Abarbanell J., Bushee B. (1997) Fundamental Analysis, Future EPS, and Stock Prices. Journal of Accounting Research, 35(1), 1-24. (Crossref)

Ahmadpour A., Etemadi H., Moshashaei S. (2015) Earnings per Share Forecast using Extracted Rules from Trained Neural Network by Genetic Algorithm. Computational Economics, 46(1), 55-63. (Crossref)

Atiya A., Shaheen S., Talaat N. (1997) An Efficient Stock Market Forecasting Model using Neural Networks. IEEE International Conference on Neural Networks - Conference Proceedings.

Ball R., Ghysels E. (2017) Automated Earnings Forecasts: Beat Analysts or Combine and Conquer? Management Science, 64(10), 4936-4952. (Crossref)

Ball R. Watts R. (1972) Some Time Series Properties of Accounting Income. The Journal of Finance, 27(3), 663-681. (Crossref)

Banerjee P. (2020) A Guide on XGBoost Hyperparameters Tuning, Accessed February 14, 2024. https://www.kaggle.com/code/prashant111/a-guide-on-xgboost-hyperparameters-tuning.

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.

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, 17(4), 944-968. (Crossref)

Bengio Y., Courville A., Goodfellow I. (2017) Deep Learning. Cambridge, Massachusetts: The MIT Press.

Bengio Y., Glorot X. (2010) Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 9, 249-256.

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, 13(2), 10-15. (Crossref)

Brooks L. D., Buckmaster D. A. (1976) Further Evidence of the Time Series Properties of Accounting Income. The Journal of Finance, 31(5), 1359-1373. (Crossref)

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, 9(1), 61-87. (Crossref)

Brown L. D., Rozeff M. S. (1979) Univariate Time-Series Models of Quarterly Accounting Earnings per Share: A Proposed Model. Journal of Accounting Research, 17(1), 179-189. (Crossref)

Cao Q., Gan Q. (2009) Forecasting EPS of Chinese Listed Companies using a Neural Network with Genetic Algorithm. 15th Americas Conference on Information Systems 2009, AMCIS 2009, 2791-2981.

Cao Q., Parry M. (2009) Neural Network Earnings per Share Forecasting Models: A Comparison of Backward Propagation and the Genetic Algorithm. Decision Support Systems, 47 (1), 32-41. (Crossref)

Cao Q., Schniederjans M. J., Zhang W. (2004) Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative Methods. Decision Sciences, 35 (2), 205-237. (Crossref)

Chen Y., Chen S., Huang H., Sangaiah A. (2020) Applied Identification of Industry Data Science using an Advanced Multi-Componential Discretization Model. Symmetry, 12(10), 1-28. (Crossref)

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, 785-794. (Crossref)

Conroy R., Harris R. (1987) Consensus Forecasts of Corporate Earnings: Analysts' Forecasts and Time Series Methods. Management Science, 33(6), 725-738. (Crossref)

Dreher S., Eichfelder S., Noth F. (2024) Does IFRS Information on Tax Loss Carryforwards and Negative Performance Improve Predictions of Earnings and Cash Flows? Journal of Business Economics, 94(1), 1-39. (Crossref)

Elend L., Kramer O., Lopatta K., Tideman S. (2020) Earnings prediction with deep learning. German Conference on Artificial Intelligence (Künstliche Intelligenz), KI 2020: Advances in Artificial Intelligence, 267-274. (Crossref)

Elton E. J., Gruber M. J. (1972) Earnings Estimates and the Accuracy of Expectational Data, Management Science, 18(8), B409-B424. (Crossref)

Foster G. (1977) Quarterly Accounting Data: Time-Series Properties and Predictive-Ability Results. The Accounting Review, 52(1), 1-21.

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, 22(1), eRAMF210164. (Crossref)

Griffin P. (1977) The Time-Series Behavior of Quarterly Earnings: Preliminary Evidence. Journal of Accounting Research, 15(1), 71-83. (Crossref)

Gupta R., Khirbat G., Singh S. (2013) Optimal Neural Network Architecture for Stock Market Forecasting. Proceedings - 2013 International Conference on Communication Systems and Network Technologies, CSNT 2013, 557-561. (Crossref)

Harris R. D. F., Wang P. (2019) Model-Based Earnings Forecasts vs. Financial Analysts' Earnings Forecasts. British Accounting Review, 51(4), 424-437. (Crossref)

Heaton J. (2008) Introduction to Neural Networks for Java, 2nd Edition. Heaton Research Inc.

