls. The chart below shows the Vega of options at different strike prices. As you probably already know, the Vega graph looks like a bell curve. However, the shape of this bell curve depends significantly on the current level of IV.

As you can see below, a higher implied volatility causes the wings to expand, meaning OTM options gain significantly more Vega exposure as volatility increases.

We can also view this concept through a different lens. If we take the derivative of Vega with respect to IV across the option strikes, we see that Volga is positive for nearly all options, peaking in the wings and remaining near zero for At-the-Money (ATM) options:

Some may not have thought about the “why” behind this before, so let’s look at the intuition. The Plinko board example is often a good analogy to explain volatility. Imagine dropping balls (stock returns) from the top of the board. In a low-volatility environment, the pegs are close together, and the balls cluster tightly in the center. Because the outcomes are so predictable, an OTM option (a slot far to the side) feels “impossible,” and it has almost no sensitivity to a change in the board’s width (Vega).

However, as you “crank up” the volatility, you are effectively widening the board and spacing out the pegs. Suddenly, the probability of a ball reaching those far-flung OTM slots isn’t just possible, it’s actually accelerating. This is why OTM options have high Volga: as the underlying has a greater chance of reaching those far out strikes, these “lottery tickets” become much more sensitive to further increases in volatility.

You can actually test this out with an option pricing model. I like this calculator online, because it displays the Greeks clearly. While it technically uses the Garman-Kohlhagen model (used for FX options) rather than standard Black-Scholes, the logic is identical. The only difference is that it uses two interest rate inputs instead of a single rate and a dividend yield. Let’s go through a few examples with this calculator:

OTM Call

Let’s choose a 15-delta option for this example, as the wings generally house the greatest Volga. We will use a call, but the effect is the same for an OTM put.

Assume USD/JPY is at 100, and we have a 1-year call with a strike price of 125. For simplicity, assume zero yield for both currencies. At 20% volatility, the option has a delta of ~15 and a Vega of ~24.

However, when we increase the implied volatility to 25%, the Vega rises to ~30:

This is the “Vega Convexity” that is so beneficial to those who hold long OTM options. Because you are “Long Volga,” your position scales in your favor: as volatility rises, your Vega exposure grows, so you make more money on the move. Conversely, if volatility falls, your Vega shrinks, meaning you lose less than a linear model would predict. You are essentially “long gamma” but for the volatility surface (this is why Volga is called Volga - it is the gamma of vol (Vol-Gamma)).

ATM Call

I also want to highlight a caveat that I would be surprised if any of you are aware of this. While we often say all long options have positive Volga exposure, this is technically incorrect at the very center of the curve.

Theoretically, ATM options can actually have slightly negative Volga.

If we use the exact same assumptions as before but move the strike to 100 (ATM) and set volatility at 20%, our Vega is 39.70.

However, if we crank implied volatility up to 25%, our Vega actually falls (ever so slightly) to 39.58.

The intuition for this is a bit abstract, but think of it this way: Vega measures how much “uncertainty” an option has. At-the-money, uncertainty is already at its absolute maximum because you are exactly on the 50/50 line of being in or out of the money. As you increase volatility to extreme levels, the distribution flattens out so much that being “exactly at 100” becomes less special relative to being at 110 or 120. The “peak” of the bell curve spreads out, actually lowering the incremental sensitivity of that specific ATM strike.

Additionally, there is a mathematical drift at play. In a lognormal distribution, as volatility increases, the median price of the distribution drops relative to the mean. This effectively makes your 'At-the-Money' strike behave as if it is slightly Out-of-the-Money, which contributes to that subtle decay in Vega.

The Delta-Hedged, Vega-Neutral Butterfly

A standard long butterfly is a common “retail” structure used to bet on low volatility or a “pin” at a specific price. It is constructed by buying one In-the-Money (ITM) call, selling two At-the-Money (ATM) calls, and buying one Out-of-the-Money (OTM) call.

You can also structure this as an Iron Butterfly using both puts and calls to achieve the same risk profile while potentially managing capital requirements differently.

However, a standard butterfly is not “pure” in terms of trading Volga because it carries significant direction (Delta) and volatility (Vega) risks. If you look at a normal butterfly’s Vega profile, you’ll see it is highly sensitive to the absolute level of implied volatility:

Normal Butterfly Vega Profile:

If you are a professional trader looking to harvest the tail premium, you don’t want to bet on where the stock price goes or whether implied vol simply goes up or down. You want to bet on the shape of the surface. To do this, institutional desks use a Delta-Hedged, Vega-Neutral Butterfly.

