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

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  • Kuppenheimer, Gregory
  • Shelly, Stuart
  • Strauss, Jack

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.

Suggested Citation

  • Kuppenheimer, Gregory & Shelly, Stuart & Strauss, Jack, 2023. "Can machine learning identify sector-level financial ratios that predict sector returns?," Finance Research Letters, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:finlet:v:57:y:2023:i:c:s154461232300613x
    DOI: 10.1016/j.frl.2023.104241
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