Financial Data mining and Tools CSCI 4333 Presentation

Financial Data mining and Tools CSCI 4333 Presentation Group 6 Date 10 th November 2003

Group Information Group members Muralikrishna Pinnaka pinnakam 5757@cl. uh. edu Prateek Bali prateek_bali@hotmail. com Azam cheema azamcheema@hotmail. com Kashif bhatti akhroatt@hotmail. com 2

Financial Data mining § § Introduction Time series analysis § § Long term or trend moment Cyclic moments or cyclic variations Seasonal moments or seasonal variations Irregular moments 3

Stock Market Prediction Stock market data Programming Models Statistical indicators Genetic programming Neural networks Trade simulator 4

Stock Market Prediction(Pictorial view) Stock market Data Trade_Creator Data Element Data mining model Trades Data Trade Simulator 5

What Is Stock Charting? Technical aspect of the stock market Identifying buy/sell signals Dow theory Primary trend is constant May be changes in stock market are secondary Elliot Wave Theory Prices move in predetermined no of waves using (fibbonacci) 6

Stock Charting 1. Grand Supercycle 2. Supercycle 3. Cycle 4. Primary 5. Intermediate 6. Minor 7. Minute 8. Minuette 9. Sub-Minuette 7

Few Applications in Data mining Individuals are likely to go bankrupt Who will be interested in buying certain products How valuable a particular customer is Who is a good risk for an auto loan What tax returns are likely to be fraudulent The probability that a particular credit card stolen 8

Classification, clustering and Prediction Two different forms of data analysis Used to extract models for predicting trends Decision trees Trends are forecasted in multiple directions Ability to model highly complex functions Ability to use more no of variable in a functions Cluster Analysis Collection of patterns which are similar Kohenen’s SOM (self organizing map) 9

JExpress-clustering 10

Algorithms § ARIMA( autoregressive integrated moving average) § § Takes time series data as input Prepares a model for extrapolating the financial market § § attempts to evaluate the stationarity of a time series Ordering the autoregression and moving average components estimation of the autoregression and moving average Neural Networks § § § Able to respond with true(1) or false(0) for a input vector Highly complex and more processing power required It consists of § § § Input layer Output layer Hiddern layer 11

Genetic programming §Automated §Writing §Genetic § method a computer program which know how to program computer algorithms Adaptive §Search and optimization problems §Survival of the fittest §Search starts from population of many points(parallell) §Dealing with broader class of functions §Rules are probabilistic but not deterministic 12

Genetic programming §Parameter §Fitness used function and value §No of individuals(112) §No of generations(max 1000, used 3) §Percentage §Probability §R cross over of function ( 30%) square value( Ex: 1. 000 means fittest) §Input §X Y §# @-/*+ 13

Genetic Programming Tool 14

Conclusions §Genetic programming §Useful in game programming §Useful in predicting the future trend of the stock market §Used in financial institutions §Statistical §ARIMA §Neural modeling techniques used for extrapolation networks §Highly complex and more processing power is needed §It is not in great practice 15

References o o o o http: //www. 5 paisa. com/abc/rsrh/Research/BSX/Technicals/SUM/ACCL. BO. html http: //www. stockcharts. com/education/index. html http: //www. stockcharts. com/education/What/Indicator. Analysis/indic_RSI. html http: //www. stockcharts. com/education/What/Indicator. Analysis/indic_williams. R. html http: //www. stockcharts. com/education/What/Indicator. Analysis/indic_Bbands. html http: //www. equis. com/Education/TAAZ/ http: //www. lascruces. com/~rfrye/complexica/dm_whatis. htm http: //www. pafis. shh. fi/~ulidau 02/SFIS/workshop 2. htm 16

Thank you Questions? 17