By adjusting the ratios of your long wings versus your short body, you can neutralize the position’s overall Vega. Because OTM options have higher Volga (Vega convexity) than ATM options, a Vega-neutral butterfly remains highly sensitive to changes in the “curvature” of the volatility smile. You also manage your deltas (by trading the underlying) in order to remain delta neutral throughout the period of the trade.

For example, if you wanted to get long volga exposure, you would trade a long, delta-hedged, vega-neutral butterfly where you buy more OTM options than you sell ATM options (because ATM options have a higher Vega than OTM options).

In a high-kurtosis regime (where the market begins to fear large, outlier events) the wings of the smile steepen. In this scenario, the Volga kicks in: the “wings” of your butterfly gain value faster than your short “body” loses it. This allows you to capture P&L purely from the market’s realization that the tails are fatter than previously expected, regardless of whether the underlying price remains stable.

Below, we will cover some academic papers on this topic, and I will give personal insight into how institutional trading desks trade these option structures.

Read more

Cookie Tracking and Sales Guidance

Firms that track users through website cookies develop a sharper internal picture of their operations, leading them to issue more frequent and higher-quality sales forecasts.

We often think of website cookies simply as tools for annoying targeted ads, but this research reveals they act as a vital data feed for corporate finance teams.

By capturing granular, real-time behavior from millions of users, cookies provide managers with an information edge that traditional accounting reports often miss. The study found that companies with higher cookie volume aren’t just better at marketing; they are significantly more likely to provide voluntary sales guidance to the public.

This relationship is particularly strong when the firm has the “data analytic technology” required to actually process that mountain of raw digital footprints into actionable insights.

However, this transparency has its limits, as managers tend to hold back these insights when industry competition is fierce or when strict privacy laws like the CCPA restrict their data collection. As the author notes, “the study highlights the role of firm-collected consumer data in shaping accounting information environments.”

Liu, Junhao, Website Cookies and Voluntary Disclosure (March 11, 2026). Journal of Accounting and Economics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=6389578 or http://dx.doi.org/10.2139/ssrn.6389578

ML Straddle Strategy

Execution mechanism design can be as important as prediction accuracy for trading strategy performance.

This study challenges the common obsession with building “perfect” forecasting models by showing that how you trade matters just as much as what you predict. The researchers developed a “straddle-then-reveal” framework where a trader enters both long and short positions simultaneously at the market open.

At the end of the day, a machine learning model predicts the next day’s direction and designates one leg as “kept” and the other as “wrong”. Instead of simply closing the wrong leg, they use a “patient trader” approach, placing limit orders at a specific buffer from intraday price extremes to capture profits from the natural daily price range.

Remarkably, this buffer mechanism allows even incorrect predictions to be profitable “rescued” when the wrong leg exits near a daily high or low before the market reverses. Testing across 48 global securities, the strategy achieved universal outperformance with win rates as high as 85%. According to the authors, “execution mechanism design can amplify returns substantially beyond what prediction accuracy alone would suggest.”

González Maiz Jiménez, Jaime and López-Herrera, Francisco and Reyes-Santiago, Adán, Machine Learning Straddle Strategy with Buffer-Based Execution: A Patient Trader Approach to Equity Trading. Available at SSRN: https://ssrn.com/abstract=6401038 or http://dx.doi.org/10.2139/ssrn.6401038

Social Media Sentiment

Machine learning models can systematically predict and monetize short-term market overreactions by integrating high-frequency emotional data from social media with price volatility signals.

In the messy world of intraday trading, markets often overextend themselves, creating brief windows where prices deviate from reality.

This research shows that by feeding a transformer-based “emotion” model a constant stream of Apple-related tweets, we can actually predict which of these price spikes will turn into profitable momentum.

While a simple overreaction rule often struggles with market noise, sophisticated models like XGBoost and neural networks excel at identifying the subtle interplay between volume, volatility, and specific feelings like fear or sadness.

Interestingly, the study found a “sweet spot” at the 10-minute frequency, where these behavioral patterns are most pronounced and tradable. As the paper concludes, “the predictive content of emotions is both statistically robust and economically actionable”.

Lis, Szymon and Ślepaczuk, Robert and Sakowski, Paweł, Overreaction as an Indicator for Momentum in Algorithmic Trading: A Case of AAPL Stocks (February 21, 2026). Available at SSRN: https://ssrn.com/abstract=6282099 or http://dx.doi.org/10.2139/ssrn.6282099

Monsoons & Stock Returns

Abnormal rainfall during India’s monsoon season acts as a significant systematic shock to equity returns, with deficient rain specifically dragging down the performance of the banking and fast-moving consumer goods sectors.

