Comparison of New Models to Predict Prices and Stock Returns

Document Type : Original Article

Authors

Department of Accounting, Ilkhchi Branch, Islamic Azad University, Ilkhchi, Iran

Abstract

Some financial professionals proposed 20th-century patterns for share price forecasting and decision-making. The primary goal of this course is to create a pricing behavior chart and analyze inflations. Recognize the sensitivities of long-term behavior and future projection. Technical analysts or chartists are members of this group. Since they relied on graphs and curves and held that supply and demand are influenced by a wide range of circumstances, they were never able to identify them correctly. The best way to find patterns is to analyze price time series and data related to transaction volume. By analyzing price past trends and historical data, we can predict share prices in the future and recognize share future price movement. This group thought that fundamental analysis should only be used for short-term opportunities and wasn't the right scale for identifying share price behavior.

Keywords


Seryasat, O. R., & Haddadnia, J. (2018). Evaluation of a new ensemble learning framework for mass classification in mammograms. Clinical breast cancer, 18(3), e407-e420. doi:10.1016/j.clbc.2017.05.009
Rahmani-Seryasat, O., Haddadnia, J., & Ghayoumi-Zadeh, H. (2015). A new method to classify breast cancer tumors and their fractionation. Ciência e Natura, 37(4), 51-57. doi:10.5902/2179460X19428
Rahmani Seryasat, O., Haddadnia, J., & Ghayoumi Zadeh, H. (2016). Assessment of a novel computer aided mass diagnosis system in mammograms. Iranian Quarterly Journal of Breast Disease, 9(3), 31-41.           Seryasat, O. R., & Haddadnia, J. (2017). Assessment of a novel computer aided mass diagnosis system in mammograms. Biomedical Research, 28(7), 3129-3135.
Haddadnia, J., Seryasat, O. R., & Rabiee, H. (2013). Thyroid diseases diagnosis using probabilistic neural network and principal component analysis. Journal of Basic and Applied Science Research, 3(2), 593-598.
Rahmani Seryasat, O., Kor, I., Ghayoumi Zadeh, H., & Shams Taleghani, A. (2021). Predicting the number of comments on Facebook posts using an ensemble regression model. International Journal of Nonlinear Analysis and Applications, 12, 49-62.               
Rahmani Seryasat, O., Ahmadi, S., Yousefi, P., Tat Shahdost, F., & Sanei, S. (2021). Recognizing phishing websites based on a bayesian combiner. International Journal of Nonlinear Analysis and Applications, 12(Special Issue), 809-823.           
Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on neural networks, 12(4), 929-935. doi:10.1109/72.935101
Aiken, M. W., & Bsat, M. (1999). Forecasting market trends with neural networks. Inf. Syst. Manag., 16(4), 1-7. doi:10.1201/1078/43189.16.4.19990901/31202.6
Chen, N. F., Roll, R., & Ross, S. A. (1986). Economic forces and the stock market. Journal of business, 383-403. doi:10.1086/296344
Lin, C. M., Huang, J. J., Gen, M., & Tzeng, G. H. (2006). Recurrent neural network for dynamic portfolio selection. Applied Mathematics and Computation, 175(2), 1139-1146. doi:10.1016/j.amc.2005.08.031
Chiang, W. C., Urban, T. L., & Baldridge, G. W. (1996). A neural network approach to mutual fund net asset value forecasting. Omega, 24(2), 205-215. doi:10.1016/0305-0483(95)00059-3
Freitas, F. D., De Souza, A. F., & De Almeida, A. R. (2009). Prediction-based portfolio optimization model using neural networks. Neurocomputing, 72(10-12), 2155-2170. doi:10.1016/j.neucom.2008.08.019
Garliauskas, A. (1999, October). Neural network chaos and computational algorithms of forecast in finance. In IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028) (Vol. 2, pp. 638-643). IEEE.           
Wang, H., & Weigend, A. S. (2004). Data mining for financial decision making. doi:10.1016/S0167-9236(03)00090-3