Big Ideas

Does lack of liquidity among stocks in anomaly portfolios effectively block exploitation? In their November 2025 paper entitled “Liquidity Constraints and the Illusion of Anomaly Profitability”, Álvaro Cartea, Mihai Cucuringu, Qi Jin and Jiexiu Zhu assess exploitability of anomaly trading strategies after accounting for individual stock liquidities. They define liquidity of a stock as its capacity to absorb incremental volume relative to recently observed average daily volume without material price impact. They estimate anomaly portfolio profitability based on liquidity-constrained dollar trade sizes/profit for each anomaly portfolio stock. They apply this approach to 128 U.S. stock return anomalies, with both in-sample (same as originally published) and out-of-sample results. They initially assume zero trading costs to isolate the impact of liquidity constraints. They then estimate trading costs (either half the bid-ask spread or price impact estimates), exclude trades expected to be unprofitable and generate the combined effects of liquidity constraints and trading costs. Using data for stocks per the 128 anomalies during January 1930 through December 2023, they find that:

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How is the increasing role of interacting algorithms changing financial markets? In his November 2025 paper entitled “Algorithmic Exuberance”, Marc Schmitt presents an Algorithmic Exuberance model, which automatically stimulates market volatility from two coupled feedback channels (see the figure below):

1. Market-algorithmic reflexivity (trading systems learning from one another).
2. Information-algorithmic reflexivity (algorithmic amplification of news, narratives and sentiment).

The model derives a Reflexivity Index (RI) that quantifies the strength and persistence of market volatility from these feedback channels, and measurable Reflexivity Share of Variance (RSV) and Implied Reflexivity (IR) components. Using broad U.S. stock market data from 1980 through 2024, he finds that: Keep Reading

A subscriber suggested review of The Book of Alternative Data: A Guide for Investors, Traders, and Risk Managers , a 2020 book by Alexander Denev and Saeed Amen. In this book, the authors address “the ever growing importance of data, and in particular, alternative data. We live in a world, which is rich with data, where many datasets are accessible and available at a relatively low cost. …This book is aimed at investors who are in search of superior returns through nontraditional approaches. These methods are different from fundamental analysis or quantitative methods that rely solely on data widely available in financial markets. It is also aimed at risk managers who want to identify early signals of events that could have a negative impact, using information that is not present yet in any standard and broadly used datasets.” Based on prior research and their experience, they conclude that:

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Different risk metrics capture different aspects of risk, and the relative importance of different aspects of risk varies across investors. Widely used risk metrics do not serve the interests of long-term investors because they destroy price series history. Is there a better risk metric for such investors? In the October 2025 draft of their paper entitled “Submergence Intensity: A Contextualized Risk Metric for Long-Term Investing”, Dane Rook and Ashby Monk introduce submergence intensity as a risk metric for long-term investors. They designed this metric to overcome the following shortcomings of existing risk metrics:

1. Many risk metrics reflect either typical/average risk or extreme (tail) risk, but not both.
2. Many risk metrics are insensitive to the order in which returns occur.
3. Many risk metrics are insensitive to asymmetries in returns (positive or negative, or part of a drawdown or a recovery.
4. Most risk metrics critically depend upon a small number of parameters, but these parameters are often not explicit, or not adaptable.
5. Many risk metrics effectively penalize liquid assets and therefore distort relative riskiness of assets.

The authors compare submergence intensity with other risk metrics, and discuss how investors can adapt it to specific preferences. Based on theoretical considerations, they conclude that: Keep Reading

The Sharpe ratio is the most widely used measure of investment efficiency. Is it truly reliable? In their September 2025 paper entitled “How to Use the Sharpe Ratio”, Marcos Lopez de Prado, Alexander Lipton and Vincent Zoonekynd review the shortcomings of conventional Sharpe ratio analysis and tackle these issues via several corrections. They also address the problems of statistical error and false discovery rates. Using theoretical analysis and Monte Carlo simulations, they conclude that:

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What are life lessons from one of the leading researchers in finance? In the August 2025 transcript of his interview entitled “My Life in Finance in 12 Questions”, Campbell Harvey offers the following notable points relevant investors regarding (1) most important findings and (2) interpretation of academic research:

