The document discusses various methods for evaluating market timing systems, including the Sharpe ratio, days ahead per year (DAPY), and Modigliani ratio. It notes that while market timing seems appealing, it is very difficult to achieve in practice due to challenges in distinguishing skill from luck, the efficiency of markets, and the psychological impact of losses from failed bets. Blackjack is provided as an analogy for how market timing could work if an investor was able to identify pockets of inefficiency, but that it is difficult to recreate historical market conditions accurately.

1. How to Tell !
if Your Market Timing System will Work
John Elder, Ph.D.
elder@datamininglab.com
@johnelder4
A New Measure of Model Quality
Charlo'esville, VA
Washington, DC
Bal6more, MD
Raleigh, NC
434-973-7673
www.elderresearch.com

2. Outline
• How likely is a result luck (vs. skill)?
• Sharpe measures the quality of returns
• DAPY measures of the quality of a timing model
• Why timing almost always doesn’t work
• Blackjack is good practice

3. 1. You should have received a BDA !
(binary decision artifact) on entry
2. Please flip (and catch) it 10 times
3. Report the #heads
4. Retain your BDA for future executive decision-
making needs
We’re Looking for Skilled Heads Flippers!

4. Find Best Flipper of “Heads” (Active Manager)
709.4
64.2
12.3
3.7
1.5
0.7
0.4
0.2
0.2
0.10.1
1.0
10.0
100.0
1000.0
1 2 3 4 5 6 7 8 9 10
Goal: Minimum #Heads (of 10)
#MonkeystoInterviewforp>.5
• Distribution of active managers vs.
market is ~ random with fat tails
(extremes) and negative mean (fees)
• If 8+ (of 10) heads is acceptable,
expect to interview 13 flippers
• 16 flippers -> 60% probability of 8+
or 95% of 7+
• Multiple targets: What if last 5 are
heads, extraordinary tail history, etc.
• Flippers can now take their record
with them. (Trainer still keeps it too.)
• (And which ones advertise?)
(1-p)m = 0.5
p = 2-10 Σ( )10
k
10
k=goal
is probability of hiring
after history of 10 flips

5. 0.8
0.9
1.0
1.1
1.2
1.3
1.4
940608
940622
940707
940721
940804
940818
940901
940916
940930
941014
941028
941111
941128
941212
941227
950111
950125
950208
950223
950309
950323
950406
950421
950505
950519
950605
B&H
Trade

6. Sharpe Ra6o
(RT - R0) /σT
RT = Trading return (annualized)
R0 = Risk-free return (e.g., T-bills), annualized
σT = standard deviation of trading system (ann.)
Conventional metric by which to score returns:

7. 0.8
0.9
1.0
1.1
1.2
1.3
1.4
940608
940622
940707
940721
940804
940818
940901
940916
940930
941014
941028
941111
941128
941212
941227
950111
950125
950208
950223
950309
950323
950406
950421
950505
950519
950605
B&H
Trade
R0 = 5.8%
RT = 27%
σT = 14%
1.56 Sharpe
RB = -11%
σB = 26%
-0.63 Sharpe

8. Efficient Fron6er
TLSB
Buy & Hold
SG-CTA
Altegris (M.Fut)
TY
SP500
0%
5%
10%
15%
20%
25%
30%
35%
0% 2% 4% 6% 8% 10% 12% 14% 16%
Annualized Return (%)
Annualized Standard Devia6on (%)

9. Days Ahead per Year
DAPY = (RT - IT*RB) /ΔB
RT
= Trading return (annualized)
IT
= % of time strategy is invested
RB = Buy & hold return (annualized)
ΔB = average absolute daily return of B&H
A new metric by which to score timing strategies:

10. 0.8
0.9
1.0
1.1
1.2
1.3
1.4
940608
940622
940707
940721
940804
940818
940901
940916
940930
941014
941028
941111
941128
941212
941227
950111
950125
950208
950223
950309
950323
950406
950421
950505
950519
950605
B&H
Trade
IT = 39%
ΔB = 1.26%
25.2 DAPY

11. 0.8
0.9
1.0
1.1
1.2
1.3
1.4
940608
940622
940707
940721
940804
940818
940901
940916
940930
941014
941028
941111
941128
941212
941227
950111
950125
950208
950223
950309
950323
950406
950421
950505
950519
950605
B&H
Trade
1st third of year:
B&H Sharpe = 5.1
Trade Sharpe = 4.7
Trade DAPY = 20
2nd third:
B&H Sharpe = -2.4
Trade Sharpe = -1.3
Trade DAPY = 6
3rd third:
B&H Sharpe = -0.5
Trade Sharpe = 1.4
Trade DAPY = 26

