the art and science of forecasting financial markets
TRANSCRIPT
HEADER DATE
The Art and Science
of
Forecasting Financial Markets
Ben Jacobsen
Outline
Part 1: Market Efficiency and unpredictability as a benchmark
Part 2: Empirical examples of forecastability
Part 3: Building quantitative forecasting models
2
Month t t-1 t-2 t-3
Future stock return
The Basic Problem
Information in the past
Academic point of view
Market efficiency: prices reflect the available information
Consequence: (changes in) stock market prices follow a random walk and are unpredictable
The study that led to the EMH
Sir Maurice Kendall (1953) seminal study on prices in financial markets:
“The series looks like a “wandering” one as if once a week the Demon of Chance drew a random number (...) and added it to the current to determine next week’s price.”
“But economists - and I cannot help sympathizing with them - will doubtless resist any such conclusion very strongly. We can at this point suggest only a few conclusions:
(a) the interval of observation may be very important.(b) it seems a waste of time to isolate a trend in data
such as these;(c) the best estimate of the change in price between
now and next week is that there is no change.”
Unpredictable? Random?
How well does that assumption work?
HEADER DATE
Virtual and Reality Virtual and Reality
We tend to see structure in randomness…
Head and Shoulder
Traditional Approaches
9
• Technical Analysis: • Looking for patterns in historical price information to predict the future
• Fundamental Analysis:• Studying macro economic, sector and company information to derive
a valuation
• Alternative approaches…..
Very fundamental analysis…
The Hemline Indicator Market efficiency may explain a lot
HEADER DATE
Mutual Fund Performance Who is the better investor?
Wall Street Dartboard Competition
16
Wall Street Dart Board Competition Results
30%
40%
50%
60%
70%
80%
90%
100%
1 3 5 7 911 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
vs Monkey
vs Market
Percentage of contests when analysts beat Monkey and Market (DJI)
Venus versus Mars
Women get higher returns 1) Women are better investors as they tend to trade less
2) This may be caused men being more overconfident
3) Differences are smaller in relationships
(Barber and Odean, “Boys will be Boys”)
Women invest less in stocks 4) Maybe caused by difference in risk tolerance but not likely
5) Women are less optimistic about the economy
6) Women tend to be less optimistic about the future in general
7) Women tend to perceive the stock market as being more risky
(Jacobsen, Lee, Marquering, Zhang, “Gender Differences in Optimismand Asset Allocation”)
Buy a well diversified portfolio of some stocks
If markets are efficient……..
Trade under no circumstances
Avoid any news that might create stimuli
HEADER DATE
• 41% of all net fund flows worldwide went into passive investments in 2012• 18% of total mutual fund and ETF assets worldwide are now passively invested• Passive investing grew at three times the rate of active investing, 9.8% to 3.2%
Source Morningstar
Passive Investing Early 1990’s
1) Assuming market efficiency/rational investors explains a lot- Poor performance of technical and fundamental analysis
2) But some anomalies:- A January effect
- A small firm effect
- Overreaction effects: Winners and Losers.
3) Academics did not like irrationality: degrees of freedom
4) Market efficiency was often not well understood.- Do we need rational investors?
- What if everyone believes markets are efficient?
- What about information?
- How can prices go up?
Market efficiency in detail
E Pt | It1 Pt1
But can this be true?
No! risk and return: nobody would invest:Prices should go up on average
Conditional on all information the best forecast of tomorrows price is today’s price
Pt-1
Pt
Pt Pt1 1
rt Pt Pt1
Pt1
Pt1 1 Pt1
Pt1
Note
rf rm rf time
Prices ‘must’ go up: risk and return
Random walk model
Price already reflect all relevant information.
What is the best forecast of tomorrows stock price given all available information today?
As consequence stock market returns follow a random walk:
t t t tr E r 1[ ]
rt t
Random Walk model
E rt | It1 Note: we assume μ is constant but it mightBe time-varying.
Et1 rt or
Thus if markets are efficient we should not be ableto come with a better forecast given all availableinformation.
How to test this. Note we have a model withExpectations which are difficult to observe.
