THE NEWS-BASED APPROACH TO
EXCHANGE RATES AND THE
ECONOMETRICS OF SHORT-TERM
TRADING
Massimo Tivegna
University of Teramo, Italy
University of Greenwich ECON1083 Global Macroeconomics - Module 5
1
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS slides
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS p. 1-5
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS p. 5-6
4. INFORMATION SOURCES OF SCHEDULED NEWS slides
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS Tab. 2.1
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES p.7-8,Tab.
7. HOW TO MEASURE NEWS slides
8. A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES p. 9-12+slides
9. FROM SIMULATION TO TRADING – OPTIMIZATION p. 12-14 + slides
10. MEASURING AND EVALUATING PERFORMANCE p.14-25+slides
2
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
A CHECKLIST OF THE FOREX MARKET
•
•
•
•
•
•
General Features
Instruments exchanged
Trading
Forecasting Techniques for trading
Forex Market Microstructure
The role of Central Banks and their operations
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
GENERAL FEATURES
•
•
•
•
•
•
Open 24 hours a day
Quoting currency pairs
Number and ranking of currencies
Who trades, where, when, why, how
Postwar history at glance
Instruments exchanged
– Spot, Forward,Swaps,Futures, Options
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
FROM MEDIUM TERM TO DAY-TRADING
• Position Trading with a Multicurrency Portfolio (several
days)
• Hedging
– On Real and Financial Assets
– On Payables and Receivables
– For Offsetting Payments Flows
– For Asset Management
• Day-trading as a separate asset class
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
•
•
•
•
•
Forecasting Techniques for Trading
Technical Analysis
Snooping “professional” Behaviours and Hints (herding
and insider trading)
Rumours and news trading
Econometric Models from Various Theories and
Frequencies
The LONG – SHORT terminology
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
Microstructure
• Order flows
• Payment and trading circuits (e.g.EBS,www.ebs.com)
• Trading over the Internet(e.g.Oanda,IG Markets,
Saxobank, etc.)
• High-frequency information (e.g. Bloomberg, Reuters,
Dow Jones, etc.)
• Online consulting services, free and fee (e.g.
Forexfactory, Forexpros, RANsquawk, etc.)
• Carry trades
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
•
•
•
•
•
Role and Operations of Central Banks
European Central Bank
The Federal Reserve System, Fed
Bank of England, The Old Lady
Bank of Japan
Reserve Bank of Australia
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
• Notation: €-$ (EUR-USD) £-$ (STG-USD)
• From the “Legacy Currencies” to the Euro:
The Role of the DM and the Bundesbank
•
The Dimension of the Currency Market
• The Euro and the British Pound
• Cross exchange rates
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
EUR-USD Daily
At 4PM EST (Source Reuters, WSJ) - From Jan. 4,1999 To Present
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
EUR-USD and GBP-USD Daily
At 4PM EST (Source Reuters, WSJ) - From Jan. 2,2008 To Present
1.6
2.16
1.5
2.04
1.4
1.92
1.80
1.2
1.68
1.1
1.56
1.0
1.44
0.9
0.8
1.32
1999
2000
2001
2002
2003
2004
2005
2006
EUR-USD
2007
2008
GBP-USD
2009
2010
2011
2012
2013
GBP-USD
EUR-USD
1.3
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
EUR-USD Daily
At 4PM EST (Source Reuters, WSJ) - From Jan. 2,2008 To Present
1.60
1.55
(2)
1.50
(8)
US Tapering
Issues
(9)
(16)
(10)
1.45
(13)
(5)
(1)
1.40
1.35
Yen Depr.
(15)
(18)
(6)
(12)
(3)
(19)
(14)
1.30
(7)
1.25
(24)
(22)
(17)
(11)
(21)
(23)
(4)
1.20
(20)
1.15
2008
2009
2010
2011
2012
2013
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
EVENTS 2008 – 2009
1. US official interest hikes in two stages
2. US Dollar repatriation for the financial crisis and “safe
heaven” flows into the US Dollar
3. Lehman
4. US official interest rates at zero
5. Negative economic news in the Eurozone. ECB
expected to lower official interest rates
6. Reduction of ECB rates
7. Quantitative easing (QE) announced by the Fed
8. Persistent Dollar weakening
9. A very rare statement by the Fed Governor Bernanke
on $ weakness: big surprise
10. First downgrade of Greece. Beginning of the Eurozone
sovereign debt crisis
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
EUR-USD Daily
At 4PM EST (Source Reuters, WSJ) - From Jan. 2,2008 To Present
1.60
1.55
(2)
1.50
(8)
US Tapering
Issues
(9)
(16)
(10)
1.45
(13)
(5)
(1)
1.40
1.35
Yen Depr.
(15)
(18)
(6)
(12)
(3)
(19)
(14)
1.30
(7)
1.25
(24)
(22)
(17)
(11)
(21)
(23)
(4)
1.20
(20)
1.15
2008
2009
2010
2011
2012
2013
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
EVENTS 2010 – 2011
11. Downgrades of Spain and Portugal
12. Hints of a second QE
13. Formal approval of a second QE by the Fed
14. Beginning of Ireland crisis and further downgrades
15. Sharp downgrades of Greece, Spain and Portugal: the
P.I.G.S
16. ECB buys Italian and Spanish bonds
17. Approval by Eurozone Heads of State of the rescue
fund EFSF
18. Further European and Greek problems
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
EUR-USD Daily
At 4PM EST (Source Reuters, WSJ) - From Jan. 2,2008 To Present
1.60
1.55
(2)
1.50
(8)
US Tapering
Issues
(9)
(16)
(10)
1.45
(13)
(5)
(1)
1.40
1.35
