Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait

Authors:
Mohamed M. Mostafa
Affiliations:
New York Institute of Technology, Global Program in Bahrain, Manama, Bahrain
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 10
Cited 5

The unpredictability of standard back propagation neural networks in classification applications

Management Science
Advanced Methods in Neural Computing

Advanced Methods in Neural Computing
A comparison between Fama and French's model and artificial neural networks in predicting the Chinese stock market

Computers and Operations Research
A comparison of global, recurrent and smoothed-piecewise neural models for Istanbul stock exchange (ISE) prediction

Pattern Recognition Letters
Modelling small-business credit scoring by using logistic regression, neural networks and decision trees: Research Articles

International Journal of Intelligent Systems in Accounting and Finance Management
Forecasting Economic Data with Neural Networks

Computational Economics
Comparison of the Levels of Accuracy of an Artificial Neural Network Model and a Logistic Regression Model for the Diagnosis of Acute Appendicitis

Journal of Medical Systems
RAILWAY PASSENGER TRAFFIC VOLUME PREDICTION BASED ON NEURAL NETWORK

Applied Artificial Intelligence
A neural-network-based nonlinear metamodeling approach to financial time series forecasting

Applied Soft Computing
A general regression neural network

IEEE Transactions on Neural Networks

A novel model by evolving partially connected neural network for stock price trend forecasting

Expert Systems with Applications: An International Journal
Stock price prediction based on procedural neural networks

Advances in Artificial Neural Systems
An empirical study of intelligent expert systems on forecasting of fashion color trend

Expert Systems with Applications: An International Journal
Using a fuzzy association rule mining approach to identify the financial data association

Expert Systems with Applications: An International Journal
A partially connected neural evolutionary network for stock price index forecasting

ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications

Quantified Score

Hi-index	12.06

Visualization

Abstract

Financial time series are very complex and dynamic as they are characterized by extreme volatility. The major aim of this research is to forecast the Kuwait stock exchange (KSE) closing price movements using data for the period 2001-2003. Two neural network architectures: multi-layer perceptron (MLP) neural networks and generalized regression neural networks are used to predict the KSE closing price movements. The results of this study show that neuro-computational models are useful tools in forecasting stock exchange movements in emerging markets. These results also indicate that the quasi-Newton training algorithm produces less forecasting errors compared to other training algorithms. Due to their robustness and flexibility of modeling algorithms, neuro-computational models are expected to outperform traditional statistical techniques such as regression and ARIMA in forecasting stock exchanges' price movements.