Investing Expertise

Can artificial intelligence (AI) models help investors quantify vague firm risks through textual analysis? In their October 2023 paper entitled “From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI”, Alex Kim, Maximilian Muhn and Valeri Nikolaev explore the value of generative AI tool ChatGPT 3.5 in quantifying firm risks based on politics, climate change and AI as conveyed in earnings conference call transcripts. For each of the three risks, they generate: (1) risk summaries based solely on the transcripts, and (2) risk assessments in full context based on the transcripts plus all ChatGPT training data. They consider risk analysis both within (before September 2021) and outside (January 2022 through March 2023) ChatGPT’s training period. They test the import of ChatGPT-based risk assessments via 5-factor (accounting for market, size, book-to-market, profitability and investment effects) alphas of hedge portfolios that are that are long the fifth (quintile) of stocks with the highest assessed risks and short the quintile with the lowest. Using earnings transcripts and monthly returns for a broad sample of U.S. stocks during January 2018 through March 2023, they find that: Keep Reading

Does machine learning reliably offer better risk-adjusted portfolio performance than traditional modern portfolio theory (MPT)? In their August 2023 paper entitled “Comparing Deep RL and Traditional Financial Portfolio Methods”, Eric Benhamou, Jean-Jacques Ohana, Beatrice Guez, David Saltiel, Rida Laraki and Jamal Atif compare principles, methodologies and risk-adjusted performances of dynamic deep reinforcement learning (DRL) and MPT. The DRL approach seeks long-only allocations that maximize Sharpe ratio (calculated assuming a zero risk-free rate). DRL training data includes individual asset returns, portfolio drawdown and contextual variables including U.S. and European interest rates, the CBOE volatility index (VIX), credit default swap prices, currency rates (U.S. dollar index), GDP and CPI forecasts, crude oil/gold/copper inventories and global, U.S., European, Japanese and emerging markets economic surprise indexes. DRL training employs an expanding window, each year training on available historical data and testing on the next year. They consider three MPT portfolios also using expanding window of historical data to estimate inputs: (1) full MPT (Markowitz); (2) minimum variance; and, (3) risk parity. Their global test data consists of daily returns of 11 futures contract series for four major equity indexes, four major bond indexes and three major commodity indexes. They assume trading frictions of 0.02% of value traded. Using the specified (groomed) data during 2000 through mid-2023, they find that: Keep Reading

Will emerging artificial intelligence (AI) tools such as the generative large language model ChatGPT have important roles in the economy, including asset management? In his September 2023 paper entitled “Generative AI: Overview, Economic Impact, and Applications in Asset Management”, Martin Luk reviews the evolution of generative AI models, their economic impact and their applications in asset management. Specifically, he covers:

• Key innovations and methodologies in large language models such as ChatGPT and in image-based, multimodal and tool-using generative AI models.
• Impacts of generative AI on jobs and productivity in various industries, with focus on uses in investment management.
• Dangers and risks associated with the use of generative AI, including the issue of hallucinations.

Based on review of nearly 200 source papers, he concludes that: Keep Reading

Will equity funds “managed” by artificial intelligence (AI) outperform human investors? To investigate, we consider the performance of AI Powered Equity ETF (AIEQ). Per the offeror, the EquBot model supporting AIEQ: “…leverages IBM’s Watson AI to conduct an objective, fundamental analysis of U.S. domiciled common stocks, including Special Purpose Acquisitions Corporations (“SPAC”), and real estate investment trusts (“REITs”) based on up to ten years of historical data and apply that analysis to recent economic and news data… Each day, the EquBot Model…identifies approximately 30 to 200 companies with the greatest potential over the next twelve months for appreciation and their corresponding weights, targeting a maximum risk adjusted return versus the broader U.S. equity market. …The EquBot model limits the weight of any individual company to 10%. At times, a significant portion of the Fund’s assets may consist of cash and cash equivalents.” We use SPDR S&P 500 (SPY) as a simple benchmark for AIEQ performance. Using daily and monthly dividend-adjusted closes of AIEQ and SPY from AIEQ inception (October 18, 2017) through September 2023, we find that: Keep Reading

Are concentrated stock portfolios inherently disadvantaged by lack of diversification? In his June 2023 paper entitled “Underperformance of Concentrated Stock Positions”, Antti Petajisto analyzes rolling future returns for individual U.S. stocks relative to the broad U.S. stock market (market-adjusted) as a way to assess implications of concentrated stock portfolios. He focuses on median return as most representative of investor experience. He considers monthly rolling investment horizons of five, 10 and 20 years because concentrated stock positions are typically long-term holdings. He looks also at the relationship between 5-year past returns and future returns for individual stocks. Using monthly returns for individual U.S. common stocks from an evolving sample similar to the Russell 3000 (no microcaps) and for the overall capitalization-weighted U.S. stock market during January 1926 through December 2022, he finds that:

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Are new machine learning portfolio strategies practically better than old stock factor ways? In their August 2023 paper entitled “Predicting Returns with Machine Learning Across Horizons, Firms Size, and Time”, Nusret Cakici, Christian Fieberg, Daniel Metko and Adam Zaremba examine the ability of various machine learning models to predict stock returns for: (1) monthly and annual return forecast horizons; (2) three ranges of firm size; and, (3) two subperiods. They apply eight machine learning models (including simple and penalized linear regressions, dimension reduction techniques, regression trees and neural networks) to 153 firm/stock characteristics following approaches typical in the finance literature. For each model, they employ rolling 11-year intervals, with:

• Model training using the first seven years.
• Model validation using the next three years.
• Out-of-sample testing the last year using hedge portfolios that are long (short) the value-weighted fifth, or quintile, of stocks with the highest (lowest) predicted returns, reformed either monthly or annually depending forecast horizon.

