Stock Market Forecasting Using Computational Intelligence: A Survey

Stock market plays a key role in economical and social organization of a country. Stock market forecasting is highly demanding and most challenging task for investors, professional analyst and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dyna...

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Vydáno v:Archives of computational methods in engineering Ročník 28; číslo 3; s. 1069 - 1101
Hlavní autoři: Kumar, Gourav, Jain, Sanjeev, Singh, Uday Pratap
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.05.2021
Springer Nature B.V
Témata:
Artificial intelligence
Artificial neural networks
Economic forecasting
Engineering
Evolutionary algorithms
Forecasting
Fuzzy logic
Genetic algorithms
Mathematical and Computational Engineering
Mathematical models
Original Paper
Performance evaluation
Performance measurement
Securities markets
ISSN:1134-3060, 1886-1784
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Shrnutí:Stock market plays a key role in economical and social organization of a country. Stock market forecasting is highly demanding and most challenging task for investors, professional analyst and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. Prediction of stock market is a crucial task and prominent research area in financial domain as investing in stock market involves higher risk. However with the development of computational intelligent methods it is possible to reduce most of the risk. In this survey paper, our focus is on application of computational intelligent approaches such as artificial neural network, fuzzy logic, genetic algorithms and other evolutionary techniques for stock market forecasting. This paper presents an up-to-date survey of existing literature on stock market forecasting based on computational intelligent methods. In this article, the selected papers are organized and discussed according to six main point of view: (1) the stock market analyzed and the related dataset, (2) the type of input variables investigated, (3) the pre-processing techniques used, (4) the feature selection techniques to choose effective variables, (5) the forecasting models to deal with the stock price forecasting problem and (6) performance metrics utilized to evaluate the models. The major contribution of this work is to provide the researcher and financial analyst a systematic approach for development of intelligent methodology to forecast stock market. This paper also presents the outlines of proposed work with the aim to enhance the performance of existing techniques.
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ISSN:1134-3060
1886-1784
DOI:10.1007/s11831-020-09413-5