Transcript Pricing Asian Options Using A Path Bundling Technique E. H

Application of Apriori Algorithm to Derive
Association Rules Over Finance Data Set
Presented
By
Kallepalli Vijay
Instructor: Dr. Ruppa Thulasiram
Agenda
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Introduction
Motivation
Background
Solution
Results
Conclusion and Future work
Introduction
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Stock exchanges maintain a log of events
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rise in stock price, option value, number of stocks
sold
Investors predict the market trends based on
available log information
Stock market is highly unpredictable
The values in the log change very drastically
with time
Introduction (Con…)
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If investors are given adequate information
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regarding stock market trends
Investors can invest money accordingly
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maximum profits.
Motivation
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Data in the log is very huge
Data contains hidden details
Manually identifying the hidden details
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cumbersome process
Apply Apriori to retrieve hidden details
Prior deriving large item data has to be
classified
Background
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Data mining: A process of extracting unknown
patterns, facts and relations from large
database
Data mining means knowledge discovery from
large databases
Association rules in data mining involves in
detecting which items tend to occur together in
transactions
Background (Con…)
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Association rules in data mining was first
proposed by Agrawal, Imielinski and Swami in
1993
Ex: Customer who purchase one item are likely
to purchase another item.
Consider A transaction is a set of items
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T = {i1, i2,……it}
T  I, where I is the set of all possible items
P Q, Where P  I, Q  I, and PQ
{i1, i2,……in}
Solution
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Finance data is quantitative
Classify data into regular intervals
Map the classified data to an index value
Index values range from 0 through 143
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0 through 35 represent stocks opening value
36 through 71 represent stocks day high value
72 through 107 represent stocks day low value
108 through 143 represent stocks closing value
Solution (Con…)
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Finance data of the Apple Computers Inc is
read from a text file
Eg1: Stock opening value ranging between
10.0 and 10.5 is mapped to an index value 0
Eg2: Stock high value ranging between 10.0
and 10.5 is mapped to an index value 36
Apply Apriori algorithm on the mapped indices
to derive association rules
Solution (Con…)
Solution (Con…)
Results
Results (Con…)
Conclusions & Future work
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Manually identifying hidden details is a tedious
process
Classified the collected data into regular
intervals
Applied apriori algorithm to derive large item
sets
Derived large item sets and projected to the
user in user readable form
Conclusions & Future work (Con…)
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Classification of data plays important role
Correctness of association rules depends on
the classification of the data
Selecting the length of the interval for
classification is difficult
Fuzzy logic can applied on the data for
classification
Thanks!