George R. Brown School of Engineering
STATISTICS
Historical Backtesting vs. Real-World
Positions
SECOND EUBANK CONFERENCE:
MODELING FINANCIAL MARKETS IN
A WORLD OF FIAT MONEY
John A. Dobelman
Rice University
October 18-19, 2010
2
Outline
•
•
•
•
The Asset Allocation Problem
The Equity Portfolio
Managing the Portfolio
Conclusion and Future Work
3
Asset Allocation
WSJ, October 19, 2030
4
Simplified Allocation
• October 14, 2009 thru October 15, 2010:
– WTC Insurance
• 32,536% 6-Yr Return
– Gold (Comex):
• 28.2% 1-Year Return
– Cotton (ICE)
• 63.5% 1-Year Return
– TGMP (American Power Group):
• 850% 1-Yr Return
– LBSV (Liberty Silver):
• 9,499,900% 1-Year Return
5
Best Allocation
LBSV
Liberty Silver,
OTC-BB
10/14/09 – 10/15/10
– Powerball ($50M Jackpot):
• 4,999,999,900% 1-Day return
6
Market Modeling
• Primarily for Trading
– Determine how much to produce/buy
– Capacity allocation
– Hedging application
– Speculation!
• Not Necessary Necessary for LT Holding
– LT holding returns → GBM model
– Given that, looking for w=(w1,…,wk)
7
BAH ≠ BAH
• Active portfolio management required
– "Indexes are not available for direct investment; therefore, their
performance does not reflect the expenses associated with the
management of an actual portfolio.“ -DFA
• Even the E-V portfolios require rebalancing to
maintain MV construction
• For S  S, SBAH  BAH :
– SBAH = BAH + DivMgt
– SBAH = BAH + DivMgt + Tender
– SBAH = BAH + DivMgt + Tender + Tax
– SBAH = BAH + DivMgt + Tender + Tax + Realloc
8
Come a Long Way Since 1973
S  GBM ( , )
2
C
P C S
S
 rt
C  S(d1 )  Xe (d 2 )
1 2
log( X )  ( r   )t
2
d1 
 t
1 2
S
log( X )  ( r   )t
2
d2 
 d1   t
 t
S
9
ACC2010
• 2010 American Control Conference
– Operations to Finance: Opportunities for
Control Theory and Application
• Control Systems Methods in Finance: Modeling
and Optimal Trading, Primbs, Stanford University,
and Barmish, Univ. Wisconsin
• Interfaces between control theory and finance
• Dynamic hedging as a stochastic control problem
• LQ and receding horizon control methodolgies
10
That’s not All!
• A model of the human as a suboptimal smoother
– WB Rouse - 1974 IEEE Conference on Decision and Control, 1974
• Trading Costs Around M&A Announcements
– L Mai, BF Van Ness, RA Van Ness, 1983
• Economic prediction using neural networks: The
case of IBM daily stock returns
– H White - Proceedings of the IEEE International
Conference on …, 1988
• Applications of statistical physics to economic and
financial topics
– M Ausloos, N Vandewalle, P Boveroux, A - Physica A:
Statistical …, 1999
11
1990’s - 2010
• “Chaos” in futures markets? A nonlinear dynamical
analysis (1991)
– Steven C. Blank, Journal of Futures Markets
• Components of multifractality in high-frequency
stock returns (2005)
– J Kwapien; Physica A: Stat Mech & Apps
• A fuzzy control model (FCM) for dynamic portfolio
management
– R Östermark – Fuzzy sets and Systems 1996
• Fluctuations and Market Friction in Financial
Trading
– Bernd Rosenow, 2001, Condensed Matter
12
1990’s - 2010
• Stochastic Lotka-Volterra Systems of Competing
Auto-Catalytic Agents Lead Generically to
Truncated Pareto Power Wealth Distribution,
Truncated Levy Distribution of Market Returns,
Clustered Volatility, Booms and Crashes
– Sorin Solomon (Hebrew University) Submitted on 30 Mar
1998) Computational Finance 97
• THE JOINT PRICING OF VOLATILITY AND
LIQUIDITY!
– F. Bandi, C.E. Moise, and J. Russell,2008
• Liquidity skewness
– R Roll, A Subrahmanyam - Journal of Banking &
Finance, 2010
13
1990’s - 2010
• Idiosyncratic Volatility, Stock Market Volatility, and
Expected Stock Returns
– Hui Guo, Robert Savickas. Journal of Business and
Economic Statistics. January 1, 2006
• A theory of power-law distributions in financial
market fluctuations
– X Gabaix, P Gopikrishnan, et.al. Nature 423 (2003)
• On fitting the Pareto–Levy distribution to stock
market index data: Selecting a suitable cutoff value
– H.F. Coronel-Brizioa, and A.R. Hernández-Montoya,
Physica A: Statistical Mechanics and its Applications
Volume 354, 15 August 2005
14
1990’s - 2010
• Predicting Stock Prices Using a Hybrid Kohonen
Self Organizing Map (SOM)
– Afolab & Olude; System Sciences, 2007. HICSS 2007
– Examples of these methods are fuzzy logic, neural
network and hybridized methods such as hybrid Kohonen
self organizing map (SOM), adaptive neuro-fuzzy
inference system (ANFIS) etc.
