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Improving Earnings Predictions With Neural Network Models

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  • RÄ‚ZVAN POPA

    (Alexandru Ioan Cuza University of Iasi, Faculty of Economics and Business Administration, Iași, Romania)

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.

Suggested Citation

  • Rä‚Zvan Popa, 2020. "Improving Earnings Predictions With Neural Network Models," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 26, pages 77-96, December.
    • Handle: RePEc:aic:revebs:y:2020:j:26:popar
    DOI: 10.47743/rebs-2020-2-0004
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    References listed on IDEAS

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