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The Role of Big Data Research Methodologies in Describing Investor Risk Attitudes and Predicting Stock Market Performance: Deep Learning and Risk Tolerance
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Author(s): Wookjae Heo (Purdue University, USA), Eun Jin Kwak (University of Georgia, USA)and John E. Grable (University of Georgia, USA)
Copyright: 2022
Pages: 23
Source title:
Handbook of Research on New Challenges and Global Outlooks in Financial Risk Management
Source Author(s)/Editor(s): Mara Madaleno (GOVCOPP, University of Aveiro, Portugal), Elisabete Vieira (GOVCOPP, University of Aveiro, Portugal)and Nicoleta Bărbuță-Mișu (University of Galati, Romania)
DOI: 10.4018/978-1-7998-8609-9.ch014
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Abstract
The purpose of this chapter is to compare the performance of a deep learning modeling technique to predict market performance compared to conventional prediction modeling techniques. A secondary purpose of this chapter is to describe the degree to which financial risk tolerance can be used to predict future stock market performance. Specifically, the models used in this chapter were developed to test whether aggregate investor financial risk tolerance is of value in establishing risk and return market expectations. Findings from this chapter's examples also provide insights into whether financial risk tolerance is more appropriately conceptualized as a predictor of market returns or as an outcome of returns.
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