On-Demand Forecasting of Stock Prices Using a Real-Time Predictor
IEEE Transactions on Knowledge and Data Engineering
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Self-organizing learning array and its application to economic and financial problems
Information Sciences: an International Journal
Information Sciences: an International Journal
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
Intelligent stock trading system by turning point confirming and probabilistic reasoning
Expert Systems with Applications: An International Journal
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
Weighted Kernel Regression for Predicting Changing Dependencies
ECML '07 Proceedings of the 18th European conference on Machine Learning
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Trading rule discovery in the US stock market: An empirical study
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evolving least squares support vector machines for stock market trend mining
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
Information Sciences: an International Journal
Clustering of time series data-a survey
Pattern Recognition
IEEE Transactions on Audio, Speech, and Language Processing
A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms
IEEE Transactions on Fuzzy Systems
System reliability forecasting by support vector machines with genetic algorithms
Mathematical and Computer Modelling: An International Journal
Computational learning techniques for intraday FX trading using popular technical indicators
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
A Hybrid Neurogenetic Approach for Stock Forecasting
IEEE Transactions on Neural Networks
Stock index tracking by Pareto efficient genetic algorithm
Applied Soft Computing
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Information Sciences: an International Journal
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Trading signal detection has become a very popular research topic in financial investment area. This paper develops a model using the Piecewise Linear Representations (PLR) and Artificial Neural Networks (ANNs) to analyze the nonlinear relationships between the stock closed price and various technical indexes, and uncovering the knowledge of trading signals hidden in historical data. Piecewise Linear Representation tools are applied to find the best stock turning points (trading signals) based on the historical data. These turning points represent short-term trading signals for selling or buying stocks from the market. This study further applies an Artificial Neural Network model to learn the connection weights from these historical turning points, and afterwards an exponential smoothing based dynamic threshold model is used to forecast the future trading signals. The stock trading signal is predicted using the neural network on a daily basis. The dynamic threshold bounds generated provide a guide for triggering a buy or sell decision when the ANN-predicted trading signal goes above or under the threshold bounds. Through a series of experiments, this research shows superior results than our previous research (Chang et al., 2009 [1]) and other benchmark researches.