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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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    1. Brown, Lawrence D., 1993. "Earnings forecasting research: its implications for capital markets research," International Journal of Forecasting, Elsevier, vol. 9(3), pages 295-320, November.
    2. Hill, Tim & Marquez, Leorey & O'Connor, Marcus & Remus, William, 1994. "Artificial neural network models for forecasting and decision making," International Journal of Forecasting, Elsevier, vol. 10(1), pages 5-15, June.
    3. Brown, Ld & Richardson, Gd & Schwager, Sj, 1987. "An Information Interpretation Of Financial Analyst Superiority In Forecasting Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 25(1), pages 49-67.
    4. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    5. Edwin J. Elton & Martin J. Gruber, 1972. "Earnings Estimates and the Accuracy of Expectational Data," Management Science, INFORMS, vol. 18(8), pages 409-424, April.
    6. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, Wiley Blackwell, vol. 38, pages 1-41.
    7. Imhoff, Ea & Pare, Pv, 1982. "Analysis And Comparison Of Earnings Forecast Agents," Journal of Accounting Research, Wiley Blackwell, vol. 20(2), pages 429-439.
    8. Fried, Dov & Givoly, Dan, 1982. "Financial analysts' forecasts of earnings : A better surrogate for market expectations," Journal of Accounting and Economics, Elsevier, vol. 4(2), pages 85-107, October.
    9. Brown, Lawrence D & Rozeff, Michael S, 1978. "The Superiority of Analyst Forecasts as Measures of Expectations: Evidence from Earnings," Journal of Finance, American Finance Association, vol. 33(1), pages 1-16, March.
    10. Brown, Ld & Rozeff, Ms, 1979. "Univariate Time-Series Models Of Quarterly Accounting Earnings Per Share - Proposed Model," Journal of Accounting Research, Wiley Blackwell, vol. 17(1), pages 179-189.
    11. Collins, Wa & Hopwood, Ws, 1980. "A Multivariate-Analysis Of Annual Earnings Forecasts Generated From Quarterly Forecasts Of Financial Analysts And Univariate Time-Series Models," Journal of Accounting Research, Wiley Blackwell, vol. 18(2), pages 390-406.
    12. Griffin, Pa, 1977. "Time-Series Behavior Of Quarterly Earnings - Preliminary Evidence," Journal of Accounting Research, Wiley Blackwell, vol. 15(1), pages 71-83.
    13. Donaldson, R.G. & Kim, K.H.Y., 1993. "Evaluating Alternative Models for Conditional Stock Volatility : Evidence from International Data," Discussion Papers dp93-06, Department of Economics, Simon Fraser University.
    14. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    15. Wiedman, CI, 1996. "The relevance of characteristics of the information environment in the selection of a proxy for the market's expectations for earnings: An extension of Brown, Richardson, and Schwager [1987]," Journal of Accounting Research, Wiley Blackwell, vol. 34(2), pages 313-324.
    16. Chatfield, Chris, 1993. "Neural networks: Forecasting breakthrough or passing fad?," International Journal of Forecasting, Elsevier, vol. 9(1), pages 1-3, April.
    17. Mendenhall, Rr, 1991. "Evidence On The Possible Underweighting Of Earnings-Related Information," Journal of Accounting Research, Wiley Blackwell, vol. 29(1), pages 170-179.
    18. J. G. Cragg & Burton G. Malkiel, 1968. "The Consensus And Accuracy Of Some Predictions Of The Growth Of Corporate Earnings," Journal of Finance, American Finance Association, vol. 23(1), pages 67-84, March.
    19. Sexton, Randall S. & Dorsey, Robert E. & Johnson, John D., 1999. "Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing," European Journal of Operational Research, Elsevier, vol. 114(3), pages 589-601, May.
    20. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
    21. Fama, Eugene F & French, Kenneth R, 1995. "Size and Book-to-Market Factors in Earnings and Returns," Journal of Finance, American Finance Association, vol. 50(1), pages 131-155, March.
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