Information Token Driven Machine Learning For Electronic Markets: Performance Effects In Behavioral Financial Big Data Analytics.

Jim Samuel

doi:10.4301/s1807-17752017000300005

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

Highlights

Exploring big data about big data, in their editorial for Information Systems Research journal, Agarwal and Dhar (2014) reported their August-2014 Google search findings for the phrases “Big data,” “Analytics,” and “Data science” which generated 822 million, 154 million, and 461 million results, respectively
Given the domain impact implications along with rising global interest as indicated above, ‘financial analytics’, ‘big data’ and ‘machine learning’ are critically relevant phenomena which are in need of significant research attention so that we can gain insights into optimal management and value creation
A new and unique perspective for identifying behavioral classifications based on Information Virtue, Information Tokens and Informational Performance has been developed and validated using a machine learning” (ML) k-Nearest Neighbors (KNN) algorithm methodology

Summary

Introduction

Exploring big data about big data, in their editorial for Information Systems Research journal, Agarwal and Dhar (2014) reported their August-2014 Google search findings for the phrases “Big data,” “Analytics,” and “Data science” which generated 822 million, 154 million, and 461 million results, respectively. Variance in number of search results search results trends have been used for successfully predicting election results, stock performance, health care trends and customer sentiment. A similar search in February of 2017 for the present study has yielded 832 million results for the term “Analytics” and 93.4 million results for the phrase “Financial Analytics”! These metrics, though vague explorative indicators, are indicators of the relatively growing importance and prominence of analytics and very ‘financial analytics’, which measures over 11% of the search results for ‘analytics’. Applying a similar search for “artificial intelligence” (AI) and “machine learning” (ML) in February of 2017 has yielded 89.6 million and 31.5 million results respectively. Given the domain impact implications along with rising global interest as indicated above, ‘financial analytics’, ‘big data’ and ‘machine learning’ are critically relevant phenomena which are in need of significant research attention so that we can gain insights into optimal management and value creation

Objectives

Findings

Conclusion