Yen Depr.
(15)
(18)
(6)
(12)
(3)
(19)
(14)
1.30
(7)
1.25
(24)
(22)
(17)
(11)
(21)
(23)
(4)
1.20
(20)
1.15
2008
2009
2010
2011
2012
2013
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
EVENTS 2012 – 2013
19 .Hollande victory in France. Two Greek elections.
Further Eurozone difficulties
20. ECB Governor Draghi’s “ECB will do whatever it takes
to preserve the Euro”
21. Improvement in the Greek economic outlook
22. A rare statement by Draghi on currencies
23. Solution of the Cyprus banking crisis
24. ECB official interest rates cut
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
50
51
43
40
42
49
44
47
39
48
41
38
45
46
37
36
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
SEPTEMBER – OCTOBER 2013
36. ECB meeting. Draghi says interest rates will remain low for an
extended period of time.
37. Nothing relevant.
38. Summers withdraws from the Fed race. FOMC surprises
markets by maintaining QE.
39. Victory of Merkel in German elections. First hints of US
shutdown.
40. Italy’s Premier Letta survives a no confidence vote. ECB
meeting. First US closings.
41. Negotiations on US shutdown. Apparent agreement on Yellen
nomination at the Fed.
42. Anomalous sharp revaluation of US $ upon the settling of the
US shutdown.
43. Bad US payrolls delayed by the shutdown.
44. FOMC meeting without any hint of a continuation of QE. Very
low inflation in Eurozone. Likely closing of long €-$ positions.
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
50
51
43
40
42
49
44
47
39
48
41
38
45
46
37
36
THE €-$ FOREX MARKET – HISTORY AND RECENT TRENDS
45.
46.
47.
48.
49.
50.
51.
NOVEMBER – DECEMBER 2013
ECB cuts rates at regular meeting
Nothing relevant
Minutes of the of the FOMC with offsetting messages
on tapering QE
Nothing relevant
ECB meeting
Nothing relevant
FOMC meeting: first steps away from QE. The Fed will
reduce its monthly buying of US
bonds from 85 to
75 billions per month.
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS
4. INFORMATION SOURCES OF SCHEDULED NEWS
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
7. HOW TO MEASURE NEWS IMPACT ON EXCHANGE RATES
8. A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
9. FROM SIMULATION TO TRADING - OPTIMIZATION
23
10. MEASURING AND EVALUATING PERFORMANCE
FROM MACROECONOMIC FUNDAMENTALS TO MACROECON. NEWS
FROM MACROECONOMIC FUNDAMENTALS TO MACROECON. NEWS
FROM MACROECONOMIC FUNDAMENTALS TO MACROECON. NEWS
FROM MACROECONOMIC FUNDAMENTALS TO MACROECON. NEWS
FROM MACROECONOMIC FUNDAMENTALS TO MACROECON. NEWS
FROM MACROECONOMIC FUNDAMENTALS TO MACROECON. NEWS
FROM MACROECONOMIC FUNDAMENTALS TO MACROECON. NEWS
• We then have a first sight of some macrovariables which
can be of interest to traders as they influence exchange
rates.
–
–
–
–
–
Relative output growth
Relative inflation rates
Relative money growth
Interest rate differential
Exchange rate forecasts and expectations
FROM MACROECONOMIC FUNDAMENTALS TO MACROECON. NEWS
• What is missing:
– A policy reaction function and the decision process of
monetary policy
– The complexities of financial markets (yield curves,
stock markets fads, derivatives, etc.)
– The formation and changes of expectations in relation
to new infomation
– The globalization of domestic financial markets
– The relation between different asset prices
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS
4. INFORMATION SOURCES OF SCHEDULED NEWS
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
7. HOW TO MEASURE NEWS
8. IMPACT: A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
9. FROM SIMULATION TO TRADING - OPTIMIZATION
32
10. MEASURING AND EVALUATING PERFORMANCE
MACROECONOMIC SURPRISES-THE SCHEDULED NEWS
• Macroeconomic news announcements (scheduled
news) are defined as “surprise effects”, that is the
difference between expectations and realizations
• Data source for expected and realized announcements
is Bloomberg
• Units of measurements differ across economic
variables. Standardized k-th news is given by
SN kt 
Akt  Ekt
ˆ k
33
MACROECONOMIC SURPRISES-THE SCHEDULED NEWS
MAIN SCHEDULED NEWS IN ETZ ED ATZ
Scheduled News
Frequency Measurement Unit
1
2
Impact on €-$
Source
3
4
US Announcements
Forward-Looking Indicators
Non-farm Payrolls
Monthly
Units
Negative
Bloomberg
Initial Jobless Claims
Monthly
Units
Negative
Bloomberg
ISM (formerly NAPM) Manufatcture Index
Monthly
Diffusion Index
Negative
Bloomberg
ISM (formerly NAPM) Services Index
Monthly
Diffusion Index
Negative
Bloomberg
ISM (formerly NAPM) Chicago
Monthly
Diffusion Index
Negative
Bloomberg
Manufacture Index Philadelphia Fed
Monthly
Diffusion Index
Negative
Bloomberg
Consumer Confidence, Conference Board
Monthly
Weighted Index
Negative
Bloomberg
Consumer Confidence, Univ. of Michigan
Monthly
Weighted Index
Negative
Bloomberg
Leading Indicators
Monthly
% Var. Monthly
Negative
Bloomberg
34
MACROECONOMIC SURPRISES-THE SCHEDULED NEWS
MAIN SCHEDULED NEWS IN ETZ ED ATZ
Scheduled News
Frequency
Measurement Unit
Impact on €-$
Source
1
2
3
4
US Announcements
Other Indicators
Unemployment Rate
Monthly
Percentage
Positive
Bloomberg
Wage Rate, Non Farm
Monthly
% Var. Monthly
Negative
Bloomberg
Deflatore del GDP
Monthly
% Var. Quarterly
Negative
Bloomberg
Consumer Price Index
Monthly
% Var. Monthly
Negative
Bloomberg
