Running head: BIG DATA AND CAPITAL MARKETS 1

Transcription

1 Running head: BIG DATA AND CAPITAL MARKETS 1 BIG DATA AND CAPITAL MARKETS Robin Roth Mills College

2 BIG DATA AND CAPITAL MARKETS 2 Big Data and Capital Markets In our modern era of technology, we are seeing a wave of technological advancements in the areas of collection, storage, aggregation, and processing of financial services data. One of biggest challenges of big data is processing it. Big data is plagued with the three V s: volume, velocity, and variety. In other words, with the advent of the Internet of things, there is an exponential rise in huge volumes of unstructured data from several different sources. Until recently, only the largest companies were able to harness, store, and process some of these data pools due to the expense. This is about to change as government policy around accessing big data is being offered as open data, along with the release of Hadoop, an open-source platform that allows separate computer tasks to be executed on large, real-time data feeds simultaneously. During the past year, the U.S. government has started opening up access to big data to foster innovation with the expectation that open data will lead to greater public sector efficiency, transparency, and accountability. According to Todd Parks, the CTO for the United States, the government is liberating data through its open data/open government policy to spur innovation and development by creating or improving products and tools in both the public and private sectors. This data is going to be freely available because, as Parks stated, this data has been paid for through your tax dollars it s yours, so use it! (Parks, 2012). The goal of having an open government /open data policy is to deliver citizen-driven innovation that will benefit both the private and public sectors. With the advent of Hadoop technology, open-source software, big data analysis is now accessible to smaller companies. In the past, only giants in the industry (Google, Amazon, Yahoo!) could process large, unstructured data because of the associated expense. Using this

3 BIG DATA AND CAPITAL MARKETS 3 open-source technology, along with the release of open data to the public, more intellectual resources can be allocated to finding even newer and more efficient ways to analyze this data. Allowing the public to access open data from the U.S. government will translate into an everincreasing pool of programmers providing a continuous flow of iterations. Capital markets have the biggest appetite for big data; they collect and analyze data to mitigate risk. Currently, the financial markets are challenged by exponentially increasing volumes of data, inconsistent data quality, and heightened regulatory scrutiny (A-Team, 2012). Having on-demand access to the right data and the ability to analyze it in real-time and at the right time are essential for intelligent decision-making investments. One of the high values big data offers is its looping ability to process real time feedback, react to it, analyze the results, and continue the loop to iterate continuous optimization. This loopback feature will allow testing large amounts of data in real time, using a variety of data from several sources, to be conducted in shorter spans of time. In capital markets, the speed at which data can be tested, analyzed, and applied will likely provide a competitive edge, specifically in the areas of prediction and forecasting. Big data also includes unstructured data collected from social media, which can be used when making trading decisions in the market. For instance, economic models are based on rational models, which exclude an important variable irrational, human behavior. By analyzing social media, such as Twitter, a broader, more complete picture can be painted of the direction a particular market may take by incorporating human sentiment (sentiment analysis) into the equation. Credit scoring is another area where big data can prove useful. Older models used to

4 BIG DATA AND CAPITAL MARKETS 4 predict the credit worthiness of a company can be supplemented with variables extracted from big data. Because big data offers a wide variety of information, these variables can be integrated into more traditional credit rating models to indentify hidden patterns that can lead to better predictive abilities regarding credit worthiness. This hybrid model would allow a more accurate way to apply credit ratings to companies and governments. While big data can offer more transparency around the markets, it also presents some questions that could turn into challenges down the road. For instance, how should U.S. open data be classified? If the United States government chooses to classify its big data as a national resource, the data could fall into the category of intellectual property (IP). IP rights could pose some complications when financial service organizations start to incorporate third party data into their analytics. In the past, these institutions have used internal or proprietary business data. Another question revolves around whether all of this big data accurately reflects real life conduct. While financial institutions can improve current models by incorporating big data analysis, they can also cause unforeseen damage by misinterpreting the data. It can be argued that even though big data can provide a huge flow of information, that data may not accurately represent the general population, or market (e.g. analysis has low external validity). Studying digital traces derived from social media will not necessarily provide accurate insights into offline dynamics. A gap can develop when digital conduct is reflected differently than real world actions. In order to mitigate these types of incongruent dynamics, unstructured data can be cleaned, however, this process often requires some subjective interpretation (which can also pose problems with biases). Until this type of unstructured data can be analyzed more objectively, it should only be used as a supplement to more traditional forms of captured data.

5 BIG DATA AND CAPITAL MARKETS 5 The way in which business and government interact in this area will have huge impacts on capital markets. Big data can provide opportunities in the financial services and markets to spur growth and development through innovation and provide more transparency. By detecting emerging patterns through big data analytics, traditional financial models can be improved as well as the creation of newer models. One example of how big data could have helped to mitigate some of the housing crisis involves pattern detection. Michael Burry, a surgeon-turnedfund manager, was one of the first people to notice patterns with mortgage-backed securities that no one else was noticing that led to the credit bubble underlying the eventual housing crisis. Because he was autistic, he had the ability to hyper-focus in such a way as to detect patterns in mortgage loans that were packaged in mortgage-backed securities. He discovered that, even though the percentage of homebuyers with stated incomes for specific AAA-rated portfolios increased each year, the high AAA ratings remained static. In other words, even as the percentage of riskier loans packed in these securities increased each year, the rating remained at the AAA level. This led to credit default swaps that were underpriced and mortgage-backed securities that were over priced. This is a case where, even though the data was available to the sophisticated money managers, it was very complicated to understand so they did not make use of the material. This examples how data can only provide predictive analysis if used. Using big data, however, provides more transparency with the ability to detect patterns to prevent disparities between perceived and actual values of financial instruments.

6 BIG DATA AND CAPITAL MARKETS 6 References A-Team Group/Platform Computing of IBM. (2012). A-Team white paper on big data solutions in capital markets A reality check. Retrieved from Parks, T. (2012) Lean Government. Lean Startup Convention. Lecture conducted from InterContinental SF, San Francisco, CA. Hilbert, Martin. (2013) Big Data for Development: From Information - to Knowledge Societies. Social Science Research Network (SSRN):