Defining the Learning Task

Whitney K. Newey and Kenneth D. West (1986)

Narasimhan Jegadeesh and Sheridan Titman (1993) — The Journal of Finance · 11065 citations

This seminal paper establishes the existence of 'Momentum' in stock prices, showing that buying past 6-month winners and selling losers generates ~1% monthly excess returns over medium horizons (3-12 months).

Yoav Benjamini and Yosef Hochberg (1995) — Journal of the Royal Statistical Society. Series B (Methodological) · 103872 citations

Active portfolio management: A quantitative approach for providing superior returns and controlling risk

Richard C.. Grinold and Ronald N.. Kahn (2000) — McGraw-Hill · 222 citations

Clifford S. Asness et al. (2013) — The Journal of Finance · 2002 citations

Value and Momentum strategies work consistently across eight diverse asset classes and markets, and their negative correlation allows a combined strategy to achieve a Sharpe ratio of 1.45, significantly outperforming either strategy alone.

David H. Bailey and Marcos Lopez de Prado (2014) · 110 citations

The paper introduces the Deflated Sharpe Ratio (DSR), a statistic that adjusts performance metrics for the probability of backtest overfitting caused by multiple testing and non-normal returns.

David H. Bailey et al. (2015) · 119 citations

This paper proposes a practical, model-free way to estimate how likely your “best” backtest is a false positive caused by trying many strategy variations on the same data.

Kent Daniel and Tobias J. Moskowitz (2016) — Journal of Financial Economics · 2 citations

Momentum strategies suffer predictable crashes during bear market rebounds because past losers behave like high-beta call options; a dynamic weighting strategy based on market volatility can double the Sharpe ratio.

Does Academic Research Destroy Stock Return Predictability?

R. David McLean and Jeffrey Pontiff (2016) — Journal of Finance · 851 citations

Stock return predictors published in academic journals lose 58% of their performance after publication due to a combination of statistical bias (data mining) and arbitrage trading.

...and the Cross-Section of Expected Returns

Campbell R. Harvey et al. (2016) — Review of Financial Studies · 1838 citations

Due to extensive data mining in asset pricing ('the factor zoo'), the standard t-statistic threshold of 2.0 is insufficient; this paper mathematically demonstrates that a t-statistic > 3.0 is required to establish true significance.

Advances in Financial Machine Learning

Marcos Lopez de Prado (2018) — John Wiley & Sons · 106 citations

Judea Pearl (2019) — Communications of the ACM · 620 citations

Pearl argues that moving from pattern recognition to causal models (especially interventions and counterfactuals) is necessary to make ML systems robust, explainable, and capable of “what if?” reasoning.

Kewei Hou et al. (2020) — The Review of Financial Studies · 759 citations

This paper replicates 452 published financial anomalies and finds that the majority fail to hold up to current empirical finance standards, particularly when controlling for microcaps and multiple testing, suggesting capital markets are more efficient than previously recognized.

Carlos Cinelli and Chad Hazlett (2020) — Journal of the Royal Statistical Society. Series B (Statistical Methodology) · 837 citations

Andrew Y. Chen and Tom Zimmermann (2021) · 185 citations

This paper provides an open-source dataset and code demonstrating that nearly 100% of published cross-sectional stock return predictability results can be successfully reproduced, challenging claims of widespread non-replicability and p-hacking.

Is There a Replication Crisis in Finance?

Theis Ingerslev Jensen et al. (2022) · 285 citations

This paper argues that the perceived replication crisis in financial economics is overstated, demonstrating through a novel hierarchical Bayesian model and global data that most asset pricing factors are replicable, cluster into 13 themes, and show external validity.

Paul Glasserman et al. (2025)

Using 2.4M Reuters articles (1996–2022), the paper shows that persistent firm-level news-topic exposures and opposite intraday vs overnight price responses to the same topics explain a large share of the long-run U.S. “overnight drift” (overnight gains vs flat/negative intraday returns) and help forecast which stocks will do best overnight and worst intraday.

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