IMPROVING EARNINGS PREDICTIONS WITH NEURAL NETWORK MODELS

Rä‚zvan Popa ()
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Rä‚zvan Popa: Alexandru Ioan Cuza University of Iasi, Faculty of Economics and Business Administration, IaÈ™i, Romania

Review of Economic and Business Studies, 2020, issue 26, 77-96

Abstract: 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.

Keywords: Comparative analysis; earnings forecasting methods; Fama French profitability model; deep neural network (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:aic:revebs:y:2020:j:26:popar

DOI: 10.47743/rebs-2020-2-0004

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