In the Indian economy, the annual monsoon is far more than a weather event; it is a critical macroeconomic driver that dictates agricultural productivity and, by extension, rural demand.

This research demonstrates that the stock market is highly sensitive to rainfall departures from the long-term average, as the monsoon directly impacts corporate earnings and inflationary expectations. When rainfall is deficient, the resulting drop in rural income and rise in food prices create a “double whammy” for sectors like banking and consumer goods, which rely heavily on the health of the rural heartland.

Interestingly, the paper finds that the market’s reaction isn’t always immediate or perfectly efficient, suggesting that investors often struggle to price in the full impact of a bad season until the data becomes undeniable.

Pandey, Utkarsh and Obuobi, Andrews Kwame and Kumar Singh, Mayannk and Bhattacharyya, Ritabrata, Effects of Monsoon on Indian Stock Market (February 19, 2026). Available at SSRN: https://ssrn.com/abstract=6270578 or http://dx.doi.org/10.2139/ssrn.6270578

Feedback

Thank you for reading this week’s edition of Recent Academic Research. Remember to fill out the poll to let me know which paper was your favorite and like the post if you enjoyed it.

Feel free to follow up with any questions, comments, or ideas for the future!

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 to the start of a new two-part series. Two and a half months ago, I explained the concept of skew and vanna, and the anomalous findings in academic papers (Part 1 and Part 2). Today, we are going to investigate Volga (not the river in Europe) and kurtosis.

Volga (also known as Vomma) is a second order option Greek (like gamma and vanna) and is very important on institutional trading desks but not understood by the average retail trader.

Let’s get into it.

The Fall of LTCM

In 1998, Long-Term Capital Management (LTCM) collapsed because its risk models were built on the assumption that market returns follow a normal (Gaussian) distribution. The fund was led by Myron Scholes and Robert Merton, two authors of the Black-Scholes model (so investors trusted that they knew what they were doing), and focused on relative value arbitrage.

Their strategy involved identifying tiny price discrepancies between related securities (such as buying “off-the-run” Treasuries and shorting “on-the-run” Treasuries) and betting that these spreads would eventually converge. Because these spreads were so small, the fund utilized extreme leverage, often reaching 30:1, to increase returns. Their models suggested that the probability of these spreads widening significantly and simultaneously was statistically zero.

Despite warnings from economists like Eugene Fama, who argued that real-world markets are prone to extreme outliers, LTCM maintained its heavy positioning. The first signs of trouble appeared in early 1998 as the fund’s capital began to shrink.

The situation worsened when Salomon Brothers shuttered its own arbitrage desk. Since Salomon held many of the same positions as LTCM, their liquidation drove prices against LTCM’s portfolio. Because other hedge funds and bank desks were crowded into these same trades, a feedback loop began.

Market participants began to anticipate LTCM’s forced liquidation, leading partner Victor Haghani to remark that “it was as if there was someone out there with our exact portfolio,... only it was three times as large as ours, and they were liquidating all at once.”

The final straw for LTCM was when Russia defaulted on its debt in August 1998. Instead of the expected convergence, the market experienced a global flight to safety. Asset classes that were supposed to be uncorrelated all diverged at the same time due to market contagion. LTCM’s models characterized this move as a “10-sigma” event, an occurrence so rare it should happen once in a million years.

In reality, the model had failed to account for kurtosis (we’ll talk about this later). The fund lost $4.6 billion in four months because it assumed the “tails” of the distribution were thin when they were actually fat. Ultimately, the Federal Reserve had to coordinate a $3.6 billion bailout by major creditors to prevent a systemic collapse of the financial markets.

We now know that these tail events occur much more frequently than a normal distribution would suggest. This is the basis for our discussion today.

Kurtosis

One of the major assumptions of the Black-Scholes Model is that the underlying stock’s returns are normally distributed (and therefore the prices of the stock are lognormally distributed). We know this is not true, and asset classes also have specific third moment (skew) and fourth moment (kurtosis) characteristics.

It’s fitting how Black and Scholes’ erroneous assumption in the option pricing model led to the downfall of their hedge fund.

While we have already talked about skew in a prior post, we have not discussed kurtosis, or fat tails. We know that “high-sigma” events occur much more often than they should. Earlier this year, we heard that silver and gold had a 6-sigma move, which should happen 3.4 times or fewer per million opportunities. The 1987 Black Monday Crash was a 22-sigma (!!) event, which should only occur about 1 in every one googol (a 1 with 100 zeros behind it) occurrences (which is a number so small it essentially exceeds the number of atoms in the visible universe).

