Predicting stock index increments by neural networks: The role of trading volume under different horizons

Authors:
Xiaotian Zhu;Hong Wang;Li Xu;Huaizu Li
Affiliations:
National University of Singapore, School of Computing, Singapore, Old Dominion University, College of Business & Public Administration, Department of Finance, 2004 Constant Hall, Norfolk, VA 23529 ...;School of Business and Economics, Department of Business Administration, North Carolina A&T State University, Greensboro, NC 27411, USA;Department of Information Technology and Decision Science, Old Dominion University, Norfolk, VA 23529, USA;School of Management, Xian Jiaotong University, Xian 710049, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 7
Cited 10

Stock Market Prediction with Backpropagation Networks

IEA/AIE '92 Proceedings of the 5th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Time Dependent Directional Profit Model for Financial Time Series Forecasting

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Towards an Artificial Technical Analysis of Financial Markets

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Analysis of the predictive ability of time delay neural networksapplied to the S&P 500 time series

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Neural networks in financial engineering: a study in methodology

IEEE Transactions on Neural Networks
Financial volatility trading using recurrent neural networks

IEEE Transactions on Neural Networks
Parallel nonlinear optimization techniques for training neural networks

IEEE Transactions on Neural Networks

Review: TAR-cointegration neural network model: An empirical analysis of exchange rates and stock returns

Expert Systems with Applications: An International Journal
An approach for moving object recognition based on BPR and CI

Information Systems Frontiers
Integrating independent component analysis-based denoising scheme with neural network for stock price prediction

Expert Systems with Applications: An International Journal
Short Communication: Forecasting changes in Korea Composite Stock Price Index (KOSPI) using association rules

Expert Systems with Applications: An International Journal
Analysis of decision making factors for equity investment by DEMATEL and Analytic Network Process

Expert Systems with Applications: An International Journal
Using artificial neural network models in stock market index prediction

Expert Systems with Applications: An International Journal
Fuzzy net present values for capital investments in an uncertain environment

Computers and Operations Research
A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach

Computers and Operations Research
A comparative study of artificial neural networks, and decision trees for digital game content stocks price prediction

Expert Systems with Applications: An International Journal
Predicting stock market returns from malicious attacks: A comparative analysis of vector autoregression and time-delayed neural networks

Decision Support Systems

Quantified Score

Hi-index	12.06

Visualization

Abstract

Recent studies show that there is a significant bidirectional nonlinear causality between stock return and trading volume. In this research, we reinforce this statement and the results presented in some earlier literatures and further investigate whether trading volume can significantly improve the prediction performance of neural networks under short-, medium-and long-term forecasting horizons. An application of component-based neural networks is used in forecasting one-step ahead stock index increments. The models are also augmented by the addition of different combinations of indices' and component stocks' trading volumes as inputs to form more general ex-ante forecasting models. Neural networks are trained with the data of stock returns and volumes from NASDAQ, DJIA and STI indices. Results indicate that augmented neural network models with trading volumes lead to improvements, at different extents, in forecasting performance under different terms of forecasting horizon. Empirical results indicate that trading volumes lead to modest improvements on the performance of stock index increments prediction under medium-and long-term horizons.