Big Ideas

Are the human choices in studies that apply machine learning models to forecast stock returns critical to findings? In other words, is there a confounding machine learning design choices zoo? In their November 2024 paper entitled “Design Choices, Machine Learning, and the Cross-section of Stock Returns”, Minghui Chen, Matthias Hanauer and Tobias Kalsbach analyze effects of varying seven key machine learning design choices: (1) machine learning model used, (2) target variable/evaluation metric, (3) target variable transformation (continuous or discrete dummy), (4) whether to use anomaly inputs from pre-publication subperiods or not, (5) whether to compress correlated features, (6) whether to sue a rolling or expanding training window and (7) whether to include micro stocks in the training sample. They examine all possible combinations of these choices, resulting in 1,056 machine learning models. For each machine learning model each month, they:

1. Rank stocks on each of 207 potential return predictors and map rankings into [-1, 1] intervals. In case of missing inputs, they set the ranking value to 0.
2. Apply rankings to predict a next-month target variable (return in excess of the risk-free rate, market-adjusted return or 1-factor model risk-adjusted return) for each stock with market capitalization above a 20% NYSE threshold during January 1987 through December 2021.
3. Reform a hedge portfolio that is long (short) the value-weighted tenth, or decile, of stocks with the highest (lowest) predicted target variable and compute next-month portfolio return.

Using monthly data as available for all listed U.S. common stocks during January 1957 through December 2021, they find that: Keep Reading

What topics are hot and cold in finance research? In their October 2024 paper entitled “Tracing the Evolution of Finance Research: A Topic Modeling Analysis of AJG-Ranked Journals”, Yang Su, Brian Lucey and Ashish Jha provide an overview of academic finance research trends since 2000. They identify the hottest and coldest topics based on publication volume, citation counts and outlier analysis. Using the content and citations for 78,822 articles published across 110 finance journals during 2000 through 2022, they find that:

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How can schemers use statistics to fool investors? In the October 2024 revision of their paper entitled “The Art of Financial Illusion: How to Use Martingale Betting Systems to Fool People”, Carlo Zarattini and Andrew Aziz illustrate use of a Martingale betting system to shape the short-term profitability of trading strategies. This system involves increasing the bet (or trade size) after every loss to recover losses and even yield a profit. Specifically, they run 10,000 trials each for three strategies trading Invesco QQQ Trust (QQQ) daily during 2022, all initially capitalized at $1,000:

1. Base – randomly initiate a 100% long or 100% short position at a random time during regular trading hours with 1:1 leverage and a stop-gain and a stop-loss both $0.20 from the entry price. When no stop triggers, close the position at 4:00PM.
2. Martingale – same as base, but double the leverage after each loss and restore it to 1:1 after a win.
3. Martingale + Target Cumulative Profit – same as base but vary the leverage (in terms of number of shares traded) to target a constant cumulative profit of $0.79 per trading day. In other words, the target profit increases by $0.79 every trading day.

They assume a commission rate of $0.0005 per share. For the second and third strategies, they limit leverage to 500:1. Using intraday prices for QQQ from the end of December 2021 through the end of December 2022, they find that:

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The market view of Bitcoin has increasingly shifted from a potentially useful currency to an investment asset with no yield but potentially high capital gain. What are the implications of its success in the latter role? In their October 2024 paper entitled “The Distributional Consequences of Bitcoin”, Ulrich Bindseil and Jürgen Schaaf model a scenario in which the price of Bitcoin rises for the foreseeable future due to persistent collective belief that its price will continue to rise. Modeling assumptions are:

• All Bitcoin has been mined, such that the supply is constant.
• Bitcoin has no impact on the capacity of the economy to produce goods and services because it has no economic value.
• Success stories of early adopters sustain a steady increase in demand from latecomers, satisfied by early adopter selling. With a fixed supply, price depends exclusively on (rising) demand.
• Latecomers finance Bitcoin purchases by reducing consumption and liquidating real assets (which are bought by early adopters).
• Bitcoin wealth stimulates higher consumption by holders, balanced by the lower consumption of others because Bitcoin does not increase economic activity (ignoring for simplicity the possibility of reduction in other investments). In other words, Bitcoin is a zero sum game.
• Everyone eventually buys some Bitcoin (people never holding Bitcoin would fare worse than latecomers).

