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Stock Market Prediction

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Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits.

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Stock Market Prediction

  1. 1. Should I Invest in Stock Market ?
  2. 2. How To Be Rich in Stock Market: A Data-Mining Approach
  3. 3. Should I BUY Reliance Ltd. Shares???? Well..!!! Ambani future Bad…
  4. 4. Jaiprakash Associates
  5. 5.  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  Web is rich textual information resource such as financial news even that is unmanageable to one. But one can use this abundance textual information to get datasets of various companies.
  6. 6.  This Project proposes a novel method for the prediction of stock market closing price.  It is an attempt to determine whether the BSE market news in combination with the historical quotes can efficiently help in the calculation of the BSE closing index for a given trading day.
  7. 7.  This project aims at predicting stock market by using financial news and quotes in order to improve quality of output.  We are combining data mining time series analysis and machine learning algorithms such as Artificial Neural Network which is trained by using back propagation algorithm.  Also, rich variety of on-line information and news make it an attractive resource from which to mine knowledge. Data mining and analysis of such financial information can aid stock market predictions.
  8. 8.  Data mining techniques are effective for forecasting future by applying various algorithms over data.  Effective prediction systems indirectly help traders by providing supportive information such as the future market direction.  Combine techniques of textual mining and numeric time series analysis will provide the research of the stock market development a new thought.  Financial analysts, investors can use this prediction model to take trading decision by observing market behaviour.
  9. 9.  Based on the principles of the Dow Theory and uses the history of prices to predict future movements.  The approach used in technical analysis can be formulated as a pattern recognition problem, where the inputs are derived from the history of prices and the output is an estimate of the price or an estimate of the prices trend.
  10. 10.  Formed by computing the average price of a security over a specified number of periods.  The calculation is repeated for each day on the chart and then joined to form a smooth curve, which makes easier to identify the direction of the trend.  SMA(t)=1/n∑x(i)
  11. 11.  It compares the magnitude of the recent gains to the magnitude of the recent losses and generates a number that ranges from 0 to 100.  The technicians say that generally when the RSI rises above 30 it is considered a bullish signal, i.e. it is considered a signal that prices trend to rise.  Conversely, when it falls below 70 it is considered a bearish signal,
  12. 12.  Fundamental data is used by analysts to apply this method of prediction to have clear idea about the market or firm for investment.  The growth, the dividend payout, the interest rates, the risk of investment, the sales level, tax rates and so on are the variables used to determine the 'real' value of the asset that they will invest in.  Main objective of this technique is the calculation of an intrinsic value of an asset.
  13. 13.  Recently, the web has rapidly emerged as a great source of financial information ranging from news articles to personal opinions.  Financial information sources on the Web.  www.yahoo.finance.com  www.stockwatch.in  Financial Times (www.ft.com) maintain excellent electronic versions of their daily issues.  Reuters (www.investools.com)
  14. 14. Prediction Module News Documents Preprocessing Numerical Representation Collection of Stock quotes and News
  15. 15. The Preprocessor unit is used to process unstructured news documents. It is fed up with a priori domain knowledge such as .txt files of stop words such as a, an, the, of etc. The preprocessor unit involves following steps I. Stop Words Removal: Its first step in conflation algorithm, input to this is .txt file containing stop words list like articles, conjunctions, prepositions etc. as well the URL of any financial news website. This website will provide the news data. II. Stemming: To fetch the exact grammatical root format of word the stemming process is used. It trims the words e.g. buying to be reduced to ‘buy’ as root. III. Key Phrases Extraction: Preprocessed news articles are generating keyword phrases corresponding to the given .txt file global list of topmost keyword phrases.
  16. 16.  Hike  Jump  Flat  Buy  Loses  Down  Lower  Steady  Recession  Scam  Swoon  Slump
  17. 17. This uses three layered Neural Network. The numerical representation of the stock quotes and the key phrases from news articles are taken as input units to the input layer of multi-layered feed-forward network. The network configuration details are as follows: 1. Input Layer: In this configuration the layer has around 4 units. Neuron 1 for Moving Average, Neuron 2 for RSI, Neuron 3 for Analyst Recommendation, Neuron 4 for news articles are considered. 2. Hidden Layer: only one hidden layer is used. 3. Output Layer: Only one output unit is used as the closing price approximation value. 4. Learning Rate: A learning rate 0.3 is used.
  18. 18. It comprises of the neural network trained using the back –propagation algorithm. Given a day’s open index, day’s high, day’s low, volume traded and the adjusted close values (all are in normalized form) along with the stock news data, the predictor module will predict the closing index value for a given trading day. The above specified inputs correspond to the data that is observed after stock market closes every day. The predicted value is then de-normalized to obtain the actual close index value.
  19. 19.  Determining the Stock market forecasts is always been challenging work for business analysts.  Thus, Project applies the data mining technology of neural network to stock price forecast and receives a preferable result, which will provide the research of the stock market development a new thought & We attempted to make use of huge textual data to predict the stock market indices .
  20. 20.  Applying other Techniques and compare the results.  Using News feeds and Experts Advices from websites and taking them as datasets for Advanced predictions.  More work on refining key phrases extraction will definitely produce better results.  Introduction of Twitter Feeds
  21. 21.  Jiawei Han and Micheline Kamber, “Data Mining concepts and Techniques”, Second Edition 2006  Zabir Haider Khan, Tasnim Sharmin Alin, Md. Akter Hussain, Department of CSE, SUST, Sylhet, Bangladesh ,“Price Prediction of Share Market using Artificial Neural Network (ANN)”, International Journal of Computer Applications (0975 – 8887) Volume 22– No.2, May 2011.  Financial Forecasting: Advanced Machine Learning Techniques in Stock Market Analysis , 9th International Multitopic Conference,IEEE INMIC 2005 in Karachi. 24-25 December, 2005.  http://ijcttjournal.org/Volume4/issue-6/IJCTT-V4I6P177.pdf  http://www.moneycontrol.com/  http://profit.ndtv.com/  B. Wüthrich, V. Cho, S. Leung, D. Permunetilleke, K. Sankaran, J. Zhang, and W. Lam, "Daily Stock Market Forecast from Textual Web Data", Proceedings of the IEEE International /Conference on Systems, Man, and Cybernetics  Marc-André, Mittermayer, “Forecasting Intraday Stock Price Trends with Text Mining Techniques”, - 0-7695-2056-1/04 $17.00 (C) 2004 IEEE.

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