same topics via google Hiromitsu Watanabe, Basabi Chakraborty, and Goutam Chakraborty (2007) Rough Neuro Voting System for Data Mining: Application to Stock Price Prediction RSKT 2007, LNAI 4481, pp. 558.565, 2007. Kyoung-jae Kim, Jin-nyoung Huh and Ingoo Han Trading rule extraction in stock market using the rough set approach "... some researchers reported that ... [1] Keywords: rough-set-theory, stock-market-prediction ================================================================================================== Golan, R.H.; Ziarko, W. (1995) "A methodology for stock market analysis utilizing rough set theory." Computational Intelligence for Financial Engineering, Proceedings of the IEEE/IAFE, pp. 32--40 Summary: Quants are aiding brokers and investment managers for stock market analysis and prediction. The Quant's black magic stems from many of the evolving artificial intelligence (AI) techniques. Extensive literature exists describing attempts to use AI techniques, and in particular neural networks, for analyzing stock market variations. The main problem with neural networks, however is the tremendous difficulty in interpreting the results. The neural nets approach is a black box approach in which no new knowledge regarding the nature of the interactions between the market indicators and the stock market fluctuations is extracted from the market data. Consequently, there is a need to develop methodologies and tools which would help in increasing the degree of understanding of market processes and, at the same time, would allow for relatively accurate predictions. The methods stemming from the research on knowledge discovery in databases (KDD) seem to provide a good mix of predictive and knowledge acquisition capabilities for the purpose of market prediction and market data analysis. This paper describes the methodology of rough sets while citing two applications which apply rough set theory (BST) for stock market analysis using Datalogic/R+. This is based on the variable precision model of rough sets (VPRS) to acquire new knowledge from market data -------------------------------------------------------------------------------------------------- Mining time series using rough sets -- A case study (1997) Lecture Notes in Computer Science, Springer, pp. 351-358 -------------------------------------------------------------------------------------------------- Kyoung-jae Kim, Jin-nyoung Huh, and Ingoo Han (1999) "Trading rule extraction in stock market using the rough set approach." ??????????? ???? No.2, 1999. 11, pp. 337-346(10) Abstract: In this paper, we propose the rough set approach to extract trading rules able to discriminate between bullish and bearish markets in stock market. The rough set approach is very valuable to extract trading rules. First, it does not make any assumption about the distribution of the data. Second, it not only handles noise well, but also eliminates irrelevant factors. In addition, the rough set approach appropriate for detecting stock market timing because this approach does not generate the signal for trade when the pattern of market is uncertain. The experimental results are ecouraging and prove the usefulness of the rough set approach for stock market analysis with respect to profitability. -------------------------------------------------------------------------------------------------- Suk Jun Lee and Jae Joon Ahn and Kyong Joo Oh and Tae Yoon Kim and Hyoung Yong Lee and Chi Woo Song (2009) "Using Rough Set to Support Investment Strategies of Rule-Based Trading with Real-Time Data in Futures Market." Hawaii International Conference on System Sciences Investment strategies in stock market have gained unprecedented popularity in major financial markets around the world. However, it is a very difficult problem because of the fluctuation of the stock market. This study presents usefulness of rough set on the rule base to develop real-time investment strategies using technical analysis in futures market. This study consists of four phases. In the first phase, meaningful technical indicators are selected to reflect market movements. In the second phase, rough set is used to extract trading rules for identification of buy and sell patterns in the stock market. In the third phase, the investment strategies are developed in order to apply selected trading rules using rule-based reasoning to unpredictable stock market. Finally, investment strategies on the basis of rule base are evaluated by real-time trading. This study then examines the profitability of the proposed model. rough-set-theory, stock-market-prediction, principal-component-analysis ================================================================================================== Chengdong Wu, Yong Yue, Mengxin Li, Osei Adjei (2004) "The rough set theory and applications." Journal: Engineering Computations Vol. 21 Issue 5 pp 488-511 You do not have rights to view the article Abstract: This paper presents a comprehensive review of the available literature on applications of the rough set theory. Concepts of the rough set theory are discussed for approximation, dependence and reduction of attributes, decision tables and decision rules. The applications of rough sets are discussed in pattern recognition, information processing, business and finance, industry, environment engineering, medical diagnosis and medical data analysis, system fault diagnosis and monitoring and intelligent control systems. Development trends and future efforts are outlined. An extensive list of references is also provided to encourage interested readers to pursue further investigations. -------------------------------------------------------------------------------------------------- Mohammed Sammany and T. Medhat (2007) "Dimensionality Reduction Using Rough Set Approach for Two Neural Networks-Based Applications." Lecture Notes in Computer Science, Springer, Volume 4585 Abstract: In this paper, Rough Sets approach has been used to reduce the number of inputs for two neural networks-based applications that are, diagnosing plant diseases and intrusion detection. After the reduction process, and as a result of decreasing the complexity of the classifiers, the results obtained using Multi-Layer Perceptron (MLP) revealed a great deal of classification accuracy without affecting the classification decisions. rough-set-theory, stock-market-prediction, principal-component-analysis, neural-network, ================================================================================================== K. Thangavela and A. Pethalakshmib (2008) "Dimensionality reduction based on rough set theory: A review." Abstract: A rough set theory is a new mathematical tool to deal with uncertainty and vagueness of decision system and it has been applied successfully in all the fields. It is used to identify the reduct set of the set of all attributes of the decision system. The reduct set is used as preprocessing technique for classification of the decision system in order to bring out the potential patterns or association rules or knowledge through data mining techniques. Several researchers have contributed variety of algorithms for computing the reduct sets by considering different cases like inconsistency, missing attribute values and multiple decision attributes of the decision system. This paper focuses on the review of the techniques for dimensionality reduction under rough set theory environment. Further, the rough sets hybridization with fuzzy sets, neural network and metaheuristic algorithms have also been reviewed. The performance analysis of the algorithms has been discussed in connection with the classification.