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Data Mining and Neural Network Techniques in Stock Market Prediction : A Methodological Review  [PDF]
Debashish Das,Mohammad Shorif Uddin
International Journal of Artificial Intelligence & Applications , 2013,
Abstract: Prediction in any field is a complicated, challenging and daunting process. Employing traditional methodsmay not ensure the reliability of the prediction. In this paper, we are reviewing the possibility of applyingtwo well-known techniques neural network and data mining in stock market prediction. As neural networkis able to extract useful information from a huge data set and data mining is also able to predict futuretrends and behaviors. Therefore, a combination of both these techniques could make the prediction muchreliable.
International Journal of Computer Engineering & Technology (IJCET) , 2016,
Abstract: Stock market is a changeable environment. Traditional data analysis techniques using of some tools can provide investors to manage stocks and predict prices. However these traditional techniques cannot determine all the possible relations between stocks and that’s why needing a different approach that will provide deeper kind of analysis. Data mining can be use comprehensively in the stock-price predicting. In this paper investigators propose a new approach with efficient preprocessing, pruning data structure techniques to discover inter-transaction association rules with business intelligence characteristics. Propose work also provides better in-depth study of intertransaction stock price movement of companies to financial research community, money managers, fund managers, investors, etc.
The Dynamics of Gold Prices, Gold Mining Stock Prices and Stock Market Prices Comovements  [cached]
Claire G. Gilmore,Ginette M. McManus,Rajneesh Sharma,Ahmet Tezel
Research in Applied Economics , 2009, DOI: 10.5296/rae.v1i1.301
Abstract: We examine the dynamic relationships between gold prices, stock price indices of gold mining companies and broad stock market indices. Evidence of cointegration between these variables is found. A vector error-correction model reveals that both gold and large-cap stock prices adjust to disturbances to restore the long-term relationship between the variables. Short-term unidirectional causal relationships are running from large-cap stock prices to gold mining company stock prices and from gold mining company stock prices to gold prices.
Time Series Analysis on Stock Market for Text Mining Correlation of Economy News  [PDF]
Sadi Evren Seker,Cihan Mert,Khaled Al-Naami,Nuri Ozalp,Ugur Ayan
Computer Science , 2014,
Abstract: This paper proposes an information retrieval method for the economy news. The effect of economy news, are researched in the word level and stock market values are considered as the ground proof. The correlation between stock market prices and economy news is an already addressed problem for most of the countries. The most well-known approach is applying the text mining approaches to the news and some time series analysis techniques over stock market closing values in order to apply classification or clustering algorithms over the features extracted. This study goes further and tries to ask the question what are the available time series analysis techniques for the stock market closing values and which one is the most suitable? In this study, the news and their dates are collected into a database and text mining is applied over the news, the text mining part has been kept simple with only term frequency-inverse document frequency method. For the time series analysis part, we have studied 10 different methods such as random walk, moving average, acceleration, Bollinger band, price rate of change, periodic average, difference, momentum or relative strength index and their variation. In this study we have also explained these techniques in a comparative way and we have applied the methods over Turkish Stock Market closing values for more than a 2 year period. On the other hand, we have applied the term frequency-inverse document frequency method on the economy news of one of the high-circulating newspapers in Turkey.
Stock Price Prediction using Neural Network with Hybridized Market Indicators
Adebiyi Ayodele A.,Ayo Charles K,Adebiyi Marion O,Otokiti Sunday O
Journal of Emerging Trends in Computing and Information Sciences , 2011,
Abstract: Stock prediction with data mining techniques is one of the most important issues in finance being investigated by researchers across the globe. Data mining techniques can be used extensively in the financial markets to help investors make qualitative decision. One of the techniques is artificial neural network (ANN). However, in the application of ANN for predicting the financial market the use of technical analysis variables for stock prediction is predominant. In this paper, we present a hybridized approach which combines the use of the variables of technical and fundamental analysis of stock market indicators for prediction of future price of stock in order to improve on the existing approaches. The hybridized approach was tested with published stock data and the results obtained showed remarkable improvement over the use of only technical analysis variables. Also, the prediction from hybridized approach was found satisfactorily adequate as a guide for traders and investors in making qualitative decisions
Accuracy Driven Artificial Neural Networks in Stock Market Prediction  [PDF]
Selvan Simon,Arun Raoo
International Journal on Soft Computing , 2012,
Abstract: Globalization has made the stock market prediction (SMP) accuracy more challenging and rewarding for the researchers and other participants in the stock market. Local and global economic situations alongwith the company’s financial strength and prospects have to be taken into account to improve the prediction accuracy. Artificial Neural Networks (ANN) has been identified to be one of the dominant data mining techniques in stock market prediction area. In this paper, we survey different ANN models that have been experimented in SMP with the special enhancement techniques used with them to improve the accuracy. Also, we explore the possible research strategies in this accuracy driven ANN models.
