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Citations
... In terms of deep probabilistic modeling of financial time series, prior work has applied deep learning approaches to model a single financial time series (e.g., [4,31]), or multivariate time series (e.g., [21,25,27,28,32]). Of this work, Tepelyan and Gopal [28] (BDG 1 ) were the first to scale up to hundreds of stocks through combining machine learning with factor modeling, specifically Fama-French factor modeling. ...
... Mean and Covariance. Unlike prior work that has worked on multivariate generative modeling (e.g., [21,25,27,28,32])), our model is able to compute the mean ( + ) and the covariance matrix without sampling (Σ + Σ ) leading to significant speedup during inference. ...
The use of machine learning for statistical modeling (and thus, generative modeling) has grown in popularity with the proliferation of time series models, text-to-image models, and especially large language models. Fundamentally, the goal of classical factor modeling is statistical modeling of stock returns, and in this work, we explore using deep generative modeling to enhance classical factor models. Prior work has explored the use of deep generative models in order to model hundreds of stocks, leading to accurate risk forecasting and alpha portfolio construction; however, that specific model does not allow for easy factor modeling interpretation in that the factor exposures cannot be deduced. In this work, we introduce NeuralFactors, a novel machine-learning based approach to factor analysis where a neural network outputs factor exposures and factor returns, trained using the same methodology as variational autoencoders. We show that this model outperforms prior approaches both in terms of log-likelihood performance and computational efficiency. Further, we show that this method is competitive to prior work in generating realistic synthetic data, covariance estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization. Finally, due to the connection to classical factor analysis, we analyze how the factors our model learns cluster together and show that the factor exposures could be used for embedding stocks.
