Commodities

Weak Factors for Commodity Futures Portfolios (Nakagawa and Sakemoto)

What explains risk premia in commodity futures? Examining 38 commodities, the authors find that market-based uncertainty measures, including VIX, credit spreads, and financial conditions, are significantly priced. In contrast, news-based measures of geopolitical risk and policy uncertainty carry no significant risk premia. Financial market conditions appear more relevant for commodity risk pricing than geopolitical headlines.

Crypto

Are Cryptocurrency Factors Investable? Reassessing Liu, Tsyvinski, and Wu (2022) (Han)

Revisiting established crypto factors using data through May 2026, this paper finds that several long-short portfolios would have been liquidated despite statistically significant average returns. Even surviving momentum strategies experience drawdowns of 74–93%. Short-term momentum holds up best out of sample, while size, price, volume, and volatility premiums lose statistical significance.

Equities

Factor Timing with Transaction Costs (Gunturu and Lalwani)

Can factor timing outperform equal weighting after transaction costs? Using 50 long-short equity factors, the authors find that annually adjusting factor weights based on past returns and covariances improves risk-adjusted performance. Over 2000–2020, the strategy achieves a Sharpe ratio of 0.61 versus 0.45 for equal weighting, after trading costs.

Lever Up Your IRA: Understanding the Long Run Performance of Leveraged ETFs (Lai and Reilly)

Leveraged equity ETFs can be attractive investments even for long-term buy-and-hold investors. Examining 191 funds, financing costs and index autocorrelation are found to matter more for expected returns than volatility alone. In a lifecycle portfolio model, optimal allocations to leveraged equity ETFs range from 35% to 100%, challenging conventional investment advice.

Do Words Matter for Wall Street? Presidential Speech and the Stock Market (Bonaparte)

Do presidential speeches move the stock market? Analyzing nearly 16,000 presidential statements from 1993–2024, absolute S&P 500 returns are found to be 75% larger than usual the day after State of the Union addresses. Unscheduled national addresses increase overnight moves by 56%. Yet speech tone and other textual measures fail to predict return direction. Presidential addresses appear to be volatility events, not directional trading signals.

Stale Data, Persistent Alpha (Fan and Li)

Stock market anomalies remain profitable even with outdated information. Examining 94 accounting-based factors, median monthly CAPM alpha declines from 36 bps using four-month-old data to 28 bps using year-old data. Even three-year-old data retains 19 bps. Much of anomaly alpha appears linked to persistent firm characteristics rather than short-lived reactions to new information.

Revisiting Momentum Crashes: Volatility Timing across Developed and Emerging Markets (Feng, Huang, and Su)

Momentum crashes often occur when markets rebound after prolonged declines. Using international equity factor data through April 2026, the authors find that volatility scaling raises the momentum Sharpe ratio from 0.53 to 0.87 in developed markets and reduces maximum drawdown from 44% to 11%. However, improvements are considerably smaller after 2010, particularly in emerging markets.

Machine Learning & Large Language Models

Reading the News Without Hindsight: NoLBERT Topics and the Cross-Section of Stock Returns (Foley, Hartadi, Prakash, Takkallapalli, and Vatsal)

Can AI extract profitable trading signals from financial news? Analyzing 1.26 million articles and 1,000 US stocks, this paper finds that measuring news attention shocks relative to the past 63 days improves return forecasts compared with a 5-day window. Combining multiple models achieves a net Sharpe of 1.12, but does not significantly outperform price-only strategies. Selecting the best model in validation leads to losses out of sample.

Trading Costs and Machine-Learning Return Forecasts (Bianchi and Jiao)

Better stock return forecasts don’t necessarily mean higher profits. This paper finds that machine-learning models predict returns best among illiquid stocks, where trading costs are highest. For a $500M portfolio, weighting positions by liquidity and avoiding trades with insufficient expected returns raises the net Sharpe ratio from −0.38 to 0.42. The largest gains come from reducing trading costs, not improving forecasts.

Regime-Aware Interpretable Ranking for Sector Rotation (Koa et al.)

Can AI improve sector rotation? Using S&P 500 sector data from 2002–2024, researchers find that a regime-aware model improves sector ranking accuracy. Technical signals, particularly volume and volatility, dominate fundamentals, while signal persistence and cross-sector relationships are especially important. Interestingly, simple 12-month momentum performs worse than random at predicting future risk-adjusted sector rankings.

Blogs

Which Trend Indicator Works Best for Sector Rotation? (Quantseeker)

Diversification All the Way Down (Alpha Architect)

Less Wrong (Quantitativo)

Gary Antonacci: From Dual Momentum to Advanced Models (Concretum Group)

Podcasts

What $450M Portfolio Manager Learned From Paul Tudor Jones · Andrew Perry (Chat with Traders)

Hedge Fund Manager Joseph Walsh: Always Play Weaker Players (Odds on Open)

Why Trend Following Rewards Patience ft. Katy Kaminski (Top Traders Unplugged)

Social Media & Industry Research

The crash that wasn’t (Owen Lamont, Acadian Asset Management)

Quant Insight: Has the Alpha in Value and Momentum Decayed? (Guido Baltussen)

Last Week’s Most Popular Links

Convexity You Can Afford (Yoav Git)

Buying Duration: Index Rebalancing and Price Pressure in Government Bond Markets (Martinetti, Soebhag, and Verwijmeren)

Bespoke Realized Correlations for Multivariate Volatility Prediction (Kotehne and Patton)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

But how should investors measure momentum?

The simplest approach is to rank assets by their historical returns. For example, a sector rotation strategy might select the three sectors with the highest trailing 12-month returns.

However, many alternatives exist. Investors can measure momentum over shorter horizons, combine multiple lookback periods, examine the distance from moving averages, or use regression-based measures of trend strength. Other approaches attempt to account for volatility, price consistency, or proximity to historical highs.

These indicators capture somewhat different characteristics of price trends. A sector with a large recent gain may rank highly on cumulative returns but less favorably on risk-adjusted momentum. Another sector may have a modest cumulative return but a particularly consistent upward trend.

Does the choice of trend indicator materially affect investment performance? And do more sophisticated measures outperform simple historical returns?

To test this, I compare 12 trend indicators within the same systematic sector rotation framework, using daily US sector ETF data from June 2009 through October 2026.

Strategy and Methodology

The investment universe consists of the 11 Select Sector SPDR ETFs, including technology (XLK), financials (XLF), energy (XLE), healthcare (XLV), and the other major S&P 500 sectors.

The strategy follows, on purpose, five simple rules:

1. At each month-end, calculate a trend score for every eligible sector ETF.

2. Rank the sectors from strongest to weakest according to the selected indicator.

3. Invest equally in the three highest-ranked sectors.

4. Rebalance at the opening price on the first trading day of the following month.

5. Deduct a one-way transaction cost of 5 basis points.

The portfolio remains fully invested in sector ETFs. There is no market-timing rule or requirement that momentum be positive.

ETFs become eligible only after 253 consecutive valid daily closing prices, ensuring sufficient history for the indicators. Consequently, the available universe expands as newer sector ETFs are introduced.

All strategies use identical selection, weighting, rebalancing, and transaction-cost assumptions. The only difference is the indicator used to rank sectors.

Performance is compared with SPY buy-and-hold. Sharpe ratios use the three-month Treasury bill rate as the risk-free benchmark.

The analysis covers approximately 17 years, with results also evaluated separately over two equal-length subperiods.

The objective is to isolate the importance of how momentum is measured, rather than differences in portfolio construction or market exposure.

Read more

Crypto

Jump Risk Premiums in Cryptocurrency Returns (Lee and Wang)

In crypto, greater exposure to positive crypto-market jumps predicts lower future returns. Studying 100 cryptocurrencies, the authors find that coins with high positive jump betas underperform those with low betas by 0.87% per week (equal-weighted). The effect survives standard risk controls and is strongest when retail investor sentiment is high. Investors seem willing to accept lower expected returns for lottery-like upside exposure.

Equities

Testing the Shiller CAPE Ratio Across 140 Years of U.S. Stock Market Data (Kotian)

Does the Shiller CAPE ratio really predict long-term stock returns? Using 140 years of US market data, the author finds that a higher CAPE predicts lower subsequent 10-year real returns, even after correcting for overlapping observations. However, predictive accuracy varies sharply across market regimes, with particularly poor performance during the 1990s bull market. CAPE helps forecast long-term returns, but its predictive power is far from stable.

Pricing the Next Surprise: Earnings Persistence and the Post-earnings Drift Window (Bonaparte)

Much of post-earnings announcement drift seems to reflect anticipation of future earnings rather than underreaction to past news. Analyzing 475,690 US earnings announcements, the author finds that controlling for the next quarter’s earnings surprise reduces the conventional 60-day drift coefficient by 58%. Moreover, about 58% of the price response to the next earnings surprise occurs before its announcement.

Infrastructure ETFs: Factor Exposures, Regime Risk, and Portfolio Roles (Feng and Meng)

Not all infrastructure ETFs offer the same exposure. Studying 48 US-listed ETFs, the authors find significant differences in risk, but no statistically significant differences in returns or alphas across seven categories. Owner/operator funds have a median market beta of 0.67 and volatility of 14%, compared with 1.14 and 24% for infrastructure development funds. ETFs carrying the same infrastructure label can have very different risk profiles.

Fixed Income

Buying Duration: Index Rebalancing and Price Pressure in Government Bond Markets (Martinetti, Soebhag, and Verwijmeren)

Nearly half of government bond futures’ average monthly returns occur on the last trading day of the month. Analyzing US and German bond futures from 2003–2024, the authors find that month-end accounts for 47% of average monthly returns. The mechanism is mechanical: Benchmark-constrained investors buy duration as bond indices rebalance, pushing prices higher before the index pricing snapshot. Prices partially reverse afterward.

