The modeling of earnings per share of Polish companies for the post-financial crisis period using random walk and ARIMA models

• Authors: Wojciech Kuryłek

Abstracts

EN

The proper forecasting of listed companies’ earnings is crucial for their appropriate pricing. This paper compares forecast errors of different univariate time-series models applied for the earnings per share (EPS) data for Polish companies from the period between the last financial crisis of 2008–2009 and the pandemic shock of 2020. The best model is the seasonal random walk (SRW) model across all quarters, which describes quite well the behavior of the Polish market compared to other analyzed models. Contrary to the findings regarding the US market, this time-series behavior is well described by the naive seasonal random walk model, whereas in the US the most adequate models are of a more sophisticated ARIMA type. Therefore, the paper demonstrates that conclusions drawn for the US might not hold for emerging economies because of the much simpler behavior of these markets that results in the absence of autoregressive and moving average parts.

Keywords

EN

earnings per share; time series; random walk; ARIMA; financial forecasting; Warsaw; Stock Exchange

• Publisher: Uniwersytet Warszawski. Wydawnictwo Naukowe Wydziału Zarządzania
• Journal: Journal of Banking and Financial Economics
• Year: 2023
• Issue: 1(19)
• Pages: 26-43

Dates

published

2023

Contributors

author

Wojciech Kuryłek

• https://orcid.org/0000000306923300

References

• Albrecht, W. S., Larry, L. L., & McKeown, J. C., (1977). The time-series properties of annual earnings. Journal of Accounting Research, 15(2), 226–244. https://doi.org/10.2307/2490350
• Asteriou, D., & Hall, S. G. (2011). ARIMA models and the Box-Jenkins methodology. In D. Asteriou & S. G Hall (Eds.), Applied Econometrics (2nd ed., pp. 265–286). Palgrave MacMillan.
• Ball, R., & Watts, R. (1972). Some time series properties of accounting income. The Journal of Finance, 27(3), 663–681. https://doi.org/10.1111/j.1540-6261.1972.tb00991.x
• Bao, B.H., Bao, D.H., & Firth, M. (1996). A note on the time series behavior of earnings per share data of Taiwanese firms. Journal of Business Finance & Accounting, 23(4), 641–650. https://doi.org/10.1111/j.1468-5957.1996.tb01030.x
• Bao, D. H., Lewis, M.T., Lin, W.T., & Manegold, J.G. (1983). Applications of time-series analysis in accounting: A review. Journal of Forecasting, 2(4), 405–423. https://doi.org/10.1002/for.3980020409
• Bathke Jr.,. A. W., & Lorek, K. S. (1984). The relationship between time-series models and the security market’s expectation of quarterly earnings. The Accounting Review, 59(2), 163–176.
• Bathke Jr., A. W., Lorek, K. S., & Willinger, G. L. (1989). Firm-size and the predictive ability of quarterly earnings data. The Accounting Review, 64(1), 49–68.
• Bathke, Jr. A. W., Lorek, K. S., & Willinger, G. L. (2004). Differential earnings behavior and the security market assessment of variation in seasonal earnings patterns. Journal of Accounting, Auditing & Finance, 19(4), 463–482. https://doi.org/10.1177/0148558X0401900407
• Beaver, W. H. (1970). The time series behavior of earnings. Journal of Accounting Research, 8, 62–99. https://doi.org/10.2307/2674693
• Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. Holden-Day.
• Bradshaw, M. T., Drake, M. S., Myers, J. N., & Myers, L. A. (2012). A re-examination of analysts’ superiority over time-series forecasts of annual earnings. Review of Accounting Studies, 17(4), 944–968. https://doi.org/10.1007/s11142-012-9185-8
• Brooks, L. D., & Buckmaster, D. A. (1976). Further evidence of the time series properties of accounting income. Journal of Finance, 31(5), 1359–1373. Wiley-Blackwell. https://doi.org/10.1111/j.1540-6261.1976.tb03218.x
• Brown, L. D., Hagerman, R. L., Griffin, P. A., & Zmijewski, M. E. (1987). Security analyst superiority relative to univariate time-series models in forecasting quarterly earnings. Journal of Accounting and Economics, 9(1), 61–87. https://doi.org/10.1016/0165-4101(87)90017-6
• Brown, L. D., & Rozeff, M. S. (1977). Univariate time-series models of quarterly accounting earnings per share: A proposed premier model (Working Paper No. 77–27). College of Business Administration, University of Iowa.
• Brown, L. D., & Rozeff, M. S. (1979a). Adaptive expectations, time-series models, and analyst forecast revision. Journal of Accounting Research, 17(2), 341–351. https://doi.org/10.2307/2490508
• Brown, L. D., & Rozeff, M. S. (1979b). Univariate time-series models of quarterly accounting earnings per share: A proposed model. Journal of Accounting Research, 17(1), 179–189. https://doi.org/10.2307/2490312
