Weekly Recap
Latest research on investing and trading
Welcome to this week’s collection of links featuring the latest research on investing and other valuable resources. Below, you’ll find a curated list, with each title linking to its source for more details.
This week, I’m experimenting with a new format. In addition to the usual brief summaries, I’ve included a more in-depth discussion of three focus papers at the top of the newsletter. The goal is to provide deeper insights on a few studies while still offering the concise summaries you’re used to in the next section.
Research in Focus
Formula Investing
(Marcel Schwartz and Matthias X. Hanauer, 2024, SSRN Working Paper 5043197.)
Background: Quantitative investing and rules-based strategies have grown in popularity among investors seeking to outperform the market. A common approach involves ranking stocks using metrics such as valuation, profitability, and momentum, an approach that has been shown to generate excess returns and attractive Sharpe ratios. While many studies have explored formula-based strategies, few have comprehensively evaluated multiple approaches side by side.
The paper: This study examines four well-known investing formulas over a 60-year period in the U.S. stock market: Piotroski’s F-Score, Greenblatt’s Magic Formula, Carlisle’s Acquirer’s Multiple, and van Vliet and Koning’s Conservative Formula. The authors find that all four formulas generate significant returns, primarily by capturing established factor premiums. While no single formula consistently outperforms across all metrics, each demonstrates unique strengths in different areas. The results also show strong performance for long-only portfolios consisting of 40 stocks, suggesting that individual investors can effectively harvest these factor premiums. However, the study notes some performance decay in recent years, underscoring the importance of ongoing strategy refinement.
Investor Implications: The paper highlights the continuing relevance of straightforward, rules-based strategies for investors seeking alpha. Despite signs of performance decay, these approaches continue to deliver excess returns, particularly in concentrated long-only portfolios. Investors can implement these methods without relying on short-selling or complex derivatives. However, the study cautions about the potential for further performance decay, consistent with the broader literature on market efficiency. As a result, investors may need to monitor strategy performance over time and consider incorporating additional signals to maintain a competitive edge.
Read the full paper here.
The Best Strategies for FX Hedging
(Pedro Castro, Carl Hamill, John Harber, Campbell R. Harvey, and Otto van Hemert, 2024, SSRN Working Paper 5047797.)
Background: Global equity investors must decide whether to hedge their foreign exchange (FX) exposure. While one strand of the literature explores static FX hedging strategies and their impact on portfolio risk and return, another focuses on currency signals, such as carry, value, and momentum, which predict currency returns. This paper proposes integrating these signals into FX hedging strategies to create a more dynamic approach.
The paper: The authors examine dynamic FX hedging strategies using signals based on carry, momentum, and value. Unlike static hedging, these active approaches adjust currency exposure based on expected currency returns. The authors demonstrate that dynamic hedging yields better risk-adjusted returns than simple hedged or unhedged strategies. The "Max Carry" strategy hedges FX exposure when foreign interest rates are lower than domestic rates and leaves it unhedged when foreign rates are higher, significantly outperforming static approaches. The addition of trend and value signals further enhances returns, leading to a comprehensive framework for optimizing FX hedging decisions.
Investor implications: The paper advocates for a flexible approach to FX hedging, leveraging established currency predictors. The findings are particularly relevant for those investors managing multi-currency portfolios. To fully harvest the FX carry premium, investors based in high-interest-rate countries investing in low-interest countries should consider active hedging. Conversely, investors based in low-interest-rate countries investing in high-interest countries may benefit from leaving their currency exposures unhedged.
Read the full paper here.
Long-Run Asset Returns
(David Chambers, Elroy Dimson, Antti Ilmanen, and Paul Rintamäki, 2024, Annual Review of Financial Economics 16, 435-458.)
Background: Research on long-term asset returns has gained momentum in recent years, building on earlier studies of returns across time and asset classes. Adopting a long-term perspective enables a broader understanding of fluctuations in risk premiums and economic regimes, although collecting and utilizing data from centuries ago presents significant challenges. The authors contribute to this growing body of literature by analyzing return data for stocks, bonds, real estate, and commodities, with records dating back to the 1800s and earlier.
The paper: The paper's key findings challenge some conventional wisdom about asset returns. The authors find that the equity risk premium was significantly lower in the 19th century compared to the 20th century, suggesting the latter may have been an anomaly. They also demonstrate that the low levels of recent bond yields are historically exceptional from a long-term perspective. Additionally, the paper questions the claim that housing returns have consistently matched equity returns over extended periods, bringing new insights into the relative performance of these asset classes.
Investor implications: For investors, these results have important implications. The lower historical equity premium suggests that future stock returns may be more modest than 20th-century data would imply. The analysis of bond yields highlights the unusual nature of the recent era of ultra-low interest rates. Lastly, the evidence on housing returns may prompt investors to reassess their expectations for residential real estate investments. Overall, the paper underscores the importance of examining very long-term data when forming expectations about future returns.