Clearly, large, outlier moves happen more than a normal distribution suggests in financial markets. This is the reason that we model distributions with fat tails.

If a distribution has fat tails, it has positive kurtosis, and is considered leptokurtic. A normal distribution is called a mesokurtic distribution. A depiction of these distributions is below.

As you can see in the leptokurtic distribution, the center is much higher and pointier than the normal distribution, but the “shoulders” are thinner. Crucially, the ends of the curve (the tails) of the leptokurtic distribution are above the normal distribution.

If the market actually followed a normal distribution, the Black-Scholes model would be “correct,” and implied volatility (IV) would be a flat line across all strike prices. If the market expected a 15% volatility for a stock, a 10% out-of-the-money (OTM) Put would cost exactly 15% IV, just like an At-the-Money (ATM) option.

However, institutional traders aren’t naive (at least anymore). They know that the “tails” are fat. To account for the fact that a 5-sigma crash happens much more often than it should, they manually bid up the price of OTM options. This creates the volatility smile.

The curvature of this smile is essentially the market’s way of “fixing” the Black-Scholes model. The steeper the smile, the more the market is pricing in high kurtosis.

This brings us to the option greek Volga, and the ways that the street trades Volga. Additionally, I will cover papers that show the relationship between implied tail risk (from options pricing) and the future returns of that underlying stock.

Read more

Minimum Wages and Robots

Does raising the floor on human wages increase the use of machines in the American factory?

While economists have long debated if minimum wage hikes reduce employment, this research shifts the focus to how such policies trigger technological substitution. Using U.S. Census data and robot import records from 1992 to 2021, the study compares manufacturing plants on opposite sides of state borders that face different wage floors.

The data reveals a clear "robot-substitution" effect: a 10% increase in the minimum wage leads to an 8% rise in robot adoption. This trend is most pronounced in routine-heavy production environments where labor costs are a significant portion of the overhead.

The authors find that "increases in minimum wages raise the likelihood of robot adoption in manufacturing". This suggests that labor-intensive industries in high-minimum-wage regions are prime candidates for rapid automation.

Erik Brynjolfsson, J. Frank Li, Javier Miranda, Robert Seamans, and Andrew J. Wang, “Minimum Wages and Rise of the Robots,” NBER Working Paper 34895 (2026), https://doi.org/10.3386/w34895.

Mutual Fund Investments and Momentum

In the Chinese A-share market, can following the “herd” of mutual funds actually generate alpha, or are institutional investors just chasing their own tails?

Herding behavior is often dismissed as irrational, but this study of the Chinese equity market suggests that institutional “flocking” can be a powerful signal.

The researchers found that when market sentiment is high, stocks heavily bought by mutual funds outperform those they sell by roughly 7% over the following six months. Interestingly, this outperformance does not suffer from a sharp reversal, suggesting that these funds are successfully incorporating fundamental information into prices, albeit slowly.

This slow “information diffusion” occurs because high sentiment often impairs objective processing, causing the market to take months to fully reflect institutional trades. The paper notes that “mutual fund herding behavior plays an important role in determining whether stock price momentum persists”. This could be an interesting filter to incorporate in screening tools.

Xu, Ruipeng, Mutual Fund Herding and Stock Price Momentum: The Role of Market Sentiment. Available at SSRN: https://ssrn.com/abstract=6360864 or http://dx.doi.org/10.2139/ssrn.6360864

Interest Rates and Consumption

Rate cuts make people spend more through the sudden surge in home equity

The “household debt channel” is a cornerstone of monetary policy, but its internal mechanics are often debated. By analyzing six million natural experiments in the UK where fixed-rate mortgages expired at different times, this study isolates exactly how rate changes move the needle on consumer spending.

A 1% drop in mortgage rates boosts household consumption by 3% over the following six months. Crucially, the primary driver is not the “mortgage cash flow effect” of lower monthly payments, but rather the ability of households to borrow against rising asset prices.

When rates fall, house prices typically climb, and households extract this new equity to fund everything from home renovations to non-durable goods. The researchers note that “monetary policy affects consumption in large part through asset prices and borrowing”. This highlights that the “wealth effect” from housing is far more potent than simple interest savings. It also explains the “long and variable lags” of policy, as the economic stimulus only kicks in when individual mortgage deals reset.

