IMPROVING EARNINGS PREDICTIONS WITH NEURAL NETWORK MODELS
RĂZVAN POPA · Review of Economic and Business Studies · 2020
In this paper we develop a generalized deep neural network model to predict quarterly earnings.Using a diverse range of predictors consisting of fundamental, technical and sentiment data the resulting model outperforms existing timeseries models such as the Fama-French 2006 regression model and comes close in prediction accuracy to sales analysts' estimates.This is achieved by handling some known issues in time series models such as seasonality and non-linearity of the earnings while improving predictions with additional explanatory variables that reflect the expectations of the market.Thus, we add to the existing literature a comprehensive and innovative neural network model that provides solutions to known challenges in forecasting and closes the gap between statistical models and sales analysts.