Stock market prediction and Portfolio selection models: a survey

被引：36
作者：
Rather A.M. [1 ]
Sastry V.N. [2 ]
Agarwal A. [1 ]
机构：
[1] School of Computer and Information Sciences, University of Hyderabad, Hyderabad
[2] Institute for Development and Research in Banking Technology, Castle Hills, Masab Tank, Hyderabad
来源：
OPSEARCH | 2017年 / 54卷 / 3期
关键词：
Artificial intelligence based models; Portfolio selection models; Stock returns; Time series prediction;
D O I：
10.1007/s12597-016-0289-y
中图分类号：
学科分类号：
摘要：
Stock data is known to be chaotic in nature and it is a challenging task to predict the non-linear patterns of such data. Forming an optimal portfolio of stocks is yet another challenging task and limitations do exist in every portfolio model in some form or the other. In order to resolve such problems, many artificial intelligence models have appeared in literature which are also known as intelligent models. Prediction of stocks as well as investing in appropriate stocks has remained in focus among investors, industrialists as well as among academicians. This paper surveys important published articles in the related area available in literature. This survey highlights traditional mathematical models available in articles which have appeared decades back till artificial intelligence based models available in recent articles. © 2017, Operational Research Society of India.
引用
收藏
页码：558 / 579
页数：21
相关论文
共 144 条
[1]  
Abu-Mostafa Y.S., Atiya A.F., Introduction to financial forecasting, Appl. Intell., 6, pp. 205-213, (1996)
[2]  
Adhikari R., Agrawal R.K., A combination of artificial neural network and random walk models for financial time series forecasting, Neural Comput. Appl., 24, pp. 1441-1449, (2014)
[3]  
Ahmed E., El-Alem M., On multiobjective optimization in portfolio management, Appl. Math. Comput., 167, pp. 616-621, (2005)
[4]  
Akcay Y., Yalcin A., Optimal portfolio selection with a shortfall probability constraint: evidence from alternative distribution functions, J. Financ. Res., 33, pp. 77-102, (2010)
[5]  
Alexander G.J., Baptist A.M., Yan S., Mean–variance portfolio selection at-risk constraints and discrete and discrete distributions, J. Bank. Finance, 31, pp. 3761-3781, (2007)
[6]  
Altaya N., Rudisillb F., Litterala L.A., Adapting wright’s modification of holt’s method to forecasting intermittent demand, Int. J. Prod. Econ., 111, pp. 389-408, (2008)
[7]  
Ammar E.E., On solutions of fuzzy random multiobjective quadratic programming with applications in portfolio problem, Inf. Sci., 178, pp. 468-484, (2008)
[8]  
Anagnostopoulos K.P., Mamanis G., Multiobjective evolutionary algorithms for complex portfolio optimization problems, Comput. Manag. Sci., 8, pp. 259-279, (2011)
[9]  
Arajo R.A., Oliveira A.L.I., Meira S., A hybrid model for high-frequency stock market forecasting, Expert Syst. Appl., 42, pp. 4081-4096, (2015)
[10]  
Araujo R.A., A hybrid intelligent morphological approach for stock market forecasting, Neural Process. Lett., 31, pp. 195-217, (2010)
12345678910