us dollar index (dxy): modeling against domestic and global macro-economic factors
DESCRIPTION
US Dollar is considered one of the safest currencies in the world and used as a benchmark for all practical purposes. DXY (US Dollar Index) is an index to measure the proxy strength of US Dollar against world currencies. The model is built to measure the factors impacting the value of the Index. Several data mining frameworks have been used and predictive analytics methods applied to improve the accuracy of the modelTRANSCRIPT
US Dollar Index – DXYModeling against Domestic and Global Macro-
Economic Factors
3/9/2013Rama KappagantulaKiran Sankuru Global Equity Fund
Kiran Sankuru | Rama Kappagantula
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Goal US Dollar Index – DXY Domestic & Global Macroeconomic Factors
o Euroo Yeno VIXo S&Po Goldo BDIo Unemploymento Inflationo LEIo GDPo Money Supplyo Current Acct
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Dollar Index
US Dollar Index – DXYo Started in 1973o Proxy for US $o Strength against world currencies
Euro, Yen, GDP, Canadian $, Swedish Krona, Swiss Franc
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History & Performance
1/3/
1994
8/3/
1994
3/3/
1995
10/3
/199
5
5/3/
1996
12/3
/199
6
7/3/
1997
2/3/
1998
9/3/
1998
4/3/
1999
11/3
/199
9
6/3/
2000
1/3/
2001
8/3/
2001
3/3/
2002
10/3
/200
2
5/3/
2003
12/3
/200
3
7/3/
2004
2/3/
2005
9/3/
2005
4/3/
2006
11/3
/200
6
6/3/
2007
1/3/
2008
8/3/
2008
3/3/
2009
10/3
/200
9
5/3/
2010
12/3
/201
0
7/3/
2011
2/3/
2012
9/3/
2012
50
60
70
80
90
100
110
120
130
DXY
Kiran Sankuru | Rama Kappagantula
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Process Data Exploration Analysis DM Methods Apply & Evaluate Interpretation & Performance Conclusion
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Data
CIA.gov Dept. of Labor US Dept. of Treasury Conference Board
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Collection
Kiran Sankuru | Rama Kappagantula
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Data
Timeline Frequency Missing values
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Exploration
Predictor(s) Frequency Adjusted Frequency
VIX, S&P,OIL, Gold, 10YrTres,Euro, UKSterling, BDI, DXY and JPYen
Daily Daily
UnEmployment, PMI, Inflation, LEI, Debt, MoneySupply, TradeBalance
Monthly Daily
GDP, CurrentAccount Quarterly Daily
Kiran Sankuru | Rama Kappagantula
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Data
Scatterplot matrix Normalization Summary Statistics Correlation Matrix PCA Analysis Regression Trees
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Analysis & Cleanup
Kiran Sankuru | Rama Kappagantula
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Data
Normalizationo Xnorm = (X – Min)/(Max - Min)
Summary Stats
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Analysis & Cleanup
Kiran Sankuru | Rama Kappagantula
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Data
Correlation Matrix
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Analysis & Cleanup
Kiran Sankuru | Rama Kappagantula
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Data
PCA Analysis
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Analysis & Cleanup
Kiran Sankuru | Rama Kappagantula
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Data
Regression Tree
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Analysis & Cleanup
Kiran Sankuru | Rama Kappagantula
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Data Mining Predictors
3/9/2013
Dependent Variable DXY
Predictors
Euro JPYen VIX S&P Gold BDI Unemployment Inflation LEI Money Supply GDP Current Acct
Kiran Sankuru | Rama Kappagantula
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Data Mining
Neural Networks
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Methodology
Kiran Sankuru | Rama Kappagantula
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Data Mining Methodology
Network Diagram
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Neural Networks
1
2
4
5
3
6
Input Layer Hidden Layer Output Layer
S&P
GDP
DXY
W13
O3
O4
O5
O6W14
W15