12. 1
2
3
4
5
08/01/95
11/01/95
02/01/96
05/01/96
08/01/96
11/01/96
02/01/97
05/01/97
08/01/97
11/01/97
02/01/98
05/01/98
08/01/98
11/01/98
02/01/99
05/01/99
08/01/99
11/01/99
02/01/00
05/01/00
08/01/00
11/01/00
02/01/01
05/01/01
08/01/01
11/01/01
02/01/02
05/01/02
08/01/02
11/01/02
02/01/03
05/01/03
08/01/03
MVP I
S&P 500
Russell 2000
Real-World Trading Results

13. 1
08/01/95
11/01/95
02/01/96
05/01/96
08/01/96
11/01/96
02/01/97
05/01/97
08/01/97
11/01/97
02/01/98
05/01/98
08/01/98
11/01/98
02/01/99
05/01/99
08/01/99
11/01/99
02/01/00
05/01/00
08/01/00
11/01/00
02/01/01
05/01/01
08/01/01
11/01/01
02/01/02
05/01/02
08/01/02
11/01/02
02/01/03
05/01/03
08/01/03
MVP I
S&P 500
Russell 2000
Log Scale be'er displays %Return

14. Can Timing Work?
For most investors, it never has [see next slide]
There are 3 extreme challenges:
1. Skill is hard to distinguish from luck [coin flip ex.]
2. Markets are nearly efficient [huge ensembles]
3. One must constantly complete failed bets!
(which wars with our cognitive bias against losses)

15. The Average Equity Investor does much worse
than the S&P500 Index (Dalbar’s QAIB Report 2014)
0
5
10
15
20
25
1 3 5 10 20 30
S&P_Over
Equity_Inv
% Annual Return
Years Invested

16. More Investment Modeling Challenges
• “Pockets of inefficiency” are trampled if found
• Historical state is hard to recreate accurately in data
[survivor bias, Bloomberg earnings reports]
• The system can change overnight [LTCM+Russia]
• Correlation increases during panics [killing hedges]

17. Best Investment Timing System Analogy is!
Blackjack
Expected losses are (only) -0.5% if playing
perfect Basic Strategy, but can grow to !
gains of 0.5% to 1.5% if able to count cards!
-- but with huge swings (risk)

18. John F. Elder IV
Founder & Chair, Elder Research, Inc.
Dr. John Elder heads the USA’s most experienced data mining consulting
team. Founded in 1995, Elder Research, Inc. has offices in Charlottesville,
VA, Washington DC, Baltimore MD, and Raleigh NC (www.datamininglab.com).
ERI focuses on Federal, commercial, and investment applications of
advanced analytics, including text mining, credit scoring, process
optimization, cross-selling, drug efficacy, market timing, and fraud
detection.
18
John earned Electrical Engineering degrees from Rice University, and a PhD in Systems Engineering
from the University of Virginia, where he’s been an adjunct professor teaching Optimization or Data
Mining. Prior to 22 years at ERI, he spent 5 years in aerospace defense consulting, 4 heading research
at an investment management firm, and 2 in Rice's Computational & Applied Mathematics department.
Dr. Elder has authored innovative data mining tools, is a frequent keynote speaker, and has chaired
International Analytics conferences. John was honored to serve for 5 years on a panel appointed by
President Bush to guide technology for National Security. His book with Bob Nisbet and Gary Miner,
Handbook of Statistical Analysis & Data Mining Applications, won the PROSE award for top book in
Mathematics for 2009. His book with Giovanni Seni, Ensemble Methods in Data Mining, was published in
2010, and his book with colleague Andrew Fast and 4 others on Practical Text Mining won the 2012
PROSE award for Computer Science.
John is grateful to be a follower of Christ and father of 5.

19. Modigliani2
M2 = (RT - RB)*(σB/σT)
RT = Trading return (annualized)
RB = Buy & hold return (annualized)
σT = standard deviation of trading system (ann.)
σB = standard deviation of buy & hold (ann.)
Another metric (equivalent to Sharpe for a given market) !
first matches risk, then compares return:
Franco Modigliani & Leah Modigliani, “Risk-Adjusted Performance”
Journal of Porlolio Management 23, no 2, (Winter 1997): 45-54.