• Collins, W. A., Hopwood, W. S., & McKeown, J. C. (1984). The predictability of interim earnings over alternative quarters. Journal of Accounting Research, 22(2), 467–479. https://doi.org/10.2307/2490659
• Collins, W. A., & William, S. H. (1980). A multivariate analysis of annual earnings forecasts generated from quarterly forecasts of financial analysts and univariate time-series models. Journal of Accounting Research, 18(2), 390–406. https://doi.org/10.2307/2490585
• Conroy, R., & Harris, R. (1987). Consensus forecasts of corporate earnings: analysts’ forecasts and time series methods. Management Science, 33(6), 725–738. https://doi.org/10.1287/mnsc.33.6.725
• Corder, G. W., & Foreman, D. I. (2009). Nonparametric statistics for non-statisticians. John Wiley & Sons. https://doi.org/10.1002/9781118165881
• Cragg, J. G., & Malkiel, B. G. (1968). The consensus and accuracy of some predictions of the growth of corporate earnings. The Journal of Finance, 23(1), 67–84. https://doi.org/10.1111/j.1540-6261.1968.tb02998.x
• Damodaran, A., & Landsman, W. R. (1989). Using shrinkage estimators to improve upon time-series model proxies for the security market’s expectation of earnings. Journal of Accounting Research, 27(1), 97–115. https://doi.org/10.2307/2491209
• Elton, E. J., & Gruber, M. J. (1972). Earnings estimates and the accuracy of expectational data. Management Science, 18(8), B409–B424. https://doi.org/10.1287/mnsc.18.8.B409
• Foster, G. (1977). Quarterly accounting data: Time-series properties and predictive-ability results. The Accounting Review, 52(1), 1–21.
• Griffin, P. (1977). The time-series behavior of quarterly earnings: Preliminary evidence. Journal of Accounting Research, 15(1), 71–83. https://doi.org/10.2307/2490556
• Grigaliūnienė, Ž. (2013). Time-series models forecasting performance in the Baltic stock market. Organizations and Markets in Emerging Economies, 4(1), 104–120. https://doi.org/10.15388/omee.2013.4.1.14261
• Hyndman, R. D., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/jss.v027.i03
• Hopwood, W. S., & McKeown, J. C. (1981). An evaluation of univariate time-series earnings models and their generalization to a single input transfer function. Journal of Accounting Research, 19(2), 313–322. https://doi.org/10.2307/2490867
• Hopwood, W. S., McKeown, J. C., & Newbold, P. (1981). Power transformations in time-series models of quarterly earnings per share. The Accounting Review, 56(4), 927–933.
• Johnson, T. E., & Schmitt, T. G. (1974). Effectiveness of earnings per share forecasts. Financial Management, 3(2), 64–72. https://doi.org/10.2307/3665292
• Kim, S., & Kim, H. (2016). A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting, 32(3), 669–679. https://doi.org/10.1016/j.ijforecast.2015.12.003
• Kao, Ch., Lee, Ch., & Wu, Ch. (1996). Time-series properties of financial series and implications for modeling. Journal of Accounting, Auditing & Finance, 11(2), 277–303. https://doi.org/10.1177/0148558X9601100207
• Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54 (1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
• Lorek, K. S. (1979). Predicting annual net earnings with quarterly earnings time-series models. Journal of Accounting Research, 17(1), 190–204. https://doi.org/10.2307/2490313
• Lorek, K. S., & Willinger, G. L. (2007). The contextual nature of the predictive power of statistically-based quarterly earnings models. Review of Quantitative Finance and Accounting, 28(1), 1–22. https://doi.org/10.1007/s11156-006-0001-z
• Pagach, D. P., & Warr, R. S. (2020). Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited. Advances in Accounting, 51. https://doi.org/10.1016/j.adiac.2020.100497
• Salamon, G. L., & Smith, E. D. (1977). Additional evidence on the time series properties of reported earnings per share: Comment. The Journal of Finance, 32(5), 1795–1801. https://doi.org/10.1111/j.1540-6261.1977.tb03377.x
• Walther, B. R. (1997). Investor sophistication and market earnings expectations. Journal of Accounting Research, 35(2), 157–179. https://doi.org/10.2307/2491358
• Watts, R. L. (1975, April). The time series behavior of quarterly earnings [Working paper]. Department of Commerce, University of New Castle.
• Watts, R. L., & Leftwich, R. W. (1977). The time series of annual accounting earnings. Journal of Accounting Research, 15(2), 253–271. https://doi.org/10.2307/2490352
• Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics, 1, 80–83. https://doi.org/10.2307/3001968

Identifiers

DOI

10.7172/2353-6845.jbfe.2023.1.2

Biblioteka Nauki

19322602