Real-time extended psychophysiological analysis of financial risk processing
Name
journal.pone.0269752.pdf
Description
Published version
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828.01 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Singh, Manish
Xu, Qingyang
Wang, Sarah J
Hong, Tinah
Ghassemi, Mohammad M
Lo, Andrew W
Date Issued
2022
Journal
PLOS ONE
Publisher
Public Library of Science (PLoS)
Citation
Singh, Manish, Xu, Qingyang, Wang, Sarah J, Hong, Tinah, Ghassemi, Mohammad M et al. 2022. "Real-time extended psychophysiological analysis of financial risk processing." PLOS ONE, 17 (7).
Version
Final published version
Abstract
We study the relationships between the real-time psychophysiological activity of professional traders, their financial transactions, and market fluctuations. We collected multiple physiological signals such as heart rate, blood volume pulse, and electrodermal activity of 55 traders at a leading global financial institution during their normal working hours over a five-day period. Using their physiological measurements, we implemented a novel metric of trader’s “psychophysiological activation” to capture affect such as excitement, stress and irritation. We find statistically significant relations between traders’ psychophysiological activation levels and such as their financial transactions, market fluctuations, the type of financial products they traded, and their trading experience. We conducted post-measurement interviews with traders who participated in this study to obtain additional insights in the key factors driving their psychophysiological activation during financial risk processing. Our work illustrates that psychophysiological activation plays a prominent role in financial risk processing for professional traders.
MIT Department
Sloan School of Management. Laboratory for Financial Engineering
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0269752
