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Stock Movement Prediction
Agenda
Introduction-Goal
Domain Description
Method
Implementation
Results
Experiences and Challenges
Questions
Stock Movement Prediction
Goal
Apply trend analysis to stock data of
in order to predict the direction of movement of stock value with time.
Stock Movement Prediction
Domain
Continuous Valued
Time series: Data for a period of ten years (1992-2002)
Data size: 2587 rows
Data Attributes: Open, Close, High, Low stock values and Volume
Stock Movement Prediction
Data Mining Technique Used
Association Rule Mining Technique has been used for the Prediction
Why Association Rule Mining Technique?
Association rule mining helps in finding interesting association relationships among large set of data items. The discovery of such associations can help develop strategies to predict.
Stock Movement Prediction
Implementation
Data Preparation Data Cleaning
Data Transformation
Data Discretization
Data Partition
Association Rule Mining
Stock Movement Prediction
Data Preparation
Data Cleaning
Not much data cleaning was required. Missing data was replaced by the correct one obtained from the internet. The data was searched for any steep changes in it which might have occurred by stock splits etc., but did not find any
Stock Movement Prediction
Data Preparation
Attributes used:
Closing stock price (Decision attribute)
Volume Derived Attributes
Two-day average
Five-day average
Ten-day average
Average True Range (ATR)
Absolute Price Oscillator (APO)
Stock Movement Prediction
Data Preparation
Data TransformationThe data has been transformed into percentage rate of change, wherein the percentages are obtained according to the increase or decrease with respect to the previous day.
The decision attribute was generalized to 0’s and 1’s according the increase or decrease of the close stock price compared to its previous day price.
Stock Movement Prediction
Data Preparation
Data Discretization
Software Used: ROSETTA
Algorithm Used: Equal Frequency Binning
The data is discretized and put into bins. Each bin was given a separate name for the purpose of increasing the ease of understanding when the rules are developed.
Stock Movement Prediction
Data Partitioning
The data tuples are analyzed, the training data set(1000 records), is selected from the data set. This learned model is represented in the form of association rules. This step is the supervised learning step. A test data set (150 records) is selected and this is independent of the training data set.
Stock Movement Prediction
Association Rule Mining
Software used: LERS
The Training data set has been fed into the LERS system to build the association rules (Machine Learning)
Total No. of Rules: 1059
Certain Rules: 532
Possible Rules: 527
Stock Movement Prediction
Association Rule Mining
Support for all the Certain and Possible rules was determined.
A threshold support value was chosen.
The rules were filtered based on the threshold support value.
Stock Movement Prediction
Association Rule Mining
After filtering
Total number of rules: 55
Certain Rules: 27
Possible Rules: 28
These rules were applied to the test data to predict the decision value
Stock Movement Prediction
Example Rules
Certain Rules:
(vol,a9) & (5day,c2) & (2day,b3) -> (close,1)
(apo,f6) & (5day,c0) -> (close,0)
Possible Rules:
(vol,a9) & (atr,e3) & (2day,b4) -> (close,1)
(5day,c6) & (apo,f7) & (10day,d7) -> (close,0)
Stock Movement Prediction
Results
No. of Records in the Test Data Set = 150
Total No. of correct matches Found = 77
Accuracy = 51.33%
No. of correct Full matches = 20 out of 36
Accuracy = 55.55%
No. of correct Partial matches = 57 out of 114
Accuracy = 50%
Stock Movement Prediction
Results
73 5716
7757
200
50
100
150
200
1 2 3
Total Partial Full
No
of R
ecor
ds
Stock Movement Prediction
Experiences & Challenges
Manual for LERS Huge Data sets Support & Confidence Measures Rule Filtering Tools Time Constraint