Producer Price Index
Monthly
% Var. Monthly
Negative
Bloomberg
Producer Price Index,excl. Food&Energy
Monthly
% Var. Monthly
Negative
Bloomberg
GDP(advance,preliminary,final)
Monthly
% Var. Quarterly
Negative
Bloomberg
35
MACROECONOMIC SURPRISES-THE SCHEDULED NEWS
MAIN SCHEDULED NEWS IN ETZ ED ATZ
Scheduled News
Frequency Measurement Unit
1
2
Impact on €-$
Source
3
4
US Announcements
Other Indicators
Retail Sales
Monthly
% Var. Monthly
Negative
Bloomberg
Retail Sales excl. Automobiles
Monthly
% Var. Monthly
Negative
Bloomberg
Industrial Production
Monthly
% Var. Monthly
Negative
Bloomberg
Durable Goods
Monthly
% Var. Monthly
Negative
Bloomberg
Factory Orders
Monthly
% Var. Monthly
Negative
Bloomberg
Personal Income
Monthly
% Var. Monthly
Negative
Bloomberg
Personal Consumption
Monthly
% Var. Monthly
Negative
Bloomberg
Trade Balance
Monthly
Billions di $
Negative
Bloomberg
Current Account Balance
Quarterly
Billions di $
Negative
Bloomberg
36
MACROECONOMIC SURPRISES-THE SCHEDULED NEWS
MAIN SCHEDULED NEWS IN ETZ ED ATZ
Scheduled News
Frequency
Measurement Unit
Impact on €-$
Source
1
2
3
4
German Announcements
Forward-Looking Indicators
IFO Index
Monthly
Diffusion Index
Positive
Various
Preliminary CPI
Monthly
% Var. Monthly
Positive
Various
Producer Price Index
Monthly
% Var. Monthly
Positive
Various
Unemployment Change
Monthly
Units
Negative
Various
Factory Orders
Monthly
% Var. Monthly
Positive
Various
Retail Sales
Monthly
% Var. Monthly
Positive
Various
Industrial Production
Monthly
% Var. Monthly
Positive
Various
GDP
Quarterly
% Var. Quarterly
Positive
Various
Other Indicators
37
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS
4. INFORMATION SOURCES OF SCHEDULED NEWS
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
7. HOW TO MEASURE NEWS
8. IMPACT: A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
9. FROM SIMULATION TO TRADING - OPTIMIZATION
38
10. MEASURING AND EVALUATING PERFORMANCE
INFORMATION SOURCES OF SCHEDULED NEWS
• Bloomberg, Reuters. The most reliable and expensive.
• ForexFactory has the best cost-quality ratio (but there
could be equivalent ones)
• Find your favoured one and stick to it (but occasionally
checking elsewhere).
• Establish (with experience) your metrics of the news
impact as trading is a highly personal business.
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS
4. INFORMATION SOURCES OF SCHEDULED NEWS
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
7. HOW TO MEASURE NEWS
8. IMPACT: A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
9. FROM SIMULATION TO TRADING - OPTIMIZATION
40
10. MEASURING AND EVALUATING PERFORMANCE
IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
THE NON-FARM PAYROLLS AND THE UNEMPLOYMENT RATE IN
THE US LABOUR MARKET
• The US Non-farm Payrolls has by far the highest impact
on world financial markets.
• It is the number of jobs added to the US economy
outside the agricultural sector, in a highly flexible labour
market.
• The impact is higher than that of Unemployment Rate
and the two numbers come from different surveys.
IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
REACTIONS TO THE US NON-FARM PAYROLLS
• In the following two slides we have the €-$ reactions in
the last two months:
• The first chart indicates a reaction to a 160.000
Expected and a 236.000 Actual with unemployment rate
going down.
• The second chart indicates a reaction to a 165.000
Expected and a 157.000 actual, with unemployment rate
going up.
IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
REACTIONS TO THE US NON-FARM PAYROLLS
NFP on March 8: Exp. 160.000 vs 236.000
IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
REACTIONS TO THE US NON-FARM PAYROLLS
NFP on February 1, 2013 Exp. 165.000 vs 157.000
IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
REACTIONS TO THE US NON-FARM PAYROLLS
Delayed NFP on October 22, 2013 Exp. 180.000 vs 148.000
IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
REACTIONS TO THE US NON-FARM PAYROLLS
NFP on November 8, 2013 Exp. 120.000 vs 204.000
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS
4. INFORMATION SOURCES OF SCHEDULED NEWS
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
7. HOW TO MEASURE NEWS
8. A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
9. FROM SIMULATION TO TRADING - OPTIMIZATION
47
10. MEASURING AND EVALUATING PERFORMANCE
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
Unscheduled news consists of an economic or
institutional event, a declaration or a disclosure,
which can be either totally unexpected or - even
though expected to occur - has an unknown
timing, or an unknown content ( or both ) and
a time-varying reaction, frequently producing
weird and ex-ante unpredictable movements in
financial markets.
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
GRADUAL RESULTS OF THE ITALIAN ELECTIONS
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
REACTION (MONDAY, JTZ) TO THE CRISIS OF THE MONTI
GOVERNMENT IN ITALY (ON FRIDAY, IN ATZ)
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
Public interventions by Central Banks in the
foreign exchange market or statements by the
same source announcing or threatening them
( Japanese Authorities is a good example).
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCH. RATES
