Can machine learning identify sector-level financial ratios that predict sector returns?

Gregory Kuppenheimer, Stuart Shelly and Jack Strauss

Finance Research Letters, 2023, vol. 57, issue C

Abstract: Academics and practitioners use fundamental ratios to evaluate an industry's financial and operating performance. WRDS has large firm-level and sector/industry-level databases that provide over 70 financial ratios that are applied in finance classes to compare and assess relative financial performance. However, there has been a lack of sophisticated econometric methods assessing these ratios' importance in predicting sector-level stock return performance. Using Elastic Net methods, we identify financial ratios that significantly forecast out-of-sample sector stock returns and find that these predictive ratios vary across sectors. We form long and long-short portfolios that consistently outperform the market over-time. Long portfolios generate significant alpha and large utility gains, boost the Sharpe and Sortino ratios, and a cumulative investment portfolio exceeds the market benchmark by five times. Long-short portfolios generate Fama-French 4-factor and 6-factor alphas between 4–9% and cumulative investment gains from six to fourteen times. Our research establishes that machine learning can identify financial ratios that significantly predict sector returns and generate profitable portfolio allocation.

Keywords: Machine learning; Sector returns; Financial Ratios (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:57:y:2023:i:c:s154461232300613x

DOI: 10.1016/j.frl.2023.104241

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