HEADER DATE
A testable model
rt t
t t t tr E r 1[ ]
Et1 rt Add on rt both sides
rt Et1 rt rtrewrite
rt rt Et1 rt Define:
t t t tr E r 1[ ]
And we have: the random walk model
with
Pt-1
Pt
time
Random deviations caused by unpredictable newsWhich affects value over time
The if’s of market efficiency
If investors are rational and if information is freely available andreaches all investors at the same time
Then
Markets are informationally efficient: prices will refelect all availableinformation and price will equal value
Then
You can trust market prices
Market Efficiency: Rationality
Note that not all investors need to be rational for markets to beefficient.
Markets can be informationally efficient even if not all investorsbehave rational. As long as they do not behave systematicallyirrational.
Systematic irrationality: sentiments or human traits likeoverreaction. This is what behavioral finance studies
Pt-1
Pt
time
Systematic deviations caused by…..
Note: Now the red line is value
Systematic Irrationality Market Efficiency: Information
Note that if information is costly
Or if not all information reaches all investors at the same time
Or, not all investors use the same information
Or, investors have limited information processing capability
Or, if there information is ambiguity
Markets may still be inefficient (partially predicatble)
We call this ‘bounded’ rationality
HEADER DATE
Testing Methodology
rt 1Itn t
t t t tr E r 1[ ]with
rt tRandom walk
Predictability
Past returns: rt 1rt1 t
Oil price changes: rt 1rt1oil t
Note: past returns are observable to investors at t-1
Note: past oil price changes are observable to investors at t-1
rt 1Jant tJanuary effect
Note: we know at time t-1 that t is January
Some Examples
Why regression?:
1) Simple and easy2) Well known properties of estimators3) Allows for control variables4) Allows for heteroscedasticity adjustments and autocorrelation in
the error terms (White s.e./Newey White s.e.): few distributional assumptions
5) Easy to communicate: broader audience6) But beware there are problems: unit roots etc.
rt 1Jant 2rt1 t
Predictable returns….
Over the years anomalies have popped up:
We very often do not know what causes them…..
- Investors being systematically irrational
- Information gradually diffusing
- Data mining, Spurious, Coincidence
- Risk related. Even if we do not know yet how (value and growth)
- Time varying risk
- Frictions (transactions costs or something else)
“It may be a bit more complicated than that” Ben Goldacre
Three Examples
Seasonal Anomalies
Gradual information diffusion
Time varying return predictability
35
The Halloween effectSell in May or go away
The Halloween indicator is a variant of the stock market adage "Sell in May and go away,” the belief that the period from November to April inclusive has significantly stronger growth on average than the other months.
(Source: Wikipedia)
With Sven Bouman; American Economic Review, 2002
HEADER DATE
Month t t-1 t-2 t-3
Future stock return
The Basic Problem
Which month is next?Hon
g Kon
g
South
Afri
ca
Denm
ark
US
Austra
lia
Norway
Sweden
Switzer
land
Canad
a
Nethe
rland
sUK
Spain
Germ
any
Belgium
Japa
n
Austri
a
Franc
e
Singap
ore
Italy
Summer
-4
-2
0
2
4
6
8
10
12
14
16
Returns Figure 1. Average Returns in May-Oct. ('Summer') and Nov.-April ('Winter') in several countries. MSCI re-investment indices 1970-August 1998.
SummerWinter
Sell in May (1970-1998)
Source: Bouman and Jacobsen, American Economic Review, 2002
Test: Methodology
rt 1St t
t t t tr E r 1[ ]
with
Statistical Significance
Halloween strategy in Italy 1970‐1998 Sell in May (1998-2007)
Belgiu
m
South
Afr
ica
Austr
alia
Denm
ark
Cana
da
Singa
pore
Hong
Ko
ng
Norwa
y US
France
Switze
rland UK
Spain
Austr
ia
Swed
en Italy
Japa
n
Nethe
rland
s
Germ
any
Summer
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
12%
14%
16%returns
country
Winter
Source: Jacobsen and Visaltanachoti, The Financial Review, 2009
HEADER DATE
-15.00
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
UA
EB
ulga
ria
Mau
ritiu
sS
ri L
anka
Kuw
ait
Cyp
rus
HK
Om
anC
hile
Qat
arP
eru
Aus
tral
iaS
love
nia
Tha
iland US
Indi
aD
enm
ark
Mex
ico
Ven
ezue
laL
atvi
aF
inla
ndS
outh
Afr
ica
UK
Phi
lippi
nes
Can
ada
Japa
nC
hina
Col
ombi
aB
ahra
inN
ZS
wed
enF
ranc
eS
wit
zerl
and
Spa
inN
ethe
rlan
dsIt
aly
Mor
occo
Kor
eaC
zech
Rep
ubli
cN
orw
ayM
alay
sia
Bel
gium
Gre
ece
Mal
taJo
rdan
Pak
ista
nL
ithua
nia
Isra
elS
inga
pore
Arg
entin
aE
gypt
Hun
gary
Ger
man
y
Aus
tria
Lux
embo
urg
Por
tuga
l
Irel
and
Indo
nesi
a
Est
onia
Tur
key
Tai
wan
Rus
sia
Pol
and
Icel
and
Rom
ania
Ret
urn
(%)
Country
Excess Returns in Summer and WinterExcess Returns in 65 Stock MarketsDuring May-October
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
0 10 20 30 40 50 60 70
Me
an
Ex
ce
ss
Re
turn
Standard deviation
Why?
