Investing Expertise

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:

Keep Reading

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

With stock portfolio construction increasingly based on “black box” machine learning models with very large numbers of inputs, how can investors decide whether portfolio recommendations make sense? In their November 2023 paper entitled “The Anatomy of Machine Learning-Based Portfolio Performance”, Philippe Coulombe, David Rapach, Christian Montes Schütte and Sander Schwenk-Nebbe describe a way to use Shapley values to estimate contributions of groups of related inputs to machine learning-based portfolio performance. Their approach applies to any fitted prediction model (or ensemble of models) used to forecast asset returns and construct a portfolio based on the forecasts. They illustrate their approach on an XGBoost machine learning model that each month:

• Uses 207 firm characteristics to forecast next-month returns of associated stocks.
• Excludes stocks in the bottom 20% of NYSE market capitalizations.
• Sorts surviving stocks into fifths, or quintiles, based on forecasted returns.
• Reforms a hedge portfolio that is long (short) the value-weighted top (bottom) quintile.

They then assign each of the 207 inputs to one of 20 groups based on similarities and estimate the contribution of each input group to portfolio performance. Using 207 monthly firm/stock characteristics for all listed U.S. firms and the monthly risk-free rate during January 1960 through December 2021, with portfolio testing commencing January 1973, they find that:

Keep Reading

Can the large language model GPT-4 help investors make investment decisions? In their October 2023 paper entitled “Can ChatGPT Assist in Picking Stocks?”, Matthias Pelster and Joel Val conduct a live test during the 2023 second quarter earnings announcements of the value and timeliness of investment advice from GPT-4 augmented with WebChatGPT for internet access. They ask GPT-4 for two separate series of ratings for each S&P 500 firm over approximately two months:

1. Considering all available information from news outlets and social media discussions, provide on a scale from -5 to +5 a forecast for the next earnings announcement.
2. Rate on a scale from -5 to +5 the attractiveness of the stock of each firm over the next month.

They apply these two series to assess the accuracy of GPT-4 earnings forecasts and the response of its stock attractiveness ratings to news. They also measure 30-day future returns of equal-weighted portfolios based on GPT-4 attractiveness ratings, reformed with each ratings update. Using the two series of GPT-4 ratings during July 5, 2023 through  September 8, 2023, they find that: Keep Reading

Can large language models (LLM) such as ChatGPT and GPT-4 pass the Chartered Financial Analyst (CFA) exam, which covers fundamentals of investment tools, asset valuation, portfolio management and wealth planning? In their October 2023 paper entitled “Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on Mock CFA Exams”, Ethan Callanan, Amarachi Mbakwe, Antony Papadimitriou, Yulong Pei, Mathieu Sibue, Xiaodan Zhu, Zhiqiang Ma, Xiaomo Liu and Sameena Shah investigate whether ChatGPT and GPT-4 could pass the CFA exam. They ask the models to respond to mock exam questions from the first two of the three levels on the exam:

• Level I – 180 standalone multiple choice questions (using questions from five mock exams).
• Level II – 22 vignettes and 88 accompanying multiple choice questions, with a higher proportion requiring interpretation of numerical data and calculations than found in Level I (using questions from two mock exams).
• Level III – a mix of vignette-related essay questions and vignette-related multiple choice questions (untested due to the difficulty of assessing essay responses).

They assess responses to the Level I and II mock exam questions via three approaches:

1. Gauging inherent model reasoning abilities without providing any correct examples.
2. Facilitating model acquisition of new knowledge by providing examples of good responses for either (a) a random sample of questions within level or (b) one question from each exam topic.
3. Prompting the models to address each question step-by-step and to show their work for calculations.

They then compared responses of the two models to approved answers and estimate whether either could pass based on proficiency thresholds reported by CFA exam takers on Reddit. Using mock CFA Level I and II exam questions and the three test approaches as described above, they find that: Keep Reading

Does financial news sentiment as interpreted by large language models (LLM) such as ChatGPT and BARD predict short-term stock market returns? In their September 2023 paper entitled “Large Language Models and Financial Market Sentiment”, Shaun Bond, Hayden Klok and Min Zhu separately test the abilities of ChatGPT and BARD to predict daily, weekly and monthly S&P 500 Index returns based on sentiments they extract from daily financial news summaries. ChatGPT is trained on information available on the web through September 2021. In contrast, BARD is connected to the web and updates itself on live information. The authors:

1. Ask each of ChatGPT and BARD to summarize the most important news from the Thomson Reuters News Archives for each trading day starting in January 2000.
2. Consolidate each set of daily summaries.
3. Ask each of ChatGPT and BARD to use their respective set of summaries to quantify market sentiment each day on a scale from 1 (weakest) to 100 (strongest) and separately evaluate the sentiment as positive, neutral or negative.
4. Relate via regressions each set of daily sentiment measurements to next-day, next-week and next-month S&P 500 Index returns. These regressions control for same-day index return, VIX, short-term credit risk and the term spread (plus additional variables when predicting monthly returns). 

For ChatGPT, analysis extends through September 2021 (the end of its training period). For BARD, analysis continues through July 2023. As benchmarks, they consider sentiment measurements from two traditional dictionary methods and two simple transformer classifiers. To estimate economic value of predictions, they compute certainty equivalent returns (CER) for a mean-variance investor who allocates between the S&P 500 Index and a risk-free asset each day according to out-of-sample sentiment measurements starting in 2006. Using Thomson Reuters News Archives and daily, weekly and monthly S&P 500 Index returns since January 2000, they find that: Keep Reading

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