Forecasting model of global stock index by stochastic time effective neural network

Authors:
Zhe Liao;Jun Wang
Affiliations:
Institute of Financial Mathematics and Financial Engineering, College of Science, Beijing Jiaotong University, Beijing 100044, PR China;Institute of Financial Mathematics and Financial Engineering, College of Science, Beijing Jiaotong University, Beijing 100044, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 2
Cited 7

Neural Network Time Series Forecasting of Financial Markets

Neural Network Time Series Forecasting of Financial Markets
Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks

Engineering Applications of Artificial Intelligence

Using artificial neural network models in stock market index prediction

Expert Systems with Applications: An International Journal
Analysis of chain reaction between two stock indices fluctuations by statistical physics systems

WSEAS Transactions on Mathematics
Original article: Voter interacting systems applied to Chinese stock markets

Mathematics and Computers in Simulation
Statistical analysis and forecasting of return interval for SSE and model by lattice percolation system and neural network

Computers and Industrial Engineering
Fluctuation prediction of stock market index by Legendre neural network with random time strength function

Neurocomputing
GDTW-P-SVMs: Variable-length time series analysis using support vector machines

Neurocomputing
Volatility clustering and long memory of financial time series and financial price model

Digital Signal Processing

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this paper, we investigate the statistical properties of the fluctuations of the Chinese Stock Index, and we study the statistical properties of HSI, DJI, IXIC and SP500 by comparison. According to the theory of artificial neural networks, a stochastic time effective function is introduced in the forecasting model of the indices in the present paper, which gives an improved neural network - the stochastic time effective neural network model. In this model, a promising data mining technique in machine learning has been proposed to uncover the predictive relationships of numerous financial and economic variables. We suppose that the investors decide their investment positions by analyzing the historical data on the stock market, and the historical data are given weights depending on their time, in detail, the nearer the time of the historical data is to the present, the stronger impact the data have on the predictive model, and we also introduce the Brownian motion in order to make the model have the effect of random movement while maintaining the original trend. In the last part of the paper, we test the forecasting performance of the model by using different volatility parameters and we show some results of the analysis for the fluctuations of the global stock indices using the model.