Can Artificial Intelligence outsmart seasoned equity analysts?
If the task is to identify a firm’s true profitability, can AI outsmart seasoned analysts?
Artificial Intelligence, Textual Analysis and Hedge Fund Performance
There’s no reason to think that the use of AI should lead to persistent fund outperformance, with any advantages gained likely being short lived.
Artificial intelligence, firm growth, and product innovation
AI-powered growth concentrates among larger firms and is associated with higher industry concentration. Our results highlight that new technologies like AI can contribute to growth and superstar firms through product innovation.
From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses
An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine,” which also substantially reduces extreme errors.
Overvalued or New Paradigm?
Without question the topic of greatest debate among investors, including investment professionals, and financial economists, is whether or not the market, and the technology sector in particular, is overvalued. There are two very strong conflicting views regarding not only the current valuation of technology stocks, but also the valuation of the entire asset class of large-cap growth stocks. One side, I’ll call the “new paradigm” or “it’s different this time” school. The other side, I’ll call “the been there, done that” school. Its theme is those that don’t learn from the past are doomed to repeat the same mistakes. No two sides could have more different viewpoints. To understand each side, let’s imagine a dialogue between the two schools.
Creating Better Factor Portfolio via AI
Trading costs, discontinuous trading, missed trades, and other frictions, along with asset management fees can cause a shortfall between live and paper portfolios. The focus of this paper is to test an effective rebalancing method that prioritizes trades with the strongest signals to capture more of the factor premia while reducing turnover and trading costs.
Complexity is a virtue in return prediction
Simple models severely understate return predictability compared to “complex” models in which the number of parameters exceeds the number of observations.
Using Machine Learning Programs to Forecast the Equity Risk Premium
To date, the best metric we have for forecasting future equity returns and the ERP is current valuations. An interesting question is whether more complicated methods using newly developed machine learning models can provide superior forecasts.
Can Machine Learning Improve Factor Returns? Not Really
Can AI models improve on the failures in predicting returns strictly from a practical point of view? In this paper, the possibilities are tested with a battery of AI models including linear regression, dimensional reduction methods, regression trees and neural networks. These machine learning models may be better equipped to address the multidimensional nature of stock returns when compared to traditional sorting and cross-sectional regressions used in factor research. The authors hope to overcome the drawbacks and confirm the results of traditional quant methods. As it turns out, those hopes are only weakly fulfilled by the MLM framework.
Valuing Artificial Intelligence (AI) Stocks
While there is literature that describes the "domain" of artificial intelligence, there are very few, if any that analyze the valuation and pricing of AI stocks. The authors attempt to fill the void with a two part methodology.
Robo-advisors: A well-researched topic
Along with the rapid growth in the utilization of robo-advisors, there has been similar growth in academic interest about robo-advisors. What is the current state and what are the main research streams in the literature?
Can ChatGPT Improve Your Stock Picks?
One use of the NLP (natural language processing) features of ChatGPT is to search out patterns in the immense amounts of news, data and other sources of information about specific stocks, and then efficiently convert them into summaries valuable for all types of investors. Can this be accomplished with useful results? The authors use the Q2_2023 period to test performance around earnings announcements. Earnings announcements and earnings surprises are informationally rich as well as challenging events for investors to analyze.
Can Machine Learning help to select mutual funds with positive alpha?
Can machine-learning methods be used to predict the performance of active mutual funds, specifically in terms of alpha net of all costs? Answer: yes.
Executives vs. Chatbots in Earnings Conference Q&A
In this study, we introduce a novel measure of information content (Human-AI Differences, HAID) by exploiting the discrepancy between answers to questions at earnings calls provided by corporate executives and those given by several context-preserving Large Language Models (LLM) such as ChatGPT, Google Bard, and an open source LLM.
Where Large Language Models and Finance Meet
BloombergGPT is a large language model (LLM) developed specifically for financial tasks. The authors trained the LLM on a large body of financial textual data, evaluated it on several financial language processing tasks and found it performed at a significantly higher level than several other state-of-the-art LLMs.
Can ChatGPT Help You Read Financial Statements? Yes.
Can chatbots, like ChatGPT, be used to interpret and condense lengthy financial disclosures into shorter but relevant documents?
Artificial Intelligence: the Past, the Present and the Future
This article examines the state of Artificial Intelligence (AI). We examine its history with an eye toward what it may mean for the world in years to come.
Machine Learning and The Cross-Section of Emerging Market Stock Returns
The paper documents that return forecasts from machine learning methods lead to superior out-of-sample returns in emerging markets.
Relative Sentiment and Machine Learning for Tactical Asset Allocation: Out-of-Sample Results
We examine Sentix sentiment indices for use in tactical asset allocation. In particular, we construct monthly relative sentiment factors for the U.S., Europe, Japan, and Asia ex-Japan by taking the difference in 6-month economic expectations between each region's institutional and individual investors. These factors (along with one-month forward equity returns) then serve as inputs to a wide array of machine learning algorithms. Employing combinatorial cross-validation and adjusting for data snooping, we find relative sentiment factors have robust and significant predictive power in all four regions; that they surpass both standalone sentiment and time-series momentum in terms of informational content; and that they demonstrate the ability to identify the subsequent best- and worst-performing global equity markets from along a cross-section. The results are consistent with previous findings on relative sentiment, discovered using unrelated datasets.
Can Machine Learning Identify Future Outperforming Active Equity Funds?
We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.