An Application of Autoregressive Distributed Lag-Models for Earnings per Share Forecasting in Poland
DOI:
https://doi.org/10.18778/0208-6018.370.01Keywords:
earnings per share, random walk, Autoregressive Distributed Lags, XGBoost, financial forecasting, Warsaw Stock ExchangeAbstract
This investigation delves into the significance of precise earnings forecasts for publicly traded companies in achieving investment success. It emphasises the importance of this aspect, particularly in markets with limited analyst coverage, such as emerging markets including Poland. The study assesses the accuracy of predictions generated using the Autoregressive Distributed Lag (ARDL) framework with the XGBoost type of modelling and various methods of combining forecasts compared to the seasonal random walk model. Positioned as an intermediate step between time series and multivariate forecasting, these models are applied to earnings per share (EPS) data of companies listed on the Warsaw Stock Exchange from 2009 to 2019, i.e. the last financial crisis and the pandemic shock. The seasonal random walk model attained the lowest error rates based on the Mean Arctangent Absolute Percentage Error (MAAPE) metric, a conclusion substantiated by rigorous statistical tests and robustness checks employing different periods and error metrics. The enhanced performance of the simpler seasonal random walk model may be ascribed to the relatively uncomplicated nature of the Polish stock market.
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References
Ball R., Ghysels E. (2017), Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?, “Management Science”, vol. 64(10), pp. 4936–4952, https://doi.org/10.1287/mnsc.2017.2864 DOI: https://doi.org/10.1287/mnsc.2017.2864
Ball R., Watts R. (1972), Some Time Series Properties of Accounting Income, “The Journal of Finance”, vol. 27(3), pp. 663–681, http://doi.org/10.1111/j.1540-6261.1972.tb00991.x DOI: https://doi.org/10.1111/j.1540-6261.1972.tb00991.x
Banerjee P. (2020), A Guide on XGBoost hyperparameters tuning, https://www.kaggle.com/code/prashant111/a-guide-on-xgboost-hyperparameters-tuning [accessed: 14.02.2024].
Bansal N., Nasseh A., Strauss J. (2015), Can we consistently forecast a firm’s earnings? Using combination forecast methods to predict the EPS of Dow firms, “Journal of Economics and Finance”, vol. 39(1), pp. 1–22, https://doi.org/10.1007/s12197-012-9234-y DOI: https://doi.org/10.1007/s12197-012-9234-y
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”, vol. 59(2), pp. 163–176.
Bentancor A., Hardy N., Pincheira-Brown P. (2023), An Inconvenient Truth about Forecast Combinations, “Mathematics”, vol. 11(1)8, 3806, https://doi.org/10.3390/math11183806 DOI: https://doi.org/10.3390/math11183806
Bradshaw M., Drake M., Myers J., Myers L. (2012), A re-examination of analysts’ superiority over time-series forecasts of annual earnings, “Review of Accounting Studies”, vol. 17(4), pp. 944–968, http://doi.org/10.1007/s11142-012-9185-8 DOI: https://doi.org/10.1007/s11142-012-9185-8
Brandon Ch., Jarrett J.E., Khumawala S.B. (1987), A Comparative Study of the Forecasting Accuracy of Holt‐Winters and Economic Indicator Models of Earnings Per Share For Financial Decision Making, “Managerial Finance”, vol. 13(2), pp. 10–15, http://doi.org/10.1108/eb013581 DOI: https://doi.org/10.1108/eb013581
