Big Data & Risk Management in Financial Markets (Part I)
I. Overview
We have seen how the interdisciplinary use of big data affected many sectors. Different examples are contagion spreading (Culotta, 2010); music albums success predictions (Dhar and Chang, 2009); or presidential election (Tumasjan et al., 2010).
In financial markets, the sentiment analysis probably represents the major and most known implementation of machine learning techniques on big datasets (Bollen et al., 2011). In spite of all the hype, though, risk management is still an exception. New information and stack of technologies did not bring as many benefits to the risk management as they did to trading for instance.
Risk is indeed usually addressed from an operational perspective, from a customer relationship angle, or specifically for fraud prevention and credit scoring. However, applications strictly related to financial markets are still not so widespread, mainly because of the following problem: in theory, more information should entail a higher degree of accuracy, while in practice it (also) exponentially…
