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Nowadays, we see an increasing number of machine learning based strategies and other related financial analyses. But can the machines replace us? Undoubtedly, AI algorithms have greater capacities to “digest” big data, but as always in the markets, everything is not rational. Cao et al. (2021) dives deeper into this topic and examines the stock analysts. Target prices and earnings forecasts are crucial parts of the investing practice and are frequently used by traders and investors (and even ML-based strategies). The novel research examines and compares the abilities of human analysts versus the AI algorithm in forecasting the target price. As a whole, AI-based analysts, on average, outperforms human analysts, but it is not that straightforward. While AI can learn from large datasets, humans do not seem to be replaced soon. There are certain fields where human uniqueness is valuable. For example, in illiquid and smaller firms or firms with asset-light business models. Moreover, it seems that rather than competing with each other, AI and human analysts are complementary. The novel technology can be used with great success to help us in areas where we lag, and the combined knowledge and forecasts of AI and humans outperform the AI analyst in each year.
Authors: Sean Cao, Wei Jiang, Junbo L. Wang and Baozhong Yang
Title: From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses
An AI analyst we build to digest corporate financial information, qualitative disclosure, and macroeconomic indicators are able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analysts. In the contest of “man vs machine,” the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent, and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI’s computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of “machine plus human” (instead of human displacement) in high-skill professions.
As always we present several interesting figures:
Notable quotations from the academic research paper:
“To trace out the path from “man v. machine” to “man + machine,” we decided to build our own AI model for year-end stock predictions so that we have a consistent and time-adapted benchmark for AI performance which we understand and are able to explain.
Our “AI analyst” is built on training a combination of current machine-learning (ML) tool kits4 using timely publicly available data and information. More specifically, we collect firm-level, industry-level, and macro-economic variables, as well as textual information from firms’ disclosure (updated to right before the time of an analyst forecast) as inputs or predictors, but deliberately exclude information from analyst forecasts (past and current) themselves. We resort to machine learning models, instead of traditional economics models (such as regressions) due to the advantages to the former in managing high dimensional unstructured data, and in their flexibility in optimizing and fitting unspecified functional forms.
The AI analyst based on the final “ensemble” model outperforms 53.7% of the target price predictions made by all IBES analysts during the sample period of 2001-2016. Moreover, a monthly rebalanced long-short portfolio based on the differences in the opinions of AI and human analysts is able to generate a monthly alpha of 0.84% to 0.92% using the Fama-French-Carhart four-factor model.
To the extent that we have, at our disposal, an AI analyst that beats the average of its human counterparts, we are able to explore the relative advantages of, and potential synergies between, the two sides. First, we examine the circumstances under which human analysts retain their advantage, in that a forecast made by an analyst beats the concurrent AI forecast in terms of lower absolute forecast error relative to the ex post realization (i.e., the actual year-end stock price). We find human analysts perform better for more illiquid, smaller firms, and firm with asset-light business models (i.e., higher intangible assets), consistent with the notion that such firms are subject to higher information asymmetry and require better institutional knowledge or industry experience to decipher. Analysts affiliated with large brokerage houses also stand a higher chance of beating the machine, a combination of their abilities and the research resources available to them. Moreover, analysts are more likely to have the upperhand when the associated industry is experiencing distress, suggesting that the AI has yet to catch up on relatively infrequent changes such as an industry recession. This is consistent with the limitation of current machine learning and AI models which lack reasoning functions and thus cannot learn effectively from infrequent events. As expected, AI enjoys a clear advantage in its capacity to process information, and is more likely to out-smart analysts when the volume of public information is larger.
If human and machine have relative advantage in information processing and decision making, then human analysts may still contribute critically to a “centaur” analyst, i.e., an analyst who makes forecasts that combine their own knowledge and the outputs/recommendations from AI models. After we add analyst forecasts to the information set of the machine learning models underlying our AI analyst, the resulting “man + machine” model outperforms 57.3% of the forecasts made by analysts, and outperforms the AI-only model in all years. Thus, AI analyst does not displace human analysts yet; and in fact an investor or analyst who combines AI’s computational power and the human art of understanding soft information can attain the best performance.”
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