Brooks L.D., Buckmaster D.A. (1976), Further Evidence of the Time Series Properties of Accounting Income, “The Journal of Finance”, vol. 31(5), pp. 1359–1373, http://doi.org/10.1111/j.1540-6261.1976.tb03218.x DOI: https://doi.org/10.1111/j.1540-6261.1976.tb03218.x
Brown L.D., Rozeff M.S. (1979), Univariate Time-Series Models of Quarterly Accounting Earnings per Share: A Proposed Model, “Journal of Accounting Research”, vol. 17(1), pp. 179–189, http://doi.org/10.2307/2490312 DOI: https://doi.org/10.2307/2490312
Brown L.D., Griffin P.A., Hagerman R.L., Zmijewski M.E. (1987), Security analyst superiority relative to univariate time-series models in forecasting quarterly earnings, “Journal of Accounting and Economics”, vol. 9(1), pp. 61–87, http://doi.org/10.1016/0165-4101(87)90017-6 DOI: https://doi.org/10.1016/0165-4101(87)90017-6
Chen T., Guestrin C. (2016), XGBoost: A Scalable Tree Boosting System, “Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining”, pp. 785–794, https://doi.org/10.1145/2939672.2939785 DOI: https://doi.org/10.1145/2939672.2939785
Conroy R., Harris R. (1987), Consensus Forecasts of Corporate Earnings: Analysts’ Forecasts and Time Series Methods, “Management Science”, vol. 33(6), pp. 725–738, http://doi.org/10.1287/mnsc.33.6.725 DOI: https://doi.org/10.1287/mnsc.33.6.725
Dou Y., Tan S., Xie D. (2023), Comparison of machine learning and statistical methods in the field of renewable energy power generation forecasting: a mini review, “Frontiers in Energy Research”, vol. 11, 1218603, https://doi.org/10.3389/fenrg.2023.1218603 DOI: https://doi.org/10.3389/fenrg.2023.1218603
Elton E.J., Gruber M.J. (1972), Earnings Estimates and the Accuracy of Expectational Data, “Management Science”, vol. 18(8), pp. 409–424, http://doi.org/10.1287/mnsc.18.8.B409 DOI: 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”, vol. 52(1), pp. 1–21.
Fourkiotis K.P., Tsadiras A. (2024), Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions, “Forecasting”, vol. 6(1), pp. 170–186, https://doi.org/10.3390/forecast6010010 DOI: https://doi.org/10.3390/forecast6010010
Gaio L., Gatsios R., Lima F., Piamenta Jr. T. (2021), Re-examining analyst superiority in forecasting results of publicly-traded Brazilian companies, “Revista de Administracao Mackenzie”, vol. 22(1), eRAMF210164, https://doi.org/10.1590/1678-6971/eramf210164 DOI: https://doi.org/10.1590/1678-6971/eramf210164
Griffin P. (1977), The Time-Series Behavior of Quarterly Earnings: Preliminary Evidence, “Journal of Accounting Research”, vol. 15(11), pp. 71–83, http://doi.org/10.2307/2490556 DOI: https://doi.org/10.2307/2490556
Harris R.D.F., Wang P. (2019), Model-based earnings forecasts vs. financial analysts’ earnings forecasts, “British Accounting Review”, vol. 51(4), pp. 424–437, https://doi.org/10.1016/j.bar.2018.10.002 DOI: https://doi.org/10.1016/j.bar.2018.10.002
Hyndman R., Kang Y., Li F., Wang X. (2023), Forecast combinations: An over 50-year review, “International Journal of Forecasting”, vol. 39(4), pp. 1518–1547, https://doi.org/10.1016/j.ijforecast.2022.11.005 DOI: https://doi.org/10.1016/j.ijforecast.2022.11.005
Jarrett J.E. (2008), Evaluating Methods for Forecasting Earnings Per Share, “Managerial Finance”, vol. 16, pp. 30–35, http://doi.org/10.1108/eb013647 DOI: https://doi.org/10.1108/eb013647
Johnson T.E., Schmitt T.G. (1974), Effectiveness of Earnings Per Share Forecasts, “Financial Management”, vol. 3(2), pp. 64–72. DOI: 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”, vol. 32(3), pp. 669–679, http://doi.org/10.1016/j.ijforecast.2015.12.003 DOI: https://doi.org/10.1016/j.ijforecast.2015.12.003
