How can we harness the value of social media when managing risk (Part II)
Following Part I – ‘How can we harness the value of social media when managing risk?’ the below looks at how we can further enhance the value of social media across; market, credit and liquidity risk by applying machine learning and trend analysis.
Machine Learning and Artificial Intelligence (AI) as a technology is becoming more widespread and mainstream as computing power increases and machine algorithms become more sophisticated. Our captured and organised social media data could potentially be used in machine learning algorithms to predict future events, discover trends and provide trading recommendations head of changes in price curves. However, it’s important to understand that different models will provide different levels of insight based on their complexity and sophistication. The accuracy of a sentiment model will depend on the quality of the subjectivity lexicons which are pre-built in the library. In other words, the number of negative and positive lexicons that have been defined has a major impact on the accuracy of the model. Another consideration is that this type of model is not a context-aware model; Tweets that have a sentence containing: “amazingly bad” may be catalogued as neutral as “amazingly” may be considered positive and “bad” negative. More advanced, context-aware models will ultimately provide better results.
When looking at the social media information regarding a particular country or counterparty over time, a sophisticated machine learning algorithm could be used to detect subtle changes in mood or sentiment that could otherwise be missed. A trend of slowly increasing negative sentiment might imply something could happen in the future. Hiring information could also be considered, for example by using information from LinkedIn, the analysis of the length of time a new senior management employee remained at a particular company might indicate all is not well at that firm.
It is clear that the relevance of big data in all areas of our lives has become very significant. The technology available to consume this data and make sense of it, is becoming more mainstream and better understood. The challenge for consumers of this information is in how to organise the data in a meaningful way and then how to visualise it. How to filter out the noise to quickly get to the relevant trend or sentiment that is interesting to the daily business. In trading and risk management, the importance and use of such data is only just being understood. If technology companies can harness this data to provide risk managers more proactive risk management techniques and insights, this will bring enormous benefit and dramatic change in the future. The emergence of machine learning, coupled with the availability of good, relevant information from social media sources, and the application of business intelligence visualisation and reporting, has the potential to change the face of risk management in the, not so distant future. This is as an important part of the future business intelligence enabled risk management landscape.