  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
