Self-aware trader: a new approach to safer trading

Martínez Fernández, Javier, Augusto, Juan Carlos ORCID logoORCID:, Trombino, Giuseppe, Seepold, Ralf and Martinez Madrid, Natividad (2013) Self-aware trader: a new approach to safer trading. Journal of Universal Computer Science, 19 (15) . pp. 2292-2319. ISSN 0948-695X [Article]

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Traders are required to work in the financial market with highly complex information and to perform efficiently under high levels of psychological pressure. Multiple disciplines, from programs with artificial intelligence to complex mathematical functions, are used to help traders in their effort to maximize profits. However, an essential problem not yet considered in this rapidly evolving environment is that traders are not supported to adequately manage how stress influences their decisions. This paper takes into consideration the negative influences of stress on individuals and proposes a system designed to support traders by providing them with information that can reduce the likelihood of poor decision-making. The system has been designed considering both technical and physiological aspects to make information available in a suitable way. Biometric sensors are used to collect data associated with stress, a software platform then analyses this information and displays it to the trader. The resulting system is capable of making individual traders, as well as teams of traders, self-aware of their levels of stress. The system has been tested in real environments and the results provide evidence that self-aware traders benefit from the system by reducing risky decision-making.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science > Intelligent Environments group
A. > School of Science and Technology > Computer Science
Item ID: 12998
Notes on copyright: author can archive publisher's version/PDF
- On Institutional Repositories
- Publisher's version/PDF must be used
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Depositing User: Juan Augusto
Date Deposited: 05 Feb 2014 13:30
Last Modified: 30 Nov 2022 00:21

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