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

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 2008. 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.

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

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

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

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

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/08 After    X Train Val Train Val

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.

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

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.

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

Data Mining Stream in Modeler

Results Before O/08 Training Set: 2635 rules After O/08 Training Set: 2643 rules Number of rules in both training sets: 79 Selected 11 example rules to discuss (rules with highest confidence in each market)

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

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

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

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

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

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

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

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

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

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

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

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.

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.

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.