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Introduction of Alternative Data in Finance

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

This chapter explains how individuals, business processes, and sensors produce alternative data. It also provides a framework to navigate and evaluate the proliferating supply of alternative data for investment purposes. It demonstrates the workflow, from acquisition to preprocessing and storage using Python for data obtained through web scraping in order to set the stage for the application of ML. It concludes by providing examples of sources, providers, and applications. After reading this chapter, we hope readers can have a detailed understanding of the application process of alternative data in finance, including the sources of alternative data generation, evaluation criteria, etc. We also provide specific application cases and Python codes for the readers’ reference.

Suggested Citation

  • Qingquan Tony Zhang & Beibei Li & Danxia Xie, 2022. "Introduction of Alternative Data in Finance," Palgrave Studies in Risk and Insurance, in: Alternative Data and Artificial Intelligence Techniques, chapter 0, pages 75-88, Palgrave Macmillan.
    • Handle: RePEc:pal:psircp:978-3-031-11612-4_4
    DOI: 10.1007/978-3-031-11612-4_4
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