Geoeconomic Fear and Sovereign Spreads (Ding, Huang, Jiang, and You)

Geoeconomic fear predicts emerging-market sovereign bond spreads. Using GPT-4o-mini to analyze over 1 million WSJ articles, the authors construct a fear index covering tariffs, sanctions, and export controls. A 1-standard-deviation increase predicts roughly 2 percentage points wider spreads one month ahead. The effect holds out of sample and is concentrated in speculative-grade debt.

Volatility

Bespoke Realized Correlations for Multivariate Volatility Prediction (Kotehne and Patton)

Not all intraday returns are equally informative for forecasting risk. Using 15-minute data on roughly 360 S&P 500 stocks, the authors find that optimally weighting intraday observations improves out-of-sample volatility and correlation forecasts. Later trading hours matter most for volatility, while midday matters most for correlations. Calmer trading periods also receive greater weight.

Blogs

Do Stocks Rally Into Midterm Elections? A Century of Evidence (Quantseeker)

The Questions That Change Everything (Robot Wealth)

Built to Survive the Waiting (Alpha Architect)

Why Long Bonds Still Belong in the Portfolio (Beyond Passive Investing)

Podcasts

The Questions that Change Everything - Kris Longmore (The Algorithmic Advantage)

Philip & Allan Maymin – Compounding Through Risk, Not Return (Flirting with Models)

Trend Following Thinking (Michael Covel)

36% of Your Portfolio Just Disappeared In the Last 30 Years (Andrew Ang Explains How) (Meb Faber Show)

Social Media & Industry Research

Not All Quants Are Alike: Exploring Where Equity Alpha Comes From (Goldman Sachs)

Convexity You Can Afford (Yoav Git)

France – A Matter of Heightened Interest (Citadel)

Last Week’s Most Popular Links

The Evolution of Quantitative Equity Investing: Half a Century of Alpha Research and Institutional Adaptation (Feng, Cardozo, and Xia)

Profit Growth and Expected Stock Returns (Ahn, Black, Huang, Lord, Rizova, and Saito)

Is the Value Premium Dead? Forecasting Value–Growth Cycles with the Implied Value Premium (Li, Ng, and Swaminathan)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

To investigate, I study daily US equity market returns from 1926 through 2025, comparing market performance around midterm elections with presidential election years and odd-numbered years.

The results suggest that US equities have historically performed particularly well around midterm elections, with roughly three-quarters of the outperformance relative to other years occurring before Election Day.

Market Returns Around November Elections

Figure 1 shows cumulative market returns over a 42-day trading-session window centered on the November election date. The window includes 21 returns ending at the last trading close before the election and 21 subsequent returns.

For each year, returns are aligned around the first Tuesday after the first Monday in November. The analysis covers 25 midterm elections, 25 presidential elections, and 50 odd-numbered years. The latter provide a comparison for the usual seasonal pattern around early November.

Historical returns are based on the CRSP value-weighted US stock market portfolio, obtained from the Kenneth R. French Data Library.

Figure 1. Cumulative US stock market returns around November elections, 1926–2025

The historical midterm pattern is distinct. Over the full event window, the market has generally delivered stronger returns in midterm election years than in other years. Much of this relative strength is concentrated in the weeks leading up to Election Day.

The chart also includes SPY’s performance in 2026. From the October 2 baseline through October 12, SPY has gained 1.10%, compared with approximately 1.45% for the historical midterm average at the same point in the election window.

How Large Is the Midterm Effect?

Table 1 reports average compounded returns for the 21 sessions before the election, the 21 subsequent sessions, and the full 42-session window.

Table 1. Average US stock market returns around November elections, 1926–2025 (%). Returns are arithmetic averages of individually compounded event-window returns.

The average 42-session return around midterm elections is 4.96%, compared with 1.37% in all other years. The difference of 3.59 percentage points is statistically significant using a two-sided Welch t-test (p = 0.041).

Midterm years also outperform odd-numbered years by 4.18 percentage points (p = 0.029). However, the difference relative to presidential election years is smaller and not statistically significant (p = 0.233).

The pre/post breakdown is particularly informative. During midterm years, the market has gained an average of 2.98% before the election and 1.96% afterward. By comparison, the average pre-election return in non-midterm years is just 0.29%.

This suggests that the historical midterm effect is not simply a post-election rally. A substantial part of the relative strength has occurred before the election itself.

Because all observations are aligned around the same November calendar dates, the comparisons also help distinguish the midterm pattern from ordinary autumn seasonality.

How Consistent Is the Pattern?

Figure 2 shows the compounded 42-session return around each midterm election from 1926 through 2022.

Figure 2. US stock market returns around individual midterm elections, 1926–2022

The market generated positive returns in 19 of the 25 midterm election windows, equivalent to 76% of observations. The pattern is also visible in more recent decades: Since 1982, 9 of the 11 midterm election windows have produced positive returns, with 1994 and 2018 being the exceptions.

However, the magnitude of returns varies considerably across elections, and some periods have produced losses. The historical average is therefore not representative of every election cycle.

The relatively small number of midterm observations is another limitation. Although the sample spans a century, it contains only 25 midterm elections, and the estimated average can be influenced by unusually strong or weak market episodes.

Why Might Markets Behave Differently Around Elections?

One possible explanation is the gradual resolution of political uncertainty. As Election Day approaches, polls and forecasts provide investors with additional information about likely outcomes, potentially reducing uncertainty and supporting risk appetite.

The concentration of returns before the election is consistent with this interpretation. A similar pre-announcement return pattern has been documented around scheduled macroeconomic events, including FOMC announcements.

More broadly, political-cycle effects, changing fiscal policy expectations, monetary conditions, and investor sentiment may also contribute to the observed return differences.

The analysis documents historical patterns rather than identifying their underlying causes. In particular, it does not establish a causal relationship between declining political uncertainty and stock market returns.

Implications for Investors

The historical evidence indicates that US equities have tended to deliver stronger returns around midterm elections than around other November dates. The effect is particularly pronounced in the weeks leading up to Election Day, while the post-election period has also generated positive average returns.

Although the pattern has been reasonably consistent across individual elections, it should be interpreted as a historical seasonal tendency rather than a standalone market-timing signal. Economic conditions, monetary policy, valuations, and the prevailing market trend are likely to matter more for the outcome of any individual election year.

The main finding is that the historical midterm election effect is primarily a pre-election phenomenon, with roughly three-quarters of the outperformance relative to other years occurring before Election Day.

The 2026 SPY comparison provides an opportunity to follow how the current election period develops relative to this historical pattern.

I’ll continue updating the comparison as we approach November 3.

Data and methodology: Historical daily market returns are constructed from the Fama–French daily factors (Mkt-RF + RF), representing the CRSP value-weighted US stock market. The sample covers 1926–2025. Returns are aligned around the first Tuesday after the first Monday in November. The event window includes 21 trading-session returns ending at the last close before the election and 21 subsequent returns. The cumulative-return chart compounds the cross-year arithmetic average daily return, while Table 1 and Figure 2 use compounded returns calculated separately for each year. The 2026 overlay uses SPY adjusted daily closing prices from FirstRate Data.

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

Asset Allocation

Adaptive Multi-Asset Allocation Conditioned on Geopolitical Risk Signals (Karadeniz)

Adding geopolitical risk to a regime-switching multi-asset model raises the out-of-sample Sharpe from 0.49 to 0.56, but the improvement is not statistically significant. Across seven robustness tests, none show a significant benefit from GPR. Key takeaway: Geopolitical risk can affect asset prices, but it seems less straightforward to use as a useful signal for portfolio allocation.

Crypto

Mispricing and Risk Compensation in Cryptocurrency Returns (Babiak and Bianchi)

Crypto returns reflect both risk compensation and systematic mispricing. Using 600+ cryptocurrencies, the authors find that speculative demand and liquidity drive daily mispricing, while reversal becomes especially important at the weekly horizon. Key takeaway: Lottery-like demand, liquidity, and reversal signals contain information about future crypto returns beyond conventional risk factors.

Fundamentals in Cryptocurrency Valuation: Evidence from DeFi Markets (Harre, Merkley, Miller, and Williams)

Crypto isn’t necessarily all speculation and momentum. Across 95 revenue-generating DeFi protocols, larger positive revenue surprises are associated with higher subsequent token returns: A 1-standard deviation surprise is associated with +29 bps abnormal returns over the next three days. Key takeaway: For some crypto assets, fundamentals do matter.

Predictability on the Move: The Cryptocurrency Cross-Section (Pang and Wang)

Crypto alpha is a moving target. Testing 1,043 signals across a point-in-time Binance universe, this paper finds that established size, momentum, and trend premia are weaker in their sample. At the same time, the strongest predictors vary substantially over time. In later periods, short-horizon liquidity and trading-activity signals dominate. Key takeaway: Crypto predictability migrates across signals rather than consistently coming from the same set of factors.

Equities

Is the Value Premium Dead? Forecasting Value–Growth Cycles with the Implied Value Premium (Li, Ng, and Swaminathan)

The value premium is cyclical and partly predictable. The authors construct an “implied value premium” from the difference in implied costs of capital between value and growth stocks. A 1-standard deviation increase predicts roughly 5–6% higher value-minus-growth returns over the next year, and the signal also works out of sample. Key takeaway: The implied value premium seems more useful for timing value vs. growth than traditional valuation spreads.