Vacations
Seasonal AffectiveDisorder: SAD
Extreme Temperature
Airline travel
-4.92
-3.28
-1.64
0
1.64
Sw
eden
Phi
lippi
nes
Net
herla
nds UK
Spa
inA
rgen
tina
Bra
zil
Bel
gium
C
hile
Ita
lyP
olan
dE
gypt
Mor
occo
Fra
nce
Jord
anG
erm
any
Aus
tral
iaA
ustr
ia US
Japa
nM
alay
sia
Sw
itzer
land
Hun
gary
Indi
aT
urke
yIr
elan
d
Mex
ico
Por
tuga
lC
olom
bia
Thai
land
Indo
nesi
aS
inga
pore
Pak
ista
nG
reec
eH
ong
Kon
gC
anad
aS
outh
Afr
ica
Den
mar
kV
enez
uela
Finl
and
Rus
sia
Nor
way
New
Zea
land
Cze
ch r
ep.
Kor
eaS
ri La
nka
Isra
elC
hina
Country
Correlation of seasonal variables Market Efficiency: Information
Note that if information is costly
Or if not all information reaches all investors at the same time
Or, not all investors use the same information
Or, investors have limited information processing capability
Or, if there information is ambiguity
Markets may still be inefficient (partially predicatble)
We call this ‘bounded’ rationality
HEADER DATE
Recent insights from theory
Gradual Information Diffusion:
– Hong and Stein (Journal of Finance, 1999)
Limited Attention of Investors
Available important information
Overlooked by investors
Intuition
50
Oil Shares Bank Shares
Oil Price Interest rate
Month t t-1 t-2 t-3
Future Small stock return
The Basic Problem
Information in the past: How large firms have performed
Lo and MacKinlay (1980)
Small follows big
52
rtSmall rt1
Big t
Callaway & Coastcast example Economic Links
Source: Cohen and Frazzini, Journal of Finance, 2008
HEADER DATE
Month t t-1 t-2 t-3
Future stock return:Coastcast
The Basic Problem
Information in the past:Callaway Golf Corporation
Gradual Information Diffusion: US Market leading other markets
56
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
Australia
Canada
France
Germany
Italy
Japan
Netherlands
Sweden
Switzerland
UK
Average
US Effect in other markets
Based on International Stock Return Predictability: What is the Role of the United States?Rapach, Strauss and Zhou, Journal of Finance,
Month t t-1 t-2 t-3
Future international market return:
The Basic Problem
Information in the past:US Market return
Month t t-1 t-2 t-3
Gradual Information Diffusion: Oil
Oil Prices and Stock Returns
With Benjamin Maat and Gerben Driesprong
Journal of Financial Economics, 2008
0
5
10
15
20
25
30
35
40
45
D a t e
The ultimate market efficiency test
“Oil is so significant in the international economy that forecasts of economic growth are routinely qualified with
the caveat: Provided there is no oil shock.”
Adelman
“It is clear our nation is reliant upon big foreign oil. More and more of our imports come from overseas.”
George W. Bush
HEADER DATE
Purpose of the paper
Do changes in oil prices predict stock market returns?
