Using data mining technologies to find currency trading rules
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Using Data Mining Technologies to find Currency Trading Rules. A. G. Malliaris M. E. Malliaris Loyola University Chicago. Multinational Finance Society, Rome, Italy, June 26-29, 2011. MOTIVATION.

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Using Data Mining Technologies to find Currency Trading Rules

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Using Data Mining Technologies to find Currency Trading Rules

A. G. Malliaris

M. E. Malliaris

Loyola University Chicago

Multinational Finance Society, Rome, Italy, June 26-29, 2011


  • If it is possible to isolate some assets that have consistent patterns of co-movement, then this knowledge can be used in two ways:

    to make money if one has moved and the other has not moved yet, or,

    to build a more diverse portfolio by including assets that move in opposite directions


  • Are there certain currency markets that move up or down together sufficiently often for us to form a conclusion about their inter-relationships?

  • Data from eight major currencies, over ten years, was studied to see if there are movement rules among these currencies that might be stable over time.


  • Data Mining is the search for meaningful patterns in large data sets

  • Meaningful patterns are easily understood, valid on new or test data with some degree of certainty, and potentially useful

  • Models are produced using a set of values of indicators at a particular time

  • The goal is to produce a model that generalizes well on future observations


    • 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.

    • Association analysis generates a set of rules of the form IF A THEN B


    • The set of rules that is generated also have values of support and confidence

    • Support: percent of times that some combination of inputs (also called antecedents) occurs in the data set.

    • Confidence: when the antecedent combination does occur, reflects the percent of time that the output, or consequent, is also true.


    • Generalized Rule Induction is an association analysis methodology that was introduced in 1992 by Smyth and Goodman

    • GRI is an effective, parsimonious method for detecting relationships in a large set of variables


    • Decision trees divide up a large collection of records into successively smaller sets of records by applying a sequence of simple decision rules.

    • A good decision tree model consists of a set of rules that results in homogeneous groups; that is, it separates records into groups where a single class predominates for each group

    • The final result of these splits is often represented graphically in a tree structure.


    All Data


    • All Decision Trees begin with a root node.

    • They employ a strategy that grows the tree by making a series of locally optimum decisions about which attribute to use for partitioning the data.

    • The goal of the algorithm is to partition the records into successively purer subsets based on the values of the target field.


    • Unlike many methods from statistics, C&RT did not exist before machine learning methods were available.

    • C&RT is a decision tree methodology that generates only binary splits at each stage

    • All decisions are based on the value of a single target variable


    • January 2000 through July 2009

    • Downloaded from Bloomberg.

    • These prices were split into two disjoint sets for training and validation.

    • Data from January 1 2000 to June 30 2008 was used as the training set (2215 rows)

    • July 1 2008 to July 21 2009, used as the validation set (276 rows).

    • To study the simultaneous market movements, all data was transformed into “Up” or “Down”


    Original data was daily cash closing prices for the price of 1 US Dollar in the foreign currency that day

    Australian Dollar, Japanese Yen

    British Pound, Euro, Swiss Franc

    Canadian Dollar, Mexican Peso, Brazilian Real


    In order to view them all in a similar scale, the Mexican Peso has been multiplied by 10 and the Japanese Yen by 100 for the graph.


    • Methodologies were run using the SPSS product Clementine

    • There were two runs of each model for GRI

      • First, the Australian dollar and Japanese Yen were inputs, with the Euro, the Swiss Franc and the British Pound as possible outputs.

      • Second, the Australian dollar, Japanese Yen, the Euro, the Swiss Franc and the British Pound were inputs, with the Mexican Peso, the Brazilian Real and the Canadian dollar as outputs.

    • One C&RT tree was created for each of the six

      GRI output targets


    • Each model developed a large number of rules and paths.

    • Here, we display one Up rule and one Down rule for each of the target markets.

    • Rules selected were those that did well not only on the training set, but also on the validation set.


    • Today, the daily volume of currency transactions in currency futures, forwards, swaps and options dominates all other types of trading volumes.

    • Whether currencies move together or independently is a matter of importance for investors wishing to spread the impact of their portfolio decisions.

    • The results of this study suggest that there is reason to believe that co-movement among some specific markets exists over relatively long periods of time.

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