Fundamental Analysis Via Machine Learning

63 Pages Posted: 22 Oct 2020 Last revised: 4 Dec 2020

See all articles by Kai Cao

Kai Cao

Hong Kong University of Science & Technology (HKUST)

Haifeng You

Hong Kong University of Science & Technology (HKUST) - Department of Accounting

Date Written: November 27, 2020

Abstract

We examine the efficacy of machine learning in one of the most important tasks in fundamental analysis, forecasting corporate earnings. We find that machine learning models, especially those accommodating nonlinearities, generate significantly more accurate and informative forecasts than a host of state-of-the-art earnings prediction models in the extant literature. Further analysis suggests that machine learning models uncover economically sensible relationships between historical financial information and future earnings, and the new information uncovered by machine learning models is of considerable economic significance. The new information component is significantly associated with both future stock returns and analyst forecast errors, with stocks in the quintiles with the most favorable new information outperforming those in the least favorable quintiles by approximately 70 bps per month. The overall results suggest that limiting to linear relationships and aggregated accounting numbers substantially understates the decision usefulness of financial statement information to investors.

Keywords: machine learning, earnings forecasts, fundamental analysis, equity valuation, market efficiency

JEL Classification: G10, G11, G14, G17, M40, M41

Suggested Citation

Cao, Kai and You, Haifeng, Fundamental Analysis Via Machine Learning (November 27, 2020). Available at SSRN: https://ssrn.com/abstract=3706532 or http://dx.doi.org/10.2139/ssrn.3706532

Kai Cao

Hong Kong University of Science & Technology (HKUST) ( email )

Clearwater Bay
Kowloon, 999999
Hong Kong

Haifeng You (Contact Author)

Hong Kong University of Science & Technology (HKUST) - Department of Accounting ( email )

Clear Water Bay
Kowloon
Hong Kong

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