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Big Data Analytics and Investment

Author

Listed:
  • S. Boubaker

    (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie)

  • Z. Liu
  • Y. Mu

Abstract

Big data has found extensive applications in various industries, including finance. It is an essential tool for investors to make high-stakes investment decisions. Using China's A-shares Market, this paper employs 76 firm characteristics to conduct descriptive analytics (factor model) and predictive analytics (long\textendashshort portfolio) through an Instrumented Principal Component Analysis (IPCA) model. According to our results, the IPCA model outperforms in both description (tangency portfolio Sharpe ratio of 2.91) and forecasting (long\textendashshort portfolio Sharpe ratio of 2.38). Moreover, our paper compares the performance of different sets of characteristics in big data analytics and concludes that sentiment is dominant, while fundamental analysis is also important. Our results can provide policymakers with valuable insights into the common trends of the stock market and assist investors in making effective investment decisions. \textcopyright 2023 Elsevier Inc.

Suggested Citation

  • S. Boubaker & Z. Liu & Y. Mu, 2023. "Big Data Analytics and Investment," Post-Print hal-04435554, HAL.
    • Handle: RePEc:hal:journl:hal-04435554
    DOI: 10.1016/j.techfore.2023.122713
    as

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