Workshop’s Topic: Firm quality is a foundational construct in the fundamental analysis literature. We examine whether it is possible to leverage the power of machine learning to construct a better measure of firm quality guided by valuation theory. We show that our machine learning model based on the same 19 ratios can outperform a linear OLS regression model based on a score constructed using simple heuristics by Asness et al. (2019) (referred to as Asness’ Q score). In addition, our measure of firm quality can better explain contemporaneous stock prices, and a value investing trading strategy based on our machine learning model outperforms the same trading strategy based on Asness’ Q score by an economically significant margin.
Time and Location 13:30-14:30 PM (GMT+8), Room A423 (School of Management)
Language: Bilingual (Chinese and English)
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Introduction of Speakers |
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Ph.D. (c) ZHAO Qi South China University of Technology, Department of Decision Science |
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ZHAO Qi is a PhD student at Department of Decision Science in School of Business Administration, South China University of Technology. She has visited Department of Accounting of NUS Business School for two years. She earned her B.E. in Electronic Information Engineering from Beijing University of Post and Telecommunication, and mainly did research on deep learning and computer vision before becoming a PhD student. Her research interests include fundamental analysis, machine-learning-based decision support, and applying machine learning to research questions in accounting and finance domains. |