Based on market experience with Bitcoin and their model, they conclude that: Keep Reading

Can market efficiency be falling despite ubiquitous data, computing and networking? In his August 2024 paper entitled “The Less-Efficient Market Hypothesis”, Clifford Asness argues that markets have become less efficient in the relative pricing of common stocks over recent decades. To make his argument, he relies on the ratio of expensive stock valuations to cheap stock valuations (the value spread). He considers two versions of this spread, one based on the conventional price-to-book ratio to measure value and the other based on five industry-neutral value metrics. He discusses three potential reasons why the value spread is rising. He closes with advice for value investors. Reflecting on 35 years of research experience, he concludes that:

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Why have stock return anomalies generally degraded over recent decades? In their August 2024 paper entitled “What Drives Anomaly Decay?”, Jonathan Brogaard, Huong Nguyen, Tālis Putniņš and Yuchen Zhang examine why stock return anomalies decay by:

• Decomposing returns into market-wide, public firm-specific and private firm-specific elements.
• Separating cash flow and discount rate effects within each of these three components.
• Accounting for noise.

This breakdown lets them determine whether changes in anomaly returns over time derive from anomaly publication, identifiable liquidity shocks (such as stock price decimalization) or a more general increase market efficiency. They apply this approach to daily returns of long-short (hedge) portfolios, reformed monthly, for 204 stock return anomalies from Open Source Asset Pricing. Using the required firm characteristics and daily prices for all NYSE/AMEX/NASDAQ common stocks during 1956 through 2021 (an average 4,029 firms per year and a total of 16,966 firms), they find that:

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How does the performance of the global multi-class market look when evaluated at a monthly frequency? In their August 2024 paper entitled “The Risk and Reward of Investing”, Ronald Doeswijk and Laurens Swinkels assess global investing rewards and risks via an exhaustive $150 trillion portfolio of investable global assets priced at a monthly frequency, enabling greater granularity of risk estimates than does the annual frequency used in prior research. They consider five asset classes: equities, real estate, non-government bonds, government bonds and commodities. For these classes and the multi-class market, they examine stability of Sharpe ratios and severity, frequency and duration of drawdowns. Their default base currency is the U.S. dollar, but they measure effects of choosing one of nine other currencies on global market portfolio performance. They calculate excess investment returns generally relative to government bill yields as a proxy for return on savings. Using monthly returns for all investable global assets with reinvested dividends during 1970 through 2022, they find that:

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Has ease of access to, and processing of, firm accounting data suppressed stock anomalies by leveling the information playing field? In their July 2024 paper entitled “The Effect of New Information Technologies on Asset Pricing Anomalies”, David Hirshleifer and Liang Ma test the effects of mandating Electronic Data Gathering, Analysis and Retrieval (EDGAR) during April 1993 to May 1996 and eXtensible Business Reporting Language (XBRL) during 2009 to 2011 on well-known stock return anomalies attributed to mispricing. EDGAR makes firm accounting data available electronically, and XBRL reduces the cost of processing such data by making it machine readable. They focus on eight anomalies, five of which rely on accounting data (accruals, net operating assets, investment-to-assets ratio, asset growth and gross profitability) and three of which rely on market data (momentum, net stock issuance and composite equity issuance). They examine effects of EDGAR/XBRL implementations on each anomaly individually, on the five accounting anomalies in aggregate and on the three non-accounting anomalies in aggregate. They carefully consider EDGAR/XBRL implementation dates and fiscal years by firm to compare anomalies for implemented and non-implemented sets of stocks. Using firm characteristics and monthly returns for a broad sample of U.S. common stocks during July 1992 through June 1997 (July 2009 through June 2012) for the EDGAR (XBRL) sample, they find that: Keep Reading

What are the best ways to apply backtesting in the development of investment strategies? In their July 2024 paper entitled “The Three Types of Backtests”, Jacques Joubert, Dragan Sestovic, Illya Barziy, Walter Distaso and Marcos Lopez de Prado offer guidance on backtesting best practices by reviewing:

• Strengths and weaknesses of the three main types of backtests (walk-forward, resampling and Monte Carlo simulation).
• Ways to enhance the quality of backtests.
• Mitigation of the confounding effects of running multiple tests on the same data.

Based on theoretical and practical considerations, they conclude that:

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Do widely used associational (rather than causal) methods used by researchers to specify factor models of asset returns work? In their March 2024 paper entitled “The Case for Causal Factor Investing”, Marcos Lopez de Prado, Alex Lipton and Vincent Zoonekynd describe the shortcomings of associational methods of factor model development. They address p-hacking (data snooping), with focus on interferences from variables called colliders (causally influenced by two or more variables) and confounders (influencing both dependent and independent variables). They further describe what can be done to correct these shortcomings. Based on logical/mathematical analysis and the body of financial markets research, they conclude that:

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