0
5
10
15
20
25
30
35
40
45
D a t e
Month t t-1 t-2 t-3
futurestock return
The Basic Problem
Oil Price Change
rt 1rt1oil t
Data
Stock market data:
MSCI reinvestment indices local currency
48 countries & World Market index: 18 developed markets and 30 ‘emerging markets’
October 1973‐ April 2003
Oil Data:
West Texas, Brent, Dubai
4 oil price series + 2 futures
Arab Light
Oil prices of different types
0
5
1 0
1 5
2 0
2 5
3 0
3 5
4 0
4 5
05/2
9/19
87
05/3
1/19
88
05/3
1/19
89
05/3
1/19
90
05/3
1/19
91
05/2
9/19
92
05/2
8/19
93
05/3
1/19
94
05/3
1/19
95
05/3
1/19
96
05/3
0/19
97
05/2
9/19
98
05/2
8/19
99
05/3
1/20
00
05/3
1/20
01
05/3
1/20
02
Oil
Pri
ce (
US
$/B
arre
l)
West Texas
Dubai
Brent
Arab Light
Results
World Market
1973‐10, 355 observations
Alpha: ‐0.081; t‐value: ‐2.90
All oil series give significant results
All countries:
Developed markets: 12 out of 18 significant
‘Emerging’markets: 8 out of 30 significant
Results: The Puzzle
HEADER DATE
Economic significance
Buy and Hold
Oil strategy
If expected return > risk free: market
If expected return < risk free: deposit
oilttt rrE 11 08.00.7%
Economic significance
0
1000
2000
3000
4000
5000
6000
Oct
-73
Oct
-75
Oct
-77
Oct
-79
Oct
-81
Oct
-83
Oct
-85
Oct
-87
Oct
-89
Oct
-91
Oct
-93
Oct
-95
Oct
-97
Oct
-99
Oct
-01
Year
En
d o
f p
eri
od
we
alt
h Oil strategy
Buy-and-hold strategy
Sectors: World Market
Sector coefficient t-value
Resources -0.04 -1.56
Utilities -0.02 -0.86
Basic Industries -0.04 -1.05
General Industries -0.06 -1.66
Cyc. Cons. Goods -0.08 -1.83
Non Cyc. Cons. Goods -0.06 -2.23
Cyc. Services -0.08 -2.38
Non Cyc. Services -0.06 -1.78
Information Techn. -0.11 -3.02
Financials -0.06 -1.67
Hypotheses
Initial reaction:negative overall World Market reaction
reaction for countries may depend on import/export
Followed by underreaction: Negative relation
Stronger for countries with high energy consumption
Less strong underreaction in oil related sectors
Conclusions
Oil price changes predict stock returns
Violating market efficiency and not as a result of time varying risk premia– Different Countries, Different Samples, Economically Significant, Robust
to the inclusion of other variables
– Also significantly predicts negative excess returns
Do not reject Gradual Information Diffusion Hypothesis
Time Varying Return Predictability
Would Industrial Metal forecast stock returns?
Month t t-1 t-2 t-3
futurestock return
Change in Industrial Metals
rt 1rt1IM t
HEADER DATE
t-statistic t-statistic Adj. R2 N
All Data (1977-2010) 0.007 2.771 0.010 0.252 -0.002 399
1977-1990 0.009 2.568 -0.023 -0.713 -0.004 166
1991-2000 0.013 4.321 -0.185 -2.281 0.037 120
2001-2010 -0.003 -0.615 0.187 2.162 0.067 113
All data: NO• First Sub-period: NO• Second Sub-period: YES - NEGATIVELY• Third Sub-period: YES - POSTIVELY
Overall predictability Main Result
The same news, different information
Whether one finds positive, negative or no predictability depends on the number of expansion versus contraction states in the sample.
Price of Copper goes up
Production costs increaseHigher demand
Contraction: Stock market goes up
Expansion: Stock market goes down
Industrial Metal Return
Return on stock market index
Dummy depending on business cycle
MethodologyExpansion Contraction
t-statistic Nt-
statistic N F-test
Panel A: NBER
IM index -0.051 -1.586 332 0.217 1.993 67 -0.2683 5.605Aluminum -0.152 -3.010 193 0.319 2.416 38 -0.4710 10.398Copper -0.045 -1.614 332 0.188 2.554 67 -0.2333 8.879Lead -0.027 -0.703 146 0.120 1.880 37 -0.1468 3.601Nickel -0.045 -1.756 170 0.110 1.741 37 -0.1557 5.517Zinc -0.003 -0.079 193 0.264 2.765 38 -0.2678 6.235
Panel B: CFNAI
IM index -0.054 -1.762 334 0.312 3.257 63 -0.3657 13.459Aluminum -0.166 -3.482 194 0.363 3.177 36 -0.5287 18.409Copper -0.046 -1.769 334 0.255 5.041 63 -0.3010 28.735Lead -0.033 -0.972 151 0.145 2.348 31 -0.1783 6.197Nickel -0.043 -1.735 175 0.123 1.758 31 -0.1659 5.287Zinc -0.013 -0.302 194 0.342 4.166 36 -0.3541 14.450
Predictability across the business cycle
Building A Quantitative Model
An example and lessons learned
My challenge…Can you do it?
