Information Token Driven Machine Learning for Electronic Markets: Performance Effects in Behavioral Financial Big Data Analytics

Jim Samuel

Papers from arXiv.org

Abstract: Conjunct with the universal acceleration in information growth, financial services have been immersed in an evolution of information dynamics. It is not just the dramatic increase in volumes of data, but the speed, the complexity and the unpredictability of big-data phenomena that have compounded the challenges faced by researchers and practitioners in financial services. Math, statistics and technology have been leveraged creatively to create analytical solutions. Given the many unique characteristics of financial bid data (FBD) it is necessary to gain insights into strategies and models that can be used to create FBD specific solutions. Behavioral finance data, a subset of FBD, is seeing exponential growth and this presents an unprecedented opportunity to study behavioral finance employing big data analytics methodologies. The present study maps machine learning (ML) techniques and behavioral finance categories to explore the potential for using ML techniques to address behavioral aspects in FBD. The ontological feasibility of such an approach is presented and the primary purpose of this study is propositioned- ML based behavioral models can effectively estimate performance in FBD. A simple machine learning algorithm is successfully employed to study behavioral performance in an artificial stock market to validate the propositions. Keywords: Information; Big Data; Electronic Markets; Analytics; Behavior

Date: 2020-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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Published in JISTEM - Journal of Information Systems and Technology Management, 2017, vol.14 no.3, On-line version ISSN 1807-1775

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2004.06642

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