Expected vs. Unexpected Extremes: The Origins of the MAX Anomaly (Haghighi Zadeh, Kahibavil, and Safari)

Not all extreme stock returns are created equal. The well-known MAX anomaly, where stocks with extreme positive daily returns subsequently underperform, appears to be driven primarily by unexpected extremes, not predictable tail risk. Stocks with surprisingly large MAX realizations earn significantly lower subsequent returns. Key takeaway: It’s the unexpected component of extreme returns that matters most.

Explaining Realized Returns: Roll’s R² 40 Years Later (Kelly, Su, and Zhang)

Even after the fact, explaining why the stock market moved is surprisingly difficult. Conventional macro, cash-flow, expectations, and flow data explain little of monthly U.S. equity returns. But newspaper-stated reasons extracted from financial news explain roughly 30–40% of daily and monthly returns out of sample. Key takeaway: Market narratives contain useful information that traditional structured data miss.

The Evolution of Quantitative Equity Investing: Half a Century of Alpha Research and Institutional Adaptation (Feng, Cardozo, and Xia)

This comprehensive survey paper traces 50 years of quantitative equity investing, from portfolio theory and factor investing to alternative data, machine learning, and AI. Key takeaway: Finding a return predictor is only the first step. Alpha must survive out-of-sample testing, trading costs, capacity, crowding, and regime change to become a viable investment strategy.

Profit Growth and Expected Stock Returns (Ahn, Black, Huang, Lord, Rizova, and Saito)

Profit growth contains information about future stock returns beyond profitability and momentum. This paper finds that firms with stronger profit growth subsequently earn higher returns, with particularly strong results among small caps. Key takeaway: Profit growth and price momentum capture complementary information about expected returns, and combining the two strengthens the signal.

Fixed Income

Cross-Sectional Corporate Bond Returns (Feng, Liu, and Zheng)

Using large jumps in Fed-funds-futures-implied rates, the authors construct a characteristics-based measure of corporate bonds’ systematic-risk exposure. Out of sample, high-minus-low bonds earn 9.1% annually, with a 1.28 Sharpe (before costs). Key takeaway: Corporate bonds’ exposure to systematic risk contains substantial information about future returns.

Blogs

Can Bond Volatility Improve a 60/40 Portfolio? (Quantseeker)

What Really Drives the Asset Growth Anomaly? New Evidence Points to Mispricing, Not Risk (Alpha Architect)

Practical transaction cost checks for macro trading strategies (Macrosynergy)

Le Boomer Spread: Why France now pays more than Italy to borrow. (Hanno Lustig)

Podcasts

"AI Won't Give You Edge" - Alpaca CEO Yoshi Yokokawa (Odds on Open)

The Game Was Rigged in Their Favor. 28% Went Bust Anyway. Kris Abdelmessih on How to Size Bets (Excess Returns)

US Trading Champion: He Made 520% Competing Against The World’s BEST Traders - Here’s how! (Words of Rizdom)

Social Media & Industry Research

Dont’ Write Off Bonds Yet (Man Group)

Taking an Active Approach in Today’s Fixed Income Market (Man Group)

Rethinking the 60/40 Portfolio (HedgeNordic)

The quants of Babylon (Owen Lamont, Acadian Asset Management)

Last Week’s Most Popular Links

The Capacity of Equity Anomalies (Avramov, Bongaerts, Crego, and Soerlie Kvaerner)

Behavioral Finance at 40 (Nicholas Barberis)

Firm-Specific Price Delay and Momentum (Parajuli)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

A new paper by Kodithyala and Rebonato (2026) takes a different approach. Rather than focusing on the level of bond volatility, the authors study changes in implied Treasury volatility, measured by the MOVE Index.

Their main finding: Increases in implied bond volatility predict lower subsequent Treasury returns, while decreases predict higher returns. Importantly, this is a short-horizon effect: The paper focuses on whether changes in implied volatility contain information about Treasury returns over the next trading day.

The authors place particular emphasis on large changes in implied volatility, which show the strongest estimated relationship with subsequent Treasury returns.

The predictive relationship is negative across Treasury maturities, although its strength varies along the yield curve.

The paper uses Treasury and MOVE data from April 1988 through May 2026 and finds that a strategy dynamically adjusting Treasury exposure based on changes in MOVE produces higher Sharpe ratios than unconditional Treasury exposure.

That raises a practical question:

Can an ETF investor use the same information to improve a conventional portfolio?

From MOVE to VXTLT

The original paper studies the MOVE Index and constant-maturity zero-coupon Treasury portfolios. I make two changes to turn the idea into something directly investable.

First, I replace MOVE with VXTLT, Cboe's implied-volatility index derived from options on the iShares 20+ Year Treasury Bond ETF (TLT), designed to measure 30-day expected volatility.

Second, I replace the paper’s constructed Treasury portfolios with TLT itself.

This is therefore not an exact replication of the paper. It is a test of whether its central insight survives when translated into instruments an ETF investor can actually trade.

There is also an important difference in sample length. The academic evidence extends back to 1988, while VXTLT limits my ETF test to the post-2012 period. The ETF results should therefore be viewed as an investable test of a relationship documented over a much longer Treasury-market history.

Constructing the Signal

Following the paper's approach, I construct the VXTLT signal as follows.

First, I calculate the daily change in VXTLT:

I then standardize that change relative to its historical distribution:

where mu and sigma are the expanding historical mean and standard deviation of daily VXTLT changes, estimated using only information available through t-1.

I use the first 252 trading days as the initial estimation period and then expand the estimation window through time.

A positive z therefore represents an unusually large increase in implied Treasury volatility. I test thresholds of 1, 1.5, and 2 standard deviations.

I apply the signal to a traditional portfolio invested 60% in SPY and 40% in TLT, rebalanced monthly. Normally, the bond allocation remains invested in TLT.

Instead, I rotate from TLT into cash when the VXTLT signal exceeds the threshold:

The signal is a short-term signal with a one-day horizon, designed to predict Treasury returns over the following trading day.

There is no leverage and no shorting in this portfolio test. The paper also increases Treasury exposure following sufficiently large declines in implied volatility; my 60/40 overlay deliberately uses only the risk-reduction side of the signal, keeping the strategy unlevered.

Cash earns the prevailing three-month Treasury-bill rate less 50 basis points, floored at zero, and I deduct 2.5 basis points for each TLT transaction.

Daily and intraday VXTLT and ETF price data are from FirstRate Data. Three-month Treasury bill rates are from FRED (DTB3).

I test three different execution assumptions:

• Daily close signal, close-to-close returns: Observe the signal at the official VXTLT close and apply it from that close to the following close (not strictly tradable).

• Daily close signal, open-to-close returns: Observe the signal at the official VXTLT close and apply it from the following day’s open to that day’s close.

• 15:45 ET signal, close-to-close returns: Observe the signal at 15:45 ET and apply it from that day’s close to the following close.

Daily Close Signal: Close-to-Close

First, I use the official daily VXTLT close and apply the signal from that close until the following close.

This serves as the clean research version of the strategy: It measures the predictive content of the official closing VXTLT value, but is not intended as a directly implementable trading rule.

The sample period is December 2013 to September 2026.

At the 1-standard-deviation threshold, CAGR increases from 8.99% to 10.40%, while the Sharpe ratio rises from 0.68 to 0.80. Maximum drawdown also improves somewhat from about−27% to −25%.

Daily Close Signal: Open-to-Close

As mentioned, the previous close-to-close signal is not fully implementable since it relies on information at the close to trade on the close.

As an alternative, I observe the official VXTLT close on day t, wait until the following day’s open, and, when the signal is active, move the TLT allocation to cash for that trading session. TLT is repurchased at the following close.

The portfolio therefore retains its normal TLT exposure overnight but moves its TLT allocation to cash during the next day, should the signal trigger.

Interestingly, the effect survives this delay. At the 2-standard-deviation threshold, CAGR reaches 10.55%, and the Sharpe ratio rises to 0.82, compared to a Sharpe of 0.68 for the standard 60/40 portfolio.

The year-by-year results are considerably less uniform, however. In particular, much of the improvement in this delayed-execution version is concentrated in 2020.

15:45 Signal: Close-to-Close

Finally, I construct an explicitly pre-close version of the signal using 5-minute VXTLT data.

Instead of waiting for the official close, I observe VXTLT at 15:45 ET and calculate:

The historical mean and standard deviation are still based only on prior daily VXTLT changes.

The signal is therefore known before the market close and can be implemented using market-on-close orders. The resulting position is held from that day’s close until the following close. The sample is shorter because 5-minute VXTLT data are available only from May 2020.

The shorter sample makes this a less powerful test, but the basic result survives in a directly implementable specification. At the 1-standard-deviation threshold, CAGR increases from 6.84% to 8.54%, the Sharpe ratio improves from 0.36 to 0.50, and maximum drawdown again improves somewhat from −27% to −25%.

Interpretation

The results are directionally consistent across the execution assumptions and signal thresholds, although these are variations of the same underlying signal rather than independent tests.

Across the three execution assumptions and three signal thresholds, all nine net-of-cost overlays improve CAGR, Sharpe ratio, and maximum drawdown relative to their corresponding 60/40 baseline.

Large increases in implied Treasury volatility seem to identify periods when Treasury exposure is less attractive over the following trading day.

The result is consistent with the paper’s empirical finding, which is somewhat surprising given the conventional view of Treasuries as a hedge during periods of market stress.

Some of the largest improvements occur during stressed bond-market periods, notably 2020 and 2022, which is consistent with the idea that large volatility shocks can identify periods of unusually poor short-term Treasury returns.

Importantly, the portfolio doesn’t need to forecast the direction of stocks, change its equity allocation, use leverage, or short Treasuries. It simply temporarily removes bond exposure when the implied-volatility signal indicates unusually high short-term Treasury risk.

