Weekly Research Recap
Latest research on investing and trading
Hi there. Here’s this week’s handpicked selection of the latest top research and resources on investing, with direct links to the original sources for easy access.
Asset Allocation
How Efficient are Static Multi-Asset Portfolios? Evidence from Institutional Capital Market Expectations (Böni, Bruggermann, and Kroencke)
Many investors rely on fixed-weight portfolios like the 60/40 split or equal-weighted, assuming these simple approaches are efficient. This paper challenges that belief, showing that they can be improved using forward-looking data. The authors incorporate institutional return forecasts from the Horizon Actuarial Services survey into a modified Black-Litterman framework to dynamically adjust portfolio weights. Their approach boosts Sharpe ratios, suggesting static portfolios can be improved upon.
Behavioral Finance
Overvaluing Simple Bets: Evidence from the Options Market (Goodman and Puri)
Retail investors are often drawn to simple bets like binary options, which offer a fixed all-or-nothing payoff. This paper finds that such bets are often overpriced compared to better alternatives like bull spreads, which provide higher payoffs in every scenario. Traditional financial and behavioral models fail to explain this preference. The findings suggest that investors may be paying a premium for simplicity, i.e. simplicity can come at a cost.
Bonds
The Expected Return of a Convertible Bond (Marmoiton)
Investors sometimes use simple heuristics or rely on past performance to gauge the returns of convertible bonds, often misunderstanding how different components contribute. This paper provides a clear formula that breaks down the expected return into parts driven by interest rates, credit risk, and expected stock returns. The key insight for investors: A convertible bond’s expected return hinges on your view of credit risk and stock returns, not just its yield or structure.
Currencies
Predictable Currency Crashes (Sun, Wang, and Wang)
Some may believe currency crashes are unpredictable and sudden. This paper challenges that view by showing that crashes are much more likely when two conditions are met: Local interest rates rise sharply and the currency has already been falling over the previous six months. When both signals are present, the chance of a crash jumps to 43%, compared to just 8% otherwise. Investors can possibly use this simple warning sign to better manage risk.
How to Maximize Momentum Returns in Foreign Exchange Markets? (Liu)
Standard long-short FX momentum strategies have weakened considerably over the past 10–15 years. This paper proposes an improvement: Double sort currencies by past 6-month returns and then by skewness and kurtosis. The approach enhances performance and reduces downside risks by avoiding currencies prone to reversals.
Equities
Breaks and Trends in Factor Premia (Cui, Feng, Ma, and Su)
This paper provides strong evidence that factor risk premia vary over time. The authors introduce a “Time-Varying Predictive Regression” model that forecasts individual stock returns using a large set of firm characteristics while accounting for time-varying coefficients and structural breaks. Long-short portfolios formed using the model’s estimated expected returns achieve significantly higher Sharpe ratios than buy-and-hold and other benchmark strategies.
Market Crash Risk and Return Predictability (Sun)
This paper develops a global indicator of stock market crash risk based on changes in local interest rate spreads relative to the U.S. and each country's past 12-month equity performance. The measure significantly predicts crash risk up to six months ahead and remains robust across both developed and emerging markets.
Understanding Dividend-Price Ratio Shifts and Stock Return Predictability (McMillan)
The dividend-price ratio is often used to forecast stock returns, but its reliability has been questioned. This paper explores whether adjusting the ratio for economic growth and inflation makes it more effective. Testing across 48 global markets, the authors find that this macro-adjusted version improves predictive power in regions like the G7 and Asia-Pacific, though results vary elsewhere.
Trade Policy Uncertainty and Stock Return Predictability: A Tale of Two Periods (Yu)
Uncertainty around trade policy has surged amid President Trump’s tariff actions. This paper shows that trade policy uncertainty predicts higher future stock returns, but only when uncertainty is unusually high, defined as the top third of its historical range. This threshold effect suggests that high uncertainty depresses prices, leading to higher expected returns going forward.
Cross-Sectional Drivers of Stock-Treasury Correlations (Luber and Lunden)
This paper shows that firms with different characteristics, such as profitability, size, and quality, exhibit different patterns of co-movement with Treasury bonds. Building on these insights, the authors use firm characteristics to forecast stock-bond correlations at both the individual stock level and the aggregate market level, achieving meaningful improvements over standard benchmark models.
Machine Learning and Large Language Models
Alpha Go Everywhere: Machine Learning and International Stock Returns (Choi, Jiang, and Zhang)
This paper shows that neural networks trained separately for each country outperform models trained on U.S. data alone when predicting cross-sectional stock returns. While some U.S. firm characteristics contain useful global signals, local return patterns vary significantly across markets. The key insight is that investors can improve international stock selection by combining country-specific models with selected U.S. predictors, particularly in economies that are more globally integrated.
Total Recall? Evaluating the Macroeconomic Knowledge of Large Language Models (Crane, Karra, and Soto)
Many investors and researchers are exploring whether large language models (LLMs) can improve macroeconomic forecasting. This paper finds that while LLMs like Claude Sonnet 3.5 recall some indicators, such as inflation and unemployment, quite accurately, they perform less well on others. Their estimates often merge initial and revised data and may even reflect future information. Investors should be cautious: LLMs show impressive recall but may unknowingly look ahead, limiting their use for real-time analysis.
LLMs Vs. Econometric Models for Nowcasting GDP Growth: A Practitioner's View (Andre, Bessec, and Goulby)
It’s well known that many nowcasting models struggle during crises or regime shifts. This paper studies whether large language models (LLMs) like ChatGPT, Claude, and Gemini can help in that respect. Compared to the Banque de France’s econometric models, LLMs perform worse at nowcasting French GDP in normal times but outperform during extreme events like the COVID-19 pandemic. For investors, this suggests LLMs may offer useful insights in turbulent periods but aren’t yet reliable as stand-alone forecasting tools.
Prediction Markets
Price Discovery and Trading in Prediction Markets (Ng, Peng, Tao, and Zhou)
Many investors use prediction markets to anticipate outcomes like elections, but it’s unclear how efficient these markets truly are. This paper analyzes trading activity around the 2024 U.S. presidential election and finds that some markets, especially Polymarket, offer more timely and accurate information than others. Large traders’ activity helps predict price movements, and persistent price gaps across platforms create profitable arbitrage opportunities.
Blogs
Weekly Research Insights - Trading Around Macro Announcements (QuantSeeker)
Economic surprises and commodity future returns (Macrosynergy)
Why Investors Underperform and How Not To (Optimal Momentum-Gary Antonacci)
An Empirical Analysis of Conference-Driven Return Drift in Tech Stocks (Quantpedia)
GitHub
Medium
Best Practices in Feature Engineering for Machine Learning (Iwai)
Podcasts
Are You Betting All Wrong? (Elm Wealth's Victor Haghani Explians) (Meb Faber)
From Lab Coat to Trend Charts: Dr. Kevin Maki (Turtle Talk)
Go Where Orders Flow (Chat With Traders)
Benjamin Hoff – Commodity Futures Surfaces and the Cash-and-Carry Glue (Flirting With Models)
Social Media
Why Some Markets Just Keep Trending (Moritz Heiden)
Stocks Can Underperform Bonds for a Long, Long Time (Meb Faber)
Michael Mauboussin on valuation, capital allocation & competitive advantage: Create More Value (Michael Mauboussin)
Last Week’s Most Popular Links
The Science and Practice of Trend-following Systems (Sepp)
Factor Investing Lecture 10: Factor Timing and Factor Allocation (Presentation Slides) (Shi)
The Volatility Edge, A Dual Approach For VIX ETNs Trading (Zarattini, Mele, and Aziz)
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