ECB RATE SETTING AND PRESS CONFERENCE FEB.7,2013
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
Unexpected – or moderately so – changes of
official interest rates or strong expectations
about their changes whenever they remain
invariant after a policy meeting or after
policy statements, frequently by lower-ranking
policy makers.
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCH. RATES
ECB RATE SETTING AND PRESS CONFERENCE MARCH 7,2013
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCH. RATES
ECB RATE SETTING AND PRESS CONFERENCE OCT. 2,2013
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCH. RATES
ECB RATE CUT on NOVEMBER 7,2013
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCH. RATES
ECB RATE SETTING AND PRESS CONFERENCE DEC. 5,2013
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCH. RATES
US FOMC COMUNIQUE’ OCTOBER 30,2013
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
Unexpected – or moderately so - upgrading or
downgrading by Rating Agencies or official
Institutions of entire countries or important financial
Institutions.
UNSCHEDULED NEWS AND THEIR IMPACT ON EXCH. RATES
FRANCE DOWNGRADING BY MOODY’S - NOVEMBER 9, 2012
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS
4. INFORMATION SOURCES OF SCHEDULED NEWS
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
7. HOW TO MEASURE NEWS
8. IMPACT: A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
9. FROM SIMULATION TO TRADING - OPTIMIZATION
61
10. MEASURING AND EVALUATING PERFORMANCE
HOW TO MEASURE NEWS
SCHEDULED NEWS
• SCHEDULED NEWS can be
measured in 2 metric scales.
• The news scale measured by
the standardized difference
between ACTUAL VALUE (A)
published in the newswires
and EXPECTED VALUE (E)
as computed by specialized
Agencies.
• The impact on exchange rate
as measured by the standard.
variation over the 3 Time
Zones.
Akt  Ekt
SN kt 
ˆ k
(€-$)ATZ – (€-$)ETZ
Stand.Error(ATZ-ETZ)
HOW TO MEASURE NEWS – SCHEDULED NEWS
ACTUAL AND EXPECTED NON-FARM PAYROLLS (NFP)
Date
2011.01.07
2011.02.04
2011.03.04
2011.04.01
2011.05.06
2011.06.03
2011.07.08
2011.08.05
2011.09.02
2011.10.07
2011.11.04
2011.12.02
2012.01.06
2012.02.03
2012.03.09
2012.04.06
2012.05.04
2012.06.01
2012.07.06
2012.08.03
2012.09.07
2012.10.05
Act.NFP
Exp.NFP
Act-Exp
STAND.(Act-Exp)
103
36
192
216
244
54
18
117
0
103
80
120
200
243
227
120
115
69
80
163
96
114
160
140
200
200
185
170
105
75
60
65
90
131
150
135
204
201
165
150
90
100
125
113
-57
-104
-8
16
59
-116
-87
42
-60
38
-10
-11
50
108
23
-81
-50
-81
-10
63
-29
1
-0.5627
-1.0267
-0.0789
0.1579
0.5824
-1.1452
-0.8589
0.4146
-0.5923
0.3751
-0.0987
-0.1085
0.4936
1.0662
0.227
-0.7996
-0.4936
-0.7996
-0.0987
0.6219
-0.2863
0.0098
STANDARD ERROR OF ACTUAL LESS EXPECTED: 101.29
HOW TO MEASURE NEWS - SCHEDULED NEWS
STANDARDIZED CHANGES OF EXCH. RATES IN 3 TIME ZONES
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
DATE
JTZ
ETZ
ATZ
2013:02:18 -0.9405 0.5110 -0.0627
2013:02:19 0.0303 -0.3537 0.8626
2013:02:20 0.7251 -0.8224 -1.5099
2013:02:21 -0.3051 -1.4272 -0.1747
2013:02:22 0.9204 -0.6358 0.0953
2013:02:25 0.0921 2.1582 -3.8326
2013:02:26 -0.1240 0.8223 -0.5288
2013:02:27 0.0619 0.8417 0.5270
2013:02:28 0.0924 -0.1796 -1.2323
2013:03:01 0.5888 0.0200 -0.8833
2013:03:04 -0.1555 -0.1410 0.1288
2013:03:05 0.5284 -0.1610 0.2572
2013:03:06 0.5274 -0.4622 -0.7085
2013:03:07 0.0623 0.4637 1.4120
2013:03:08 -0.4328 0.0000 -1.4941
HOW TO MEASURE NEWS - UNSCHEDULED NEWS
+1
€-$-Positive, $-¥-Negative, £-$-Positive News
- 1
€-$ or £-$ - Negative News, $-¥-Positive
0
No news
65
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS
4. INFORMATION SOURCES OF SCHEDULED NEWS
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
7. HOW TO MEASURE NEWS
8. IMPACT: A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
9. FROM SIMULATION TO TRADING - OPTIMIZATION
66
10. MEASURING AND EVALUATING PERFORMANCE
A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
3 TRADING ZONES IN THE GLOBAL TRADING DAY
GT Day
(t-1)
21:00
(t-1)
Global Trading Day
(t)
5:00
JAPANESE
TIME ZONE
13:00
EUROPEAN
TIME ZONE
21:00
AMERICAN
TIME ZONE
67
A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
A NEWS APPROACH TO ECHANGE RATE DETERMINATION
r
m
n
s 1
j 1
i 1
Et ( St 1 )  St    sYs,t    j [ Z j ,t 1  Et ( Z j ,t 1 )]    iU i,t 1   t
FOREIGN
EXCHANGE
VARIATION
FUNDAMENT.
SCHEDULED
NEWS
UNSCHED.
NEWS
t:JTZ
ETZ
ATZ
SCHEDULED NEWS typically consist of macroeconomic data
releases
UNSCHEDULED NEWS consists of an economic or institutional
event, a declaration or a disclosure, which can be either totally
unexpected or – even though expected to occur – has an unknown
timing, or an unknown content or both
A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
MODELS IN EUROPEAN TIME AND AMERICAN TIME ZONES
EUROPEAN TIME ZONE(ETZ) 6AM(T) – 14(T), CET
• rEU,ETZ = F[ Lags(rEU) , (rUS) t-1,SKEU ]
• (€-$ETZ)=F[ (€-$ATZ)t-1, rEU, (rUS)t-1,
SKETZ,UNSKETZ]
AMERICAN TIME ZONE( ATZ) 14(T) – 22(T), CET
• DJATZ = F[ Lags(DJATZ)t-1 , SKATZ ]
• rUS,ATZ = F[ Lags(rUS)t-1 , SKATZ ]
• (€-$US)=F[ (€-$ETZ)t, rUS,rEU ,Lags(DJ) ,SKATZ ,UNSKATZ ]
• GARCH ERROR MODELS
A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
NEWS-BASED MODEL DEVELOPMENT AND NOVEL FEATURES
UK
$ / ¥, E