There are important caveats. The investable VXTLT sample is much shorter than the underlying MOVE evidence in the paper. TLT also represents the long end of the Treasury curve, where the paper’s predictive evidence is weaker than at some shorter maturities. And the signal is explicitly short-term; it should not be interpreted as a long-run Treasury market-timing indicator.

Conclusion

The usual approach to volatility management asks whether investors should hold less of an asset when the level of volatility is high.

For bonds, a potentially more useful question may be:

Is the daily change in bond implied volatility unusually large?

The evidence from VXTLT and TLT suggests that this information can be useful for managing bond exposure over very short horizons.

In my tests, moving from TLT to cash after large increases in Treasury implied volatility improves the performance of a 60/40 portfolio across different thresholds and execution assumptions, even after transaction costs.

The improvement isn’t enormous, and some periods contribute disproportionately.

But for a signal requiring only one volatility index and a simple threshold rule, the consistency across implementations is encouraging.

References

Kodithyala, Siddartha, and Riccardo Rebonato, 2026, Can one volatility-manage fixed-income portfolios?, SSRN Working Paper 7526798.

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

Behavioral Finance

Behavioral Finance at 40 (Nicholas Barberis)

This review of 40 years of behavioral finance highlights several useful insights for investors. For example, investors tend to underreact to near-term earnings news but overreact to long-term growth expectations. That combination can help explain both momentum and value: Near-term fundamentals are incorporated too slowly, while long-term growth stories can get priced too aggressively. Key takeaway: Markets can be slow with facts and fast with stories.

Equities

Survivorship Bias in the Cross-Section of Winner Portfolios (Jha, Jaffri, and Butt)

Survivorship bias can dramatically inflate the performance of past winners. Among the top 20 U.S. stocks ranked by trailing returns, a survivor-only backtest outperformed the full-universe portfolio by 6.2 percentage points after 1 year and 37.4 percentage points after 5 years. Key takeaway: Survivorship bias in backtests can be sizeable.

The Capacity of Equity Anomalies (Avramov, Bongaerts, Crego, and Soerlie Kvaerner)

A high-alpha strategy isn’t necessarily scalable. Across 35 equity anomalies, the highest-alpha long legs could support less than $2B, while lower-alpha profitability strategies could support $20 to 33B before trading costs erase returns. Key takeaway: A great backtest can become a much less attractive strategy once you try to deploy serious capital.

Size Anomalies in U.S. Bank Stock Returns: A Fiscal Explanation (Gandhi and Lustig)

Bigger banks have historically earned lower risk-adjusted returns. In U.S. bank stocks, a portfolio long the largest banks and short the smallest underperformed by roughly 7–8% per year, even though larger banks were more levered. The authors’ explanation: Too-big-to-fail protection lowers the tail-risk premium investors demand for owning large banks. Key takeaway: The small-minus-big risk premium is alive in bank stocks.

Firm-Specific Price Delay and Momentum (Parajuli)

Momentum is highly conditional on how efficiently stocks incorporate firm-specific information. It is strong among stocks with high price delay, but essentially absent among low-delay stocks. Price delay measures how persistently a stock’s own past returns predict future returns, controlling for market and industry effects. Takeaway: Momentum works best where information diffuses slowly.

Speculative Leverage and Factor Momentum (Sun and Xia)

Factor momentum is strongest when speculation is being fueled by leverage. Since 1997, a one-standard-deviation increase in quarterly margin-debt growth predicts 49 bps/month higher abnormal returns for short-horizon factor momentum. The effect fades as the momentum signal ages and is strongest in hard-to-arbitrage factors. Takeaway: Leverage-financed performance chasing could explain why factor momentum persists.

Risk Mitigation of Momentum Strategy Using Stop-Loss Rules (Sadaqat, Butt, Demirer)

Exit rules can radically change the performance of momentum strategies. Across five Asian markets where conventional momentum was essentially unprofitable, adding a 15% stop-loss increased average monthly returns to 2.8–5.3% and cut average momentum crashes to below 10%. Key takeaway: Momentum’s failure in some markets can partly be resurrected by properly managing downside risk.

Asymmetric Reversals (Baldi-Lanfranchi, Collin-Dufresne, and Daniel)

Short-term reversal is more nuanced than “losers rebound and winners fall.” Decomposing stock returns shows that systematic and news-driven moves tend to continue, while residual moves reverse. Negative residual shocks reverse quickly, while positive shocks unwind much more slowly. Key takeaway: Short-term reversal is mostly about idiosyncratic price moves, not systematic or news-driven returns.

Fixed Income

Credit When it’s Due: Corporate Bond Factors on a Schedule (Dickerson and Nozawa)

Corporate bond returns have a striking calendar pattern. The first 5 trading days of the month, just 24% of trading days, account for 83% of the U.S. corporate bond market’s credit premium and 51% of premia across 103 bond factors. Key takeaway: In corporate bonds, it matters considerably when you take credit risk. Much of the credit premium is concentrated in the first few trading days of the month.

Options

Taming the Option Factor Zoo: A High-Dimensional Analysis (Walter, Zimmer, and Ulrich)

Most option-implied equity factors don’t add much beyond the traditional factor zoo. Across 137 option characteristics, equity factors explain about 91% of the variation in the 20 selected option factors, while adding them does not significantly improve the maximum Sharpe ratio. Key takeaway: Most option signals overlap with what we already know from equity factors. The real added information seems to be in jumps, kurtosis, and the shape of the volatility surface.

Blogs

The Holdout That Made the Sharpe Bigger (Jonathan Kinlay)

Timing Short Volatility with VIX Curve Momentum (Quantseeker)

The NAAIM Exposure Index: Contrarian or Continuation Indicator? (Portfolio Optimizer)

Global bond markets. Marked to Market. Part I. (Hanno Lustig)

Podcasts

Retail Trader vs. Hedge Fund Manager: Where Is the Real Alpha? (Odds on Open)

50 Years of Trading Lessons in 90 Minutes | Exclusive with Market Wizard Peter Brandt (TraderLion)

Trend Following Re-education (Michael Covel)

Last Week’s Most Popular Links

Option Prices, Analyst Expectations, and Stock Returns (Martin, Rodenkirchen, Wagner, and Wang)

Label alchemy: Target engineering for improved stock selection (Coqueret and Guida)

The Common Source of Option Return Anomalies (Gerchik)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

When the curve is in contango, short-volatility strategies tend to benefit from positive roll yield. When it moves into backwardation, the environment is typically much less favorable.

In an earlier post, “Timing Volatility with the VIX Term Structure”, I tested using the relationship between the VIX and 3-month VIX to dynamically switch between short- and long-volatility exposure. The strategy produced a Sharpe ratio of around 0.6 and, importantly, provided diversification during major equity drawdowns.

But the level of the volatility curve may not contain all the relevant information.

Consider two days on which the VIX curve has the same slope. On one day, the curve may have been steadily steepening over the previous few weeks. On the other, it may have been rapidly flattening. The curve looks identical, but the paths getting there are very different.

This raises a natural question:

Does the momentum of the VIX curve contain information that its current shape misses?

In this post, I test a simple curve-momentum signal and apply it to timing short-volatility exposure. I then compare it directly with the curve-level signal from my earlier work and examine whether momentum contains incremental information after controlling for the current state of the curve.

Read more

Equities

Noisy factors? The retroactive impact of methodological changes on the Fama–French factors (Akey, Robertson, and Simutin)

The authors show that historical Fama-French factor returns have changed across data vintages, partly because of revisions to the construction methodology. Those changes can materially alter estimated alphas, betas, and even the statistical significance of some anomalies. Key takeaway: For reproducible factor research, the data vintage matters.

Option Prices, Analyst Expectations, and Stock Returns (Martin, Rodenkirchen, Wagner, and Wang)

Using S&P 500 stocks from 1996 to 2025, the authors find that an option-implied expected-return measure predicts future returns from 1 to 12 months and substantially outperforms analyst price-target-based forecasts. At 12 months, it delivers a 21.3% out-of-sample R² relative to price-target forecasts. Key takeaway: Option prices contain information about expected returns that analyst forecasts largely miss.

Stale Data, Persistent Alpha (Fan and Li)

Accounting-based anomalies remain surprisingly strong even with stale information. Across 94 anomalies, median monthly CAPM alpha falls from 36 bps using the conventional 4-month data lag to 28 bps using year-old accounting data. Even at 2–3 year lags, substantial alpha remains. Key takeaway: Information releases explain part of anomaly returns, but much of the alpha persists long after the underlying information becomes public.

Hedge Funds

Hedge Fund Performance and Interest Rate Conditions: Evidence from Regulatory Data (Banegas)

Hedge funds are far from uniformly exposed to interest-rate risk. Using regulatory data on 4,883 hedge funds, the paper finds that higher Treasury volatility generally hurts returns, while a steeper yield curve helps. But the effects vary sharply across styles: A 100 bp steepening is associated with +1.75 pp monthly returns for equity funds, but lower returns for relative-value funds. Key takeaway: The same rate environment can produce very different outcomes across hedge fund strategies.

Machine Learning & Large Language Models

Fedspeak, LLM-Derived Signals, and High-Frequency Trading (Wei)

Using LLMs to measure shifts in tone and semantic content between FOMC statements and subsequent communications, this paper finds that new information, particularly in the minutes, is associated with significant intraday price movements across Treasuries, equities, and FX. Key takeaway: The initial FOMC statement does not contain the full policy signal; later communication can provide additional information that markets price.