Angus K. Foulis, Jonathon Hazell, Atif R. Mian, and Belinda Tracey, “How Do Interest Rates Affect Consumption? Household Debt and the Role of Asset Prices,” NBER Working Paper 34911 (2026), https://doi.org/10.3386/w34911.

Long Term Macro Models

Simple models are superior for long term forecasting.

Long-run forecasting is often viewed as a “fool’s errand” due to the compounding uncertainty of structural shifts, yet it remains vital for pension funds and climate policy. This research evaluates six forecasting models using over 150 years of data across 10 macroeconomic variables, including GDP growth and interest rates.

The study finds that for stationary variables (those that tend to return to a long-term average, like GDP growth) a basic first-order autoregressive model (AR(1)) remains best for 10 to 25 years.

Conversely, for non-stationary variables like nominal interest rates, a simple random walk model (which assumes the future will look like the current value) outperforms other models.

The paper demonstrates that while no model remains perfectly accurate at the 50-year mark, simple univariate models with constant parameters can effectively quantify uncertainty for decades.

Kurt G. Lunsford and Kenneth D. West, “An Empirical Evaluation of Some Long-Horizon Macroeconomic Forecasts,” NBER Working Paper 34904 (2026), https://doi.org/10.3386/w34904.

Feedback

Thank you for reading this week’s edition of Recent Academic Research. Remember to fill out the poll to let me know which paper was your favorite and like the post if you enjoyed it.

Feel free to follow up with any questions, comments, or ideas for the future!

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.

Crypto Straddles

Is at-the-money really the best place to sell crypto volatility?

Most volatility sellers default to at the money strikes because that is where the premium is thickest, but crypto return dynamics (heavy tails and frequent jumps) make this "standard" approach surprisingly suboptimal. By analyzing years of Bitcoin option data, this study finds that the sweet spot for a daily short strangle is actually about 3% out of the money.

The researchers move beyond static rules of thumb by training machine learning models to adjust strikes based on current market states (like implied volatility levels and the shape of the surface). Their smoothed classification model consistently beats the standard benchmark by reducing the impact of daily noise and focusing on persistent regimes.

Wen, Hongzhe and Yu, Xitian, Optimal Strike Distance for Daily Crypto Strangles: Theory and Empirical Analysis (January 26, 2026). Available at SSRN: https://ssrn.com/abstract=6151986

CEO Tone Impacts Returns

Can the nervous tremor in a CEO’s voice tell you more about future stock returns than the actual words in the transcript?

Traditional sentiment analysis focuses on what executives say, but this research suggests that the way they say it is far more predictive of future performance. By isolating earnings calls where the transcript text was entirely neutral, the authors analyzed paralinguistic features like pitch, energy, and vocal tension.

They found that even when the language is devoid of optimism or pessimism, vocal cues of assertiveness or nervousness predict significant stock moves. Specifically, portfolios built on high conviction vocal tones generated alpha between 40 and 70 basis points over the following month.

This suggests that voice provides an entirely independent channel of information that text based algorithms completely miss. The subtle, nonverbal cues of the CEO and CFO (captured during the heat of the Q&A session) act as a high resolution window into their true confidence level and cognitive load.

Pope, David, Voice Beyond Words: Evidence That Managerial Tone Predicts Returns When Text Does Not (January 26, 2026). Available at SSRN: https://ssrn.com/abstract=6135066

Bonds and Climate Risk

Bond return differs based on the “climate risk” associated with the bond.

This research uncovers a pattern where bonds that are most sensitive to climate policy uncertainty actually yield less than their peers. While we might expect a "risk premium" for holding uncertainty, the reality is that institutional investors view these specific bonds as valuable insurance policies.

Because these assets tend to perform well when climate regulations tighten, risk averse portfolios are willing to pay a premium for them (driving the initial price up and the future return down). This effect is particularly concentrated in high carbon sectors and has intensified significantly since the 2015 Paris Agreement.

This means that policy resilient bonds might be more expensive than they look (offering safety at the cost of yield). This negative premium remains significant even after controlling for traditional macroeconomic factors.

Yang, Wenhan and Wu, Chunchi, Climate Policy Uncertainty and the Cross Section of Corporate Bond Returns. Available at SSRN: https://ssrn.com/abstract=6277839 or http://dx.doi.org/10.2139/ssrn.6277839

Stock Valuation from Interest Rate Discounting

Why have stocks and bonds seemingly broken their historical link, and which part of interest rates actually drives stock prices?

We often assume that falling interest rates should mechanically boost stock valuations (a one for one trade), yet the historical data across major economies shows a muddled relationship.

