Does Big Data Influence the Efficiency of the Capital Markets?

Date01 April 2018
AuthorSingh, Rajesh Kumar

Introduction

Within the financial services sector, 'big data' has gained far more traction within retail banking and insurance sectors due to the increasing desire of these financial institutions to profile their customers in a similar manner to early adopters of big data strategy such as Amazon, Baidu or Google. Research by the International Data Corporation indicates that the global data volume is expected to reach 35 zettabytes (ZB) (1) by 2020. Beyond that, the trend of growth of doubling the data every 2 years will be maintained. This implies that we have entered the era of big data. On the institutional side of the capital markets, there had traditionally been far more customer stickiness, hence there had been less incentive to apply big data in this manner.

Big data is defined as, "data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data" (Snijders et.al, 2012). However, big data strategies had begun to make some impact in a selected few areas of the capital markets including the sentiment analysis for trading and growth in volume, risk analytics, fraud prevention and market surveillance. The business landscape has been going through constant change since the financial crisis in 2008. The emergence of internet and social media networking services combined with the extensive dissemination of smart phones has revolutionized the way we communicate and exchange information. Most of the firms have been using the information obtained from the vast oceans of available structured and unstructured data to gain customer knowledge, anticipate market conditions, and better gauge customer preferences and behavior ahead of time so that firms could offer highly personalized customer-centric products and services such as sentiment analysis-enabled brand strategy management and real-time location-based product offerings as opposed to the historically offered product-centric services. The global financial markets had increasingly been fragmented due to rapid globalization and technological changes (Funk et.al, 2014). In one of his studies on liquidity, Blocher et.al (2016) identified three key components of the financial market like 1) fund management for long term investors 2) low-frequency trading (LFT) by the traditional brokers and 3) high-frequency trading (HFT) by proprietary financial firms which used the big data and aggregated information to set the trading strategy. Continuous flow of the big data from increasing online activities by users has become a buzz word for recent years because of its potentials for various uses including marketing, trading, political predictions, disease epidemics, social dynamics, etc. This is the era of information explosion and a world overwhelmed by numbers and digits. Also the market events, technology changes and regulatory reforms keep on throwing new challenges for the capital markets. Solution to many of these issues which capital market faces lies within house by analyzing and leveraging the huge volume of the data accumulated on day to day basis. The capital market industry has varied data sources, which included structured data like traditional banking transaction data and market data. At the same time, it also generates mammoth volume of unstructured data through corporate news, feeds, macro and micro economic indicators, social media updates and contents. In recent years, capital markets have gone through an unprecedented change, resulting in the generation of massive amounts of high-velocity and heterogeneous data. Similar trends could be observed in the financial services sector as well, where big data has been increasingly becoming the most significant, promising, and differentiating asset for the financial services enterprises (Seth et.al, 2015). These massive data troves could be processed through big data strategy, tools and techniques which could be game changer. Traditional tools cannot process such large datasets, however the Big Data based approach can analyze structured and un-structured data and create logical patterns to help business to take decisions. The speed and agility of this processing is exponentially faster than that could be done by the traditional mechanism. This provides near real time information with the actionable intelligence which could be used in the decision making process. The analytical and predictive power of information generated from online big data for the capital market activity is supported by numerous studies ranging from stock markets to housing markets.

Big data strategy could help the capital market to address key use cases in the area of: 1) trading strategy, 2) reporting, 3) compliance and 4) operational simplifications. Investment banks have an untapped opportunity to use big data to solve many of their business problems. The age of big data offers new creative opportunities across the board. For finance researchers, big data allows the field to settle old debates and discover new puzzles. For investment practitioners, the big data approach offers limitless possibilities to gain informational advantage over the competition by cleverly analyzing public data sources. Big data strategies in the capital markets tend to be synonymous with analytical tasks or those related to reporting or governance functions but in recent years the consumption of text-based, audio and video unstructured data had also been a significant driver for some projects. The big data story in the capital markets is directly tied to the rising importance of data management as a function within financial institutions. Regulatory, client, and internal drivers have forced most firms to re-evaluate the core reference data sets on which they are basing their trading, risk management, and operational decisions.

Capital Markets & Technologies

Big data had been a much misused and misunderstood term within the financial services industry and capital markets for some time, applied to everything from traditional relational databases (RDBMS) to web-based sentiment...

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