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Smart Beta and Risk Factors Based on Textural Data and Machine Learning

In: Alternative Data and Artificial Intelligence Techniques

Author

Listed:
  • Qingquan Tony Zhang

    (University of Illinois Urbana-Champaign)

  • Beibei Li

    (Carnegie Mellon University)

  • Danxia Xie

    (Tsinghua University)

Abstract

As one of the main sources of data, text plays an important role in various fields. This chapter mainly introduces the application of textural analysis in the financial field. Firstly, we introduce two techniques of text analysis, including natural language processing and Machine Learning/Deep Learning. Secondly, we also introduce factors for finance built on textural dataset analysis, which includes readability, tone and sentiment factors, similarity, semantic, uncertainty, accuracy, and popularity. Through this article, we have explained the importance and potential of textural analysis in finance.

Suggested Citation

  • Qingquan Tony Zhang & Beibei Li & Danxia Xie, 2022. "Smart Beta and Risk Factors Based on Textural Data and Machine Learning," Palgrave Studies in Risk and Insurance, in: Alternative Data and Artificial Intelligence Techniques, chapter 0, pages 111-128, Palgrave Macmillan.
    • Handle: RePEc:pal:psircp:978-3-031-11612-4_6
    DOI: 10.1007/978-3-031-11612-4_6
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