TITLE:
A Deep Dive: Does Big Data Improve Maturity in the Developed Capital Markets?
AUTHORS:
Rajesh Kumar Singh, Subrata Kumar Mitra
KEYWORDS:
Efficient Market Hypothesis (EMH), Capital Market Line (CML), Capital Asset Pricing Model (CAPM), Security Market Line (SML), High Frequency Trading (HFT), Low Frequency Trading (LFT), Ultra High Frequency Data (UHFD), Data Science, Big Data, Natural Language Processing (NLP), Machine Learning (ML), Artificial Intelligence (AI)
JOURNAL NAME:
Theoretical Economics Letters,
Vol.9 No.1,
January
29,
2019
ABSTRACT: Over this decade, the concept of big data has been
applied to industries but the capital markets have been traditionally laggard
to adoption. Within the financial services’ sector, Big Data has gained far
more traction within retail banking and insurance due to the increasing desire
of these financial institutions to profile and analyze their customers in a
similar manner to early adopters of Big Data strategy such as Amazon, Baidu or
Google. However, Big Data strategies have begun to make some impacts on few selected
areas of the capital markets, including the social media sentiment analysis on
the structured and unstructured data for trading, growth in volume, risk
analytics, fraud prevention, market surveillance, predictability and
forecasting of the equity prices; those are the early sign of the maturity of
the capital markets. Technical and theoretical measures have evolved, but still
these dimensions of the capital markets have been a mystery for the human beings
till now. The Big Data in the form of structured, semi-structured and
unstructured socio-economic and demographic information from social media and
blogs from consumers has started indicating impacts on the capital markets which can lead to improving the real-time systems
and transaction processing, and improving operational efficiency and
maturity. The intent of this paper is threefold. First, it aims to bring the
clear inference from the past researches to take a holistic analysis of the
work done in the emerging area of Big Data and its implications on capital markets.
Second, it’s to perform a deep analysis on how the influences of Big Data
affect the assumptions in connection with Random Walk theory and Efficient
Market Hypothesis. Third, it will provide a conclusive theoretical analysis of
past research work by the scholars, which can establish the model to refine the
nexus between investors’ sentiments and assets’ prices with advanced techniques
in the Big Data. The paper has been divided into 4 broad sections. In the first
section, the paper sets the introduction of connecting the dots and setting the
context for the two different fields like Big Data and its influences on the
capital markets. The second section explains the theoretical premises and
frameworks needed for this research and does deep studies of the previous works
in this area to establish conclusive references for the future study. The third
section carries out the studies of emerging social media and technologies,
analysis of the previous research works from the social media and the capital
markets perspective. Finally, the fourth section concludes findings with
recommendations.