Co-Movement and Index Changes - Evidence from The Emerging Indian Stock Market  [cached]
Srikanth Parthasarathy
Asian Journal of Finance & Accounting , 2011, DOI: 10.5296/ajfa.v3i1.835
Abstract: This study investigates the return co-movement around the benchmark Nifty index changes for the period 1999-2010 in the Indian stock market. We find evidence of significant increase in co-movement between the added stocks and the Nifty index subsequent to additions to the benchmark Nifty index. On the contrary stocks deleted from the Nifty index do not evidence decreased co-movement between the deleted stocks and the Nifty index. We have employed various methodologies used by Vijh(1994), Barberis et al(2002) and Greenwood and Sosner (2002) and the results suggest that the information related views explain the Nifty index changes in the emerging Indian stock market.
Market-wide price co-movement around crashes in the Tokyo Stock Exchange  [PDF]
Jun-ichi Maskawa,Joshin Murai,Koji Kuroda
Quantitative Finance , 2013,
Abstract: As described in this paper, we study market-wide price co-movements around crashes by analyzing a dataset of high-frequency stock returns of the constituent issues of Nikkei 225 Index listed on the Tokyo Stock Exchange for the three years during 2007--2009. Results of day-to-day principal component analysis of the time series sampled at the 1 min time interval during the continuous auction of the daytime reveal the long range up to a couple of months significant auto-correlation of the maximum eigenvalue of the correlation matrix, which express the intensity of market-wide co-movement of stock prices. It also strongly correlates with the open-to-close intraday return and daily return of Nikkei 225 Index. We also study the market mode, which is the first principal component corresponding to the maximum eigenvalue, in the framework of Multi-fractal random walk model. The parameter of the model estimated in a sliding time window, which describes the covariance of the logarithm of the stochastic volatility, grows before almost all large intraday price declines of less than -5%. This phenomenon signifies the upwelling of the market-wide collective behavior before the crash, which might reflect a herding of market participants.
Mining the Web for the Voice of the Herd to Track Stock Market Bubbles  [PDF]
Aaron Gerow,Mark Keane
Computer Science , 2012,
Abstract: We show that power-law analyses of financial commentaries from newspaper web-sites can be used to identify stock market bubbles, supplementing traditional volatility analyses. Using a four-year corpus of 17,713 online, finance-related articles (10M+ words) from the Financial Times, the New York Times, and the BBC, we show that week-to-week changes in power-law distributions reflect market movements of the Dow Jones Industrial Average (DJI), the FTSE-100, and the NIKKEI-225. Notably, the statistical regularities in language track the 2007 stock market bubble, showing emerging structure in the language of commentators, as progressively greater agreement arose in their positive perceptions of the market. Furthermore, during the bubble period, a marked divergence in positive language occurs as revealed by a Kullback-Leibler analysis.
Forecasting the Tehran Stock Market by Artificial Neural Network
Reza Aghababaeyan,Tamanna Siddiqui,Najeeb Ahmad Khan
International Journal of Advanced Computer Sciences and Applications , 2011,
Abstract: 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. In this paper, computational data mining methodology was used to predict seven major stock market indexes. Two learning algorithms including Linear Regression and Neural Network Standard feed-forward back prop (FFB) were tested and compared. The models were trained from four years of historical data from March 2007 to February 2011 in order to predict the major stock prices indexes in the Iran (Tehran Stock Exchange). The performance of these prediction models was evaluated using two widely used statistical metrics. We can show that using Neural Network Standard feed-forward back prop (FFB) algorithm resulted in better prediction accuracy. In addition, traditional knowledge shows that a longer training period with more training data could help to build a more accurate prediction model. However, as the stock market in Iran has been highly fluctuating in the past two years, this paper shows that data collected from a closer and shorter period could help to reduce the prediction error for such highly speculated fast changing environment.
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