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Financial ratios and stock returns reappraised through a topological data analysis lens

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  • Pawel Dlotko
  • Wanling Qiu
  • Simon Rudkin

Abstract

Firm financials are well established as return predictors, being the inspiration for a large set of anomalies in the asset pricing literature. Employing topological data analysis we revisit the question of association between seven of the most commonly studied financial ratios and stock returns. Specifically the TDA Ball Mapper algorithm is applied to visualise the point cloud of financial ratios as an abstract two-dimensional graph readily allowing for identification of interdependencies between factors. These relationships are seldom monotonic, opportunities for investors to profitably exploit this knowledge provided by TDA abound. Clear potential offered by the tools of TDA to shed new light on asset pricing models is demonstrated. Scope for benefit is limited only by the availability of information to the analyst.

Suggested Citation

  • Pawel Dlotko & Wanling Qiu & Simon Rudkin, 2019. "Financial ratios and stock returns reappraised through a topological data analysis lens," Papers 1911.10297, arXiv.org.
    • Handle: RePEc:arx:papers:1911.10297
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    References listed on IDEAS

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    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. John Lintner, 1965. "Security Prices, Risk, And Maximal Gains From Diversification," Journal of Finance, American Finance Association, vol. 20(4), pages 587-615, December.
    3. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    4. Black, Fischer & Scholes, Myron, 1974. "The effects of dividend yield and dividend policy on common stock prices and returns," Journal of Financial Economics, Elsevier, vol. 1(1), pages 1-22, May.
    5. Ou, Jane A. & Penman, Stephen H., 1989. "Financial statement analysis and the prediction of stock returns," Journal of Accounting and Economics, Elsevier, vol. 11(4), pages 295-329, November.
    6. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    7. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    8. Serhiy Kozak & Stefan Nagel & Shrihari Santosh, 2018. "Interpreting Factor Models," Journal of Finance, American Finance Association, vol. 73(3), pages 1183-1223, June.
    9. Basu, S, 1977. "Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis," Journal of Finance, American Finance Association, vol. 32(3), pages 663-682, June.
    10. Jeremiah Green & John R. M. Hand & X. Frank Zhang, 2017. "The Characteristics that Provide Independent Information about Average U.S. Monthly Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4389-4436.
    11. Xuemin (Sterling) Yan & Lingling Zheng, 2017. "Fundamental Analysis and the Cross-Section of Stock Returns: A Data-Mining Approach," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1382-1423.
    12. Ball, Ray, 1992. "The earnings-price anomaly," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 319-345, August.
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