A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5

Authors:
Jao-Hong Cheng;Huei-Ping Chen;Yi-Min Lin
Affiliations:
Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliou, Yunlin 64002, Taiwan;Department of Information Management, National Yunlin University of Science and Technology, 123, Section 3, University Road, Douliou, Yunlin 64002, Taiwan;Department of Information Management, Transworld Institute of Technology, 1221, Jen-Nang Rd., Doulin, Yunlin, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 17
Cited 6

Artificial intelligence (3rd ed.)

Artificial intelligence (3rd ed.)
C4.5: programs for machine learning

C4.5: programs for machine learning
An intelligent approach to time series identification by a neural network-driven decision tree classifier

Decision Support Systems
Forecasting S&P 500 stock index futures with a hybrid AI system

Decision Support Systems
Genetic Algorithms and Investment Strategies

Genetic Algorithms and Investment Strategies
Ensembling neural networks: many could be better than all

Artificial Intelligence
Rough Sets: Mathematical Foundations

Rough Sets: Mathematical Foundations
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Induction of Decision Trees

Machine Learning
Rough set methods in feature selection and recognition

Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Application of neural networks to an emerging financial market: forecasting and trading the Taiwan stock index

Computers and Operations Research - Special issue: Emerging economics
A Compact and Accurate Model for Classification

IEEE Transactions on Knowledge and Data Engineering
An illustration of variable precision rough sets model: an analysis of the findings of the UK Monopolies and Mergers Commission

Computers and Operations Research
Forecasting the volatility of stock price index

Expert Systems with Applications: An International Journal
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting

Expert Systems with Applications: An International Journal
NeC4.5: Neural Ensemble Based C4.5

IEEE Transactions on Knowledge and Data Engineering
Improved use of continuous attributes in C4.5

Journal of Artificial Intelligence Research

Using artificial neural network models in stock market index prediction

Expert Systems with Applications: An International Journal
A novel model by evolving partially connected neural network for stock price trend forecasting

Expert Systems with Applications: An International Journal
Review: Hybrid expert systems: A survey of current approaches and applications

Expert Systems with Applications: An International Journal
An intelligent supplier evaluation, selection and development system

Applied Soft Computing
A hybrid intelligent approach for modeling brand choice and constructing a market response simulator

Knowledge-Based Systems
Granular neural networks

Artificial Intelligence Review

Quantified Score

Hi-index	12.06

Visualization

Abstract

One of the major difficulties in investment strategy is to integrate supply chain with finance for controlling the marketing timing. The present study uses not only the different indexes in fundamental and technical analysis, but also the rough set theory and artificial neural networks inference system to construct three investment market timing classification models. This includes probabilistic neural network classification model, rough set classification model and hybrid classification model combining probabilistic neural network, rough sets and C4.5 decision tree. We use the forecasting accuracy and investment return to evaluate the efficacy of these three classification models. Empirical experimentation shown hybrid classification model help construct a better predictive power trading system in terms of stock market timing analysis.