[1] M.N. Ashtiani and B. Raahmei, News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review, Expert Syste. Appl. (2023), 119509.
[2] H. Bang and D. Selva, Discovering generalized design knowledge using a multi-objective evolutionary algorithm with generalization operators, Expert Syste. Appl. 143 (2020), 113025.
[3] M.H. Chen, W.G. Kim, and Ch.Y. Chen, An investigation of the mean reversion of hospitality stock prices towards their fundamental values: The case of Taiwan, Int. J. Hospital. Manag. 26 (2007), no. 2, 453–467.
[4] J. Cunado and F. P. de Gracia, Oil price shocks and stock market returns: Evidence for some european countries, Energy Econ. 42 (2014), 365–377.
[5] J.H.V.H. De Wet and E. Du Toit, Return on equity: A popular, but flawed measure of corporate financial performance, South Afr. J. Bus. Manag. 38 (2007), no. 1, 59–69.
[6] P. Farshadi, Analyzing the performance of insurance companies and predicting future performance: a hybrid approach of fuzzy TOPSIS and artificial neural networks, Master’s thesis, Tarbiat Modares University, Tehran, Iran, 2017.
[7] D. Furlaneto, L. Soares de Oliveira, D. Menotti, and G. Cavalcanti, Bias effect on predicting market trends with emd, Expert Syste. Appl. 82 (2017).
[8] M.A. Goldberg, R.W. Helsley, and M.D. Levi, The location of international financial activity: An interregional analysis, Reg. Stud. 23 (1989), no. 1, 1–7.
[9] J.H. Holland, Genetic algorithms and adaptation, Adapt. Control Ill-defined Syst. (1984), 317–333.
[10] L. Huang and J. Wang, Forecasting energy fluctuation model by wavelet decomposition and stochastic recurrent wavelet neural network, Neurocomputing 309 (2018), 70–82.
[11] Ch.E. Jordan, S.J. Clark, and W.R. Smith, Should earnings per share (eps) be taught as a means of comparing intercompany performance?, J. Educ. Bus. 82 (2007), no. 6, 343–348.
[12] J. Kang, K. Kim, and W.C. Henderson, Economic value added (EVA): A financial performance measure, J. Account. Finance Res. 10 (2002), no. 1, 48.
[13] S. Kumar and D.P. Warne, Parametric determinants of price-earnings ratio in Indian capital markets, IUP J. Appl. Finance 15 (2009), no. 9, 63.
[14] L. Mohammadi-Karizek, Investigating the impact of new product strategy on company performance with the moderating role of CEO optimism (evidence from Tehran Stock Exchange), Master’s thesis, Khord Higher Education Institute, Bushehr-Iran, 2021.
[15] M. Motallebi, M. Alizadeh, and S. Faraji-Dizaji, Estimating shadow economy and tax evasion by considering the variables of government financial discipline and behavioral factors in Iran’s economy, Iran. Econ. Rev. 24 (2020), no. 2, 515–544.
[16] M. Pasha, Using the probabilistic approach to improve the prediction of financial performance of companies, Master’s thesis, Islamic Azad University, Chalous, Iran, 2022.
[17] Y. Peng, P.H.M. Albuquerque, H. Kimura, and C.A.P.B. Saavedra, Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators, Machine Learn. Appl. 5 (2021), 100060.
[18] S. Salimzadeh, Comparison of statistical algorithms and machine learning to predict the financial performance of companies, Master’s thesis, Urmia University, Urmia, Iran, 2022.
[19] C.J. Simon and M.W. Sullivan, The measurement and determinants of brand equity: A financial approach, Market. Sci. 12 (1993), no. 1, 28–52.
[20] M. Vogl, P. G. Rotzel, and S. Homes, Forecasting performance of wavelet neural networks and other neural network topologies: A comparative study based on financial market data sets, Machine Learn. Appl. 8 (2022), 100302.
[21] X.SH. Yang, Cuckoo search and Firefly Algorithm: Theory and Applications, vol. 516, Springer, 2007.
[22] Y. Yang and J. Wang, Forecasting wavelet neural hybrid network with financial ensemble empirical mode decomposition and mcid evaluation, Expert Syst. Appl. 166 (2021), 114097.
[23] O.C. Yolcu and U. Yolcu, A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series, Expert Syst. Appl. 215 (2023), 119336.