Crowding in an Artificial World: AI and the Future of Investing (Chincarini, Falvey, and Moneta)

AI can make investing more crowded, not more diverse. Four independent AI agents received the same 209 published return signals and were asked to build long-short equity strategies. Their six distinct strategies ultimately relied on just five signals, producing highly correlated holdings, trades, and returns. Key takeaway: Independent AI investors can converge on remarkably similar strategies when working from the same set of published return signals.

Label alchemy: Target engineering for improved stock selection (Coqueret and Guida)

In ML for stock selection, the prediction target can matter more than the model. Using 1,960 U.S. stocks and 122 features, the authors keep the features and model fixed but change how forward returns are represented. Transforming returns into normalized cross-sectional ranks raises the long-short Sharpe from 0.68 to 1.69. Key takeaway: Improving the return target can be more valuable than building a more sophisticated model.

Options

The Common Source of Option Return Anomalies (Gerchik)

Most option return anomalies seem to be different versions of the same trade. Across 18 characteristics, the paper finds that about 80% of the average long-short return is explained by exposure to stocks with different implied volatility. Much of the effect is concentrated around the monthly expiration window. Key takeaway: Many known option anomalies largely reflect a common volatility-related effect around option expiration.

Portfolio Construction

Inference-Based Performance Evaluation Using the Sortino Ratio (Ledoit and Wolf)

The Sharpe ratio can penalize strategies for upside volatility and hide important differences in downside risk. This paper develops statistical inference for the Sortino ratio, allowing investors to test whether differences in downside-risk-adjusted performance are actually significant. Key takeaway: A higher estimated Sortino ratio is not enough; investors should ask whether the difference is statistically meaningful.

Don’t Fix Your Fixed Income Allocation (Lucas)

For long-horizon investors, bonds have historically been a substantial drag on returns. Using 155 years of U.S. data, the paper finds that all-equity portfolios beat 70/30 portfolios in 71% of 10-year periods, 90% of 30-year periods, and 95% of 40-year periods, though with larger drawdowns. Key takeaway: The longer the investment horizon, the weaker the historical case for a constant bond allocation.

Blogs

Macro demand factors and rates trading strategies (Macrosynergy)

Do Airline Stocks Take Off Around U.S. Holidays? (Quantpedia)

Tom Carlson’s Adaptive 60/40 Portfolio: Momentum-based Selection of Stock Diversifiers (Portfolio Optimizer)

Better Debt (John H. Cochrane)

Is Trend Still Your Friend? A Microstructural Explanation for the Demise of Short-Term Trend-Following (Alpha Architect)

Competition for Capital (Goldman Sachs)

Vanguard Capital Markets Model (Vanguard)

Podcasts

$242 Billion Investor on AI, Market Bubbles & How the Best Investors Build Portfolios (How I Invest)

RE-RELEASE: Texas Trend Following with Salem Abraham (RCM Alternatives)

Last Week’s Most Popular Links

Taking Carry Trading Seriously (Breedon and Vitale)

Intermediate-Term Reversal (Huang, Vincent, and Yeh)

Binary Bias and Stock Returns (Fattinger, Hanspal, Koval, and Steshkova)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

Commodities

Commodity Futures and the Limits to Arbitrage (Cooper, Day, Lewis, and Molyboga)

Commodity index investing appears to have changed the economics of futures curves. Since financialization took off after 2003, index commodities developed significant term premiums, while premiums in non-index commodities are not statistically significant. The premiums also increase with maturity and passive index participation. Key takeaway: Passive investment flows can systematically influence expected returns across the futures curve.

Equities

Retail Trading and the Varying Predictive Power of Aggregate Short Interest (Froymovich, Gong, Wasley, and Xiao)

One of the strongest historical predictors of market returns has become much weaker. Aggregate short interest’s ability to predict future S&P 500 returns declined substantially from 2012 to 2023, alongside a sharp rise in retail trading. The authors link this decline to more retail short selling and greater short-squeeze risk. Key takeaway: The predictive value of market signals can change as the investors behind them change.

Binary Bias and Stock Returns (Fattinger, Hanspal, Koval, and Steshkova)

Stocks with a high frequency of down days over the previous month subsequently outperform those dominated by up days. A long-short strategy earned 12.3% annually from 1963–2023, and the effect is not explained by conventional short-term reversal. Key takeaway: The frequency of up vs. down days predicts returns beyond the magnitude of recent price moves.

Does Weinstein Stage Analysis Beat a Moving Average? (Roskill)

Stan Weinstein’s four-stage framework is widely used in technical analysis, but its edge may come almost entirely from simple trend following. Testing Weinstein’s method on survivorship-free S&P 500 data since 1992, the author finds that its useful information comes from whether the 30-week moving average is rising, not from identifying bases and tops. Key takeaway: The trend signal works; the additional stage classification in Weinstein’s framework adds little forecasting value.

Quantifying the Contributions of Clustering to Statistical Arbitrage (Zhu, He, and Cucuringu)

How you group stocks may matter more than the algorithm used to group them. Across 27 years of U.S. equities, peer-based statistical arbitrage produces higher alpha and substantially lower portfolio volatility than no clustering or arbitrary groups. Yet 12 very different clustering methods lead to surprisingly similar portfolios. Key takeaway: Much of the value comes from identifying informative peers, not fine-tuning the clustering method.

Intermediate-Term Reversal (Huang, Vincent, and Yeh)

Ranking U.S. stocks by their largest peak-to-trough decline between 12 and 24 months before portfolio formation produces a strong reversal effect: The high-minus-low portfolio earns 1.16% per month, with 0.87% eight-factor alpha. The effect survives among large caps and after controlling for momentum and other characteristics. Key takeaway: The path of past prices contains predictive information that cumulative returns can miss.

ETFs

In Defense of Leveraged and Inverse Funds (Carpenter, Lu, and Whitelaw)

This paper argues volatility decay in leveraged ETFs is widely misunderstood. Daily rebalancing does not inherently destroy value; it creates a convex payoff: Leveraged and inverse ETFs underperform a linear leverage benchmark when index moves are modest, but outperform when moves are sufficiently large. Key takeaway: Leveraged ETF returns are nonlinear by design, not systematically eroded by daily rebalancing.

FX

Taking Carry Trading Seriously (Breedon and Vitale)

The standard “high-minus-low” carry strategy does not capture carry as well as commonly assumed. Using FX data from 1983–2025, the paper constructs an optimized carry portfolio accounting for risk and trading frictions. Its net Sharpe is 0.71 vs. 0.32 for conventional high-minus-low carry, and its returns show little exposure to the risk factors typically associated with carry. Key takeaway: How you construct a carry portfolio fundamentally changes both its performance and sources of risk.

Machine Learning & Large Language Models

Return-Optimal Regime Labels and Progressive Risk Overlays: A Hierarchical Framework for Industry Allocation (Li and Mulvey)

Machine learning can be especially valuable for dynamic risk allocation, not just return prediction. This paper trains an XGBoost model across 46 industry pairs, then combines within-equity rotation with defensive overlays and volatility scaling. Out-of- sample from 1995–2025, Sharpe rises from 0.56 to 1.01 while max drawdown falls from 57% to 22%. Key takeaway: Portfolio construction can be as important as prediction: The largest incremental Sharpe improvement comes from the defensive overlay.

The Return Impact of Form 10-K Releases: Evidence from a Large Language Model (Frey, Qiu, and Zhao)

Markets do not appear to fully digest 10-K filings when they arrive. A fine-tuned LLM predicts returns over the subsequent 20 trading days from the filing text alone. Stocks in the highest predicted-return quintile outperform the lowest by about 5 percentage points, value-weighted. Key takeaway: Long corporate disclosures contain return-relevant information that is not immediately reflected in prices.

Blogs

What are U.S. Treasury markets really telling us? Part II. (Hanno Lustig)

Improving Fixed-Weight Portfolios with Dynamic Rules (Quantseeker)

Can AI Agents Beat the Random Walk? Not So Fast (CFA Institute)

Podcasts

Lucas Schuermann - Swapping Out Perpetual Futures (Flirting with Models)

Hedge Fund Manager Alix Pasquet: How Small Funds Can Outsmart Multi-Managers (Odds on Open)

Why Governments Are Afraid of the Bond Market (Robin Wigglesworth Explains) (Meb Faber)

Zigs, Zags, and Finding Micro Cap Winners with Ian Cassel (RCM Alternatives)

Social Media & Industry Research

Inflation Redux? (AQR)

IPOs of doom (Owen Lamont, Acadian Asset Management)

The Quant Renaissance Part III: Who's Ready for the Next Winter? (Man Group)

Last Week’s Most Popular Links

A Sharpe of 2.1 From Nothing: The Second Number Your Agent Doesn’t Log (Jonathan Kinlay)

Rethinking Predictability: Distributional Forecasts of the Market Risk Premium (Sinha)

Correlation and Commodity Risk Premia (Fan and Zhang)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

Recent research examines whether relatively simple dynamic rules can improve on these portfolios. I test and extend this idea across several different investment universes.

The approach is straightforward: First, allow portfolios to reduce exposure when risk is high, then add a simple momentum signal and dynamic portfolio optimization.

Across several very different portfolios, these modifications substantially improve risk-adjusted returns in most cases while reducing drawdowns.

Read more

Asset Allocation

From Strategic Policy to Tactical Trades: Dynamic Asset Allocation with Predictable Returns and Trading Costs (Kolm and Ritter)

This paper develops a dynamic framework for tactical asset allocation that jointly accounts for expected returns, portfolio risk, trading costs, and how today’s positions affect future decisions. It derives when optimal trades should be smaller or larger than standard one-period TAA and how quickly portfolios should adjust. Key takeaway: Optimal tactical trades depend on both today’s opportunity and the cost of repositioning tomorrow.

