MLP, CNN, LSTM and Hybrid SVM for Stock Index Forecasting Task to INDU and FTSE100

2020 ◽  
Author(s):  
Xiangyu Zong
Keyword(s):  
Stock Index ◽  
Stock Index Forecasting

Fuzzy dual-factor time-series for stock index forecasting

2009 ◽  
Vol 36 (1) ◽  
pp. 165-171 ◽  
Author(s):  
Hsing-Hui Chu ◽  
Tai-Liang Chen ◽  
Ching-Hsue Cheng ◽  
Chen-Chi Huang
Keyword(s):  
Time Series ◽  
Stock Index ◽  
Stock Index Forecasting

Higher Order Neural Networks for Stock Index Modeling

2009 ◽  
pp. 113-131 ◽  
Author(s):  
Yuehui Chen ◽  
Peng Wu ◽  
Qiang Wu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Prices ◽  
Higher Order ◽  
Stock Index ◽  
Training Algorithms ◽  
Window Method ◽  
Higher Order Neural Networks ◽  
Artificial Neural ◽  
Stock Index Forecasting

Artificial Neural Networks (ANNs) have become very important in making stock market predictions. Much research on the applications of ANNs has proven their advantages over statistical and other methods. In order to identify the main benefits and limitations of previous methods in ANNs applications, a comparative analysis of selected applications is conducted. It can be concluded from analysis that ANNs and HONNs are most implemented in forecasting stock prices and stock modeling. The aim of this chapter is to study higher order artificial neural networks for stock index modeling problems. New network architectures and their corresponding training algorithms are discussed. These structures demonstrate their processing capabilities over traditional ANNs architectures with a reduction in the number of processing elements. In this chapter, the performance of classical neural networks and higher order neural networks for stock index forecasting is evaluated. We will highlight a novel slide-window method for data forecasting. With each slide of the observed data, the model can adjusts the variable dynamically. Simulation results show the feasibility and effectiveness of the proposed methods.

NEURAL NETWORKS FOR TECHNICAL ANALYSIS: A STUDY ON KLCI

1999 ◽  
Vol 02 (02) ◽  
pp. 221-241 ◽  
Author(s):  
JINGTAO YAO ◽  
CHEW LIM TAN ◽  
HEAN-LEE POH
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Exchange ◽  
Emerging Market ◽  
Neural Nets ◽  
Stock Index ◽  
Range Analysis ◽  
The Neural Network ◽  
Rescaled Range Analysis ◽  
Stock Index Forecasting

This paper presents a study of artificial neural nets for use in stock index forecasting. The data from a major emerging market, Kuala Lumpur Stock Exchange, are applied as a case study. Based on the rescaled range analysis, a backpropagation neural network is used to capture the relationship between the technical indicators and the levels of the index in the market under study over time. Using different trading strategies, a significant paper profit can be achieved by purchasing the indexed stocks in the respective proportions. The results show that the neural network model can get better returns compared with conventional ARIMA models. The experiment also shows that useful predictions can be made without the use of extensive market data or knowledge. The paper, however, also discusses the problems associated with technical forecasting using neural networks, such as the choice of "time frames" and the "recency" problems.

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