Generative Machine Learning for Multivariate Equity Returns
Explores using machine learning models for multivariate equity returns, focusing on risk analysis and portfolio optimization.
What it examines
This paper explores using modern machine learning methods, specifically conditional importance-weighted autoencoders and conditional normalizing flows, to model the multivariate distribution of stock returns, focusing on the S&P 500. The goal is to improve risk forecasting, volatility estimation, and portfolio optimization.
What it concludes
The research demonstrates that deep generative models are effective for financial applications, such as risk analysis and portfolio optimization. Future work could explore additional architectures and features. Potential applications include creating optimal portfolios and improving risk management strategies.
Evidence objects
The research demonstrates that deep generative models are effective for financial applications, such as risk analysis and portfolio optimization. Future work could explore additional architectures and features. Potential applications include creating optimal portfolios and improving risk management strategies.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: The use of machine learning to generate synthetic data has grown in popularity with the proliferation of text-to-image models and especially large language models. 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 work, we explore the efficacy of using modern m… ▽ More The use of machine learning to generate synthetic data has grown in popularity with the proliferation of text-to-image models and especially large language models. 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 work, we explore 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. The main problem we work to address is modeling the joint distribution of all the members of the S&P 500, or, in other words, learning a 500-dimensional joint distribution. 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. △ Less
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