Factor MAX: A New Signal for Predicting Factor Returns

Investment professionals have long relied on factor investing—strategies built around characteristics like value, momentum, and quality—to generate returns beyond the broad market. But predicting which factors will perform well in the future has remained challenging. Liyao Wang and Ming Zeng, authors of the December 2025 study “Factor MAX and Predictable Factor Returns,” introduced an intriguing predictor: factor MAX, which captures extreme daily returns within a month.

What the Researchers Examined

Research findings (see for example here and here) have found that extreme returns have been found to be a source of useful information at the individual stock level. The factor MAX study investigated whether the maximum daily return a factor experiences within a month contains valuable information about that factor’s future performance. Unlike traditional predictors like factor momentum (which looks at cumulative performance over recent periods), factor MAX isolates a single data point: the highest daily return a factor achieves in a given month.

The researchers analyzed 172 equity factors from 1963 to 2023, sorting them monthly into quintiles based on their prior month’s maximum daily return. They then tracked how these portfolios performed in the following month.

Key Findings

1. Strong Return Predictability

Factors with the highest maximum daily returns significantly outperformed those with the lowest maximum returns. The strategy of buying high-MAX factors and selling low-MAX factors generated:

• 0.32% per month in raw returns (with a t-statistic of 5.9)—the monthly average returns of the factor MAX portfolios increased from 0.09% for quintile 1 to 0.41% for quintile 5.
• Statistically significant risk-adjusted returns ranging from 0.24% (behavioral model of Daniel, Hirshleifer, and Sun) to 0.37% per month (q-factor model of Hou, Xue, and Zhang) depending on which of the five factor models were used (the other models used were the CAPM and the five– and six-factor models of Fama and French).
• Over the full sample period, this translated to cumulative alpha of $9.58 per dollar invested.
• Limited drawdowns, suggesting that factor MAX returns are not concentrated in some specific historical periods.

2. Distinct from Factor Momentum

While factor MAX  correlates with factor momentum (since extreme returns often coincide with strong average performance), it captures different information. The factor MAX  strategy continued to generate significant abnormal returns even after controlling for both time-series and cross-sectional factor momentum.

3. Not Driven by Stock-Level Lottery Effects

The researchers ruled out the possibility that factor MAX  simply repackages stock-level anomalies related to extreme returns or “lottery-like” characteristics. The factor-level effect remained robust after controlling for seven various stock-level anomalies, including maximum returns, idiosyncratic volatility, and return skewness.

4. The Power of Investor Attention

The study provides compelling evidence that limited investor attention drives the factor MAX  effect through underreaction to factor-level news. The strategy proved most profitable when:

• Factors received low investor attention—0.42% monthly return (t-stat of 2.93) vs. statistically insignificant returns for high-attention factors
• The extreme return day didn’t coincide with salient macroeconomic announcements or earnings releases
• Factors were more systematic—closer to broad, non‑diversifiable risk and show up pervasively across securities, asset classes, and time.

5. A Tale of Two MAXes

Perhaps most intriguingly, investors appear to react differently to extreme returns at different levels:

• Stock-level MAX: Investors overreact, leading to subsequent return reversals (consistent with lottery-seeking behavior)
• Factor-level MAX: Investors underreact, leading to positive return continuation

This asymmetry explains why stock MAX negatively predicts stock returns while factor MAX  positively predicts factor returns.

6. A Contrarian–Momentum Split in MAX

At the individual stock level, the original MAX anomaly documented by Turan Bali and co‑authors is a contrarian signal: lottery‑like stocks with very high past maximum daily returns tend to underperform subsequently, so the profitable trade is effectively long low‑MAX, short high‑MAX. In contrast, factor MAX  is used as a momentum‑style signal in factor space: factors that experience an extreme positive daily return within a month tend, on average, to continue outperforming in the next month, much like a time‑series factor‑momentum strategy.​

This “level‑of‑aggregation flip” echoes a broader pattern in the literature: single‑stock returns often display short‑term reversal (STREV‑type effects), whereas diversified factor returns display positive autocorrelation that supports factor momentum and factor MAX ‑type continuation bets. For practitioners, the key is to recognize that a characteristic that is contrarian in the stock cross‑section can legitimately be traded as a momentum signal once it is embedded in a factor and treated as a time‑series.

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged and do not reflect management or trading fees, and one cannot invest directly in an index.

Key Investor Takeaways

1. Monitor Extreme Factor Returns

Investors should pay attention not just to average factor performance but also to extreme daily realizations. A factor experiencing unusually high daily returns may signal continued outperformance in the following month.

2. Combine with Existing Strategies

Factor MAX complements rather than replaces factor momentum strategies. Investors can potentially enhance returns by incorporating both signals into their factor timing decisions.

3. Focus on Low-Attention Environments

The factor MAX  effect is strongest when investor attention is limited. Consider emphasizing this signal for:

• Factors receiving less market attention
• Periods without major macroeconomic announcements
• Factors whose constituents aren’t announcing earnings

4. Systematic Factors Matter Most

The predictive power of factor MAX  concentrates in more systematic factors (those explaining broader market variation). This suggests the strategy works best when applied to well-established, liquid factors rather than niche anomalies.

5. Robust Across Specifications

The effect proves remarkably stable across different portfolio construction methods (such as quintiles, quartiles or terciles, definitions (such as redefining the signal as MAX5—the sum of the five largest daily returns within a month), time periods, and factor universes. This robustness suggests it’s not a data-mining artifact but reflects a genuine market pattern.

Implementation Considerations

While the academic evidence is compelling, practical implementation requires consideration of:

• Transaction costs: Monthly rebalancing across many factors could be expensive
• Factor universe selection: Focusing on the most liquid, systematic factors may improve real-world performance
• Combination strategies: Integrating factor MAX  with momentum and other signals could optimize risk-adjusted returns

Conclusion

Factor MAX represents an innovative approach to predicting factor returns by focusing on extreme within-month performance rather than cumulative returns. The evidence suggests that investors systematically underreact to factor-level news embedded in these extreme returns, creating exploitable return predictability. For sophisticated investors engaged in factor investing, incorporating factor MAX  signals—particularly during low-attention environments and for systematic factors—may offer meaningful performance enhancement opportunities.

The finding that investors process stock-level and factor-level extreme returns differently also provides valuable insight into market psychology and the limits of investor attention in processing complex, portfolio-level information.

Larry Swedroe is the author or co-author of 18 books on investing, including his latest, Enrich Your Future: The Keys to Successful Investing. This article is for informational and educational purposes only and should not be construed as specific investment, accounting, legal, or tax advice.