14–17 Oct 2019

Extracting trading signals from alternative data using machine learning

Arun Verma (Bloomberg)
14:3515:15 Wednesday, 16 October 2019
Location: Blenheim Room - Palace Suite
Average rating: ***..
(3.50, 4 ratings)

Who is this presentation for?

  • Quantitative analysts, machine learning scientists, AI product managers, and quantitative portfolio managers

Level

Intermediate

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.

Prerequisite knowledge

  • A basic understanding of probability, statistics, and finance

What you'll learn

  • Understand the broad application of machine learning in finance
Photo of Arun Verma

Arun Verma

Bloomberg

Arun Verma is the head of the quantitative research solutions team at Bloomberg. He also serves on the board of a nonprofit that helps with humanitarian projects in India serving impoverished children and women in the areas of education and vocational training. Since he joined the Bloomberg Quantitative Research Group, Arun has worked on stochastic volatility models for derivatives and exotics pricing and hedging (e.g., variance swaps and VIX Futures fair pricing and cross-currency volatility surface construction) and at the intersection of diverse areas such as data science, innovative quantitative finance models across asset classes, and using machine learning methods to help reveal embedded signals in traditional and alternative data that can be used to construct quantitative trading strategies. He holds a PhD in computer science and applied mathematics from Cornell University and a bachelor of technology in computer science from IIT Delhi. Arun lives in central New Jersey with his lovely wife and two children.

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