Investing Expertise

Should stock return model complexity guide breadth of input data? In their May 2025 paper entitled “Model Complexity and the Performance of Global Versus Regional Models”, Minghui Chen, Matthias Hanauer and Tobias Kalsbach assess the predictive performance of global versus regional inputs for stock return models based on linear and machine learnings algorithms: ordinary least squares regression (OLS); elastic net (ENET); random forest (RF); gradient-boosted regression trees (GBRT); and, neural networks (NN). Monthly model inputs include 36 firm-level characteristics and associated stock trading data in U.S. dollars for 24 developed market countries, suppressing effects of megacaps and excluding microcaps (the smallest stocks per country comprising 3% of overall market capitalization).  They segment country markets into four regions: North America, Europe, Japan and Asia Pacific. Model training employs an expanding window (initially six years, extended year by year), followed by a 6-year validation interval and a 1-year test interval. For each model, each month, they reform a portfolio that is long (short) the fifth, or quintile, of stocks with the highest (lowest) predicted returns. Using the specified monthly firm/stock inputs during July 1990 through December 2021, they find that:

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Can investors exploit analyst stock price targets by finding the best analysts and overweighting the most extreme target-implied returns? In their March 2025 paper entitled “Alpha in Analysts”, Álvaro Cartea and Qi Jin test the informativeness and exploitability of sell-side analyst stock price targets. To test informativeness of target prices, they each month for each analyst:

• Use price targets to deduce 12-month return forecasts.
• Form a hedge portfolio that is long (short) stocks with positive (negative) return forecasts, with weights proportional to magnitudes of forecasted returns and absolute value of the sum of weights equal to one.
• Compare analyst portfolio performance to that of an equal-weighted, long-only portfolio of the same stocks.

To test exploitability of results, they each month:

• Predict portfolio profitability for each analyst via an inception-to-date regression of six analyst performance metrics up to 12 months ago (capturing historical performance and breadth of stock coverage) versus next-month portfolio return.
• Construct a portfolio of analyst portfolios with higher (lower) allocations to those with higher (lower) predicted returns.

Using daily analyst price targets and associated stock returns/firm characteristics as available for common NYSE/AMEX/NASDAQ stocks during January 1999 through November 2024, they find that: Keep Reading

Are more factors better for predicting stock market returns? In their April 2025 paper entitled “The Limited Virtue of Complexity in a Noisy World”, Álvaro Cartea, Qi Jin and Yuantao Shi analyze the interactions between stock factor noisiness (errors/uncertainties) and factor model complexity in portfolio optimization. Specifically, they study how factor noise affects the predictive power of factor-based return models. They postulate that noise arises from issues such as inconsistent data collection, processing errors or insufficient computing power. Their conclusions derive from three examples:

1. Employ market data to simulate ideal noiseless stock factors, and combine them via ridge regression to predict stock returns.
2. Apply a neural network to actual NYSE/AMEX/NASDAQ data to predict monthly stock returns via a varying number of factor inputs during 1991 through 2023.
3. Use simulated stock factors which have either fixed noise levels or noise levels that increase with model complexity to explore how predictive power varies with noise and complexity. 

Using monthly excess returns for the broad value-weighted U.S. stock market and values for 15 factors used in other studies during 1926 through 2023, they find that: Keep Reading

Should those aiming to exploit machine learning for portfolio construction focus model training on the broad market or specific industries? In their April 2025 paper entitled “Do Machine Learning Models Need to Be Sector Experts?”, Matthias Hanauer, Amar Soebhag, Marc Stam and Tobias Hoogteijling examine return predictability using several machine learning (ML) models trained on a comprehensive set of firm/stock characteristics in three ways:

1. Generalist – trained on all stocks in the sample.
2. Specialist – trained on stocks only within one of 12 industry classifications.
3. Hybrid – integrates overall sample and industry information via industry-neutral mappings from stock characteristics to expected returns.

