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Speaker "Arun Verma" Details Back

 

Topic

Quantitative Trading Strategies and Asset Pricing using Alternative Data and Machine learning

Abstract

To gain an edge in the markets quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly non-traditional sources of data. The nature of these "alternative data" sources presents challenges that are comfortably addressed through Machine Learning techniques. We illustrate use of AI and ML techniques that help extract derived signals that have significant risk premium and lead to profitable trading strategies. 
This talk will cover the following topics:
 
  • The broad application of machine learning in finance
  • Extracting sentiment from textual data such as news stories and social media content using machine learning algorithms and using sentiment to build trading strategy
  • Construction of scoring models and factors from complex data sets such as supply chain graph, options (implied volatility skew, term structure) and ESG (Environmental, Social and Governance)
  • Use of Alternative data such as extreme weather (Cyclone, Snowfall) to quantify impact on companies that own retail stores and factories.
  • Robust portfolio construction using multi-factor models by blending in factors derived from alternative data with the traditional factors such as those in Fama-French five-factor model.
  • Machine Learning techniques for efficient pricing of derivative securities.
 

Profile

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.