Commodities

Correlation and Commodity Risk Premia (Fan and Zhang)

Commodity momentum struggles when commodities start moving together. Across 29 futures markets, scaling down momentum exposure during high-correlation regimes reduces drawdowns and improves risk-adjusted returns. Combining correlation, volatility, and stop-loss rules raises net Sharpe from 0.42 to 0.67. Key takeaway: Cross-commodity correlation can be a useful signal for timing momentum exposure.

Crypto

Bitcoin option expiration, gamma exposure, and intraday price reversals (Weiss, Gaudiosi, Zhou, and Webb)

Bitcoin options appear to move the underlying around expiration. The paper finds that when ATM open interest is high and dealer gamma exposure is negative, Bitcoin tends to fall before the 8:00 UTC expiry and then fully reverse within two hours. Key takeaway: Bitcoin option positioning can create systematic, short-lived price pressure around expiration.

Equities

Financial-Statement Signals and the Cross-Section of Stock Returns (Dong)

The accounting “factor zoo” may be much smaller than it looks. This survey catalogs 155 financial-statement signals, but argues that many reflect overlapping economic ideas such as valuation, profitability, accruals, investment, financing, and fundamental momentum. Key takeaway: Don’t treat every accounting signal as an independent factor. Combine signals by economic concept to avoid repeatedly loading on the same information.

The Implied Equity Term Structure (Baele, Driessen, and Jankauskas)

This paper asks how the price of equity risk changes with the horizon of corporate cash flows. Using U.S. stocks since 1980, the authors find that longer-dated cash flows generally command higher expected returns. But the pattern reverses for value firms and companies with high credit risk. Key takeaway: Equity risk is priced differently across horizons, and the shape depends strongly on firm characteristics.

Rethinking Predictability: Distributional Forecasts of the Market Risk Premium (Sinha)

Expected-return forecasts may miss much of what matters for investors. Using S&P 500 data, this paper finds limited ability to predict next-month excess returns, yet forecasts of the full return distribution contain useful risk information. Distribution-based allocation cuts max drawdown from 56.9% to as low as 16.0%. Key takeaway: Forecasting uncertainty can be more useful for controlling downside risk than forecasting returns.

Factor Time (Bowles, Reed, Ringgenberg, and Thornock)

Fama–French factors may be more time-sensitive than they look. Standard portfolios are refreshed annually, leaving accounting information increasingly stale. Monthly updates materially change value and investment factors, but the greater improvement comes from modeling the returns generated by these updates separately. Key takeaway: How often factor portfolios are updated significantly affects measured alpha and performance.

Timing the Market: A Skewed Perspective (Chan)

“Time in the market beats timing the market” is not universal. Across six developed markets over 25 years, waiting for a 20% dip to buy beat monthly investing in Japan, the UK, and Germany, but produced 12.6% less final wealth in the U.S. Key takeaway: The cost of waiting to buy a dip depends on the market drift. The stronger the long-run return, the more expensive it is to sit in cash waiting for a better entry.

Blogs

Gold and macro factors (Macrosynergy)

Are Covered Calls Just Expensive De-Risking? (Quantseeker)

Not another one! My fifth book... (Rob Carver)

A Sharpe of 2.1 From Nothing: The Second Number Your Agent Doesn’t Log (Jonathan Kinlay)

Podcasts

How Top Prop Traders Find and Scale Their Edge · Jeff Holden (Chat With Traders)

Professional Trading Roundtable: “A 50% Market Crash Is Mathematically Inevitable!” (Words of Rizdom)

Social Media & Industry Research

What Daily Stock Returns Tell Us About the Economy (Alpha Architect)

What I Learned About Asset Pricing (Pedro Santa Clara)

Investing in private equity: A decision framework for drawdown versus semiliquid funds (Vanguard)

Last Week’s Most Popular Links

Agentic Quantitative Trading: A Survey of Workflows, Systems, and Evaluation (Hua et al.)

Expected market risk premiums in the international cross-section (Berkman and Malloch)

Two Heads Are Better Than One: t-Statistics and Monotonicity in the Factor Zoo (Fan)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

But covered-call strategies also have substantially lower market beta than the equities they own. That raises an interesting question: Does option writing actually improve the risk-return tradeoff, or could investors do better by simply holding less equity and making withdrawals themselves?

I test this using long histories of S&P 500 and Nasdaq-100 covered-call strategies, covering both benchmark indexes and investable ETFs.

The results challenge some of the conventional arguments for covered calls, not only on returns, but also on downside protection and the income they generate.

Read more

Equities

Expected market risk premiums in the international cross-section (Berkman and Malloch)

Options markets contain a surprisingly strong signal for international equity allocation. Across international markets, option-implied expected market risk premia predict next-month returns: A 1%-point higher risk premium corresponds to roughly 1.1% higher subsequent excess return in the cross-section. Key takeaway: Local index options help predict next-month index returns.

Calendar Effects of Asset Pricing Factors (Fan, Li, and Miao)

Factor returns have calendar effects, but almost exactly opposite to the market. Across 153 U.S. equity factors, premia are weaker overnight, on Fridays, in January, around month-end, and near major macro announcements. Key takeaway: Market seasonality and factor seasonality tend to run in opposite directions.

Has the Correlation Risk Premium Been Competed Away? Evidence from Implied and Realized Correlation, 2006-2026 (Bawa)

The economics of dispersion trading have changed dramatically. In 2010–14, option-implied correlation exceeded subsequent realized correlation by 10.7 points on average. By 2022–26, that spread had flipped to -1.9 points. Key takeaway: The historical premium for selling correlation across S&P 500 stocks has largely vanished.

Two Heads Are Better Than One: t-Statistics and Monotonicity in the Factor Zoo (Fan)

A high t-stat isn’t enough to identify robust equity factors. Across 150 U.S. characteristics, t-stats and monotonicity, the share of months returns rise across factor quintiles, are nearly unrelated (correlation −0.08). Combining both better predicts out-of-sample returns and alphas. Key takeaway: Factor robustness is better judged by both statistical strength and the shape of returns across portfolios.

ETFs

Volatility and Returns to Leveraged ETFs (Bessembinder)

Leveraged ETFs don’t necessarily suffer from high volatility. What matters is serial correlation in the underlying returns. When returns tend to persist, more volatility can boost expected leveraged returns; when they reverse, it hurts. Without serial correlation, volatility itself has no effect. Key takeaway: Serial correlation, not volatility alone, determines the expected impact of daily rebalancing.

Twenty Years of Leveraged and Inverse Exchange-Traded Products: What Have We Learned? (Chang and Madhavan)

Twenty years after leveraged ETFs were launched, the authors review what investors have learned. Daily leverage works as advertised, but long-run returns are highly path-dependent. Hidden financing costs can create additional performance drag. Key takeaway: With leveraged ETFs, the return path and hidden costs matter greatly for realized returns.

Machine Learning & Large Language Models

Agentic Quantitative Trading: A Survey of Workflows, Systems, and Evaluation (Hua et al.)

This survey paper covers how agentic AI is being applied to quantitative trading. Across 20 systems, signal discovery is nearly universal, but only 2 consider all five core areas: Factor research, signals, portfolios, execution, and risk management. Key takeaway: Strong AI forecasts and backtests do not reliably translate into live trading performance as risk control, liquidity, and execution costs matter greatly.

Option-Implied Signals and Crash Risk: Predictability and Machine-Learning Evidence from U.S. Equity Options (Li and Wang)

Machine learning is uncovering option signals that traditional measures miss. Using U.S. equity options from 2015–2026, XGBoost beats linear models for next-month return prediction in the recent 2023–26 regime: 1.29% vs. 0.07% OOS R². The most important predictors also change across market regimes. Key takeaway: Option-market predictability is regime-dependent, with nonlinear ML adding value in the most recent period.

Options

Volatility Salience: Evidence from the Options Market (Ding, Li, Shi, and Zhai)

Investors appear to overpay for options when a stock’s recent volatility stands out relative to market volatility. Across U.S. equity options from 1996–2025, greater “volatility salience” predicts lower subsequent delta-hedged returns. The effect is strongest in OTM options: High-minus-low spreads reach −1.11% for calls and −1.83% for puts. Key takeaway: Unusually attention-grabbing volatility appears to predict option overpricing.

OPIN and the Cross Section of Equity Option Returns (Chen, Hu, and Yang)

Informed options trading appears to be priced. A new measure of informed trading intensity (OPIN) strongly predicts delta-hedged option returns: Options with more informed activity subsequently earn lower returns. For ATM calls, the high-minus-low OPIN spread is about −2.3% over 52 days. Key takeaway: Option order flow contains information about subsequent option returns.

Portfolio Construction

The Marginal Cost of Diversification (Sanford)

Diversification can eventually increase portfolio risk. In U.S. equities, nearly half of stock additions that reduce idiosyncratic risk also increase systematic risk. As portfolios grow, the idiosyncratic benefit shrinks faster than the systematic-risk effect. Key takeaway: More holdings do not necessarily mean less total risk; what matters is how each addition changes factor exposure.

Prediction Markets

The Cross-Section of Price Changes in Prediction Markets (Hou, Li, and Zheng)

Prediction markets exhibit short-term reversal. Across nearly 97,000 Kalshi contracts, recent 1–6 day price moves tend to reverse, with the strongest effect at the 1-day horizon: Contracts with the largest prior increases underperform those with the largest declines by 0.77 cents the next day. Key takeaway: Prediction-market prices show short-run mean reversion.

