How Big Data Has Changed Finance

The vast proliferation of data and increasing technological complexities continue to transform the way industries operate and compete. Over the past few years, 90 percent of the data in the world has been created as a result of the creation of 2.5 quintillion bytes of data on a daily basis. Commonly referred to as big data, this rapid growth and storage creates opportunities for collection, processing and analysis of structured and unstructured data.

Following the 3 V’s of big data, organizations use data and analytics to gain valuable insight to inform better business decisions. Industries that have adopted the use of big data include financial services, technology, marketing and health care, to name a few. The adoption of big data continues to redefine the competitive landscape of industries. An estimated 89 percent of enterprises believe those without an analytics strategy run the risk of losing a competitive edge in the market.

Financial services, in particular, have widely adopted big data analytics to inform better investment decisions with consistent returns. In conjunction with big data, algorithmic trading uses vast historical data with complex mathematical models to maximize portfolio returns. The continued adoption of big data will inevitably transform the landscape of financial services. However, along with its apparent benefits, significant challenges remain in regards to big data’s ability to capture the mounting volume of data. (For more, see: The Big Play In Big Data.)

The 3 V’s are fundamental to big data: volume, variety and velocity. Facing increasing competition, regulatory constraints and customer needs, financial institutions are seeking new ways to leverage technology to gain efficiency. Depending on the industry, companies can use certain aspects of big data to gain a competitive advantage.

Velocity is the speed at which data must be stored and analyzed. The New York Stock Exchange captures 1 terabyte of information during each day. By 2016, there was an estimated 18.9 billion network connections, with roughly 2.5 connects per person on Earth. Financial institutions can differentiate themselves from the competition by focusing on efficiently and quickly processing trades.

Big data can be categorized as unstructured or structured data. Unstructured data is information that is unorganized and does not fall into a pre-determined model. This includes data gathered from social media sources, which help institutions gather information on customer needs. Structured data consists of information already managed by the organization in relational databases and spreadsheets. As a result, the various forms of data must be actively managed in order to inform better business decisions.

The increasing volume of market data poses a big challenge for financial institutions. Along with vast historical data, banking and capital markets need to actively manage ticker data. Likewise, investment banks and asset management firms use voluminous data to make sound investment decisions. Insurance and retirement firms can access past policy and claims information for active risk management. (For more, see: Quants: The Rocket Scientists Of Wall Street.)

Algorithmic trading has become synonymous with big data due to the growing capabilities of computers. The automated process enables computer programs to execute financial trades at speeds and frequencies that a human trader cannot. Within the mathematical models, algorithmic trading provides trades executed at the best possible prices and timely trade placement, and reduces manual errors due to behavioral factors.

Institutions can more effectively curtail algorithms to incorporate massive amounts of data, leveraging large volumes of historical data to backtest strategies, thus creating less risky investments. This helps users identify useful data to keep as well as low value data to discard. Given that algorithms can be created with structured and unstructured data, incorporating real-time news, social media and stock data in one algorithmic engine can generate better trading decisions.  Unlike decision making, which can be influenced by varying sources of information, human emotion and bias, algorithmic trades are executed solely on financial models and data.

Robo advisors use investment algorithms and massive amounts of data on a digital platform. Investments are framed through Modern Portfolio theory, which typically endorses long term investments to maintain consistent returns, and require minimal interaction with human financial advisors. (For more, see: Basics of Algorithmic Trading: Concepts and Examples.)

Despite the financial services industry increasing embrace of big data, significant challenges still exist in the field. Most importantly, the collection of various unstructured data supports concerns over privacy. Personal information can be gathered about an individual’s decision making through social media, emails and health records.

Within financial services specifically, the majority of criticism falls onto data analysis. The sheer volume of data requires greater sophistication of statistical techniques in order to obtain accurate results. In particular, critics overrate signal to noise as patterns of spurious correlations, representing statistically robust results purely by chance. Likewise, algorithms based off of economic theory typically point to long-term investments opportunities due to trends in historical data. Efficiently producing results supporting a short-term investment strategy are inherent challenges in predictive models.

Big data continues to transform the landscape of various industries, particularly financial services. Many financial institutions are adopting big data analytics in order to maintain a competitive edge. Through structure and unstructured data, complex algorithms can execute trades using a number of data sources. Human emotion and bias can be minimized through automation; however, trading with big data analysis has its own specific set of challenges  The statistical results produced so far have not been fully embraced due to the field’s relative novelty. However, as financial services trend towards big data and automation, sophistication of statistical techniques will increase accuracy.