Stock Movement Prediction

Deepathi Lingala

Sathindra K. Kamepalli

Sudhir K. V. Potturi

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

Results

No Match

Partial Match

Full Match

Stock Movement Prediction

Experiences & Challenges

Manual for LERS Huge Data sets Support & Confidence Measures Rule Filtering Tools Time Constraint

Stock Movement Prediction

QUESTIONS ??

Stock Movement Prediction

THANK YOU !Have a Happy Thanks

Giving!