This paper resolves the confusion by splitting real interest rates into three distinct drivers: expected growth, risk, and “pure discounting.” It turns out that only the pure discounting component (the part of the rate tied to time preference) transmits directly to equity yields. In fact, this single factor explains 80% of cross country valuation changes since 1990.

Meanwhile, about two thirds of the decline in U.S. rates were driven by slowing growth and shifting risk (which often drag stock prices down, cancelling out the benefit of lower rates). This finding matters because it suggests that blindly buying stocks whenever rates fall is a poor decision, and that you have to know why the rates are moving to know if the equity “duration” will actually kick in. Isolating the pure discounting component is the only way to accurately measure how much a change in rates has passed through to equities.

Gormsen, Niels Joachim and Lazarus, Eben, Interest Rates and Equity Valuations (February 02, 2026). Available at SSRN: https://ssrn.com/abstract=6171489

Feedback

Thank you for reading this week’s edition of Recent Academic Research. Remember to fill out the poll to let me know which paper was your favorite and like the post if you enjoyed it.

Feel free to follow up with any questions, comments, or ideas for the future!

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.

U.S. Equity Valuations

Does the decline of the labor share mean the stock market isn’t actually expensive?

Traditional valuation metrics have signaled an overheated U.S. market for nearly 30 years, yet the expected mean reversion has failed to materialize. By shifting the lens from standard accounting to macroeconomic data, researchers found that while the "earnings yield" has plummeted, the "free cash flow yield" has remained remarkably stable around its historical mean of 3.6%.

This divergence is mechanically driven by two factors: a significant drop in the share of corporate output paid to labor and a lack of significant growth in measured corporate investment.

Effectively, corporations are generating more cash for owners without needing to reinvest it into physical capital, a shift that makes the market look expensive on paper while the underlying cash flows remain grounded. This suggests that "low" earnings yields might be a permanent fixture of the modern economy rather than a bubble waiting to burst. As the authors note, "persistently low earnings yields need not reflect mispricing".

Atkeson, Andrew, Jonathan Heathcote, and Fabrizio Perri. “A Macroeconomic Perspective on Stock Market Valuation Ratios.” Staff Report 682. Federal Reserve Bank of Minneapolis, January 2026. https://doi.org/10.21034/sr.682.

Geopolitical Power and Market Returns

Can the geopolitical strength of a nation predict how closely its stock market moves with the world?

While investors often look to local volatility or GDP to explain market co-movements, a new study suggests that a nation's "Global Power" is a primary driver of financial integration. Using an index that combines military, political, and economic strength, researchers discovered that as a country's global power increases, its stock market becomes significantly more correlated with other G20 nations.

This relationship likely stems from the fact that powerful nations exert more influence over global trade policies and international agencies, which in turn shapes the risk premiums and returns for their domestic firms.

The findings indicate that shifts in the global order, such as the rising power of China and the relative decline of the US, directly impact the diversification benefits available to international investors.

Gupta, Rakesh and Haddad, Sama and Selvanathan, E. A., Global Power and Stock Market Co-Movements: A Study of G20 Markets (July 15, 2024). Available at SSRN: https://ssrn.com/abstract=6236638

Extreme Valuation Multiples

Should you stop worrying about valuation multiples when they are in the middle of the pack?

Market multiples like the dividend yield or CAPE are famous for forecasting long-term returns, but their predictive power isn’t constant across all levels. New evidence shows that these metrics are far more useful when they are at historical extremes, specifically in the top or bottom quartiles of their range.

When multiples are in the “middle,” the relationship to future returns effectively breaks down, providing almost no actionable signal for investors.

A dividend yield only provides a strong signal when it is roughly below 3% or above 6%, whereas the “middle” range fails to convey meaningful information about future market performance. As the research highlights, “multiples are far more useful when they are relatively high or low”.

Estrada, Javier, Multiples for Valuation: Go High, Go Low, Ignore the Middle (February 01, 2026). Available at SSRN: https://ssrn.com/abstract=6152048

Taleb’s Newest Paper

Are American options providing a layer of “hidden” protection that your pricing model is missing?

Conventional pricing systems often treat interest rates as fixed inputs, a simplification that systematically underestimates the value of the early-exercise feature in American options. By introducing randomness into funding and dividend rates, this research (by Nassim Taleb!) develops a framework to quantify “unaccounted optionality” that remains invisible to standard risk systems.

The study highlights that the American contract acts as a “smart” instrument, adapting its exercise timing to benefit from transient peaks in the interest rate differential or unexpected market squeezes.

