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 alpha or risk premium and lead to profitable trading strategies.
This session 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 (implied volatility skew, term structure), Geolocational datasets and ESG (Environmental, Social and Governance)
Robust portfolio construction using multi-factor models by blending in factors derived from alternative data with the traditional factors such as fama-french five-factor model.
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.