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Financial indicators analysis using machine learning: Evidence from Chinese stock market

Author

Listed:
  • Zhao, Chencheng
  • Yuan, Xianghui
  • Long, Jun
  • Jin, Liwei
  • Guan, Bowen

Abstract

This study employs machine learning models to explore the predictive power of 10 categories of financial indicators on the Chinese stock market. We examine whether influential financial indicators fall into distinct categories of greater importance for predicting stock returns. The findings demonstrate that financial indicators across 10 categories hold predictive power for stock returns on Chinese market, with neural network models outperforming linear ones. Profitability and growth indicators are among the most influential indicators. This study contributes to a better understanding of financial indicators and demonstrates the effectiveness of machine learning models in the Chinese stock market.

Suggested Citation

  • Zhao, Chencheng & Yuan, Xianghui & Long, Jun & Jin, Liwei & Guan, Bowen, 2023. "Financial indicators analysis using machine learning: Evidence from Chinese stock market," Finance Research Letters, Elsevier, vol. 58(PD).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pd:s1544612323009625
    DOI: 10.1016/j.frl.2023.104590
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    References listed on IDEAS

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    1. Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019. "Characteristics are covariances: A unified model of risk and return," Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
    2. Novy-Marx, Robert, 2013. "The other side of value: The gross profitability premium," Journal of Financial Economics, Elsevier, vol. 108(1), pages 1-28.
    3. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    4. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    5. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    6. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    7. Lettau, Martin & Ludvigson, Sydney C., 2005. "Expected returns and expected dividend growth," Journal of Financial Economics, Elsevier, vol. 76(3), pages 583-626, June.
    8. 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.
    9. Tian Ma & Cunfei Liao & Fuwei Jiang, 2023. "Timing the factor zoo via deep learning: Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 485-505, March.
    10. Lakshmanan Shivakumar, 2006. "Accruals, Cash Flows and the Post‐Earnings‐Announcement Drift," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(1‐2), pages 1-25, January.
    11. Ham, Charles G. & Kaplan, Zachary R. & Leary, Mark T., 2020. "Do dividends convey information about future earnings?," Journal of Financial Economics, Elsevier, vol. 136(2), pages 547-570.
    12. James A. Ohlson, 2001. "Earnings, Book Values, and Dividends in Equity Valuation: An Empirical Perspective," Contemporary Accounting Research, John Wiley & Sons, vol. 18(1), pages 107-120, March.
    13. Lakshmanan shivakumar, 2006. "Accruals, Cash Flows and the Post-Earnings-Announcement Drift," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(1-2), pages 1-25.
    14. Peng, Emma Y. & Yan, An & Yan, Meng, 2016. "Accounting accruals, heterogeneous investor beliefs, and stock returns," Journal of Financial Stability, Elsevier, vol. 24(C), pages 88-103.
    15. Yue Hu & Haosheng Guo & Wenli Huang & Yueling Xu, 2023. "Yield Forecasting by Machine Learning Algorithm: Evidence from China’s A-share Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 59(6), pages 1767-1781, May.
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    Cited by:

    1. Moitra, Agnij, 2024. "Directional Stock Price Forecasting Based on Quantitative Value Investing Principles for Loss Averted Bogle-Head Investing using Various Machine Learning Algorithms," OSF Preprints y3mr6, Center for Open Science.

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    More about this item

    Keywords

    Financial indicators; Machine learning; Return prediction;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G39 - Financial Economics - - Corporate Finance and Governance - - - Other
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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