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

Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges—New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies—Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

show abstract

“…Later Wang in 2003 also utilized it in their work [16]. It was then proposed in the methodology by Nair et al in 2010 [17].…”

Section: Artificial Intelligence Systemsmentioning

confidence: 99%

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

et al. 2018

View full text Add to dashboard Cite

show abstract

“…SVMs have also been successfully combined with decision trees (Nair et al, 2010b) for predicting stock market trends. A decision tree-rough set based stock market trend prediction system is presented in Nair et al (2010a). Adaptive filters have also been used for stock market prediction (Nair et al, 2010c).…”

Section: Introductionmentioning

confidence: 99%

“…The current day's trend, i.e., Up, down and no trend is determined according to Nair et al (2010aNair et al ( , 2010b in the following way:…”

Section: Introductionmentioning

confidence: 99%

Predicting stock market trends using hybrid ant-colony-based data mining algorithms: an empirical validation on the Bombay Stock Exchange

Nair

Mohandas

Sakthivel

2011

IJBIDM

View full text Add to dashboard Cite

Ant Colony Optimisation (ACO) algorithms use simple mutually cooperating agents (ants) to produce a robust and adaptive search system, which can be used for knowledge discovery. In this paper, a Support Vector Machine (SVM)-cAnt-Miner-based system for predicting the next-day's trend in stock markets is proposed. The trend predicted by the proposed system is then used to identify the appropriate time to buy and sell securities. Performance of the proposed system is evaluated against SVM-Ant-Miner, SVM-Ant-Miner2, Naïve-Bayes and an Artificial Neural Network (ANN)-based trend prediction system. The results indicate that the proposed system outperforms all the other techniques considered.

show abstract

“…Nair B.B. et al (2010) [6] suggested the design and performance evaluation of hybrid decision tree-rough set based system for predicting the next day's trend in the Bombay Stock Exchange (BSESENSEX).The proposed hybrid decision tree-rough set based trend prediction system produces better performance.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Data Mining Techniques for Stock Price Forecasting

Shrivastava¹,

Shrivas²

2017

IJERT

View full text Add to dashboard Cite

Prediction of stock price is very challenging task due to dynamic changes value of company stock. The successfully stock prediction helps to profit the company otherwise face the problem of loss. This paper presents the analytical study of various data mining based predictive models to predict the stock in financial domain and find the most consistent prediction model among them. In Stock Market, Data mining play very important role to analysis of data. Data mining techniques can be applied on past and present financial data to generate pattern and decision making. In this paper, we have used predictive techniques like Decision Tree (DT), Random Forest(RF), Support Vector Machine (SVM), Random Tree(RT) and Multilayer Perceptron (MLP) for analysis and perdition of stock market. We have used Bombay Stock Exchange (BSE) data set to analysis of stock market prediction.

show abstract

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

Cited by 68 publications

References 17 publications

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets

Predicting stock market trends using hybrid ant-colony-based data mining algorithms: an empirical validation on the Bombay Stock Exchange

Performance Analysis of Data Mining Techniques for Stock Price Forecasting

Contact Info

Product

Resources

About