Speaker "Arun Verma" Details Back



Using Machine Learning to derive financial trading signals from alternative data


There is a lot of use of alternative data sets (both structured and unstructured) e.g. Supply chain network data, retail footfalls, credit card and loan data as well as news & social media data. High volume and time sensitivity of news and social media stories requires automated processing to quickly extract actionable information. However, the unstructured nature of textual information presents challenges that are comfortably addressed through machine learning techniques. This talk would cover: • The application of machine learning in finance • Extracting sentiment from news stories and social media content using machine learning algorithms • Quantitative techniques for constructing aggregated sentiment scores and other derived metrics (e.g., sentiment dispersion) • Demonstrating the sentiment signal based trading strategies that have high risk-adjusted returns • Illustrating variation in sensitivity of sentiment with respect to industry sector, market cap, trading volume, etc.


Dr. Arun Verma joined the Bloomberg Quantitative Research group in 2003. Prior to that, he earned his Ph.D from Cornell University in the areas of computer science and applied mathematics. At Bloomberg, Arun's work initially focused on Stochastic Volatility Models for pricing & hedging Derivatives & Exotic financial Instruments. More recently, he has enjoyed working at the intersection of diverse areas such as data science, cross-asset quantitative finance models and machine learning & AI methods to help reveal embedded signals in traditional & alternative data.