A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls

Earnings call summarizes the financial performance of a company, and it is an important indicator of the future financial risks of the company. We quantitatively study how earnings calls are correlated with the financial risks, with a special fo-cus on the financial crisis of 2009. In par-ticular, we perform a text regression task: given the transcript of an earnings call, we predict the volatility of stock prices from the week after the call is made. We pro-pose the use of copula: a powerful statis-tical framework that separately models the uniform marginals and their complex mul-tivariate stochastic dependencies, while not requiring any prior assumptions on the distributions of the covariate and the de-pendent variable. By performing probabil-ity integral transform, our approach moves beyond the standard count-based bag-of-words models in NLP, and improves pre-vious work on text regression by incor-porating the correlation among local fea-tures in the form of semiparametric Gaus-sian copula. In experiments, we show that our model significantly outperforms strong linear and non-linear discriminative baselines on three datasets under various settings. 1