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 the use of AI and ML techniques that help extract derived signals that have a 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
• Construction of scoring models and factors from complex data sets such as supply chain graph, options prices and ESG (Environmental, Social and Governance)
• Use of Alternative data such as extreme weather (Cyclone, Snowfall) to quantify the impact on companies that own retail stores and factories (geolocational).
• Robust portfolio construction by blending in factors derived from alternative data with traditional factors such as those in the Fama-French five-factor model.
• Machine Learning techniques for asset pricing, e.g. learning the complex quant models (PDE, Monte Carlo) via machine learning approach for efficient pricing of derivative securities.
Bio: 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 & applied mathematics. At Bloomberg, Mr. Verma’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing. More recently, he has enjoyed working at the intersection of diverse areas such as data science (with structured & unstructured data), innovative quantitative models across all asset classes & using machine learning methods to help reveal embedded signals in financial data.