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HEADER DATE
The Art and Science
of
Forecasting Financial Markets
Ben Jacobsen
ben.jacobsen@ed.ac.uk
Outline
Part 1: Market...
HEADER DATE
Virtual and Reality Virtual and Reality
We tend to see structure in randomness…
Head and Shoulder
Traditional ...
HEADER DATE
Mutual Fund Performance Who is the better investor?
Wall Street
Dartboard
Competition
16
Wall Street Dart Boar...
HEADER DATE
• 41% of all net fund flows worldwide went into passive investments in 2012
• 18% of total mutual fund and ETF...
HEADER DATE
A testable model
rt   t
t t t tr E r  1[ ]
Et1 rt    Add on rt both sides
rt  Et1 rt     r...
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Testing Methodology
rt   1Itn  t
t t t tr E r  1[ ]with
rt   t
Random walk
Predictability
Past ...
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Month t t-1 t-2 t-3
Future stock return
The Basic Problem
Which month is next?
Hong
Kong
South
Africa
Denm
ark...
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-15.00
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
UAE
Bulgaria
Mauritius
SriLanka
Kuwait
Cyprus
HK
Oman
Chile
Qa...
HEADER DATE
Recent insights from theory
Gradual Information Diffusion:
– Hong and Stein (Journal of Finance, 1999)
Limited...
HEADER DATE
Month t t-1 t-2 t-3
Future stock return:
Coastcast
The Basic Problem
Information in the past:
Callaway Golf Co...
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...
HEADER DATE
Economic significance
Buy and Hold 
Oil strategy
If expected return > risk free: market
If expected return < r...
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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 2.5...
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How does it work?
Start of every month
Data
Feeds
Software/Model
Predictions:
- Indicators
- % prediction
Data...
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Markets used
Stock Markets: S&P500, FTSE100, STOXX600, Nikkei, SMI,
VIX
FX: Euro, Yen, GBP, Australian Dollar,...
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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%
...
HEADER DATE
While there are all sorts of detailed
and specific adjustments…
Bonferroni, Holm, White reality check, etc….
B...
HEADER DATE
Opportunities
• We are moving from the question “whether markets are
predictable?” to “how to predict them?”.
...
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The Art and Science of Forecasting Financial Markets

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Slides from the recent ACQuFRR Masterclass delivered by Ben Jacobsen from the University of Edinburgh Business School on 13 April 2015 at UCT's GSB

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The Art and Science of Forecasting Financial Markets

