Big Data in Finance

  • Bin Fang
  • Peng ZhangEmail author


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.


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.


  1. 1.
    Aldridge I (2015) Trends: all finance will soon be big data financeGoogle Scholar
  2. 2.
    Iati R (2009) The real story of trading software espionage. WallStreet and Technology. Available: AdvancedTrading.comGoogle Scholar
  3. 3.
    (2012) Times Topics: high-frequency trading. The New York TimesGoogle Scholar
  4. 4.
    Lewis M (2014) An adaption from ‘Flash Boys: A Wall Street Revolt’, by Michael Lewis, The New York TimesGoogle Scholar
  5. 5.
    Egan M (2013) Survey: ‘Hash Crash’ didn’t seriously erode market structure confidence, FoxBusinessGoogle Scholar
  6. 6.
    Kilburn F (2013) 2013 review: social media, ‘Hash Crash’ Are 2013’s trendingtopicsGoogle Scholar
  7. 7.
    Gutierrez DD (2015) InsideBIGDATA guide to big data for financeGoogle Scholar
  8. 8.
    (2014) Big data: profitability, potential and problems in banking, Capgemini ConsultingGoogle Scholar
  9. 9.
    Groenfeldt T (2013) Banks use big data to understand customers across channels, ForbesGoogle Scholar
  10. 10.
    Zagorsky V (2014) Unlocking the potential of Big Data in banking sectorGoogle Scholar
  11. 11.
    Yu P, McLaughlin J, Levy M (2014) Big Data, a big disappointment for scoring consumer creditworthiness. National Consumer Law Center, BostonGoogle Scholar
  12. 12.
    Algorithmic tradingGoogle Scholar
  13. 13.
    (2014) Retail banks and big data: big data as the key to better risk management, A report from the Economist Intelligence UnitGoogle Scholar
  14. 14.
    Arnold Veldhoen SDP (2014) Applying Big Data To Risk Management: transforming risk management practices within the financial services industryGoogle Scholar
  15. 15.
    Andreas Huber HH, Nagode F (2014) BIG DATA: potentials from a risk management perspectiveGoogle Scholar
  16. 16.
    Jackson J (2015) IBM and Deloitte bring big data to risk management, ComputerworldGoogle Scholar
  17. 17.
    O’Shea V (2014) Big Data in capital markets: at the start of the journey, Aite Group Report (commissioned by Thomson Reuters)Google Scholar
  18. 18.
    M a Celent (2013) How Big is Big Data: big data usage and attitudes among North American financial services firmsGoogle Scholar
  19. 19.
    (2013) BBRS 2013 banking customer centricity studyGoogle Scholar
  20. 20.
    Jean Coumaros JB, Auliard O (2014) Big Data alchemy: how can banks maximize the value of their customer data? Capgemini ConsultingGoogle Scholar
  21. 21.
    (2013) Deutsche bank: big data plans held back by legacy systems, Computerworld UKGoogle Scholar
  22. 22.
    (2012) How ‘Big Data’ is different, MIT Sloan Management Review and SASGoogle Scholar
  23. 23.
    C. P. Finextra research, NGDATA (2013) Monetizing payments: exploiting mobile wallets and big dataGoogle Scholar
  24. 24.
    King R (2014) BNY Mellon finds promise and integration challenges with Hadoop. Wall Street JGoogle Scholar
  25. 25.
    Holley E (2014) Cloud in financial services – what is it not good for?Google Scholar
  26. 26.
    Aberdeen (2013) Analytics in bankingGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

Personalised recommendations