This flexibility provides a crucial lower bound against adverse movements, effectively acting as an option on the funding environment itself. This feature “embodies an additional, often unrecognized, layer of convexity”.

Hassan, Noura El, Bacel Maddah, and Nassim N. Taleb. "Hidden Risks and Optionalities in American Options." arXiv preprint arXiv:2602.14350 (2026).

Feedback

Thank you for reading this week’s edition of Recent Academic Research. Remember to fill out the poll to let me know which paper was your favorite and like the post if you enjoyed it.

Feel free to follow up with any questions, comments, or ideas for the future!

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-subscriber only post. Today, we will be discussing how prediction markets can be modeled as exotic options, and how we can systematically exploit the structural inefficiencies within them.

Let’s get into it.

Modeling Prediction Markets as Exotic Options

Prediction markets are often misunderstood by retail traders as simple sports-betting parlors for geopolitical or macroeconomic events. However, from a structural standpoint, they are sophisticated derivatives exchanges trading exotic options. By mapping these prediction contracts to their traditional options counterparts, we can better understand their pricing dynamics and the mathematical edge required to trade them systematically.

Digital Option

A digital option, also known as a binary or cash-or-nothing option, provides a fixed payout if the underlying asset is trading above (for a call) or below (for a put) the strike price precisely at expiration. It is a European-style option with a strictly binary outcome: $1 or $0.

In standard Black-Scholes framework, the price of a digital call option is the discounted risk-neutral probability that the option will expire in-the-money. Mathematically, this is represented by:

While platforms like Kalshi do provide interest on open positions, the time-value discount factor (e^(-rT) in short-duration prediction markets is negligible. As it approaches 1, the price of a near-term contract simplifies directly to N(d2), which is the market-implied probability of the event occurring.

Below, I have an example of a market on Polymarket that can be described with digital options. Note: Please click “View Polymarket” to get the updated market. The values may be stale and the layout below may not be in the correct order for S&P 500 price.

Each line item represents a distinct digital call option. If you buy "Yes" for the ">$6,860" strike at 57 cents, you are paying a $0.57 premium for a digital call that pays $1.00 if the SPX prints above $6,860 at the exact moment of expiry. The 57-cent price directly reflects a 57% market-implied probability. Because the payout is entirely binary at expiration, the Gamma of this position will become incredibly explosive as the February deadline approaches, creating massive price swings on small underlying index movements.

Digital Range (Corridor) Option

A digital range, or corridor option, pays a fixed cash amount only if the underlying asset's price settles within a specifically defined lower and upper boundary at expiration. Structurally, you can create a range option by executing a spread: going long a digital call at the lower boundary (K1) and short a digital call at the upper boundary (K2).

The value of a corridor option is the probability that the final fixing price ($S_T$) lands between the two strikes:

In pricing terms, it is simply the price of the lower-strike digital call minus the price of the upper-strike digital call:

The market for US strikes in Somalia is structured as a series of mutually exclusive corridor options. When you purchase the "14-17" bracket for 60 cents, you need the final count to fall strictly within that corridor. If the final number is 13 or 18, the “14-17” option expires completely worthless. The pricing here functions as a probability density function across the different brackets, with the market currently pricing a 60% probability that the final settlement falls within the 14-17 boundaries.

One Touch Option

A one-touch option is a path-dependent barrier option. Unlike standard digitals that only examine the underlying price at the moment of expiration, a one-touch option pays out if the underlying asset breaches a predefined barrier price at any single point during the life of the contract.

Pricing a one-touch option is significantly more complex than a standard digital because it accounts for the entire path of the asset. It relies heavily on the volatility of the underlying and time to expiration. Because the asset only needs to touch the barrier once, the probability of a one-touch option paying out is always strictly greater than or equal to a standard digital option at the same strike.

A One-Touch Call Option at the same strike as a Digital Call Option should be roughly 2x the value of the Digital Call Option (however, this is actually incorrect, as the "one touch = 2x digital at the same strike" only holds in a very special case (driftless Brownian motion via the reflection principle), while for a real asset price, even in plain Black Scholes, log spot has nonzero risk-neutral drift, which breaks the symmetry that gives the factor of 2, so the touch probability is not generally 2x, it’s a different expression that depends on drift (and in practice on skew/local vol/stoch vol, etc)).

The Silver by end of June market perfectly illustrates this. Notice the specific wording: "Will Silver hit..." rather than "Will Silver close above...". If you buy the "$120" strike for 29 cents, it does not matter where Silver trades on June 30th. If a volatility spike causes Silver to touch $120 in mid-April before crashing back down to $30, the barrier is breached, the option is immediately triggered, and the $1.00 payout is locked in. You are buying an American-style barrier payout, making Vega (sensitivity to implied volatility) a massive driver of this contract's pricing.

