Generative AI / Large Language Models Stream
12.00 – 12.45: Generative Machine Learning for Multivariate Equity Returns
The use of machine learning to generate synthetic data has grown in popularity significantly in the last few years. The core methodology these models use is to learn the distribution of the underlying data, similar to the classical methods common in finance of fitting statistical models to data. In this presentation, we discuss the efficacy of using modern machine learning methods, specifically conditional importance weighted autoencoders (a variant of variational autoencoders) and conditional normalizing flows, for the task of modeling the returns of equities.
We apply our method to learn a 500 dimensional joint distribution for S&P 500 members. We show that this generative model has a broad range of applications in finance, including generating realistic synthetic data, volatility and correlation estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization.
Achintya Gopal:
Machine Learning Quant Researcher, Bloomberg
Achintya Gopal: Machine Learning Quant Researcher, Bloomberg
Achintya Gopal is a Machine Learning Quant Researcher in the Quantitative Research group in the Office of the CTO at Bloomberg, where he works on applying machine learning within the financial domain. Prior to that, he worked on estimating carbon emissions using machine learning, developing new models in normalizing flows, and exploring new methods to evaluate statistical models with model uncertainty. More recently, he has been working on a variety of projects ranging from volatility modeling using neural networks, causal inference for investing, generative models in differential privacy, active learning for NLP, and the interpretability of large language models.
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