IRE major project group 22 IIITH
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Over the years there have been a lot of attempts to predict stock market movements using various techniques and hundreds of parameters. Some of the algorithms used are Exponential Moving Average and Head & Shoulders. Artificial Neural Networks and Genetic Algorithms are also used heavily. Many analysts use more traditional techniques such as P/E Ratio too. All these techniques used Stock Market prices, stock volumes traded and dividends paid etc. However there has been no single solution which has been perfected, generally an ensemble of algorithms are used for this purpose. However our attempt was to highlight how market and news/blogs sentiment can be harnessed and used for predicting Stock Movements without these traditional techniques
IRE major project group 22 IIITH
- 2. What ? Introduction Why ? Uses How ? Steps Experiments Results Conclusion References Agenda
- 3. Prediction of Stock Market movements and approx. percentage changes without use of traditional stock algorithms. Using the cauldron of emotions, thought processes and sentiments expressed in news articles and blogs related to financial news. Multiple techniques of IRE used. Statistical Models derived and experimented with. Exciting results What? - Introduction
- 4. Attempt to find out whether sentiments have direct relationship with Stock Market movements techniques for disambiguation of entities extracted from free text RealWorld Uses Guide a stock market trader/investor to gauge the market approximately as expressed by co-traders, journalists , bloggers etc. Why ? - Uses
- 5. How ? Google News Alerts (For Stock Market News). Parse news articles / blogs etc. Extract entities using entity extraction libraries from GATE. Calculate multiple sentiments using entity position tags and various keywords such as Stock, English andContextual etc. Mine sentiment of the news article as a whole using NLP libraries from Stanford. CollectTraining datasets Create models usingWEKA ( 10 Fold Cross Validation )
- 6. How ? UBUNTU/LINUX PENTAHO JAVA WEKA GATE ETL MACHINE LEARNING PREDICTIVE ANALYSIS ENTITY EXTRACTION MySQL NEWS FEED ETL DB MACHINE LEARNING PREDICTIVE ANALYSIS STOCK MARKET PREDICTIONS Training Date Range 1/1/2013 to 31/12/2013 No. of news articles ~35000 No. of unique company/stock code extracted from news/blog articles ~1400 No. of Stock Keywords ~2156 No. of English Keywords ~2838 No. of records used for building model ~7000
- 7. Experiments (Classification) ===Stratified cross-validation === ===Summary === Correctly Classified Instances 4535 59.6397 % Incorrectly Classified Instances 3069 40.3603 % Kappa statistic 0.0271 Mean absolute error 0.4853 Root mean squared error 0.499 Relative absolute error 103.6642 % Root relative squared error 103.1445 % Total Number of Instances 7604 === DetailedAccuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.846 0.822 0.633 0.846 0.724 0.531 DOWN 0.178 0.154 0.408 0.178 0.248 0.531 UP WeightedAvg. 0.596 0.572 0.549 0.596 0.546 0.531 ===Stratified cross-validation === ===Summary === Correctly Classified Instances 4623 60.7969 % Incorrectly Classified Instances 2981 39.2031 % Kappa statistic 0.1212 Mean absolute error 0.4342 Root mean squared error 0.5242 Relative absolute error 92.753 % Root relative squared error 108.3439 % Total Number of Instances 7604 === DetailedAccuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.757 0.641 0.664 0.757 0.707 0.578 DOWN 0.359 0.243 0.468 0.359 0.406 0.578 UP WeightedAvg. 0.608 0.492 0.591 0.608 0.595 0.578 NATIVE BAYES RANDOM FOREST
- 8. === Cross-validation === === Summary === Correlation coefficient 0.0666 Mean absolute error 2.9873 Root mean squared error 6.4104 Relative absolute error 99.4892 % Root relative squared error 99.8253 % Total Number of Instances 7604 Experiments (Regression) SMOreg
- 9. Results NATIVE BAYES RANDOM FOREST Classification of Stock Movements
- 10. Results Prediction of Stock Percentages (SMOreg) APPLE STOCK INDEX MICROSOFT STOCK INDEX
- 11. The experiment was successful to a large extent for classifying the stock movements over a period of time for both the classification models The accuracy percentage achieved for classification was about 80% which is significant. However a lot challenges remain for predicting the percentage changes just by using stock news.This maybe due the lack of following factors Not using any stochastic parameters in the model Disambiguation of Stock Code entities Classification of Stock Companies in groups The prediction is not RealTime as and when news is published hence it may introduce a lot of noise. There are a lot of uncertainties involved in predicting stock indices some of them are not addressable by sentiments only Conclusions