1. How a Financial Crisis Affects Data Mining Results: A Case Study
M.E. Malliaris
A.G. Malliaris
Loyola University Chicago
July 17, 2012
The 8th International Conference on Data Mining, Las Vegas

2. Question
Can we find any stable pattern on each side of a period of instability?
Specifically, are there any Apriori rules for directional movement of eight major currencies common to the years prior to and following October 2008?
Successful trading of currencies over time often depends on a strategy that remains consistent. Identifying patterns that occur among such currencies is a basis for the strategy. However, major financial crises can cause shifts in trading patterns that interfere with even the best approaches. This paper uses the association analysis data mining technique to compare rules related to eight major currencies and their co-movements before and after the financial crisis of October The currencies included in the search for rules are the Australian dollar, the Japanese yen, the euro, the Swiss franc, the British pound, the Canadian dollar, the Mexican peso, and the Brazilian real. Some of the rules that remained stable, during a seven-year period, on both sides of the 2008 financial crisis are examined and compared.

3. Motivation for Rule Search
Daily volume of currency transactions dominates all other types of trading volumes
Increased financial globalization offers opportunities for portfolio diversification
This global significance drives a search for understanding currency co-movement

4. Currencies Australian Dollar Japanese Yen Euro Swiss Franc
British Pound
Canadian Dollar
Mexican Peso
Brazilian Real

5. Original Data Bank of Canada nominal noon Exchange Rates, 12:30 ET
Original numbers: amount of the currency equal to one US dollar on that day at that time
Period: November 2005 to September 2011

6. Data Transformation
Converted into category-type data representing the direction of movement of each currency (relative to the US dollar) from day to day. Up
Even
Down

7. Data Sets Before O/08 After Train Val Train Val Set Begin Date
End Date
# Rows
Before O/08 Training
11/1/2005
9/28/2007
480
Before O/08 Validation
10/1/2007
9/30/2008
252
After O/08 Training
11/3/2008
9/17/2010
471
After O/08 Validation
9/20/2010
9/19/2011
Before O/ After  X
Train Val Train Val

8. Same-Direction Movement Over Time
. In 0 and 8, all the markets move together, and this has increased since the crash. In 4, half the markets move one way, and half the other. This percent has decreased over this time period. With the increasing flow of information made possible in today’s world, it seems that markets are more likely to agree than not. However, when they do not, then data mining can help us to understand the markets that do and do not move together.

9. Methodology Apriori Association Analysis
Association analysis is a popular data mining method that originated with the study of market baskets to see which items people purchased at the same time.
Generally, it looks for things that occur together
Apriori generates a set of rules of the form IF A THEN B

10. Apriori, continued
The set of rules that is generated also depends on the values of support and confidence.
Support: percent of times that some combination of inputs [the IF side of the statement] occurs in the data set.
Confidence: when the IF combination does occur, reflects the percent of time that the output [or THEN side] is also true.

11. Minimum Settings Support: 7%
That is, this pattern must occur in at least 7% of the rows in the data set
Confidence: 65%
When the IF part of the rule is true, the THEN part must hold at least 65% of the time

12. Data Mining Stream in Modeler

13. Results Before O/08 Training Set: 2635 rules
After O/08 Training Set: rules
Number of rules in both training sets: 79
Selected 11 example rules to discuss (rules with highest confidence in each market)

14. Rule 1 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Japan Mexico Brazil Australia IF
THEN
 15.63
16.67
17.20
25.50
Support
 84.00
80.95
79.01
93.75
Confidence

15. Rule 2 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Mexico Swiss Australia Euro IF
THEN
 15.83
14.29
7.86
9.16
Support
 92.11
86.11
81.08
56.52
Confidence

16. Rule 3 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Brazil Swiss Mexico Euro IF
THEN
 7.29
9.52
9.13
6.37
Support
 85.71
87.50
90.70
Confidence

17. Rule 4 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Mexico Swiss Japan IF
THEN
 23.75
25.40
17.41
24.30
Support
 74.56
93.75
70.73
85.25
Confidence

18. Rule 5 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Australia Swiss Mexico Japan IF
THEN
 14.17
13.49
9.98
7.57
Support
 88.24
67.65
70.21
78.95
Confidence

19. Rule 6 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Swiss Canada Australia Pound IF
THEN
 26.25
23.81
28.03
24.70
Support
 84.13
80.00
82.58
70.97
Confidence

20. Rule 7 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Australia Canada Swiss Mexico Pound IF
THEN
 7.50
8.73
7.01
2.39
Support
 94.44
95.45
78.79
66.67
Confidence

21. Rule 8 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Brazil Mexico Euro Swiss IF
THEN
 15.21
12.30
8.07
7.17
Support
 89.04
100.00
84.21
77.78
Confidence

22. Rule 9 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Euro Mexico Swiss IF
THEN
 23.13
24.21
19.32
15.94
Support
 91.89
88.52
82.42
60.00
Confidence

23. Rule 10 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Swiss Canada Australia Mexico IF
THEN
 9.58
11.51
11.04
14.34
Support
 76.09
89.66
75.00
86.11
Confidence

24. Rule 11 12:30 ET Bank of Canada Nominal Noon Exchange Rates
Brazil Canada Swiss Mexico IF
THEN
 14.17
19.05
23.57
15.54
Support
 75.00
68.75
75.68
89.74
Confidence

25. Summary
To uncover stable currency relationships and to examine their robustness over time, an Apriori association analysis was performed on two sets of data, on set prior to the October 2008 financial crisis and the other set after.
Association analysis found patterns that have occurred in data sets both before and after the financial crash of October 2008.

26. Summary
There are 11 rules that this paper identified for further analysis.
Each rule confirms a stable relationship among currencies that would allow hedging and speculative activities.
For example, if certain currencies are moving in a specific direction in a given day then some other currency’s direction is predictable.

27. Conclusion
These rules demonstrate that stable and robust relationships exist among groups of currencies
These can form the fundamentals for global banking and investment, hedging and speculative activities.