Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Network Time Series Forecasting of Financial Markets
Neural Network Time Series Forecasting of Financial Markets
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
A New Genetic Algorithm Using Large Mutation Rates and Population-Elitist Selection (GALME)
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Forecasting the volatility of stock price index
Expert Systems with Applications: An International Journal
Extracting classification rule of software diagnosis using modified MEPA
Expert Systems with Applications: An International Journal
Effective vaccination policies
Information Sciences: an International Journal
A dynamic threshold decision system for stock trading signal detection
Applied Soft Computing
Learning approaches for developing successful seller strategies in dynamic supply chain management
Information Sciences: an International Journal
A novel model by evolving partially connected neural network for stock price trend forecasting
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A novel parallel hybrid intelligence optimization algorithm for a function approximation problem
Computers & Mathematics with Applications
A rough set approach for estimating correlation measures in quality function deployment
Information Sciences: an International Journal
Rough set-based approach for modeling relationship measures in product planning
Information Sciences: an International Journal
Information Sciences: an International Journal
A parallel method for computing rough set approximations
Information Sciences: an International Journal
Rough sets in the Soft Computing environment
Information Sciences: an International Journal
Information Sciences: an International Journal
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
Information Sciences: an International Journal
Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction
Information Sciences: an International Journal
Information Sciences: an International Journal
High-order fuzzy-neuro expert system for time series forecasting
Knowledge-Based Systems
Core set analysis in inconsistent decision tables
Information Sciences: an International Journal
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Information Sciences: an International Journal
Fuzzy artificial neural network p, d, q model for incomplete financial time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have been found in many of the past forecasting models: (1) statistical assumptions about variables are required for time series models, such as the autoregressive moving average model (ARMA) and the autoregressive conditional heteroscedasticity (ARCH), to produce forecasting models of mathematical equations, and these are not easily understood by stock investors; and (2) the rules mined from some artificial intelligence (AI) algorithms, such as neural networks (NN), are not easily realized. In order to overcome these drawbacks, this paper proposes a hybrid forecasting model, using multi-technical indicators to predict stock price trends. Further, it includes four proposed procedures in the hybrid model to provide efficient rules for forecasting, which are evolved from the extracted rules with high support value, by using the toolset based on rough sets theory (RST): (1) select the essential technical indicators, which are highly related to the future stock price, from the popular indicators based on a correlation matrix; (2) use the cumulative probability distribution approach (CDPA) and minimize the entropy principle approach (MEPA) to partition technical indicator value and daily price fluctuation into linguistic values, based on the characteristics of the data distribution; (3) employ a RST algorithm to extract linguistic rules from the linguistic technical indicator dataset; and (4) utilize genetic algorithms (GAs) to refine the extracted rules to get better forecasting accuracy and stock return. The effectiveness of the proposed model is verified with two types of performance evaluations, accuracy and stock return, and by using a six-year period of the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) as the experiment dataset. The experimental results show that the proposed model is superior to the two listed forecasting models (RST and GAs) in terms of accuracy, and the stock return evaluations have revealed that the profits produced by the proposed model are higher than the three listed models (Buy-and-Hold, RST and GAs).