- 25 years of experience
- Feel for what might work and what not
- Combine everything that I felt might work
- Longer term (transactions costs)
- Simplicity is the ultimate form of sophistication
- Stock markets- Other markets
HEADER DATE
How does it work?
Start of every month
Data Feeds
Software/ModelPredictions:- Indicators
- % prediction
Datastream Matlab
Directional Forecasts
Creating an Edge
81
A Coin-Flipping Exercise: 50% vs. 58% in USD over 120 months
Hedgefund
What is in the model?
• New insights from recently developed strands in the academic literature: • Cross Asset Return Predictability & Gradual Information Diffusion• Time Varying Return Predictability• Calendar effects
• Studies published in top academic journals and yet new unpublished working papers
• My own published and unpublished academic work
• Insights obtained from studying the predictability of stock markets for over two decades.
• Insights gathered as an academic and practitioner
• Insights from building the models
• So far some 5 years of blood, sweat and tears.
Indicator Selection
What to predict?Monthly directional forecastsWhat window to use
Extensive back testing: statistical significance; historical performance of potential indicators in different periods (more recent periods get more weight);Sign consistency in the different back tests; Robustness of results also during 2008-2009 financial crisis;Robustness across estimation methodsRobustness of measurement intervalsRobustness of rolling window length
Economic reasons to include the variable in the model: the variable itself; as a proxy for underlying fundamentals;
Consistency with economic theory and academic studies; Interaction with other variables in the model; Likelihood that the variable also predicts in the future;Availability of data at the proper time.
HEADER DATE
Markets used
Stock Markets: S&P500, FTSE100, STOXX600, Nikkei, SMI, VIX
FX: Euro, Yen, GBP, Australian Dollar, Swiss Franc
Commodities: Nymex, Heating Oil, Natural Gas, Copper, Platinum, Sugar, Cocoa
Bonds: 2 yr Note, 10 yr Note, 10 yr Japan T, gilt, Bond
European Sectors (Based on Stoxx sector indices)
Too Farfetched: UK consumerservices two months ago
Likely indicators:Japan Industrials, S&P500, Yen
Too Obvious: Last month Nikkei returns
Search Algorithm for Gradual Information Diffusion Indicators
Market: Nikkei
Gradual Information Diffusion Indicators for the CHF/USD based on
CHF/USD
5 Sector Indices- US Utilities- EU Financials
1 Market Variable-Japanese bond
2 Stock Market Indices - Stoxx600
2 Currencies- AUS dollar
2 Commodity Market Indices - GS Industrial Metals
Some backtest results
Monthly S&P FTSE STOXX Euro Nymex Vix2000-2011 67.3% 79.1% 66.4% 70.0% 76.4% 68.2%2010-2011 57.1% 85.7% 64.3% 71.4% 78.6% 71.4%2008-2009 80.0% 92.0% 72.0% 76.0% 68.0% 72.0%2000-2007 65.8% 74.0% 65.8% 68.5% 78.1% 67.1%
Actual versus in sample
Stocks Currencies Commodities Bonds
Out of sample 63.24% 49.73% 54.26% 61.59%
In Sample 62.07% 63.13% 65.22% 64.22%
Correct Predictions Out of Sample
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
Correct: 56.73%
HEADER DATE
Over time (in and out of sample)
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Jan-
00
Jul-0
0
Jan-
01
Jul-0
1
Jan-
02
Jul-0
2
Jan-
03
Jul-0
3
Jan-
04
Jul-0
4
Jan-
05
Jul-0
5
Jan-
06
Jul-0
6
Jan-
07
Jul-0
7
Jan-
08
Jul-0
8
Jan-
09
Jul-0
9
Jan-
10
Jul-1
0
Jan-
11
Jul-1
1
Jan-
12
Jul-1
2
Jan-
13
Jul-1
3
Jan-
14
Jul-1
4Based on 36 month moving average
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
20.0%
Jan-
06
Apr
-06
Jul-0
6
Oct
-06
Jan-
07
Apr
-07
Jul-0
7
Oct
-07
Jan-
08
Apr
-08
Jul-0
8
Oct
-08
Jan-
09
Apr
-09
Jul-0
9
Oct
-09
Jan-
10
Apr
-10
Jul-1
0
Oct
-10
Jan-
11
Apr
-11
Jul-1
1
Oct
-11
Jan-
12
Apr
-12
Jul-1
2
Oct
-12
Jan-
13
Apr
-13
Per
cent
age
over
50%
Gradual Information Diffusion
Seasonals
Attribution of Correct Predictions
Lessons Learned
• Predict what you know
• Your indicators may only work for specific markets
• Simplicity is the ultimate form of sophistication
• Beware: you will datamine!
