A Hybrid Approach To Evaluate Stock Returns Using Data Mining Techniques (original) (raw)
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Developing an approach to evaluate stocks by forecasting effective features with data mining methods
In this research, a novel approach is developed to predict stocks return and risks. In this three stage method, through a comprehensive investigation all possible features which can be effective on stocks risk and return are identified. Then, in the next stage risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function- based clustering; the important features in risk and return prediction are selected then risk and return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature selection and these features are good indicators for the prediction of risk and return. To illustrate the approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002 to 2011.
Stock Market Prediction using Data Mining Techniques
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.
Application of data mining techniques in stock markets: A survey
One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Potential significant benefits of solving these problems motivated extensive research for years. The research in data mining has gained a high attraction due to the importance of its applications and the increasing generation information. This paper provides an overview of application of data mining techniques such as decision tree, neural network, association rules, factor analysis and etc in stock markets. Also, this paper reveals progressive applications in addition to existing gap and less considered area and determines the future works for researchers.
Prediction of Stock Performance Using Analytical Techniques
Journal of Emerging Technologies in Web Intelligence, 2013
With an easy access to share information and data nowadays, many investors worldwide are interested in predicting stock prices. The prediction of stock prices using data mining techniques applied to technical variables has been widely researched but not much research to date has been done in applying data mining techniques to both technical and fundamental information. This paper is based on a personal approach to stock selection, using both technical and fundamental information. In this paper we construct a framework that enables us to make class predictions about industrial stock performances. In order to have a systemized approach for the selection of stocks and a high likelihood of the performance of the stock price increasing, several analytical techniques are applied. A trading strategy is also designed and the performance of the stocks evaluated. Our two goals are to validate our stock selection methodology and to determine whether our trading strategy allows us to outperform the Australian market. Simulation results show that our selected stock portfolios outperform the Australian All-Ordinaries Index. Our findings justify the use of analytics for classification and prediction purposes. Further, in conclusion, we can safely say that our stock selection and trading strategy outperformed the Australian Ordinary index.
Prediction of stock price return is a highly complicated and very difficult task because there are many factors such that may influence stock prices. An accurate prediction of movement direction of stock index is crucial for investors to make effective market trading strategies. However, because of the high nonlinearity of the stock market, it is difficult to reveal the inside law by the traditional forecast methods. In response to such difficulty, data mining techniques have been introduced and applied for this financial prediction. This study attempted to develop three models and compared their performances in predicting the direction of movement in daily Tehran Stock Exchange (TSE) index. The models are based on three classification techniques, Decision Tree, Random Forest and Naïve Bayesian Classifier. Ten microeconomic variables and three macroeconomic variables were chosen as inputs of the proposed models. Experimental results show that performance of Decision Tree model (80.08%) was found better than Random Forest (78.81%) and Naïve Bayesian Classifier (73.84%).
An insight of stock market trends has been an area of vast interest both for those who wish to make profit by trading stocks in the stock market. Generally there is an opinion about stock market like high risk and high returns.Eventhough we have a huge number of potential investors, only very few of them are invested in the stock market. The main reason is the inability of risk taking skill of investors. Though get low returns they want to save their money. One important reason for this problem is that, they don't have a proper guidance for making their portfolio. In this paper we focus the real world problem; we had selected three indices such as CNX Realty, BANK NIFTY and MIDCAP 50. The analysis is purely based on the data collected from past three years. The Data mining technique, Time series interpretation is applied for the Data analysis to show the ups and downs of a particular index. The correlation and Beta are the tools which gives the suggestion about the stock and its risk. The correlation tool is used to identify the relationship between the index and company individually. This Beta is used to identify the risk associated with the stock.
Application of Data Mining Techniques to Classify World Stock Markets
It is imperative for the participants of stock markets to understand the characteristics of stock markets for effective decision making. However, it is difficult to understand the dynamics of every single market, so the classification of stock markets with similar characteristics as a group would be a great help for the stakeholders of the stock markets. There exist some classifications based on various socioeconomic and financial factors, which involve huge costs and also this cannot be verified as it is very complex to obtain the data on these variables. There are very few studies in the literature on the classification of markets with quantitative variables. In this context, this study aims at classifying the stock markets into different groups based on their key financial factors. This study considers forty-five stock markets for classification by using data mining techniques, viz. K-Means, Hierarchical, and Fuzzy C-Means. The results show that Fuzzy C-Means clustering is found to be the most suitable method.
Predicting the Price of Tehran Stock Market Using Data Mining Algorithms
Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, instead of a single method, traders need to use various appropriate techniques to gain more information about the future of the markets. In this paper, three different techniques of data mining are discussed and applied to predict price of Tehran stock market. The approaches include decision trees (CART and CHAID) and Neural Network that execute on Saipa, Iran Khodro, Telecommunication, Mapna and Saderat Bank datasets. The aim of this paper is generating an effective predicting model to forecasting future price in Tehran stock market. Price prediction in stock market helps investors for exact and quick identification and more investment on valuable portion. Finally it causes portion basket of investors optimized. Results show that ...
Developing Decision Model by Mining Historical Prices Data of Infosys for Stock Market Prediction
Stock market analysis is the process of analyzing and monitoring stocks so it is also a process of calculating the future trends on the basis of historical trends. This whole concept is volatile, as the stock prices having the tendency to rise and fall. However, we know that there is a defined pattern in insight of any sequenced event therefore we can extract some hidden pattern thorough analysis. In this paper we have developed a decision support model to classify and predict the stock market by data mining techniques like classification and prediction. In this way we have developed some decision rules as model to increase the probability of right decision so that an investor can took profit in the stock investment. in this study we analyze the historical price data of the specific industry group Named Infosys Pvt. Ltd. to make sure that the investors is moving with right decision in order to increase the possibility of profit in their investments. Therefore the main task is to predict and classify the stock prices of Infosys Company on the basis of past prices.
Stock Market Direction Prediction Using Data Mining Classification
ARPN Journal of Engineering and Applied Sciences, 2014
The key of success in stock trading is to buy and sell stocks at the right time for the right price. “Buy Low, Sell High” sounds easy, but it is so difficult to carry out since the direction of stock market in the near future is almost unpredictable. With the advances in data mining, it has now become possible to predict the future market direction based on historical data. In this study, different approaches are used to predict the future market direction of the Stock Exchange of Thailand (SET). Time series forecasting is conducted and a suitable span of time for the stock market data is examined. A novel approach to predict future market direction has been introduced based on chart patterns recognition by using data mining classification. Models are built through different methods including neural network, decision tree, naïve Bayes and k-nearest neighbors. Results were obtained, compared and discussed in details. Important chart patterns to support decision making in stock trading had been found out. In order to visualize the result, a visualization technique is also introduced.