Hou K., van Dijk M., Zhang Y. (2012) The Implied Cost of Capital: A New Approach. Journal of Accounting and Economics, 53(3), 504-526. (Crossref)

Jarrett J. E. (2008) Evaluating Methods for Forecasting Earnings per Share. Managerial Finance, 16, 30-35. (Crossref)

Johnson T. E., Schmitt T. G. (1974) Effectiveness of Earnings per Share Forecasts. Financial Management, 3(2), 64-72. (Crossref)

Kim S., Kim H. (2016) A New Metric of Absolute Percentage Error for Intermittent Demand Forecasts. International Journal of Forecasting, 32(3), 669-679. (Crossref)

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, 1(19), 26-43. (Crossref)

Kuryłek W. (2023b) Can Exponential Smoothing Do Better than Seasonal Random Walk for Earnings per Share Forecasting in Poland? Bank & Credit, 54(6), 651-672. (Crossref)

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 (pp. 77-101), Bingley, UK, Emerald. (Crossref)

Lai S., Li H. (2006) The Predictive Power of Quarterly Earnings per Share based on Time Series and Artificial Intelligence Model. Applied Financial Economics, 16(18), 1375-1388. http://dx.doi.org/10.1080/09603100600592752. (Crossref)

Laurent C. (1979) Improving the Efficiency and Effectiveness of Financial Ratio Analysis. Journal of Business Finance & Accounting, 6(3), 401-413. (Crossref)

Lev B., Souginannis T. (2010) The Usefulness of Accounting Estimates for Predicting Cash Flows and Earnings. Review of Accounting Studies, 15(4), 779-807. (Crossref)

Lev B., Thiagarajan S. (1993) Fundamental Information Analysis. Journal of Accounting Research, 31(2), 190-215. (Crossref)

Li K. K. (2011) How Well Do Investors Understand Loss Persistence? Review of Accounting Studies, 16(3), 630-667. (Crossref)

Li K. K., Mohanram P. (2014) Evaluating Cross-Sectional Forecasting Models for the Implied Cost of Capital. Review of Accounting Studies, 19(3), 1152-1185. (Crossref)

Lorek K. S. (1979) Predicting Annual Net Earnings with Quarterly Earnings Time-Series Models, Journal of Accounting Research, 17(1), 190-204. (Crossref)

Lorek K. S, Willinger G. L. (1996) A Multivariate Time-Series Model for Cash-Flow Data. Accounting Review, 71, 81-101.

Ohlson J. A. (1995) Earnings, Book Values, and Dividends in Equity Valuation. Contemporary Accounting Research, 11(2), 661-687. (Crossref)

Ohlson J. A. (2001) Earnings, Book Values, and Dividends in Equity Valuation: An Empirical Perspective. Contemporary Accounting Research, 18(1), 107-120. https://doi.org/10.1092/7tpj-rxqn-tqc7-ffae. (Crossref)

Ohlson J. A., Juettner-Nauroth B. E. (2005) Expected EPS and EPS Growth as Determinants of Value. Review of Accounting Studies, 10(2-3), 349-365. (Crossref)

Pagach D. P., Warr R. S. (2020) Analysts Versus Time-Series Forecasts of Quarterly Earnings: A Maintained Hypothesis Revisited. Advances in Accounting, 51, 1-15. (Crossref)

Pasini A. (2015) Artificial Neural Networks for Small Dataset Analysis. Journal of Thoracic Disease, 7(5), 953-960.

Pope P. F., Wang P. (2005) Earnings Components, Accounting Bias and Equity Valuation. Review of Accounting Studies. 10(4), 387-407. (Crossref)

Pope P., Wang P. (2014) On the Relevance of Earnings Components: Valuation and Forecasting Links. Review of Quantitative Finance and Accounting. 42, 399-413. (Crossref)

Rosenblatt F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, 65(6), 386-408. (Crossref)

Ruland W. (1980) On the Choice of Simple Extrapolative Model Forecasts of Annual Earnings. Financial Management, 9(2), 30-37. (Crossref)

Suler P., Vochozka M., Vrbka J. (2020) Bankruptcy or Success? The Effective Prediction of a Company's Financial Development using LSTM. Sustainability, 12(18), 2299-2314. https://doi.org/10.3390/su12187529. (Crossref)

Watts R. L. (1975) The Time Series Behavior of Quarterly Earnings, Working paper, Department of Commerce, University of New Castle, April 1975.

Werbos P. (1988) Backpropagation: Past and Future. IEEE International Conference on Neural Networks, 343-353. (Crossref)

Wilcoxon F. (1945) Individual Comparisons by Ranking Methods. Biometrics, 1, 80-83. (Crossref)

Xiaoqiang W. (2022) Research on Enterprise Financial Performance Evaluation Method based on Data Mining. [In:] 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI). Accessed February 14, 2024. https://doi.org/10.1109/icetci55101.2022.9832404. (Crossref)