That was a lot, and I could discuss this with much more depth if it is of interest.

Below, I will introduce a trading strategy that I have been working on over the last two weeks. The strategy looks quite promising, and I will launch the systematic trading strategy live on Kalshi in the next few days.

Read more

Government Bond Performance During Wars

History suggests that government debt is far from a safe haven during major geopolitical conflicts or pandemics.

We naturally assume that sovereign bonds offer absolute protection during times of extreme crisis. However, an analysis of centuries of financial data from the United States and the United Kingdom proves otherwise. During major wars and the recent pandemic, real returns on government bonds consistently collapsed and fell significantly below economic growth rates.

When nations face massive and unexpected spending needs, they rarely raise taxes enough to cover the bill (printing money is always the path of least resistance). Instead, governments quietly pass the cost onto bondholders through surprise inflation and financial repression policies that keep yields artificially low while purchasing power erodes.

Over a four year crisis window, these combined forces typically destroy about 31 percent of a bond portfolio's real value. As the authors conclude, "governments force bondholders to bear a substantial share of that fiscal burden" during these extreme spending shocks.

Zhengyang Jiang, Hanno Lustig, Stijn Van Nieuwerburgh, and Mindy Z. Xiaolan, “Are Government Bonds Safe in Times of War and Pandemic?,” NBER Working Paper 34820 (2026), https://doi.org/10.3386/w34820.

Financial Literacy and Dividend Anomaly

Does a basic misunderstanding of exponential math cause investors to chronically undervalue high yield dividend stocks?

The value premium found in high dividend stocks is one of the oldest anomalies in finance, but its root cause might simply be human cognitive limits. This research shows that investors broadly fail to grasp the mechanics of compound growth, leading them to severely underestimate the future value of reinvested dividends.

Using a global survey of financial literacy, the study proves that the dividend anomaly is wildly profitable in regions where the public struggles with compounding math. In these areas, the strategy generates nearly triple the returns compared to highly literate regions.

Because the average investor cannot intuitively model exponential growth, they simply refuse to pay a fair price for cash flow generating assets. The authors conclude that "our paper highlights financial education as a key tool for improving market efficiency."

Chen, Xin and yin, xueqian and Wang, Wei, Unveiling the Value Investing: The Crucial Role of Compound Rate Understanding. Available at SSRN: https://ssrn.com/abstract=6211283 or http://dx.doi.org/10.2139/ssrn.6211283

Advanced Filtering for Mean Reversion

A superior model to measure the mean-reverting capabilities of the underlying.

Mean reversion trading relies on the assumption that an asset will eventually return to its fair price after being pushed around by short term noise. The challenge is always defining that fair price. This research introduces a flexible filtering technique that acts as an intelligent smoothing tool.

By adjusting how much weight it gives to local price vol versus the bigger picture, the model extracts a cleaner signal of the true underlying trend. When the market behaves normally, the filter hugs the price closely.

Ultimately, this refined spread between the erratic spot price and the smooth latent fair value creates a highly measurable trading signal. As the authors state, this approach "expands the expressiveness of the kernel beyond simple exponential smoothing."

Xu, Zhichen and Firoozye, Nick and Koukorinis, Andreas and Treleaven, Philip and Zhu, Wilbur, Advanced Signal Filtering for Mean Reversion trading (February 12, 2026). Available at SSRN: https://ssrn.com/abstract=6225198

Interest Rates and Foreign Demand

Global investors seeking safe assets systematically drive down domestic real interest rates independently of local economic fundamentals.

We often attribute falling real interest rates to domestic forces like inflation expectations or monetary policy adjustments. This paper proves that foreign appetite for domestic Treasury securities plays a massive role in moving the market.

By tracking cross border capital flows, the research isolates the specific yield changes driven purely by international portfolio balancing rather than domestic news. When global uncertainty spikes, foreign capital floods into sovereign safe assets, pushing down yields regardless of what the local economy is doing (proving that domestic yields are often hostage to global anxieties).

The researchers conclude that "foreign demand is not a peripheral factor but a systematic contributor" rather than a temporary crisis anomaly.

Crooker, John, Global Portfolio Demand and the Dynamics of U.S. Real Interest Rates. Available at SSRN: https://ssrn.com/abstract=6211282 or http://dx.doi.org/10.2139/ssrn.6211282

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