A Financial Trading System Using Rough Set Classifier (original) (raw)

Discovering Stock Price Prediction Rules Using Rough Sets

The use of computational intelligence systems such as neural networks, fuzzy set, genetic algorithms, etc. for stock market predictions has been widely established. This paper presents a generic stock pricing prediction model based on rough set approach. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data, which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. Using a data set consisting of daily movements of a stock traded in Kuwait Stock Exchange, a preliminary assessment indicates that rough sets is shown to be applicable and is an effective tool to achieve this goal. For comparison, the results obtained using rough set approach were compared to that of neural networks algorithm and it was shown that Rough set approach have a higher overall accuracy rate and generates more compact and fewer rules than neural networks.

Rough Set Generating Prediction Rules for Stock Price Movement

2008

This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movements. These rules then could be used to guide investors whether to buy, sell or hold a stock. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree and neural networks algorithms have been made. Rough sets show a higher overall accuracy rates reaching over 97%and generate more compact rules.

A Generic Scheme for Generating Prediction Rules Using Rough Sets

2009

This chapter presents a generic scheme for generating prediction rules based on rough set approach for stock market prediction. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data, which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts.

A predictive model construction applying rough set methodology for Malaysian stock market returns

This paper describes the invention about the stock market prediction for use of investors. More specifically, the stock market's movements are analyzed and predicted in order to retrieve knowledge that could guide investors on when to buy and sell. Through a case study on trading Kuala Lumpur Composite Index and individual firms listed in Bursa Malaysia, rough sets is shown to be an applicable and effective tool for stock market analysis. The ability of rough set approach to discover dependencies in data while eliminating superfluous factors in noisy stock market data deems very useful to extract trading rules. This is very crucial to detect market timing for market timing is detected by capturing the major turning points in data. Nevertheless, one failure of the predictive system developed in this research is its inability to detect numerous minor trends displayed by volatile individual firms, thus the failure to produce effective trading signals to generate profits above the naive strategy for these firms.

A Rough Set Based Classification Model for the Generation of Decision Rules

International Journal of Database Theory and Application, 2014

This paper introduces a very important classification aspect for the analysis of huge amount of data stored in databases and other repositories. Numerous classification models are available in the literature, to predict the class of objects whose class level is unknown. Literature reveals that most of the available models are not capable in handling imperfect data. In view of this, present paper proposes a new rough set based classification model to derive the classification (IF-THEN) rules. Furthermore, developed model has been applied to handle bank-loan applications database as either safe, unsafe or risky. However, proposed model can also be used for the analysis of data from other domains.

New learning models for generating classification rules based on rough set approach

2000

Data sets, static or dynamic, are very important and useful for presenting real life features in different aspects of industry, medicine, economy, and others. Recently, different models were used to generate knowledge from vague and uncertain data sets such as induction decision tree, neural network, fuzz y logic, genetic algorithm, rough set theory, and others. All of these models take long time to learn for a huge and dynamic data set. Thus, the challenge is how to develop an efficient model that can decrease the learning time without affecting the quality of the generated classification rules. Huge information systems or data sets usually have some missing values due to unavailable data that affect the quality of the generated classification rules. Missing values lead to the difficulty of extracting useful information from that data set. Another challenge is how to solve the problem of missing data.

Rough Set Approach in Machine Learning: A Review

International Journal of Computer Applications, 2012

The Rough Set (RS) theory can be considered as a tool to reduce the input dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there has been a rapid growth in interest in rough set theory and its applications in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, data preprocessing, knowledge discovery, decision analysis, and expert systems. This paper discusses the basic concepts of rough set theory and point out some rough set-based research directions and applications. The discussion also includes a review of rough set theory in various machine learning techniques like clustering, feature selection and rule induction.

A Decision tree- Rough set Hybrid System for Stock Market Trend Prediction

2010

Prediction of stock market trends has been an area of great interest both to those who wish to profit by trading stocks in the stock market and for researchers attempting to uncover the information hidden in the stock market data. Applications of data mining techniques for stock market prediction, is an area of research which has been receiving a lot of attention recently. This work presents the design and performance evaluation of a hybrid decision tree-rough set based system for predicting the next days" trend in the Bombay Stock Exchange (BSE-SENSEX). Technical indicators are used in the present study to extract features from the historical SENSEX data. C4.5 decision tree is then used to select the relevant features and a rough set based system is then used to induce rules from the extracted features. Performance of the hybrid rough set based system is compared to that of an artificial neural network based trend prediction system and a naive bayes based trend predictor. It is observed from the results that the proposed system outperforms both the neural network based system and the naive bayes based trend prediction system. 2 algorithm. Forecasting was then done based on rule support values from rough set algorithm.

Building a Rough Sets-Based Prediction Model of Tick-Wise Stock Price Fluctuations

Time Series Analysis, Modeling and Applications

Rough sets enable us to mine knowledge in the form of IF-THEN decision rules from a data repository, a database, a web base, and others. Decision rules are used to reason, estimate, evaluate, and forecast. The objective of this paper is to build the rough sets-based model for analysis of time series data with tick-wise price fluctuations where knowledge granules are mined from the data set of tickwise price fluctuations. We show how a method based on rough sets helps acquire the knowledge from time-series data. The method enables us to obtain IF-THEN type rules for forecasting stock prices.