Investing Expertise

In late 2022, Forbes “tapped Morningstar to identify top-performing fund managers who have either beat their benchmarks this year or on a longer-term basis over three-year, five-year or ten-year periods. Here are their best stock ideas for the coming year…” as published at the beginning of January 2023 in “20 Great Stock Ideas for 2023 from Top-Performing Fund Managers”:

Air Lease (AL)
Atlanta Braves Holdings Inc Series C (BATRK)
Becton Dickinson (BDX)
Bill.com Holding (BILL)
CACI International (CACI)
Chord Energy (CHRD)
Comcast (CMCSA)
Dish Network (DISH)
DoubleVerify (DV)
Duolingo (DUOL)
HCA Healthcare (HCA)
Insulet (PODD)
Permian Basin Royalty Trust (PBT)
Philip Morris International (PM)
Royal Caribbean Cruises (RCL)
Shopify (SHOP)
TJX (TJX)
UnitedHealth Group (UNH)
Waste Connections (WCN)
Zebra Technologies (ZBRA)

How did these ideas perform? To check, we collect end-of-2022 and end-of-2023 dividend-adjusted prices for the 20 ideas and calculate the annual total return for each. We then compare the average of these returns to the annual total return for SPDR S&P 500 ETF Trust (SPY). Using the specified annual data for 2022 and 2023, we find that:

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Is ChatGPT useful for predicting stock market returns based on financial news headlines? In the December 2023 version of their paper entitled “ChatGPT, Stock Market Predictability and Links to the Macroeconomy”, Jian Chen, Guohao Tang, Guofu Zhou and Wu Zhu investigate whether ChatGPT 3.5 can predict U.S. stock market (S&P 500 Index) returns based on Wall Street Journal front-page news headlines/alerts. The instruction they give ChatGPT 3.5 is:

“Forget all previous instructions. You are now a financial expert giving investment advice. I’ll give you a news headline, and you need to answer whether this headline suggests the U.S. stock prices are GOING UP or GOING DOWN. Please choose only one option from GOING UP, GOING DOWN, UNKNOWN, and do not provide any additional responses.”

They first compute monthly ratios of good news to total news (NR^G) and bad news to total news (NR^B) and then relate these ratios to S&P 500 Index excess returns over the next 1, 3, 6, 9 or 12 months. They compare the ability of ChatGPT to predict returns to that of traditional human interpretation and to those of BERT and RoBERTa as ChatGPT alternatives. Using daily Wall Street Journal front-page news headlines/alerts and monthly S&P 500 Index excess returns during January 1996 through December 2022, they find that:

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Can enhanced machine learning models accurately time popular equity factors? In their January 2024 paper entitled “Multi-Factor Timing with Deep Learning”, Paul Cotturo, Fred Liu and Robert Proner explore equity factor timing via a multi-task neural network model (MT) to capture the commonalities across factors and a dynamic multi-task neural network model (DMT) to extract financial and macroeconomic states. They attempt to time six well-known factors: (1) excess market return, size, value, profitability, investment and momentum. They employ 272 model inputs (123 macroeconomic and 149 financial) to predict each month:

1. The sign of next-month return for each factor.
2. The return for an equal-weighted portfolio that holds the factors (the risk-free asset) for factors with positive (negative) predicted returns.

The compare performances of MT and DMT with those of seven simpler off-the-shelf machine learning models: logistic regression (LR), penalized logistic regression (EN), random forest (RF), extremely randomized trees (XRF), gradient boosted trees (GBT), support vector machine (SVM) and feed-forward neural network (NN). For all models, they use the first 20 years of their sample period for training, the next five years for validation and the remaining years for out-of-sample testing. Their benchmark is an equal-weighted portfolio of all six factors. Using monthly data for the 272 model inputs and monthly returns for the six factors during January 1965 through December 2021, with out-of-sample testing starting January 1990, they find that: Keep Reading

Can ChatGPT find red flags in firm earnings calls? In their January 2024 paper entitled “Unusual Financial Communication – Evidence from ChatGPT, Earnings Calls, and the Stock Market”, Lars Beckmann, Heiner Beckmeyer, Ilias Filippou, Stefan Menze and Guofu Zhou test the ability of ChatGPT-4 Turbo to identify and analyze unusual content and tone aspects of S&P 500 earnings calls. Unusualness has 25 dimensions derived from executive behaviors, analyst questions, specific content or technical issues. They examine correlations of unusualness with firm characteristics, industry and macroeconomic indicators across business cycles. They validate unusualness by looking at associated stock returns and trading volumes from one day before through one day after earnings calls. Using transcripts of S&P 500 earnings calls from Refinitiv, firm characteristics/stock trading data and macroeconomic data during January 2015 through December 2022, they find that:

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Can machine learning models pick stocks that unequivocally generate alpha out-of-sample? In their November 2023 paper entitled “The Expected Returns on Machine-Learning Strategies”, Vitor Azevedo, Christopher Hoegner and Mihail Velikov assess expected net returns and alphas of machine learning-based anomaly trading strategies. They use nine machine learning models to predict next-month stock returns based on inputs for up to 320 published anomalies, added to the mix according to respective publication dates:

• Ordinary Least Squares with Huber Loss Function (OLS-HUBER).
• Elastic Net (ENET).
• Feedforward Neural Network (FFNN) with two to five hidden layers (FFNN2, FFNN3, FFNN4 and FFNN5).
• Long Short-Term Memory (LSTM) with one or two hidden layers (LSTM1 and LSTM2).
• ENSEMBLE, a combination of all other models.