Kuryłek W. (2023a), The modeling of earnings per share of Polish companies for the post-financial crisis period using random walk and ARIMA models, “Journal of Banking and Financial Economics”, vol. 1(19), pp. 26–43, http://doi.org/10.7172/2353-6845.jbfe.2023.1.2 DOI: https://doi.org/10.7172/2353-6845.jbfe.2023.1.2
Kuryłek W. (2023b), Can exponential smoothing do better than seasonal random walk for earnings per share forecasting in Poland?, “Bank & Credit”, vol. 54(6), pp. 651–672. DOI: https://doi.org/10.5604/01.3001.0054.5724
Kuryłek W. (2024), Can we profit from BigTechs’ time series models in predicting earnings per share? Evidence from Poland, “Data Science in Finance and Economics”, vol. 4(2), pp. 218–235, http://doi.org/10.3934/DSFE.2024008 DOI: https://doi.org/10.3934/DSFE.2024008
Lacina M., Lee B., Xu R. (2011), An Evaluation of Financial Analysts and Naïve Methods in Forecasting Long-Term Earnings, [in:] K.D. Lawrence, R.K. Klimberg (eds.), Advances in Business and Management Forecasting, Emerald, Bingley, pp. 77–101, http://doi.org/10.1108/S1477-4070(2011)0000008009 DOI: https://doi.org/10.1108/S1477-4070(2011)0000008009
Lorek K.S. (1979), Predicting Annual Net Earnings with Quarterly Earnings Time-Series Models, “Journal of Accounting Research”, vol. 17(1), pp. 190–204, http://doi.org/10.2307/2490313 DOI: https://doi.org/10.2307/2490313
Lorek K.S, Willinger G.L. (1996), A multivariate time-series model for cash-flow data, “Accounting Review”, vol. 71, pp. 81–101.
Pagach D.P., Warr R.S. (2020), Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited, “Advances in Accounting”, vol. 51, pp. 1–15, http://doi.org/10.1016/j.adiac.2020.100497 DOI: https://doi.org/10.1016/j.adiac.2020.100497
Pope P.F., Wang P. (2005), Earnings Components, Accounting Bias and Equity Valuation, “Review of Accounting Studies”, vol. 10(4), pp. 387–407, https://doi.org/10.1007/s11142-005-4207-4 DOI: https://doi.org/10.1007/s11142-005-4207-4
Pope P.F., Wang P. (2014), On the relevance of earnings components in valuation and forecasting links, “Review of Quantitative Finance and Accounting”, vol. 42, pp. 399–413, https://doi.org/10.1007/s11156-013-0347-y DOI: https://doi.org/10.1007/s11156-013-0347-y
Qian X.Y. (2017), Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods, pp. 1–9, https://doi.org/10.48550/arXiv.1706.00948
Ruland W. (1980), On the Choice of Simple Extrapolative Model Forecasts of Annual Earnings, “Financial Management”, vol. 9(2), pp. 30–37. DOI: https://doi.org/10.2307/3665165
Simon J., Pochetti F. (2020), Learn Amazon SageMaker. A guide to building, training, and deploying machine learning models for developers and data scientists, Packt, Birmingham–Mumbai.
Timmermann A. (2006), Forecast combinations, [in:] G. Elliott, C. Granger, A. Timmermann, Handbook of Economic Forecasting, vol. 1, Elsevier, Amsterdam, pp. 135–196. DOI: https://doi.org/10.1016/S1574-0706(05)01004-9
Watts R.L. (1975), The Time Series Behavior of Quarterly Earnings, “Working Paper, Department of Commerce, University of New Castle”, April.
Wilcoxon F. (1945), Individual Comparisons by Ranking Methods, “Biometrics”, vol. 1, pp. 80–83, http://doi.org/10.2307/3001968 DOI: https://doi.org/10.2307/3001968
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