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Big Data in Finance

  • Bin Fang
  • Peng ZhangEmail author
Chapter

Abstract

Quantitative finance is an area in which data is the vital actionable information in all aspects. Leading finance institutions and firms are adopting advanced Big Data technologies towards gaining actionable insights from massive market data, standardizing financial data from a variety of sources, reducing the response time to real-time data streams, improving the scalability of algorithms and software stacks on novel architectures. Today, these major profits are driving the pioneers of the financial practitioners to develop and deploy the big data solutions in financial products, ranging from front-office algorithmic trading to back-office data management and analytics.

Not only the collection and purification of multi-source data, the effective visualization of high-throughput data streams and rapid programmability on massively parallel processing architectures are widely used to facilitate the algorithmic trading and research. Big data analytics can help reveal more hidden market opportunities through analyzing high-volume structured data and social news, in contrast to the underperformers that are incapable of adopting novel techniques. Being able to process massive complex events in ultra-fast speed removes the roadblock for promptly capturing market trends and timely managing risks.

These key trends in capital markets and extensive examples in quantitative finance are systematically highlighted in this chapter. The insufficiency of technological adaptation and the gap between research and practice are also presented.

To clarify matters, the three natures of Big Data, volume, velocity and variety are used as a prism through which to understand the pitfalls and opportunities of emerged and emerging technologies towards financial services.

Keywords

Cloud Service Financial Industry Public Cloud Private Cloud Unstructured Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.QuantCloud Brothers Inc.SetauketUSA
  2. 2.Stony Brook UniversityStony BrookUSA

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