sk t
u 
 $/ ¥ t i 
 $/ ¥ t 
 €/$ E    Β E   €/$    Γ SK, E sk GE   Γ UNSK, E  u € / $, E   ε E
i
t 
t
t
t i



 39 
 312 




 33
 31
E
US
£ / $, E
u 
sk t 2 
 £ /$ t i 
 £ /$ t 




( 31 )
 31
E
t
3
t
i 0
t
 91
εt
E
N  0, H t
E

H   + ( ) H
E
t
E
E 2
t
121
E
t 1
70
A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
NEWS-BASED MODEL DEVELOPMENT AND NOVEL FEATURES
71
A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
NEWS-BASED MODEL DEVELOPMENT AND NOVEL FEATURES
 DJ t 
DJ t i 
 r US 
US
¥ /$, A
US 



¥
/$






t
sk
u
rt i
t i
t
t

 3 
5
 UK  SK, A  GE  UNSK, A  €/$, A  A

A
A
A
 $/ ¥ t    Βi   €/$     Θi  r   Γ sk t 1  Γ
u


t
t
t i




t i
514 
511


51
i 0  55 

 i 053 
A
UK
£/$,
A


EU
sk t 1 
u t 
r
  €/$ t 
 £/$ t i 
t i 
 
 

JA
141
111
 31
A

r


 t 1 
 £/$ t 
51
εt
A
N  0, H t
A

H   + ( ) H
A
t
A
A 2
t
A
t 1
72
A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
NEWS-BASED MODEL DEVELOPMENT AND NOVEL FEATURES
73
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS
4. INFORMATION SOURCES OF SCHEDULED NEWS
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
7. HOW TO MEASURE NEWS
8. IMPACT: A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
9. FROM SIMULATION TO TRADING - OPTIMIZATION
74
10. MEASURING AND EVALUATING PERFORMANCE
FROM SIMULATION TO TRADING - OVERVIEW
• In this course we will examine four protocols which can
be used with a short-term econometric model:
• Directional Trading
• Contrarian Trading, news-assisted mode
• Contrarian trading, automatic mode
• Mixed Trading: Directional plus “Contrarian” at Stop Loss
• Baseline (for a benchmark)
FROM SIMULATION TO TRADING - DIRECTIONAL
•
•
1.
2.
3.
4.
5.
•
Directional Trading consists of taking a long or short
position according to the model forecast.
Procedure:
In ETZ, update, before 7AM, your data base with the
5AM €-$ rate and simulate the model.
In ATZ, update, before 1PM, your data base with the
1PM €-$ rate and simulate the model.
If the model predicts an appreciating €, you look for a
suitable €-$ rate and take take a long position, or else
short €-$.
You set up Stop Loss (SL) and Take Profit (TP).
The ETZ trade is closed at at SL or TP, ore else at
1PM. For ATZ, the trade is closed at SL or TP or else
at 9PM.
This trading strategy can be implemented by a
computer programme.
FROM SIMULATION TO TRADING - CONTRARIAN NEWS-ASSISTED
• News-assisted contrarian trading consists of
starting a Long or Short trade if the €-$ goes
beyond GARCH-computed lower or upper
thresholds (respectively), in the ETZ-ATZ time
span after 7AM.
• All that must occur in a no-news situation (as
there is no reason for the € to go beyond
thresholds) or in conjunction with a Europositive or Euro-negative news (respectively).
• The above two trading strategies (mutually
exclusive, in principle) can be implemented by
human intervention or in automated mode.
FROM SIMULATION TO TRADING - CONTRARIAN AUTOMATED
• Automated contrarian trading consists of
starting a Long or Short trade whenever the €-$
goes beyond GARCH-computed lower or upper
thresholds (respectively), in the ETZ-ATZ time
span after 7AM.
• Contrary to the news-assisted protocol, the
trade begins mechanically if the € goes beyond
GARCH thresholds.
• This trading strategy can be implemented by a
computer programme.
FROM SIMULATION TO TRADING - MIXED
•
•
•
•
•
•
•
This kind of trade starts Directional in ETZ. If the trade
is closed regularly at TP or at 1PM, you start a trade in
ATZ, with the normal Directional protocol.
If a SL is touched, the the procedure gets Mixed:
Revert the trade (with respect to the movement of the €)
in the same direction of the original Directional trading
signal, setting SL and TP.
In case of a Directional Long trade, SL is reached if the
€ goes in the other direction with respect to this signal.
So you revert the trade and go Long again.
In case of a Directional Short trade, SL is reached if
the € goes in the other direction with respect to this
signal. So you revert the trade and go Short again.
In both Long or Short Mixed trading protocols, the trade
is closed at SL or TP or at 9PM.
This trading strategy can be implemented by a
computer programme.
FROM SIMULATION TO TRADING - BASELINE
•
•
1.
2.
3.
4.
5.
•
The Baseline follows a Directional Trading scheme. It
consists of taking a long or short position according to the mode
forecast.
Procedure:
In ETZ, update, before 7AM, your data base with the 5AM €-$
rate and simulate the model.
In ATZ, update, before 1PM, your data base with the 1PM €-$
rate and simulate the model.
If the model predicts an appreciating €, the suitable €-$ rate to
start your Long trade is an average of the opening rate and the
low rate between 7-8. For a Short, you take the average of the
opening rate between 7-8 with the High €-$ rate, in the same
time span.
You set up Stop Loss (SL) and Take Profit (TP).
The ETZ trade is closed at at SL or TP, ore else at 1PM. For
ATZ, the trade is closed at 9PM.
This trading strategy cannot be implemented by a computer
programme.
FROM SIMULATION TO TRADING
CONTRARIAN TRADING WITH GARCH BANDS
r
m
n
s 1
j 1
i 1
Et ( St 1 )  St    sYs,t    j [ Z j ,t 1  Et ( Z j ,t 1 )]    iU i,t 1   t
FOREIGN
EXCHANGE
VARIATION
FUNDAMENT.
SCHEDULED
NEWS
UNSCHED.
NEWS
t:JTZ
ETZ
ATZ
Model is simulated setting Fundamentals and Sched. News equal
to zero and by setting the Unsched. News variable with the highest
coefficient equal to +1 (for simulating a “strong €-$”) or -1 (for
simulating a “weak €-$”). We thus have 3 values of Exchange
Rate:
1. “Baseline Value of €-$”
2. “Strong €-$”
3. “Weak €-$”
The GARCH model of the error term determines bands around
these values-
FROM SIMULATION TO TRADING
CONTRARIAN TRADING WITH GARCH BANDS
• GARCH structure of the error term
i
t
2,i
t