• Keep a truly out of sample period if you can
• Statistical significance won’t tell you everything
• Common Sense
• Use graphs of parameter estimates over time
• Judgement calls: Do you believe, (Gold…)
• Will the world change
• Trust your model
• Model uncertainty
• You’ll make (silly) mistakes
Lessons learned
Stocks Currencies Commodities Bonds
Out of sample 63.24% 49.73% 54.26% 61.59%
In Sample 62.07% 63.13% 65.22% 64.22%
Some markets are easier than othersWhat may work in one market may not work in others
Currencies: If you are wrong with the dollar…..Commodities: Too many other influences?? Unrelated to economyBonds: Surprising… more than interest rate dependence?
Data mining
In sample results Out of sample results
Performance
Data snooping or mining
Number of variables tested: Data snooping
Number of time periods: sample selection
Combining in sample and out of sample estimation
Different models (choosing the ‘best’ methodology)
Optimisation (choosing the ‘best’ interval)
Number of researchers…
“. . . and the Cross-Section of Expected Returns,” Harvey. Liu, Zhu, 2015
HEADER DATE
While there are all sorts of detailedand specific adjustments…
Bonferroni, Holm, White reality check, etc….
Based on all sorts of assumptions
The problem in the real world goes beyond the statisticalprocedures
A t-stat of 3?
Beware up front
Data snooping and all sorts of biases will enter your equation. No matter how hard you try. If you do not control there are hugeeffects. It may happen in the data you select, the method youchoose.
Judge every decision you make on whether some bias may enter
This is a judgement call.
Keep a true out of sample period that you do not use for anyestimation whatsoever
Nobody benefits from a system that does not work
Economic safeguards
- Consistency with economic theory and academic studies;
- Interaction with other variables in the model;Likelihood that the variable also predicts in the future;
- Availability of data at the proper time.
- Economic reasons to include the variable in the model: - the variable itself;- as a proxy for underlying fundamentals;
Once you have first out of sample results
You can make comparison based on in sample and out of sample results.
In sample correct: 64%
Out of sample: 57%
Bias 7%: What is the impact????
Datamining Adjustment in the Backtest
101
Correct Back Test Predictions:
64.05%
Back Test Return of 39.4%
Correct Real Time Predictions:
58% (Feb. 2012 – April 2012)
Adjusted Back Test Return of 24.6%
100.00%
1000.00%
10000.00%
100000.00%
Jan-
03
Au
g-0
3
Ma
r-0
4
Oct
-04
Ma
y-05
Dec
-05
Jul-0
6
Fe
b-0
7
Se
p-0
7
Ap
r-0
8
Nov
-08
Jun-
09
Jan-
10
Au
g-1
0
Ma
r-1
1
Oct
-11
Ma
y-12
Dec
-12
Jul-1
3
Fe
b-1
4
58.00%
64.05%
Actual
Adjusted
Outline
Part 1: Market Efficiency and unpredictability as a benchmark
Part 2: Some empirical examples of forecastability
Part 3: Building quantitative forecasting models
102
HEADER DATE
Opportunities
• We are moving from the question “whether markets are predictable?” to “how to predict them?”.
• Many people do not seem to realise this yet
• There is a tremendous terra incognita out there both from a practical and an academic perspective
• First mover advantage for every institution that gets ahead of the curve
103
Thank you!
Random or not…..
Correct predictions