Fuzzy Presentation
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Transcript of Fuzzy Presentation
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1
INFORMS PhiladelphiaNovember 2015
Mohamed Abraar Ahmed (Email: [email protected])M.S. Candidate, Industrial and Systems Engineering
Stock Price Prediction Using Disparate Data Sources in Fuzzy Systems
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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
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3Stock Market Prediction How?
Guess? Fundamental Analysis
Technical Analysis (Charting) Technological Methods
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4Data Sources
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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?
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6Feature Selection• Simplification of model• Shorter training times• Improve accuracy• Enhanced generalization by reducing overfitting
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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
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8Experimental Result• Comparison of Model Accuracy by information
input
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9Evaluation 10 – fold cross validation
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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?
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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
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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
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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
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14Results
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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
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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
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17Thank you! Questions?
(Oh c’mon, you knew it from the first slide that this was coming.)