Extracting trading signals from alternative data using machine learning

Description

To gain an edge in the markets, quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly nontraditional sources of data. The nature of these “alternative data” sources presents challenges that are comfortably addressed through machine learning techniques.

Arun Verma shares AI and ML techniques that help extract derived signals that have significant alpha or risk premium and lead to profitable trading strategies. You’ll learn the broad application of machine learning in finance; how to extract sentiment from textual data such as news stories and social media content using machine learning algorithms; the construction of scoring models and factors from complex datasets such as supply chain graph, options (implied volatility skew, term structure), geolocational datasets, and environmental, social, and governance (ESG); and robust portfolio construction using multifactor models by blending in factors derived from alternative data with the traditional factors such as the Fama-French five-factor model.