They train the models using an expanding window, with the last seven years reserved for six years of validation and one year of out-of-sample-testing. During the test year, they each month reform a portfolio that is long (short) the value-weighted tenth, or decile, of stocks with the highest (lowest) predicted next-month returns. They then calculate actual next-month gross returns and 6-factor (market, size, value, profitability, investment and momentum) alphas during the test year. To calculate net returns and alphas, they multiply trading frictions estimated from historical bid-ask spreads times monthly portfolio turnovers. Using returns and firm characteristics for a broad sample of U.S. common stocks having data covering at least 20% of the 320 anomalies during March 1957 through December 2021, with out-of-sample tests starting January 2005, they find that:

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Can explainable artificial intelligence (AI) bridge the gap between complex machine learning predictions and economically meaningful interpretations? In their December 2023 paper entitled “Empirical Asset Pricing Using Explainable Artificial Intelligence”, Umit Demirbaga and Yue Xu apply explainable artificial intelligence to extract the drivers of stock return predictions made by four machine learning models: XGBoost, decision tree, K-nearest neighbors and neural networks. They use 209 firm/stock-level characteristics and stock returns, all measured monthly, as machine learning inputs. They use 70% of their data for model training, 15% for validation and 15% for out-of-sample testing. They consider two explainable AI methods:

1. Local Interpretable Model-agnostic Explanations (LIME) – explains model predictions by approximating the complex model locally with a simpler, more interpretable model.
2. SHapley Additive exPlanations (SHAP) – uses game theory to determine which stock-level characteristics are most important for predicting returns.

They present a variety of visualizations to help investors understand explainable AI outputs. Using monthly data as described for all listed U.S stocks during March 1957 through December 2022, they find that:

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Can causal discovery algorithms (which look beyond simple statistical association, and instead consider all available data and allow for lead-lag relationships) make economically meaningful contributions to equity investing? In their December 2023 paper entitled “Causal Network Representations in Factor Investing”, Clint Howard, Harald Lohre and Sebastiaan Mudde assess the economic value of a representative score-based causal discovery algorithm via causal network representations of S&P 500 stocks for three investment applications:

1. Generate causality-based peer groups (e.g., to account for characteristic concentrations) to neutralize potentially confounding effects in long-short equity strategies across a variety of firm/stock characteristics.
2. Create a centrality factor represented by returns to a portfolio that is each month long (short) peripheral (central) stocks.
3. Devise a monthly network topology density market timing indicator.

Using daily and monthly data for S&P 500 stocks and monthly returns for widely used equity factors during January 1993 through December 2022, they find that: Keep Reading

Does the Fama-French 5-factor model of stock returns stand up to causality analyses? Do the factors cause the returns? In their December 2023 paper entitled “Re-Examination of Fama-French Factor Investing with Causal Inference Method”, Lingyi Gu, Ellen Zhang, Andrew Heinz, Jingxuan Liu, Tianyue Yao, Mohamed AlRemeithi and Zelei Luo construct causal graphs to analyze the relationship between future (next-month) stock return and each of the five factors in the model, which are:

1. Market – value-weighted market return minus the risk-free rate.
2. Size – return on small stocks minus the return on big stocks.
3. Value –  return on high book-to-market ratio stocks minus the return on low book-to-market ratio stocks.
4. Profitability – return on robust profitability stocks minus the return on weak profitability stocks.
5. Investment – return on conservative investment stocks minus the return on aggressive investment stocks.

They consider a constraint-based algorithm, a score-based algorithm and a functional model to estimate causality. For each approach, they evaluate the stability and strength of the causal relationships across different conditions by explore robustness to data loss or alterations. Their goal is to replicate initial conditions and datasets used in the 2015 paper that introduced the 5-factor model. Using monthly returns for a broad sample of U.S. common stocks and the five specified factors during July 1963 through December 2013, they find that:

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Do linear factor model specification choices inherently produce out-of-sample underperformance of investment strategies seeking to exploit factor premiums? In their January 2024 paper entitled “Why Has Factor Investing Failed?: The Role of Specification Errors”, Marcos Lopez de Prado and Vincent Zoonekynd examine whether standard practices induce factor specification errors and how such errors might explain actual underperformance of popular factor investing strategies. They consider potential effects of confounding variables and colliding variables on factor model out-of-sample performance. Based on logical derivations, they conclude that: Keep Reading

How might Large Language Models (LLM), trained to understand, generate and interact with human language via billions or trillions of tuned parameters, impact accounting and finance? In their December 2023 paper entitled “A Scoping Review of ChatGPT Research in Accounting and Finance”, Mengming Dong, Theophanis Stratopoulos and Victor Wang synthesize recent publications and working papers on ChatGPT and related LLMs to inform practitioners and researchers of the latest developments and uses. They also provide a brief history of LLMs. Based on review of about 200 papers released during January 2022 through October 2023, they conclude that: Keep Reading