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

Qingquan Tony Zhang (), Beibei Li () and Danxia Xie
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Qingquan Tony Zhang: University of Illinois Urbana-Champaign
Beibei Li: Carnegie Mellon University

Chapter Chapter 6 in Alternative Data and Artificial Intelligence Techniques, 2022, pp 111-128 from Palgrave Macmillan

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:pal:psircp:978-3-031-11612-4_6

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DOI: 10.1007/978-3-031-11612-4_6

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