Read the full paper here.
Research Snapshots
Bonds
Debt Dictionaries (Addoum et al.)
Investors in stocks and bonds are shown to interpret company information differently, even when assessing the same data, suggesting that textual information in corporate communications predicts stock and bond returns differently.
Understanding the Excess Bond Premium (Benson et al.)
The paper finds that variation in news attention to specific topics, like financial crises and politics, explains a large part of the variation in bond risk premiums and predicts economic growth.
Data
Chapter 16 How Alternative Data Are Changing Finance (Glatzer and Cumming)
The authors offer a comprehensive discussion of the rising use of alternative data in finance, sources of unconventional data, and its implications for forecasting and finance in general.
Equities
An Investigation into the Causes of Stock Market Return Deviations from Real Earnings Yields (Murphy et al.)
The difference between the S&P 500 earnings yield and the TIPS real yield is a significant predictor of long-term equity returns, with the predictability being related to inflation and monetary policy.
Exploring the Low-Volatility Anomaly (QuantSeeker)
This article explores the low-volatility effect in stocks, reviews key findings, tests a variety of low-volatility signals, and examines how combining low-volatility signals with other strategies can further improve performance.
Commodity Sentiment in Predicting the Index Futures Returns (Zhang)
The author constructs a measure of commodity sentiment based on millions of news articles and finds it to predict S&P 500 returns out-of-sample with a negative sign.
Nominal Rigidity and the Inflation Risk Premium: Identification from the Cross Section of Equity Returns (Ai et al.)
This paper shows theoretically and empirically that sorting stocks on their profit margins captures exposure to inflation risk induced by monetary policy shocks and serves as a proxy for the inflation risk premium, where a long-short portfolio based on profit margins delivers a significant return spread.
Bad Beta, Good Beta: A Replication (Maio)
The author challenges the effectiveness of the bad beta, good beta model of Campbell and Vuolteenaho (2004), finding its success relies on “questionable” methods and fails to price a richer set of equity anomalies.
Market-Based Short-Rate Uncertainty and Time-Varying Expected Returns (Yu et al.)
The cyclical part of the short-rate uncertainty measure developed by Bauer et al. (2022) is a significant predictor of stock market returns.
ESG
Sustainable Investing (Pastor et al.)
This is a comprehensive review paper on sustainable investing and climate risk, exploring how investors’ preferences affect asset prices and ESG investing.
Machine Learning and Large Language Models
Learning Fundamentals from Text (Kim et al.)
The authors train a Transformer model on firms’ 10-K filings and the corresponding stock price reaction around filing dates, identifying the textual content in filings that attracts investors attention and drives stock market reactions.
Large Language Models for Financial Time Series Forecasting (Noguer I Alonso and Pereira Franklin)
The study evaluates the performance of various Large Language Models for time series forecasting of stock returns, showing promising results but encountering problems with volatile stocks and market conditions.
The author evaluates the merits of using machine learning (ML) models instead of traditional methods in portfolio construction, finding that some ML models outperform but the choice of model(s) requires careful consideration.
A Scoping Review of ChatGPT Research in Accounting and Finance (Dong et al.)
This review paper explores the current and future use of Large Language Models in finance and accounting research and in practice, providing an extensive reference list for further reading.
Market Microstructure
Lead-Lag Relationships in Market Microstructure (Schmidt et al.)
The authors document significant lead-lag effects in high-frequency data between transaction prices and order book data, across different assets.
Blogs
U.S. Treasuries and macro-enhanced trend following (Macrosynergy)
Taking an income from your trading account - probabilistic Kelly with regular withdrawals (Rob Carver)
GitHub
Medium
Backtesting Made Easy: A Complete Guide to Evaluate Trading Strategies with Python (Bauer)
The Portfolio that Got Me a Data Scientist Job (Chapman)
AI in Options Trading (Mercanti)
Podcasts
Wayne Himelsein - Logica Capital Advisors (The Algorithmic Advantage)
Running 55+ Systematic Trading Strategies Simultaneously w/ Laurens Bensdorp (Chat with Traders)
Victor Haghani - The Last of the Tactical Allocators (Flirting with Models)
Why Adaptive Strategies Matter Now More Than Ever ft. Graham Robertson (Top Traders Unplugged)
Last Week’s Most Popular Links
End-of-Day Reversal (Soebhag et al.)
AWS Trading Part 2 - The Strategy (Blackarbs)
Forecasting and Managing Volatility: An S&P 500 Case Study (Dai et al.)
Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse any specific securities or investments mentioned. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.
This content does not offer 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 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 are the author's own interpretations of the findings and content. These summaries should not be considered as definitive or comprehensive representations of the original works. Readers are encouraged to refer to the original sources for complete and authoritative information.
This newsletter contains 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 these external sites or for that of subsequent links. Users click on these links at their own risk.
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