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Blockchain is a decentralized, secure digital ledger that automatically records transactions across many computers. Each block of data contains a list of transactions, with blocks linked to form a chain. Cryptography ensures integrity and immutability of block data. Could broad use of blockchain technology make financial markets better? In their May 2025 paper entitled “Crypto and the Evolution of the Capital Markets”, Tuongvy Le and Austin Campbell explore how use of blockchain to trade conventional assets could mitigate or eliminate many of the risks that drive inefficiencies, opaqueness, embedded rent-seeking (frictions), conflicts of interest and concentrations of data and permissions within current market infrastructure. Drawing on lessons from the historical evolution of markets, they conclude that:

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How can investors avoid out-of-sample factor investing strategy failures driven by use of non-causal research methods? In their May 2025 paper entitled “A Protocol for Causal Factor Investing”, Marcos Lopez de Prado and Vincent Zoonekynd introduce the concept of the factor mirage, a factor model that appears statistically valid but is causally mis-specified. They then provide a practical 7-step protocol for causal factor investing that exploits advances in econometrics and machine learning. Based on theoretical analysis, they find that:

• Collider and confounder biases embedded in standard regressions can generate misleading inferences, poor out-of-sample performance and bad investment decisions.
  • Colliders are variables that are causally downstream for both an independent variable and the dependent variable.
    • They often change the sign of coefficients, inducing investors to buy (sell) when they should sell (buy).
    • They are particularly dangerous for standard two-pass or three-pass regressions.
    • Standard evaluation metrics such as adjusted R-squared and t-statistic may reward misspecification and penalize simplicity even when extra variables introduce collider bias.
  • Confounders are uncontrolled variables that cause both an independent variable and the dependent variable. For example, if leverage influences both book-to-market and returns, and is ignored, the estimated role of book-to-market may be biased in magnitude and sign.
• The seven steps for proper causal factor investing are:

• 1. Identify variables potentially associated with future returns.
  2. Create a causal graph that captures variable interdependencies.
  3. Apply do-calculus to the graph to select appropriate control variables.
  4. Use double machine learning to identify causal effects. 
  5. Construct a portfolio that reflects resulting return and risk forecasts.
  6. Backtest portfolio performance via: (a) walk-forward testing; (b) resampling; and, (c) Monte Carlo simulation.
  7. Correct for the elevated risk of false discoveries due to multiple hypothesis testing through p-value and Sharpe ratio adjustments

In summary, investors can produce robust factor investing strategies by using causal reasoning in strategy development.

The paper includes a due diligence questionnaire to assess whether a factor investing scheme is based on causal reasoning.

Cautions regarding findings include:

• The study does not demonstrate/quantify performance improvements in factor investing strategies attributable to use of causal reasoning.
• The methodology described is beyond the reach of most investors, who would bear administrative costs and management fees for delegating to an expert fund manager. Use of the methodology may be costly.

For related research, see results of this search. See also the Compendium of Live ETF Factor/Niche Premium Capture Tests.

Does the evolution of empirical asset pricing point inevitably to machine learning methods? In his February 2025 paper entitled “From Econometrics to Machine Learning: Transforming Empirical Asset Pricing”, Chuan Shi summarizes the transition from traditional methods to machine learning in empirical asset pricing. He traces the historical development of traditional asset pricing models and their roles as benchmarks for decades of research. He compares the strengths and weaknesses of traditional methods and machine learning, explaining why the latter is well-suited to address challenges of the big data era. Finally, he introduces an approach based on the stochastic discount factor (SDF), melding the simplicity of traditional models and the flexibility/predictive power of machine learning. Based on the body of research on asset pricing, he concludes that: Keep Reading

Every review and analysis, including updates of items in “Compendium of Live ETF Factor/Niche Premium Capture Tests”, brings to mind…

• Wherefrom data snooping bias?
  • Data that involve considerable randomness (luck to be discovered).
  • Brute force experimentation with samples/sample periods, model formulas and model parameter values (finding the luck).
  • Reusing and tweaking models and parameter values previously snooped by others (compounding the luck).
  • A community of thousands (millions?) in loose collaboration analyzing the same samples with uncounted model variations (socializing/amplifying luck discovery).
• Implementation frictions are hard to model and often excluded.
• Markets adapt.