Blogs

Does Short-Term Mean Reversion Work Across Asset Classes? (Quantseeker)

Can ChatGPT Forecast Stock Price Movements? (Alpha Architect)

Podcasts

David Booth: What True Wealth Actually Means (Rational Reminder)

Jack Schwager: 40 Years of Market Wizards (Capital Horizons)

The Art of Simple Investing | Tips from Ben Carlson (Bogleheads)

Social Media & Industry Research

September Setup: The Asymmetry Has Changed (Citadel)

Academic Alpha (AQR)

Last Week’s Most Popular Links

Rethinking Time-Series Momentum in Equity Portfolios: Common Signals, Long-Only Positioning, and No Need for Volatility Timing (Zakamulin)

On the Anatomy of Trend (Kjaer)

Prior Sentiment and Returns Around Earnings Announcements (Kazemi and Makridis)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

This week, I extend that analysis and ask: Does the same type of short-term mean reversion work across asset classes?

Using intraday data for liquid ETFs covering equities, bonds, currencies, commodities, real estate, and Bitcoin, I test the same signal across markets.

The results reveal large differences across asset classes, and an interesting pattern in when those returns are earned.

Read more

Crypto

Skewness Illusion in Cryptocurrency Portfolios (Horvath and Rigamonti)

Crypto’s lottery-like payoff largely disappears with diversification. Across 2,359 coins, 96% have positively skewed weekly returns. But that skewness largely disappears once portfolios reach around 25 coins. As in equities, diversification washes out the extreme positive skew of individual assets. Key takeaway: Positive skewness is largely an individual-coin phenomenon, and not a feature of crypto portfolios.

Equities

Prior Sentiment and Returns Around Earnings Announcements (Kazemi and Makridis)

Investor sentiment before earnings predicts returns after the announcement, even after accounting for the earnings surprise. Across 83,293 U.S. earnings events, low-sentiment stocks had 1.38 percentage points higher abnormal returns than high-sentiment stocks over the next 10 days. The pattern holds after both earnings beats and misses. Key takeaway: Prior sentiment contains return information beyond the earnings surprise.

Rethinking Time-Series Momentum in Equity Portfolios: Common Signals, Long-Only Positioning, and No Need for Volatility Timing (Zakamulin)

Across U.S. size, value, investment, and profitability portfolios, a single 12-month market momentum signal works better than calculating momentum separately for each portfolio. The best Sharpe comes from staying long when market momentum is positive and moving to cash otherwise, without volatility scaling. Key takeaway: Within equities, trade the market trend, not each portfolio’s own trend.

Sentiment and Return Predictability in Global Value Chains (Heckmann)

Across global equities, FinBERT sentiment from a firm’s customers and suppliers predicts its next-month returns beyond the firm’s own tone. Optimistic customers predict higher returns; optimistic suppliers often predict lower returns, consistent with greater input-cost pressure and bargaining power. Key takeaway: Customer optimism can be good news for a stock; supplier optimism can be bad news.

FX

Option-implied Hurst exponent for FX timing (Petric and Schadner)

FX options can tell investors when to take currency risk. Across 9 major currencies since 2001, using an option-implied Hurst measure derived from the volatility term structure to time FX exposure cut average volatility from 10.6% to 5.3% and max drawdown from 41.4% to 20.3% versus passive FX exposure. Key takeaway: FX option term structures seem useful for timing downside risk.

Options

The Price Impact of Hedging Expiring Index Options (Amaya, Garcia-Ares, Pearson, and Vasquez)

0DTE options can move the S&P 500 into the close through dealer hedge unwinding. On the highest- vs. lowest-dealer-delta days, S&P 500 futures returns differed by 17 bps over the final 30 minutes and 12.5 bps over the final 15 minutes. Key takeaway: Dealer delta in expiring options can create systematic price pressure into the close.

When Does Intraday Options Trading Predict Stock Returns? (Lin, Luo, and Shao)

Options traders seem to know more near the closing bell. Across 3,317 stocks, bullish options order imbalance in the final 30 minutes predicts higher next-day returns more than twice as strongly as the average 30-minute options order-imbalance measure. Key takeaway: For predicting next-day stock returns, late-day options positioning contains the strongest signal.

Trend Following

On the Anatomy of Trend (Kjaer)

Trend-following’s crisis performance is distinct from its average timing ability. Across 81 markets over 25 years, trend provides increasingly strong protection during large market declines. Yet, its signals show much less evidence of consistently timing subsequent returns. Key takeaway: Trend’s most robust feature is its downside protection, not its average timing ability.

Blogs

Wealth management with macro factors (Macrosynergy)

The Sharpe Stability Ratio: Evaluating the Sharpe Ratio Temporal Consistency (Portfolio Optimizer)

Interest Rate Surge? (John H. Cochrane)

Podcasts

Mebane Faber Interview with Michael Covel on Trend Following Radio (Michael Covel)

Why Trend Following Feels Wrong (But Could Boost Your Portfolio) (Meb Faber)

Learning from Buffett and Munger (Masters in Business)

Social Media & Industry Research

Elastic Expectations (Citadel)

Crazy days in the stock market (Acadian Asset Management, Owen Lamont)

Passive-Investing Boom is Making Life Harder for Stock Pickers (Justina Lee, Bloomberg)

What’s Pushing Long-Term Bond Yields Higher? (PIMCO)

Last Week’s Most Popular Links

Unexpected Gross Profit and Cross-Sectional Stock Returns (Yang, Cai, Rhee, and Wu)

VIX and Trend Following Revisited: Nearly a Decade of Out-of-Sample Evidence (Alpha Architect)

AgonAlpha: Autonomous Alpha Discovery via Prompt Economy and Scalable Agentic Search (Ye, Sun, Ren, Yu, Yi, and Yang)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

Crypto

Are Day-of-the-Week Effects in Cryptocurrencies Real? Intraday Evidence from Active and Less Active Cryptocurrencies (Aalipour, Mehdian, and Rezvanian)

Crypto’s day-of-the-week effect isn’t really a full-day effect. Across 12 cryptocurrencies, daily anomalies are concentrated in just a few specific hours and differ by asset. Bitcoin’s Monday effect, for example, is driven by only two significant hourly intervals. Key takeaway: Predictability in daily returns can actually come from just a few specific trading hours.

The Bitcoin Polar Pricing Model: Cycles, Prices, and Predictability (Bonaparte)

Bitcoin’s four-year cycle isn’t just imposed by the halving calendar. An unrestricted search of the price data estimates a 3.86-year cycle, with two alternative methods finding 3.74 and 4.14 years. The pattern is much weaker at short horizons and becomes more informative over longer horizons, but overlapping returns can exaggerate this predictability. Key takeaway: Bitcoin’s long-run price cycle independently converges remarkably close to its protocol-driven halving schedule.

Equities

Text-Based Risk Similarity and Economic Links (Chen and Yang)

Firms don’t need to be in the same industry to transmit return-relevant information. Using 10-K risk disclosures to identify firms with similar risk exposures, this paper finds that stocks with the best-performing “risk peers” outperform those with the worst-performing peers by 1.78% per month. The effect is strongest across industries. Key takeaway: Shared risks create predictive links between seemingly unrelated stocks.

Buy the Rumor, Sell the News: When Is News Priced In? (Kargarzadeh, Khaledian, Parvini, Ghatak, and Khaledian)

Most stock news is already priced in by the time you read it. Studying 1.68 million U.S. stock-news events, the authors find that the price move is concentrated before and on publication day. But what happens next depends on the news: Quantified fundamentals keep drifting, while softer narrative news tends to reverse. Key takeaway: The post-news opportunity depends less on sentiment than on whether the news contains hard information or narrative.

Unexpected Gross Profit and Cross-Sectional Stock Returns (Yang, Cai, Rhee, and Wu)

A surprisingly simple profitability signal predicts stock returns: Current gross profit minus its 8-quarter moving average. This “unexpected gross profit” measure rivals standardized earnings surprises and is particularly strong among smaller stocks. Key takeaway: Profit surprises contain information about expected returns beyond traditional earnings surprises, with the effect attributed to systematic risk rather than mispricing.

Machine Learning & Large Language Models

MacroAllocAgent: From macro narratives to strategic asset allocation via a multi-agent LLM system (Wang, Deng, Li, and Yang)

LLMs work better for asset allocation when they form views, not portfolio weights. Across 13 Chinese macro assets, adding LLM-generated views to Black–Litterman modestly improved Sharpe: From 0.90 to 0.95, and from 0.67 to 0.76 with tighter allocation bounds. Direct LLM allocation achieved a Sharpe of just 0.32. Key takeaway: Let AI form the views; let conventional optimization build the portfolio.

Big Data Machine Learning Portfolios in Thirty Minute SPX Options (Maisch, Zhang, Ulrich, and Zimmer)

Machine learning finds predictability in 30-minute SPX option returns. Using 14M observations, the best models reach a 30-minute gross Sharpe of 0.72. Fewer inputs often work better: Theta, implied vol, bid/ask size, and vega dominate. Key takeaway: There are significant intraday mispricings in option markets, but they are hard to monetize after spreads.

AgonAlpha: Autonomous Alpha Discovery via Prompt Economy and Scalable Agentic Search (Ye, Sun, Ren, Yu, Yi, and Yang)

AI agents are getting surprisingly good at autonomous alpha discovery. AgonAlpha searched U.S. equities without humans writing factor formulas. Across 60 submissions, 17 earned WorldQuant BRAIN’s highest grade. The discovered signals used inputs such as option-implied volatility, trading volume, short interest, returns, and option positioning. Key takeaway: Alpha discovery can be automated from idea generation through verification.

