(NY) - Arun Verma - Statistical arbitrage using news and social sentiment

Statistical arbitrage using news and social sentiment based quant trading strategies

Arun Verma

Seminar Program

5:45pm Registration
6:00pm Seminar
7:30pm Reception

Abstract

To explore the value embedded in News & Social Sentiment data, we build three types of equity trading strategies based on sentiment data and show that strategies based on sentiment outperform the corresponding benchmark indexes significantly.

Biography

Arun Verma joined the Bloomberg Quantitative Research group in 2003. Prior to that, he earned his Ph.D from Cornell University in the computer science & applied mathematics.

At Bloomberg, Dr. Verma's work initially focused on Stochastic Volatility Models for Equity/FX Derivatives and Exotics pricing, e.g. Arbitrage free Volatility interpolation, Variance Swaps and VIX Futures/Options pricing and Cross Currency Volatility Surface construction. More recently, he has enjoyed working at the intersection of such areas as data science, innovative quantitative techniques and interactive visualizations for help reveal embedded signals in financial data, e.g., building quant trading strategies for statistical arbitrage.

Disclaimer

This a joint IAQF/Thalesians seminar, and not an instructional program of New York University.