Deep Learning Based Statistical Models for Business and Financial Data

Abstract

We investigate a wide range of statistical models commonly used in many business and financial econometrics applications and propose flexible ways to combine these highly interpretable models with powerful predictive models in the deep learning literature to leverage the advantages and compensate the disadvantages of each of the modelling approaches. Our approaches of utilizing deep learning techniques for financial data are different from the recently proposed deep learning-based models in the financial econometrics literature in several perspectives. First, we do not overlook well-established structures that have been successfully used in statistical modelling. We flexibly incorporate deep learning techniques to the statistical models to capture the data effects that cannot be explained by the simple linear components of those models. Our proposed modelling frameworks therefore normally include two components: a linear part to explain linear dependencies and a deep learning-based part to capture data effects rather than linearity possibly exhibited in the underlying process. Second, we do not use the neural network structures in the same fashion as they are implemented in the deep learning literature but modify those black-box methods to make them more explainable and hence improve the interpretability of the proposed models. As the results, our hybrid models not only perform better than the pure deep learning techniques in term of interpretation but also often produce more accurate out-of-sample forecasts than the counterpart statistical frameworks. Third, we propose advanced Bayesian inference methodologies to efficiently quantify the uncertainty about the model estimation and prediction. For the proposed high dimensional deep learning-based models, performing efficient Bayesian inference is extremely challenging and is often ignored in the engineer-oriented papers, which generally prefer the frequentist estimation approaches mainly due to the simplicity.