Book Chapter10.1007/978-3-642-14831-6_54
Developing an Evolutionary Neural Network Model for Stock Index Forecasting
Esmaeil Hadavandi,Arash Ghanbari,Salman Abbasian-Naghneh +2 more
- 18 Aug 2010
- pp 407-415
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TL;DR: Results show that the proposed approach is able to cope with the fluctuation of stock market values and it also yields good forecasting accuracy, so it can be considered as a suitable tool to deal with stock market forecasting problems.
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Abstract: The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the movement of the market. This paper proposes a hybrid artificial intelligence model for stock exchange index forecasting, the model is a combination of genetic algorithms and feedforward neural networks. Actually it evolves neural network weights by using genetic algorithms. We also employ preprocessing methods for improving accuracy of the proposed model. We test capability of the proposed method by applying it to forecast Tehran Stock Exchange Prices Indexes (TEPIX) which is used in literature, and compare the results with previous forecasting methods and Back-propagation neural network (BPNN). Results show that the proposed approach is able to cope with the fluctuation of stock market values and it also yields good forecasting accuracy. So it can be considered as a suitable tool to deal with stock market forecasting problems.
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Citations
Predicting stock market index using fusion of machine learning techniques
TL;DR: The paper proposes two stage fusion approach involving Support Vector Regression (SVR) in the first stage and second stage of the fusion approach uses Artificial Neural Network (ANN), Random Forest (RF) and SVR resulting into SVR-ANN, Svr-RF and S VR-SVR fusion prediction models.
564
Stock Market Forecasting Using Computational Intelligence: A Survey
TL;DR: This paper presents an up-to-date survey of existing literature on stock market forecasting based on computational intelligent methods and presents the outlines of proposed work with the aim to enhance the performance of existing techniques.
155
Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models
Milad Shahvaroughi Farahani,Seyed Hossein Razavi Hajiagha +1 more
- 25 Apr 2021
TL;DR: In this article, the authors predict stock price indices using artificial neural network (ANN) and train it with some new metaheuristic algorithms such as social spider optimization (SSO) and bat algorithm (BA).
A calibration procedure for analyzing stock price dynamics in an agent-based framework
Maria Cristina Recchioni,Maria Cristina Recchioni,Gabriele Tedeschi,Gabriele Tedeschi,Mauro Gallegati +4 more
TL;DR: In this article, a calibration procedure for validating of agent-based models is introduced, based on the Brock and Hommes model, which can be solved numerically via a gradient-based method.
72
A Grey Wolf Optimizer-based neural network coupled with response surface method for modeling the strength of siro-spun yarn in spinning mills
TL;DR: The prediction accuracy of the GWNN was compared with that of a MLP neural network trained with Back-Propagation algorithm and a Multiple Linear Regression model as well as three evolutionary-based neural networks and it was found that the proposed GWNN enjoys higher accuracy as compared with other models.
48
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