They employ four ML models, including elastic nets, gradient boosted regression trees, 3-layer neural networks and an equal-weighted ensemble of the three. They train and tune these models with an expanding window with an initial 18-year training set, 12-year validation set and 1-year test set, shifted forward each year but retaining the initial training start point. Input data consists of monthly stock returns and monthly values of 153 firm-level characteristics for U.S. stocks each month at or above the 20th percentile of NYSE market capitalizations . They assign stocks to the 12 industries (including Other), with average weights ranging from 22.5% for Tech to 1.4% for Durables. They then each month sort stocks into tenths (deciles) by machine learnings ensemble-predicted next-month return and reform a volatility-scaled, value-weighted hedge portfolio that is long the decile with the highest expected returns and short the decile with the lowest. Using the specified inputs during January 1957 (January 1986 for a non-U.S. sample) through December 2023, they find that:

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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

Is the ability of artificial intelligence (AI) platforms such as ChatGPT to summarize and interpret large volumes of financial data altering investor trading behaviors and thereby changing financial markets? In the April 2025 revision of their paper entitled “ChatGPT and the Stock Market”, Jenny Stanco and Kee Chung examine the impact of ChatGPT on stock trading, volatility, liquidity, and price efficiency. For their analysis, they separate firms into those with abundant publicly available information (high-info) and those with limited information (low-info), employing firm size and age as proxies for information availability. They further use Google search volumes to estimate levels of attention firms may get from ChatGPT. They use the year before (after) ChatGPT launch on November 30, 2022 as the pre-launch (post-launch) subperiod. Using daily trading volumes and return volatilities, and earnings forecasts/announcements/actuals data, for a broad sample of U.S. stocks from the end of November 2021 through the end of November 2023, they find that: Keep Reading

Can large language models (LLM) help improve portfolio performance metrics, portfolio optimization and strategy feature discovery? In his three January-February 2025 papers entitled “AlphaSharpe: LLM-Driven Discovery of Robust Risk-Adjusted Metrics”, “AlphaPortfolio: Discovery of Portfolio Optimization and Allocation Methods Using LLMs” and “AlphaQuant: LLM-Driven Automated Robust Feature Engineering for Quantitative Finance”, Kamer Yuksel explores use of specially trained LLMs to discover new:

• Enhanced portfolio risk-return metrics that outperform traditional approaches such as Sharpe ratio.
• Better portfolio optimization methods.
• Robust investment strategy features.

The development processes are iterative for continuous improvement. He assesses usefulness of discoveries with 15 years of historical data for 3,246 US stocks and ETFs, of which he uses the last few years for out-of-sample equal-weighted portfolio testing. Using these methods and this dataset, he finds that:

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Are plans to use nuclear power to provide electricity for proliferating data centers driving attractive performance for uranium exchange-traded-funds (ETF)? To investigate, we consider four such ETFs, all currently available:

• VanEck Uranium and Nuclear ETF ETF (NLR) – picks stocks and depositary receipts of firms involved in uranium and nuclear energy.
• Global X Uranium ETF (URA) – picks stocks of global companies involved in the uranium industry.
• Sprott Uranium Miners ETF (URNM) – picks stocks of firms devoting at least 50% of assets to mining of uranium, holding physical uranium, owning uranium royalties or engaging in other activities that support uranium mining.
• Sprott Junior Uranium Miners ETF (URNJ) – picks stocks of small firms devoting at least 50% of assets to mining of uranium, holding physical uranium, owning uranium royalties or engaging in other activities that support uranium mining.

We use Energy Select Sector SPDR Fund (XLE) as a benchmark. We also look at some performance results for SPDR S&P 500 ETF Trust (SPY) for perspective. We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly total returns for the four uranium ETFs as available and for XLE and SPY through February 2025, we find that: Keep Reading

Does Warren Buffett consistently keep Berkshire Hathaway in market-beating form? If so, how does he do it? In his annual letters to stockholders, he includes company performance and benchmark data and describes in general terms how he goes about investing. He sometimes shares his thoughts on the current state of and prospects for the U.S. markets. Using annual performance data from his 2024 letter for 1965 through 2024 (60 years) and the investing approach/outlooks described in his letters of 1977 through 2024, we find that: Keep Reading

Does finance professor David Kass, who presents annual lists of stock picks on Seeking Alpha, make good selections? To investigate, we consider his picks of “10 Stocks for 2020”, “16 Stocks For 2021”, “12 Stocks For 2022”, “10 Stocks For 2023” and “10 Stocks For 2024”. For each year and each stock, we compute total (dividend-adjusted) return. For each year, we then compare the average (equal-weighted) total return for a David Kass portfolio to that of  SPDR S&P 500 ETF Trust (SPY). Using dividend-adjusted returns from Yahoo!Finance for SPY and most stock picks and returns from Barchart.com and Investing.com for three picks during their selection years, we find that: Keep Reading