They focus on gross 6-factor (market, size, book-to-market, profitability, investment, momentum) alpha to assess machine learning effectiveness. Using data for the selected 153 firm/stock characteristics and associated stock returns, measured monthly, for all listed U.S. stocks during January 1972 through December 2020, they find that: Keep Reading

Should investors trust artificial intelligence (AI) models such as ChatGPT to pick stocks? In their August 2023 paper entitled “ChatGPT-based Investment Portfolio Selection”, Oleksandr Romanko, Akhilesh Narayan and Roy Kwon explore use of ChatGPT to recommend 15, 30 or 45 S&P 500 stocks, with portfolio weights, based on textual sentiment as available to Chat GPT via web content up to September 2021. For robustness, they ask ChatGPT to repeat recommendations for each portfolios 30 times and select the 15, 30 or 45 most frequently recommended stocks for respective portfolios. They then test out-of-sample performance of the following five implementations of each portfolio during September 2021 to July 2023, mid-March 2023 to July 2023, and May 2023 to July 2023:

1. ChatGPT picks and ChatGPT weights.
2. ChatGPT picks weighted equally.
3. ChatGPT picks weighted based on minimum variance (Min Var) weights from a 5-year rolling weekly history.
4. ChatGPT picks weighted based on maximum return (Max Ret) weights from a 5-year rolling weekly history.
5. ChatGPT picks weighted based on maximum Sharpe ratio (Max Sharpe) weights from a 5-year rolling weekly history.

For benchmarking, they consider:

• Long-only portfolios that incorporate all possible combinations of 15, 30 or 45 S&P 500 stocks weighted as above for Min Var, Vax Ret or Max Sharpe.
• The S&P 500 Index, Dow Jones Industrial Average and the NASDAQ Index.
• Average performance of 13 popular equity funds.

Using weekly data as specified up to September 2021 for training and subsequent weekly data through June 2023 for out-of-sample testing, they find that:

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Is dispersion of stock return forecasts from different machine learning models trained on the same history (as a proxy for variation in human beliefs) a useful predictor of stock returns? In their August 2023 paper entitled “Machine Forecast Disagreement”, Turan Bali, Bryan Kelly, Mathis Moerke and Jamil Rahman relate dispersion in 100 monthly stock return predictions for each stock generated by randomly varied versions of a machine learning model applied to 130 firm/stock characteristics. They measure machine return forecast dispersion for each stock as the standard deviation of predicted returns. They then each month sort stocks into tenths (deciles) based on this dispersion, form either a value-weighted or an equal-weighted portfolio for each decile and compute average next-month portfolio return. Their key metric is average next-month return for a hedge portfolio that is each month long (short) the stocks in the lowest (highest) decile of machine return forecast dispersions. Using the 130 monthly firm/stock characteristics and associated monthly stock returns for a broad sample of U.S. common stocks (excluding financial and utilities firms and stocks trading below $5) during July 1966 through December 2022, they find that:

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While prior research indicates that analyst forecasts of future stock returns are substantially biased upward, might the relative rankings of return forecasts be informative? In their June 2023 paper entitled “Analysts Are Good at Ranking Stocks”, Adam Farago, Erik Hjalmarsson and Ming Zeng apply within-analyst 12-month stock price targets to rank stocks in two ways:

1. Average Demeaned Return – each month, demean the returns implied by target prices from an analyst by subtracting from each return the average forecasted return for that analyst. Then, average the demeaned returns for a given stock across all analysts.
2. Average Ranking – each month, rank stocks by forecasted return for each analyst. Then, average the rankings for a given stock across all analysts covering that stock.

Both approaches remove the upward biases observed in raw target prices. To test analyst forecast informativeness, they then form hedge portfolios that are each month long (short) the equal-weighted or value-weighted fifths, or quintiles, of stocks with the highest (lowest) demeaned returns or rankings that month. Using 12-month target prices for each analyst who issues targets for at least three stocks during a month and associated monthly firm characteristics and stock prices during March 1999 through December 2021, they find that:

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What is the state of machine learning in finance? In their July 2023 paper entitled “Financial Machine Learning”, Bryan Kelly and Dacheng Xiu survey studies on the use of machine learning in finance to further its reputation as an indispensable tool for understanding financial markets. They focus on the use of machine learning for statistical forecasting, covering regularization methods that mitigate overfitting and efficient algorithms for screening a vast number of potential model specifications. They emphasize areas that have received the most attention to date, including return prediction, factor models of risk and return, stochastic discount factors and portfolio choice. Based on the body of machine learning research in finance, they conclude that: Keep Reading