Machine Learning Models for Quantitative Finance and Trading


Machine Learning (ML) and Predictive Analytics are now embedded in a broad variety of use cases in Quantitative Finance, from information extraction to sentiment analysis, from factor scoring models to complex instrument pricing methods, and from risk premium mining to portfolio construction models. ML is also being used for asset pricing, e.g, option pricing or illiquid bond pricing where we need to learn the pricing function using data driven techniques as a further enhancement of methods rooted in stochastic modeling.

Quant traders and data scientists require automated ML & AI technologies to quickly extract actionable information from unstructured and increasingly non-traditional sources of data. We will illustrate the use of such derived signals in constructing promising trading strategies through novel use cases.

Session Outline:

This talk will provide a brief overview of the following topics:
• The broad application of machine learning in finance: opportunities and challenges.
• Use of alternative data such as News and Geo-locational/Extreme weather data to build signals and trading strategies for the financial markets.
• Machine Learning techniques for asset pricing, enhancing complex quant models (i.e., PDE solutions, Monte Carlo Simulations) for an efficient pricing of derivative and illiquid securities using data driven methods.


Arun heads the Bloomberg Quantitative Research Solutions Team. Arun's work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing/hedging and more generally around asset pricing using traditional quantitative finance methods. More recently, he has enjoyed working at the intersection of diverse areas such as data science, innovative quantitative finance models and using AI/Machine Learning methods to help reveal embedded signals in traditional & alternative data such as Company Financials, ESG, News/Social, Supply Chain, Geolocational & Extreme Weather and their potential impact on capital markets. Most recently in an attempt to complete a full circle, he has been exploring use of ML methods in asset pricing , e.g. Derivatives pricing and illiquid instrument pricing.

Prior to joining Bloomberg, he earned his Ph.D from Cornell University in the areas of computer science and applied mathematics and a B. Tech in Computer Science from IIT Delhi, India. Arun is also an editorial board member of The Journal of Financial Data Science.

Open Data Science




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