2,i
N ( 0,  t )

2, i
  t 1
i => ETZ ATZ

2,i
 t 1
FROM SIMULATION TO TRADING
CONTRARIAN TRADING WITH GARCH BANDS
BASELINE
(LO)BA
(€)BA
(UP)BA
€-WEAK
(LO)WE
(€)WE
€-STRONG
(UP)WE
(LO)ST
(€)ST
(UP)ST
FROM SIMULATION TO TRADING
DIRECTIONAL TRADING IN ETZ
IF ER7-8<THRL
THRL
SL
TP
LONG
LONG
TP
IF ER7-8>THRL
Model forecast
at 7 AM or
before
NO TRADE
IF NOT
REACHED
SL
THRS
IF ER7-8>THRS
SL
TP
SHORT
IF ER7-8<THRS
NO TRADE
SHORT
CLOSE
AT 1 PM
FROM SIMULATION TO TRADING
DIRECTIONAL TRADING IN ATZ
IF ER1-2<THRL
THRL
SL
TP
LONG
LONG
TP
IF ER1-2>THRL
Model forecast
at 1 PM or
before
NO TRADE
IF NOT
REACHED
SL
THRS
IF ER1-2>THRS
SL
TP
SHORT
IF ER1-2<THRS
NO TRADE
SHORT
CLOSE
AT 9 PM
FROM SIMULATION TO TRADING
MIXED TRADING IN ETZ
TP
IF ER7-8<THRL
THRL
SL
TP
IF SL IS
REACHED
IF ER7-8>THRL
Model forecast
at 7 AM or
before
IF NOT
REACHED
CLOSE
AT 1 PM
SL
LONG
IF SL IS
NOT REACHED
LONG
LONG
FROM SL
CLOSE
AT TP
AT 1 PM
NO TRADE
TP
IF ER7-8>THRS
THRS
SL
TP
IF SL IS
REACHED
IF ER7-8<THRS
NO TRADE
IF NOT
REACHED
SL
SHORT
IF SL IS
NOT REACHED
SHORT
SHORT
FROM SL
CLOSE
AT TP
AT 1 PM
CLOSE
AT 1 PM
FROM SIMULATION TO TRADING
MIXED TRADING IN ATZ
TP
IF ER1-2<THRL
THRL
SL
TP
IF SL IS
REACHED
IF ER1-2>THRL
Model forecast
at 1 PM or
before
IF NOT
REACHED
CLOSE
AT 9 PM
SL
LONG
IF SL IS
NOT REACHED
LONG
LONG
FROM SL
CLOSE
AT TP
AT 9 PM
NO TRADE
TP
IF ER1-2>THRS
THRS
SL
TP
IF SL IS
REACHED
IF ER1-2<THRS
NO TRADE
IF NOT
REACHED
SL
SHORT
IF SL IS
NOT REACHED
SHORT
SHORT
FROM SL
CLOSE
AT TP
AT 9 PM
CLOSE
AT 9 PM
FROM SIMULATION TO TRADING
AUTOMATIC CONTRARIAN TRADING
IF ER<
Computation of
Garch
Thresholds at 7
AM or before
LOWER-G
THR
LOWER-G
IF ER> THR
UPPER-G
IF ER>
THR
UPPER-G
IF ER<
THR
LOWER-G
THR
SL
TP
LONG
TP
NO TRADE
IF NOT
REACHED
SL
UPPER-G
THR
SL
TP
NO TRADE
SHORT
CLOSE
AT 9 PM
FROM SIMULATION TO TRADING
NEWS ASSISTED CONTRARIAN TRADING
LOWER-G
IF ER< THR AND
NO NEWS
OR
€ - POSITIVE NEWS
Computation of
Garch
Thresholds at 7
AM or before
IF ER<
LOWER-G
THR
LONG
LOWER-G
THR AND
€ - NEGATIVE
NEWS
SL
TP
NO TRADE
TP
IF NOT
REACHED
UPPER-G
THR AND
IF ER>
NO NEWS
OR
€ - NEGATIVE
NEWS
UPPER-G
THR AND
IF ER> € - POSITIVE
NEWS
UPPER-G
THR
SHORT
SL
TP
NO TRADE
SL
CLOSE
AT 9 PM
FROM SIMULATION TO TRADING
CUMULATIVE PROFITS IN THE 4 TRADING RULES
Euro-$,Cumulative Total Profits in All Trading Rules
January 3, 2011 - November 29, 2013
1.50
1.0
33.4%
0.8
1.40
22.6%
1.35
0.6
0.4
8.5%
1.30
0.2
6.1%
1.25
0.0
1.20
-0.2
J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N
2011
2012
2013
E-$
Contrarian-News
Contr.Autom.
Mixed
Directional
Cumulative Profits
Exchange Rate
1.45
FROM SIMULATION TO TRADING
CUMULATIVE PROFITS IN THE 4 TRADING RULES PLUS BASELINE
Euro-$,Cumulative Total Profits in All Trading Rules Plus Baseline
January 3, 2011 - November 29, 2013
1.50
1.2
49.9%
1.45
1.0
1.40
22.6%
0.8
0.6
1.35
8.5%
1.30
0.4
0.2
6.1%
1.25
0.0
1.20
-0.2
J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N
2011
2012
2013
E-$
Contrarian-News
Contr.Autom.
Mixed
Directional
Baseline
Cumulative Profits
Exchange Rate
33.4%
FROM SIMULATION TO TRADING
OPTIMIZING AUTOMATED TRADING VIA A GENETIC ALGORITHM
• Once you are able to devise a protocol which can be
automated and you build a computer program able to
execute your trading instructions sequentially, you may try to
