This document summarizes research on using disparate data sources and fuzzy logic systems to predict stock prices. It discusses using feature selection techniques like recursive feature elimination to select relevant inputs from multiple data sources for modeling stock movement and price. Experimental results show the fuzzy logic models can accurately predict stock movement and price changes based on selected features from technical analysis, fundamental data, and other sources. The document proposes combining the movement and price prediction models to help with stock selection decisions. Future work is suggested to expand the data sources, rules, and use error-driven methods to improve the models.

1. 1
INFORMS Philadelphia
November 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

2. 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

3. 3Stock Market Prediction
 How?
Guess? Fundamental Analysis
Technical Analysis (Charting) Technological Methods

4. 4Data Sources

5. 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?

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

7. 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

8. 8Experimental Result
• Comparison of Model Accuracy by information input

9. 9Evaluation
 10 – fold cross validation

10. 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?

11. 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

12. 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

13. 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

14. 14Results

15. 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

16. 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

17. 17Thank you! Questions?
(Oh c’mon, you knew it from the first slide that this was coming.)