Clustering Stock Market Companies via K- Means Algorithm
نویسندگان
چکیده
One of the main concepts in pattern recognition is clustering. This technique is used as important knowledge discovery tools in modern machine learning process. Clustering of high-performance companies is very important not only for investors, but also for the creditors, financial creditors, stockholders, etc. Hence, firms’ clustering is considered as one of the important issues in Tehran Stock Exchange (TSE). To this end, we have used financial statement data of three industries in TSE for the year 2012. After selecting profit criteria (attributes) and prioritizing them using AHP, k means clustering algorithm is used to classify these companies. Also, to obtain the optimal number of clusters, different validity measures are presented. The identification of clusters of companies of TSE can be exploited to improve planning and get to more comprehensive decision making about companies.
منابع مشابه
Designing a smart algorithm for determining stock exchange signals by data mining
One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock exchange market. This research proposes a smart algorithm by means of valuable big data that is generated by stock exchange market and different kinds of methodology to present a smart model.In this paper, we investigate relationships between the data and access to their lat...
متن کاملPrediction-Based Portfolio Optimization Model for Iran’s Oil Dependent Stocks Using Data Mining Methods
This study applied a prediction-based portfolio optimization model to explore the results of portfolio predicament in the Tehran Stock Exchange. To this aim, first, the data mining approach was used to predict the petroleum products and chemical industry using clustering stock market data. Then, some effective factors, such as crude oil price, exchange rate, global interest rate, gold price, an...
متن کاملClustering stock market companies via chaotic map synchronization
A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series are associated to the coupling strengths between maps. The simulati...
متن کاملClustering-Classification Based Prediction of Stock Market Future Prediction
Stock market values keeps on changing day by day, so it is very difficult to predict the future value of the market. Although there are various techniques implemented for the prediction of stock market values, but the predicted values are not very accurate and error rate is more. Hence an efficient technique is implemented for the prediction of the stock market values using hybrid combinatorial...
متن کاملStock Price Prediction using Machine Learning and Swarm Intelligence
Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...
متن کامل