optimize the parameters of our rule which condition the profit
performance.
• A widely used optimization technique in finance is the
Genetic Algorithm (GA), which is adapted from a “selection
of the species” to a “selection of the profit-maximizing
parameters”
FROM SIMULATION TO TRADING
OPTIMIZING AUTOMATED TRADING VIA A GENETIC ALGORITHM
• It is basically a data mining program which iteratively
finds the best set of parameters from a group of numbers
within boundary conditions.
• In order to avoid “data snooping” (namely use the same
set of numbers to optimize and compute trading results),
you will need to split your data set into two parts.
FROM SIMULATION TO TRADING
OPTIMIZING AUTOMATED TRADING VIA A GENETIC ALGORITHM
• A Training Set (TNS), made up by the first part of your
data set, where you find the optimal parameters.
• A Trading Set (TRS), made up of the second part of
your data set still within the sample, where you will
compute the performance (generally in terms of
cumulative profits, their volatility and their Drawdown)
FROM SIMULATION TO TRADING
OPTIMIZING AUTOMATED TRADING VIA A GENETIC ALGORITHM
• Ideally the time series of exchange rate (ER) in the two
Sets should have comparable time series properties (e.g
mean, volatility, cyclical properties, outliers) and they
should be usable in the trading environment of the
immediate future.
• After some use of the optimized day-trading rule, the GA
will have to be trained again, using the ER actually used
for trading as a Training/Trading Set.
FROM SIMULATION TO TRADING
OPTIMIZING AUTOMATED TRADING VIA A GENETIC ALGORITHM
• The following slide shows what to optimize in the
Automated Rule shown before (slide 66). The
parameters are thresholds and SL and TP, besides a
time length to decide to “Close and Reverse” the trade.
• The subsequent two slides show a TNS and a TRS
where charts of cumulative profits and of Drawdown
features are depicted, together with the observed ER
series
FROM SIMULATION TO TRADING
OPTIMIZING AUTOMATED TRADING VIA A GENETIC ALGORITHM
TP
IF ER7-8<THRL
THRL
SL
TP
IF SL IS
REACHED
IF ER7-8>THRL
Model forecast
at 7 AM or
before
IF NOT
REACHED
CLOSE
AT 1 PM
SL
LONG
IF SL IS
NOT REACHED
LONG
LONG
FROM SL
CLOSE
AT TP
AT 1 PM
NO TRADE
TP
IF ER7-8>THRS
THRS
SL
TP
IF SL IS
REACHED
IF ER7-8<THRS
Genetic algorithm
optimization
NO TRADE
IF NOT
REACHED
SL
SHORT
IF SL IS
NOT REACHED
SHORT
SHORT
FROM SL
CLOSE
AT TP
AT 1 PM
CLOSE
AT 1 PM
COURSE OUTLINE
1. THE €-$ FOREX MARKET - HISTORY AND RECENT TRENDS
2. FROM MACROECONOMIC FUNDAMENTALS TO MACRO NEWS
3. MACROECONOMIC SURPRISES – THE SCHEDULED NEWS
4. INFORMATION SOURCES OF SCHEDULED NEWS
5. IMPACT ON EXCHANGE RATES OF SCHEDULED NEWS
6. UNSCHEDULED NEWS AND THEIR IMPACT ON EXCHANGE RATES
7. HOW TO MEASURE NEWS
8. IMPACT: A 3-ZONES ECONOMETRIC MODEL OF EXCHANGE RATES
9. FROM SIMULATION TO TRADING - OPTIMIZATION
98
10. MEASURING AND EVALUATING PERFORMANCE
MEASURING AND EVALUATING PERFORMANCE
• The profitability of a day-trading rule is measured as any
standard financial investment.
• Profit Rate over a given time horizon and its standard
deviation for evaluating statistical significance and
drawing confidence bands. A Sharpe Ratio (i.e.
standardized excess return over the risk-free return) is