W23
W24
W25
W36
W46
W56
Outputj = 1 / (1 + e-(Oj + ∑ Wij * Xi ))
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Data Mining Methodology
Training the Modelo Input nodes: 12 predictorso # of Hidden layers: 1o # of Hidden layer nodes: 12o # of Epochs: 30o Output nodes: 1
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Neural Networks
Kiran Sankuru | Rama Kappagantula
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Data Mining Methodology
Data Partition: Training – 80%, Validation – 20%
Runs
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Neural Networks - Implementation
#Run#Hidden layer
#Hidden Layer Nodes
#No of epochs/iterations
RMSE - Training
RMSE – Validation
RMSE Chg Return
1 1 12 30 0.107477474 0.111156007 3.423%
2 1 24 30 0.121486949 0.126022016 3.733%
3 1 6 30 0.099382581 0.102438743 3.075%
4 1 4 30 0.103504299 0.107414073 3.777%
5 1 6 45 0.066935626 0.067200881 0.396%
6 1 6 150 0.019869902 0.019022021 -4.267%
7 1 6 300 0.01733016 0.01701691 -1.808%
12:30 24:30 6:30 4:30 6:45 6:150 6:3000
0.02
0.04
0.06
0.08
0.1
0.12
0.14
-5.000%
-4.000%
-3.000%
-2.000%
-1.000%
0.000%
1.000%
2.000%
3.000%
4.000%
5.000%
RMSE
RMSE - Training RMSE - Validation RMSE Chg Rt
Kiran Sankuru | Rama Kappagantula
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Data Mining Methodology
Final Designo Input nodes: 12 predictorso # of Hidden layers: 1o # of Hidden layer nodes: 6o Output nodes: 1
Xactual = (Xnorm)*(b - a) + a for each predicted periodNote: a – minimum in the original range & b – maximum in the original range
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Neural Networks
Kiran Sankuru | Rama Kappagantula
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Model
3/9/2013
Results
1/3/
1994
6/3/
1994
11/3
/199
44/
3/19
959/
3/19
952/
3/19
967/
3/19
9612
/3/1
996
5/3/
1997
10/3
/199
73/
3/19
988/
3/19
981/
3/19
996/
3/19
9911
/3/1
999
4/3/
2000
9/3/
2000
2/3/
2001
7/3/
2001
12/3
/200
15/
3/20
0210
/3/2
002
3/3/
2003
8/3/
2003
1/3/
2004
6/3/
2004
11/3
/200
44/
3/20
059/
3/20
052/
3/20
067/
3/20
0612
/3/2
006
5/3/
2007
10/3
/200
73/
3/20
088/
3/20
081/
3/20
096/
3/20
0911
/3/2
009
4/3/
2010
9/3/
2010
2/3/
2011
7/3/
2011
12/3
/201
15/
3/20
1210
/3/2
012
0
20
40
60
80
100
120
140
-5.000%
-4.000%
-3.000%
-2.000%
-1.000%
0.000%
1.000%
2.000%
3.000%
4.000%
5.000%
Training Data
Regular Value - Predicted Regular Value - Actual % Prediction Error
8eoQSyDyMCjQVwoNGtOKpp
1/7/
1994
6/7/
1994
11/7
/199
44/
7/19
959/
7/19
952/
7/19
967/
7/19
9612
/7/1
996
5/7/
1997
10/7
/199
73/
7/19
988/
7/19
981/
7/19
996/
7/19
9911
/7/1
999
4/7/
2000
9/7/
2000
2/7/
2001
7/7/
2001
12/7
/200
15/
7/20
0210
/7/2
002
3/7/
2003
8/7/
2003
1/7/
2004
6/7/
2004
11/7
/200
44/
7/20
059/
7/20
052/
7/20
067/
7/20
0612
/7/2
006
5/7/
2007
10/7
/200
73/
7/20
088/
7/20
081/
7/20
096/
7/20
0911
/7/2
009
4/7/
2010
9/7/
2010
2/7/
2011
7/7/
2011
12/7
/201
15/
7/20
1210
/7/2
012
0
20
40
60
80
100
120
140
-4.000%
-3.000%
-2.000%
-1.000%
0.000%
1.000%
2.000%
3.000%
4.000%
Validation Data
Regular Value - Predicted Regular Value - Actual % Prediction Error9fGpnYAGToIf8cclCaKZh8
Kiran Sankuru | Rama Kappagantula
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Model
Model Performance
3/9/2013
Validation
1/1/
2013
1/2/
2013
1/3/
2013
1/4/
2013
1/5/
2013
1/6/
2013
1/7/
2013
1/8/
2013
1/9/
2013
1/10
/201
3
1/11
/201
3
1/12
/201
3
1/13
/201
3
1/14
/201
3
1/15
/201
3
1/16
/201
3
1/17
/201
3
1/18
/201
3
1/19
/201
3
1/20
/201
3
1/21
/201
3
1/22
/201
3
1/23
/201
3
1/24
/201
3
1/25
/201
3
1/26
/201
3
1/27
/201
3
1/28
/201
3
1/29
/201
3
1/30
/201
3
1/31
/201
377.5
78
78.5
79
79.5
80
80.5
81
-1.200%
-1.000%
-0.800%
-0.600%
-0.400%
-0.200%
0.000%
0.200%
0.400%
DXY - Test Data 01/01/2013 - 01/31/2013
DXY - Actual DXY - Predicted % Prediction Error
Kiran Sankuru | Rama Kappagantula
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Model
Q1 2013 & Q2 2013
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Prediction
Date DXY_Norm ActualDXY_Norm Predicted
DXY Actual
Time SeriesDXY
Predicted
Q1 2013 0.175558586 0.174875784 79.765 79.731
Q2 2013 0.187186009 0.234279779 80.345 82.693