  1. 1. HEADER DATE The Art and Science of Forecasting Financial Markets Ben Jacobsen ben.jacobsen@ed.ac.uk 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?
  2. 2. 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
  3. 3. 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 9 11 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 Optimism and 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
  4. 4. 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 | It1   Pt1 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  Pt1 1  rt  Pt  Pt1 Pt1  Pt1 1  Pt1 Pt1   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 | It1    Note: we assume μ is constant but it might Be time-varying. Et1 rt   or Thus if markets are efficient we should not be able to come with a better forecast given all available information. How to test this. Note we have a model with Expectations which are difficult to observe.
  5. 5. HEADER DATE A testable model rt   t t t t tr E r  1[ ] Et1 rt    Add on rt both sides rt  Et1 rt     rt rewrite rt    rt  Et1 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 news Which affects value over time The if’s of market efficiency If investors are rational and if information is freely available and reaches all investors at the same time Then Markets are informationally efficient: prices will refelect all available information 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 be efficient. Markets can be informationally efficient even if not all investors behave rational. As long as they do not behave systematically irrational. Systematic irrationality: sentiments or human traits like overreaction. 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
  6. 6. HEADER DATE Testing Methodology rt   1Itn  t t t t tr E r  1[ ]with rt   t Random walk Predictability Past returns: rt   1rt1 t Oil price changes: rt   1rt1 oil 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 easy 2) Well known properties of estimators 3) Allows for control variables 4) 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 audience 6) But beware there are problems: unit roots etc. rt   1Jant 2rt1 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 effect Sell 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
  7. 7. HEADER DATE Month t t-1 t-2 t-3 Future stock return The Basic Problem Which month is next? Hong Kong South Africa Denm ark USAustraliaNorwaySweden Switzerla ndCanada Netherla nds UK SpainG erm anyBelg iu m Japan Austria FranceSin gapore 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. Summer Winter 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) BelgiumSouth AfricaAustralia Denmark CanadaSingaporeHong Kong Norway US FranceSwitzerland UK Spain Austria Sweden Italy Japan Netherlands Germany Summer -6% -4% -2% 0% 2% 4% 6% 8% 10% 12% 14% 16%returns country Winter Source: Jacobsen and Visaltanachoti, The Financial Review, 2009
  8. 8. HEADER DATE -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 20.00 UAE Bulgaria Mauritius SriLanka Kuwait Cyprus HK Oman Chile Qatar Peru Australia Slovenia Thailand US India Denmark Mexico Venezuela Latvia Finland SouthAfrica UK Philippines Canada Japan China Colombia Bahrain NZ Sweden France Switzerland Spain Netherlands Italy Morocco Korea CzechRepublic Norway Malaysia Belgium Greece Malta Jordan Pakistan Lithuania Israel Singapore Argentina Egypt Hungary Germany Austria Luxembourg Portugal Ireland Indonesia Estonia Turkey Taiwan Russia Poland Iceland Romania Return(%) Country Excess Returns in Summer and Winter Excess Returns in 65 Stock Markets During May-October -35 -30 -25 -20 -15 -10 -5 0 5 10 15 0 10 20 30 40 50 60 70 MeanExcessReturn Standard deviation Why? Vacations Seasonal Affective Disorder: SAD Extreme Temperature Airline travel -4.92 -3.28 -1.64 0 1.64 Sweden Philippines Netherlands UK Spain Argentina Brazil Belgium Chile Italy Poland Egypt Morocco France Jordan Germany Australia Austria US Japan Malaysia Switzerland Hungary India Turkey Ireland Mexico Portugal Colombia Thailand Indonesia Singapore Pakistan Greece HongKong Canada SouthAfrica Denmark Venezuela Finland Russia Norway NewZealand Czechrep. Korea SriLanka Israel China 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
  9. 9. 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 rt Small   rt1 Big t Callaway & Coastcast example Economic Links Source: Cohen and Frazzini, Journal of Finance, 2008
  10. 10. 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
  11. 11. 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 future stock return The Basic Problem Oil Price Change rt   1rt1 oil 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 05/29/1987 05/31/1988 05/31/1989 05/31/1990 05/31/1991 05/29/1992 05/28/1993 05/31/1994 05/31/1995 05/31/1996 05/30/1997 05/29/1998 05/28/1999 05/31/2000 05/31/2001 05/31/2002 OilPrice(US$/Barrel) 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
  12. 12. HEADER DATE Economic significance Buy and Hold  Oil strategy If expected return > risk free: market If expected return < risk free: deposit     oil ttt 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 Endofperiodwealth 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 future stock return Change in Industrial Metals rt   1rt1 IM  t
  13. 13. 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 increase Higher demand Contraction: Stock market goes up Expansion: Stock market goes down Industrial Metal Return Return on stock market index Dummy depending on business cycle Methodology Expansion Contraction t-statistic N t- statistic N F-test Panel A: NBER IM index -0.051 -1.586 332 0.217 1.993 67 -0.2683 5.605 Aluminu m -0.152 -3.010 193 0.319 2.416 38 -0.4710 10.398 Copper -0.045 -1.614 332 0.188 2.554 67 -0.2333 8.879 Lead -0.027 -0.703 146 0.120 1.880 37 -0.1468 3.601 Nickel -0.045 -1.756 170 0.110 1.741 37 -0.1557 5.517 Zinc -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 13.459 Aluminu m -0.166 -3.482 194 0.363 3.177 36 -0.5287 18.409 Copper -0.046 -1.769 334 0.255 5.041 63 -0.3010 28.735 Lead -0.033 -0.972 151 0.145 2.348 31 -0.1783 6.197 Nickel -0.043 -1.735 175 0.123 1.758 31 -0.1659 5.287 Zinc -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
  14. 14. HEADER DATE How does it work? Start of every month Data Feeds Software/Model Predictions: - 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 forecasts What 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 methods Robustness of measurement intervals Robustness 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.
  15. 15. 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/U SD 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 Vix 2000-2011 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%
  16. 16. 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-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 Jan-12 Jul-12 Jan-13 Jul-13 Jan-14 Jul-14 Based 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-06 Oct-06 Jan-07 Apr-07 Jul-07 Oct-07 Jan-08 Apr-08 Jul-08 Oct-08 Jan-09 Apr-09 Jul-09 Oct-09 Jan-10 Apr-10 Jul-10 Oct-10 Jan-11 Apr-11 Jul-11 Oct-11 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Percentageover50% 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 others What 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 economy Bonds: 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
  17. 17. HEADER DATE While there are all sorts of detailed and specific adjustments… Bonferroni, Holm, White reality check, etc…. Based on all sorts of assumptions The problem in the real world goes beyond the statistical procedures 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 huge effects. It may happen in the data you select, the method you choose. 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 any estimation 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 Aug-03 Mar-04 Oct-04 May-05 Dec-05 Jul-06 Feb-07 Sep-07 Apr-08 Nov-08 Jun-09 Jan-10 Aug-10 Mar-11 Oct-11 May-12 Dec-12 Jul-13 Feb-14 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
  18. 18. 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

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