also widely used.
MEASURING AND EVALUATING PERFORMANCE
• But a high-frequency trading rule (as a protocol
replicating itself without any change) needs also
monitoring indicators for checking the stability of profits
and their volatility overtime and, in particular, in turbulent
periods.
MEASURING AND EVALUATING PERFORMANCE
The monitoring indicators can be:
1. Equity Line (a Cumulative Profit Line)
2. Moving Standard Deviation of Profit along the
Equity Line (Volatility)
3. Drawdown Analysis
4. Policy and event analysis from the perspective of
an historical evaluation of quiet and turbulent
periods and related policy measures and market
sentiment.
MEASURING AND EVALUATING PERFORMANCE
Turbulent periods follow:
1. Unexpected Policy measures (e.g. US ultra expansionary
monetary policy after Lehman and QE later)
2. Ongoing financial distress from contagion (e.g.
Greece=>Ireland=>Portugal=>Italy and Spain, PIIGS)
3. Rumors, Fads, Myths, Excuses
4. Irrational Exuberance or Irrational Pessimism
MEASURING AND EVALUATING PERFORMANCE
TURBULENCE POST-LEHMAN
Fig. 3.1 - Euro-$,Cumulative Total Profits and Drawdowns
2008 - 2010
1.60
1.55
4
5
0.8
6
7
8
0.6
1.45
0.5
1.40
1
2
3
0.4
1.35
0.3
1.30
0.2
1.25
0.1
1.20
1.15
0.0
J
F M A M J J A S O N D
2008
J F M A M J J
2009
E-$
Cum.Tot.Prof.
A S O N D J F M A M J J
2010
Cumulative Profits
Exchange Rate
1.50
0.7
MEASURING AND EVALUATING PERFORMANCE
TURBULENCE EPISODES IN 2011-12 - BASELINE
Euro-$,Cumulative Total Profits and Drawdowns in the Baseline Rule
January 3, 2011 - August 3, 2012
1.50
1
2
3
0.72
4
0.60
0.48
1.40
0.36
1.35
12
0.24
1.30
0.12
1.25
0.00
5
6
7
8
9
10
11
1.20
-0.12
Jan Feb Mar
Apr May
Jun
Jul
2011
Aug
Sep Oct Nov Dec
E-$
Cum.Tot.Prof.
Jan
Feb Mar
Apr
May
Jun Jul
2012
Aug
Cumulative Profits
Exchange Rate
1.45
MEASURING AND EVALUATING PERFORMANCE
TURBULENCE EPISODES IN 2011-12 – 4 TRADING RULES PLUS BASELINE
Euro-$,Cumulative Total Profits and Drawdowns in All Trading Rules Plus Baseline
January 3, 2011 - August 3, 2012
1.50
1
2
3
0.8
4
0.7
1.45
1.40
0.5
0.4
1.35
12
0.3
1.30
0.2
0.1
1.25
5
6
7
8
9
10
0.0
11
1.20
-0.1
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul
2011
2012
E-$
Contrarian-News
Contr.Autom.
Mixed
Directional
Baseline
Aug
Cumulative Profits
Exchange Rate
0.6
MEASURING AND EVALUATING PERFORMANCE
TURBULENCE EPISODES IN 2012-13 – BASELINE
Euro-$,Cumulative Total Profits and Drawdowns in the Baseline Rule
July 30, 2012 - November 29, 2013
1.40
13
14
15
16
17
18
19
20
21
24
23
22
0.56
0.48
1.35
0.40
0.32
1.30
0.24
0.16
1.25
0.08
0.00
1.20
-0.08
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
2013
E-$
Cum.Tot.Prof.
Aug
Sep
Oct
Nov
Cumulative Profits
Exchange Rate
0.64
MEASURING AND EVALUATING PERFORMANCE
TURBULENCE EPISODES IN 2012-13 – 4 TRADING RULES PLUS BASELINE
Euro-$,Cumulative Total Profits and Drawdowns in All Trading Rules Plus Baseline
August 6, 2012 - November 29, 2013
1.40
13
14 15
16
17
18
19
20
21
24
23
22
0.7
0.6
0.5
0.4
1.30
0.3
0.2
1.25
0.1
0.0
1.20
-0.1
Jul
Aug
Sep
E-$
Oct
Nov
Dec
Contrarian-News
Jan
Feb
Mar
Contr.Autom.
Apr
May
Mixed
Jun
Jul
2013
Aug
Directional
Sep
Oct
Baseline
Nov
Cumulative Profits
Exchange Rate
1.35
THE END
Goodnight
And Good Luck !
Shakespeare “Julius Caesar”
(Brutus)