Fuzzy Presentation

1

INFORMS PhiladelphiaNovember 2015

Mohamed Abraar Ahmed (Email: mza0068@auburn.edu)M.S. Candidate, Industrial and Systems Engineering

Stock Price Prediction Using Disparate Data Sources in Fuzzy Systems

2Stock Market Prediction Why?• The stock market is

one of the most important way for companies to raise money• About 48% Americans

invested in the stock market in 2015 (CNBC)• The successful

prediction of a stock’s future price could yield significant profit

3Stock Market Prediction How?

Guess? Fundamental Analysis

Technical Analysis (Charting) Technological Methods

4Data Sources

5Motivation and Previous Process Overview• Which sources of data have the most correlation with the

stock market time series?• Which logical target has the best prediction capability with

regards to the stock movement? • Which technological model is best at predicting the stock

movement?• Can we construct a better model using disparate data

sources?

6Feature Selection• Simplification of model• Shorter training times• Improve accuracy• Enhanced generalization by reducing overfitting

7Feature Selection Method : Recursive feature elimination (RFE)

Coding : Python with multiple feature selection package Pseudo Code of RFE

* Code is available on https://github.com/binweng/SFS

8Experimental Result• Comparison of Model Accuracy by information

input

9Evaluation 10 – fold cross validation

10Motivation Could predict movement quite

accurately, can it be done for price? Movement can tell buy or sell, price will

tell whether it is worth it Will application of Fuzzy Logic to the

disparate data sources improve, maintain or reduce accuracy compared to other implementations?

Can the movement and price models be used in conjunction for better decision making in stock selection?

11Membership Functions for Input and Output Cluster Analysis

• Cluster analysis or segmentation analysis forms clusters such that data points in the same cluster are very similar

• K-means clustering

• Clusters were used to form ranges of membership functions

• Coding: On Matlab

12Rules Made categories of levels based on input

membership functions Got the input and output rules for each

pair based on historical real data Checked for input-output pairs, that

formed rules, which were repeated Picked most repeated Coding: On VBA

Part of results for Output Low MF

13Results

• Using error as a marker of performance, the results are convincing

• There are situations where it looks like more rules are required for predicting the market

• The system looks to be reacting well even when the stock price range has changed

14Results

15Model Combination The main idea behind adding Fuzzy Logic

to the chosen movement model is to predict the close price after movement is known

If predicted close price is in the opposite direction of the movement prediction, close price resets to previous day price

16Future Work Gather data by other methods such as

Twitter sentiments and textual analysis of financial reports

Scan for more rules via the input-output

pairing method

Use error in prediction in genetic algorithms to modify rules

17Thank you! Questions?

(Oh c’mon, you knew it from the first slide that this was coming.)