Financial Data mining and Tools CSCI 4333 Presentation Group 6 Date10th November 2003

2 Group Information Group members Muralikrishna Pinnaka Prateek Bali Azam cheema Kashif bhatti

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

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

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

6 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)

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

8 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

9 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)

10 JExpress-clustering

11 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

12 Genetic programming  Genetic programming  Automated method  Writing a computer program which know how to program computer  Genetic 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

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

14 Genetic Programming Tool

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

16 References        

17 Thank you Questions?