Stock Movement Prediction

1. Stock Movement Prediction Deepathi Lingala Sathindra K. Kamepalli Sudhir K. V. Potturi

2. Agenda • Introduction-Goal • Domain Description • Method • Implementation • Results • Experiences and Challenges • Questions Stock Movement Prediction

3. Goal Apply trend analysis to stock data of in order to predict the direction of movement of stock value with time. Stock Movement Prediction

4. Domain • Continuous Valued • Time series: Data for a period of ten years (1992-2002) • Data size: 2587 rows • Data Attributes: Open, Close, High, Low stock values and Volume Stock Movement Prediction

5. Data Mining Technique Used • Association Rule Mining Technique has been used for the Prediction Why Association Rule Mining Technique? • Association rule mining helps in finding interesting association relationships among large set of data items. The discovery of such associations can help develop strategies to predict. Stock Movement Prediction

6. Implementation • Data Preparation • Data Cleaning • Data Transformation • Data Discretization • Data Partition • Association Rule Mining Stock Movement Prediction

7. Data Preparation Data Cleaning Not much data cleaning was required. Missing data was replaced by the correct one obtained from the internet. The data was searched for any steep changes in it which might have occurred by stock splits etc., but did not find any Stock Movement Prediction

8. Data Preparation Attributes used: • Closing stock price (Decision attribute) • Volume Derived Attributes • Two-day average • Five-day average • Ten-day average • Average True Range (ATR) • Absolute Price Oscillator (APO) Stock Movement Prediction

9. Data Preparation Data Transformation • The data has been transformed into percentage rate of change, wherein the percentages are obtained according to the increase or decrease with respect to the previous day. • The decision attribute was generalized to 0’s and 1’s according the increase or decrease of the close stock price compared to its previous day price. Stock Movement Prediction

10. Data Preparation Data Discretization Software Used: ROSETTA Algorithm Used: Equal Frequency Binning The data is discretized and put into bins. Each bin was given a separate name for the purpose of increasing the ease of understanding when the rules are developed. Stock Movement Prediction

11. Data Partitioning The data tuples are analyzed, the training data set(1000 records), is selected from the data set. This learned model is represented in the form of association rules. This step is the supervised learning step. A test data set (150 records) is selected and this is independent of the training data set. Stock Movement Prediction

12. Association Rule Mining • Software used: LERS • The Training data set has been fed into the LERS system to build the association rules (Machine Learning) • Total No. of Rules: 1059 Certain Rules: 532 Possible Rules: 527 Stock Movement Prediction

13. Association Rule Mining • Support for all the Certain and Possible rules was determined. • A threshold support value was chosen. • The rules were filtered based on the threshold support value. Stock Movement Prediction

14. Association Rule Mining • After filtering Total number of rules: 55 Certain Rules: 27 Possible Rules: 28 • These rules were applied to the test data to predict the decision value Stock Movement Prediction

15. Example Rules Certain Rules: (vol,a9) & (5day,c2) & (2day,b3) -> (close,1) (apo,f6) & (5day,c0) -> (close,0) Possible Rules: (vol,a9) & (atr,e3) & (2day,b4) -> (close,1) (5day,c6) & (apo,f7) & (10day,d7) -> (close,0) Stock Movement Prediction

16. Results No. of Records in the Test Data Set = 150 Total No. of correct matches Found = 77 Accuracy = 51.33% No. of correct Full matches = 20 out of 36 Accuracy = 55.55% No. of correct Partial matches = 57 out of 114 Accuracy = 50% Stock Movement Prediction

17. Results Stock Movement Prediction

18. Results Stock Movement Prediction

19. Experiences & Challenges • Manual for LERS • Huge Data sets • Support & Confidence Measures • Rule Filtering Tools • Time Constraint Stock Movement Prediction

20. QUESTIONS ?? Stock Movement Prediction

21. THANK YOU ! Have a Happy Thanks Giving! Stock Movement Prediction