Options & Volatility

Last Fifteen Minutes: Equity Options Volatility at the Close (Clark, Palepu, Poti, and Siddique)

The market close may no longer be the best time to measure SPX implied volatility. With the rise of 0DTE options, 3:45 PM implied vol predicts next-day realized variance far better than the raw 4:00 PM measure: Correlation 0.68 vs. 0.07. At expiration, collapsing time value makes implied-volatility estimates increasingly unstable. Key takeaway: In the 0DTE era, the last 15 minutes can distort rather than improve volatility signals.

One-Day Volatility Indices and the Variance Risk Premium in European Equity Markets (Wilkens)

Europe now has a VIX1D equivalent. This paper constructs one-day volatility indices for the EURO STOXX 50 and DAX from daily-expiry options. They add substantial information: Including them raises explanatory power for next-day realized variance by about 25 percentage points. But selling one-day variance is less attractive as quoted spreads absorb a large part of the premium. Takeaway: 0DTE options are potentially more useful for forecasting volatility than harvesting variance premia.

Blogs

Beyond Volatility Scaling: Does “Good” and “Bad” Volatility Matter? (Quantseeker)

Your Research Agent Is an Undisclosed Factor Exposure — And So Is Everyone Else’s (Jonathan Kinlay)

Sectoral Intramonth Momentum Cycle: Exploiting Turn-of-the-Month Patterns in Sector ETF Strategies (Quantpedia)

Podcasts

Toby Crabel - Short-Term Futures Trading with Size! (The Algorithmic Advantage)

Barry Ritholtz: How Not to Invest (Rational Reminder)

Social Media & Industry Research

VIX and Trend Following Revisited: Nearly a Decade of Out-of-Sample Evidence (Alpha Architect)

August Checklist (Citadel)

Setting the record straight: The truths about index fund investing (Vanguard)

Last Week’s Most Popular Links

Latent Characteristic Factors in Commodity Markets (Sakkas and Stavroglou)

Factor Neutralization and Risk-Budgeted Scaling: Evidence from Long-Short Anomaly Portfolios (Kosmakov)

International Yield Curves and Exchange Rates (Wang and Zhu)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

The evidence appears particularly strong for market and momentum portfolios. For example, Schwarz (2025) studies nine factors across 45 international equity markets and finds that, after realistic transaction costs, the clearest benefits remain for market and momentum portfolios.

Harvey et al. (2018), using more than 60 assets with data extending back to 1926, find that Sharpe-ratio improvements are concentrated in equities and credit. For bonds, currencies, and commodities, volatility scaling does little for Sharpe ratios, although it can still reduce tail risk.

In short, volatility scaling appears most useful for equity-like risk assets and much less reliable elsewhere. That raises a natural question: What if we distinguish between upside and downside volatility rather than treating all volatility equally?

In this post, I discuss recent research on this idea and test it across asset classes.

Read more

Commodities

Latent Characteristic Factors in Commodity Markets (Sakkas and Stavroglou)

Many commodity signals tend to reflect the same underlying forces. Using 11 characteristics across 25 commodity futures, this paper finds that much of their variation can be reduced to just two factors: One related to how risk is shared among market participants, and another related to broader macroeconomic conditions. Key takeaway: The commodity factor zoo seems to be much smaller than it appears.

Crypto

Agentic AI Nowcasting and Cryptocurrency Return Predictability (Sun, Wang, and Zhang)

Agentic AI can turn real-time public information into investable signals. In a real-time test across coins in the CoinMarketCap 100 Index, AI-generated scores predicted crypto returns from 1-hour to 1-week horizons. A Top 10–Bottom 10 portfolio earned 1.78% at the 1-day horizon. Key takeaway: AI can uncover return-relevant information not yet fully reflected in crypto prices.

Equities

Factor Neutralization and Risk-Budgeted Scaling: Evidence from Long-Short Anomaly Portfolios (Kosmakov)

Factor neutralization can improve anomaly portfolios, but only when the risk reduction outweighs the return sacrificed. Across 133 long–short strategies, removing exposure to the Fama-French five factors plus momentum raised average Sharpe from 2.25 to 3.57. Key takeaway: Neutralization is most effective when factor exposure contributes more to portfolio risk than to expected returns.

Fixed Income

Overnight Corporate Bond Trading (Abdi, Rossi, and Wu)

Corporate bond trading contains an unusual overnight signal. When public trade reporting is temporarily paused, institutional trades become much larger, and their order flow carries substantially more information about future prices than daytime trading. The signal even spills across bonds from the same issuer. Key takeaway: Overnight order flow contains information about future bond returns that is only gradually incorporated into prices.

Machine Learning and Large Language Models

International Yield Curves and Exchange Rates (Wang and Zhu)

Exchange rates are more predictable than commonly thought. Using machine learning and yield curves from 17 countries, this paper beats the random-walk benchmark OOS for 8 of 9 G10 currencies at 1 month, and all 9 at longer horizons. Key takeaway: Currency predictability depends on information across global yield curves, not just bilateral interest-rate differences.

Machine learning for realised volatility forecasting (Rahimikia and Poon)

Machine learning can improve volatility forecasts, but its advantage disappears when volatility becomes extreme. Across 23 NASDAQ stocks, ML models using order-book, news, and historical volatility data beat traditional HAR models during 90% of the out-of-sample period, but underperformed on extreme-volatility days. Key takeaway: Combining ML with traditional models is more robust than replacing them.

On the predictability of ETF returns with technical predictors (Gong and Muller)

This paper trains a random forest model on decades of individual-stock data, then transfers the learned relationships to international equity ETFs. The resulting top-minus-bottom decile portfolio earned 0.76% per month out of sample, with momentum and volatility signals particularly effective. Key takeaway: Predictive patterns learned from individual stocks can transfer to ETFs.

Yield Curve Prediction with Machine Learning: Forecasting Approaches and the Role of Macroeconomic Predictors (Kang)

Machine learning can improve Treasury yield-curve forecasts, but the gains are highly concentrated. Using 2015–2025 out-of-sample data, the greatest improvements appear at short maturities and in curve-slope forecasts, especially when macro data are included. Key takeaway: Macro information is most valuable for predicting changes in the shape of the yield curve, particularly its slope.

Do Market Regimes Improve Machine-Learning Stock Ranking? (Pascual Miralles and Alfeus)

Machine learning can rank stocks effectively without elaborate regime timing. An XGBoost model ranking 200 U.S. stocks produced a 34.2% CAGR and 1.28 Sharpe out of sample from 2021–2025. Adding market regimes or portfolio optimization failed to improve results. Key takeaway: Adding sophistication to a strong investment signal doesn’t necessarily improve performance.

Prediction Markets

Prediction Markets as Event Hedges: Can They Actually Be Used in Practice? (Kirillov)

Prediction markets can potentially hedge event risk, not just forecast it. A Trump-win contract reduced the one-day loss on a $100k solar ETF position from −9.32% to +0.53%. But limited liquidity made the approach impractical at institutional scale. Key takeaway: Prediction markets can provide targeted event hedges for smaller portfolios.

Blogs

Revisiting Intraday Momentum (Quantseeker)

Bond indices and systematic duration management (Macrosynergy)

Most Asset Managers Already ‘Use AI.’ Few Turn It Into Alpha (CFA Institute)

Podcasts

How a 50-Year Veteran Thinks About Risk Management · Peter Brandt (Chat with Traders)

Trading 80 Synthetic Markets with Trend, Carry & Skew: Jiro Fujisawa, Asset Management One USA (RCM Alternatives)

Portable Alpha: Asking the Questions That Matter ft. Harry Moore (Top Traders Unplugged)

Nicolas Mirjolet, CEO of Quantica on Why the Most Profitable Trends Face the Most Skepticism (StrandGlobalMacro)

Social Media & Industry Research

Traders on Defense (Citadel)

Skewness as a Hidden Driver of Anomaly Returns (Alpha Architect)

Portable Alpha: Ask the Hard Questions (Man Group)

Last Week’s Most Popular Links

Disagreement on Volatility (Yang and Zhu)

A Seat at the Table: Turning AI Macroeconomic Views into Portfolios with a Factor Framework (Elkamhi and Lee)

The Costs and Benefits of Leveraged ETFs (Murray and Sammon)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

This content does not constitute personalized financial, legal, or investment advice and may not be suitable for your individual circumstances. Investing carries risks, and past performance does not guarantee future results. The author and affiliates may hold positions in securities discussed, and these holdings may change at any time without prior notification.

The author is not affiliated with, sponsored by, or endorsed by any of the companies, organizations, or entities mentioned in this newsletter. Any references to specific companies or entities are for informational purposes only.

The brief summaries and descriptions of research papers and articles provided in this newsletter should not be considered definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.

This newsletter may contain links to external websites and resources. The inclusion of these links does not imply endorsement of the content, products, services, or views expressed on these third-party sites. The author is not responsible for the accuracy, legality, or content of external sites or for that of any subsequent links. Users access these links at their own risk.

The author assumes no liability for losses or damages arising from the use of this content. By accessing, reading, or using this newsletter, you acknowledge and agree to the terms outlined in this disclaimer.

Paid subscriptions may not be available in all jurisdictions and may change without notice.

A few years ago, researchers proposed that this pattern isn’t simply random noise. They argued it reflects systematic trading flows from market participants who are forced to rebalance as markets move, creating predictable short-term price pressure.

The results attracted considerable attention, but one obvious question remains:

Does the effect still hold today, and is it actually tradable after accounting for implementation costs?

In this post, I test the original idea, focusing on SPY and QQQ, extend the sample well beyond the original study, and test whether the edge has survived out of sample. I also examine whether simple filters improve the strategy and how much transaction costs it can absorb.

Read more