Arun Verma, PhD

Arun Verma, PhD

Head of Quant Research Solutions Team, CTO Office at Bloomberg

    Arun heads the Bloomberg Quantitative Research Solutions Team in the CTO office. Arun has worked extensively on Stochastic Volatility Models for Derivatives & Exotics Valuation, as well as factor investing, asset allocation, portfolio optimization and risk models. More recently, he has enjoyed working at the intersection of quantitative finance and using AI/Machine Learning to help reveal embedded signals in traditional & alternative data such as Company Financials, ESG, News/Social, Supply Chain, and Geo-locational & Extreme Weather data among others to study their potential impact on financial markets. In an attempt to complete a full circle, Arun has most recently been exploring use of ML methods in traditional quant space, e.g. Derivatives pricing, prediction of illiquid instruments fair value, Economic indicators Now-casting & high-dimensional Data Imputation. Prior to joining Bloomberg, he earned his PhD 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.

    All Sessions by Arun Verma, PhD

    Day 2 04/24/2024
    11:00 am - 11:30 am

    Imputation of Financial Data Using Collaborative Filtering and Generative Machine Learning

    <span class="etn-schedule-location"> <span class="firstfocus">Generative AI</span>

    Quant traders and data scientists regularly user automated ML & AI technologies to extract a variety of information from large datasets, e.g. sentiment from news data, or scoring methods for complex data sets like Supply Chain and ESG. ML methods are also being used for imputation of financial data as well as prediction of asset prices. This talk will provide a brief overview of the following topics: The broad application of machine learning in finance: opportunities and challenges. Machine Learning techniques for Imputation e.g. estimating granular Geographical Exposure of companies given partial & high-level disclosure from the company financial statement Collaborative filtering techniques for illiquid asset pricing, use of data driven methods to inform price movements in a target instrument from observations on related liquid instruments

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