– This paper presents a number of methods used to predict
the stock price of the day. These methods are
backpropagation, Kohonen SOM, and a hybrid Kohonen
SOM...the hybrid Kohonen SOM is a better predictor
compared to Kohonen SOM and backpropagation
15
Orthodoxy
• Departures from the EMH Market Portfolio
– Market
– Departure 1
– Departure 2
– Departure 3
– Departure 4
– Departure 5
– Departure 6
– Departure 7
Ω=Ω
Ω=ΩE
Ω=ΩE\Priv
Ω=ΩS
Ω=ΩIndex
Ω→Your E-V portfolio,  and 
Ω→Your E-V portfolio, ˆ,ˆ
Ω→ Some other portfolio P
16
Portfolio Construction
• Remark: Ω=Ωindex
– Wilshire 5000, SP500, RUT3000, Value-Line,
DOW30, etc., are ALL actively determined
portfolios.
– Only “recently” could you buy into a mutual
fund/ETF which attempts to replicate these
indexes
• Unless you inherit a portfolio, you must
create one, or build one over time.
17
Portfolio Construction
• Fundamental analysis
– Slow and time-consuming
• Technical Analysis
– Value Line
– O’Neil /Investors Business Daily
– Efficacy in question
• Quantitative Portfolio Management
– Formulation
– Management
– Allows statistics-based portfolio strategies
18
Portfolio Formulation
You Must Pick 10 Stocks
19
How Much Would You Pay?
20
For This?
21
Fundamental Analysis
Valuation Measures
Market Cap (intraday)5:
Enterprise Value (Oct 4, 2010)3:
Trailing P/E (ttm, intraday):
Forward P/E (fye Dec 31, 2011)1:
PEG Ratio (5 yr expected)1:
Price/Sales (ttm):
Price/Book (mrq):
Enterprise Value/Revenue (ttm)3:
Enterprise Value/EBITDA (ttm)3:
ABC
DEF
ABC
DEF
238.79M
185.34M
130.57
29.48
2.81
3.59
4.25
2.7
38.84
2.10B
3.86B
15.83
13.71
1.49
1.01
1.46
1.82
7.58
Income Statement
68.60M
Revenue (ttm):
2.65
Revenue Per Share (ttm):
3.00%
Qtrly Revenue Growth (yoy):
50.14M
Gross Profit (ttm):
4.77M
EBITDA (ttm):
1.83M
Net Income Avl to Common (ttm):
0.07
Diluted EPS (ttm):
N/A
Qtrly Earnings Growth (yoy):
2.12B
8.36
8.00%
421.18M
508.55M
136.66M
0.54
74.40%
Balance Sheet
Total Cash (mrq):
Total Cash Per Share (mrq):
Total Debt (mrq):
Total Debt/Equity (mrq):
Current Ratio (mrq):
Book Value Per Share (mrq):
60.77M
2.33
0
N/A
2.51
2.21
152.24M
0.62
1.86B
125.54
1.03
5.97
9.40M
3.97M
346.98M
263.52M
Financial Highlights
Fiscal Year
Fiscal Year Ends:
Most Recent Quarter (mrq):
31-Dec
30-Jun-10
31-Dec
30-Jun-10
Profitability
Profit Margin (ttm):
Operating Margin (ttm):
2.67%
5.01%
6.46%
16.10%
Management Effectiveness
Return on Assets (ttm):
Return on Equity (ttm):
2.47%
3.40%
2.50%
9.61%
Cash Flow Statement
Operating Cash Flow (ttm):
Levered Free Cash Flow (ttm):
22
Fundamental Analysis
Variable
ABC
DEF
Shares Outstanding
Market Cap
Avg $M Volume
Closing Price
Act. Price
Exchange
Ind Name
F0 Sales Hist
F0 Sales IBES Act
Working Capital Ann
Working Capital Qtr
LTD $M
BV PS
EV $M
DA LTM
DA Annual
DA PS
CAPX LTM
CAPX Annual
CAPX PS
EBIT $M Ann
EBIT $M Qtr
EBITDA $M
EPS LTM
F0 EPS
FCF $M Ann.
FCF $M LTM
ROE
Dividend Yield
25.8
280.2
506.2
3,936.2
3.13
9.72
19.6
14.1
19.6
14.1
NYSE
NYSE
Software & Programming
Personal Services
2.2
7.7
62.1
2,285.3
27.6
-169.6
28.1
-73.2
0.0
1,779.8
1.1
5.6
506.2
5,716.1
0.05
0.44
0.05
0.35
0.05
0.44
0.08
0.54
0.04
0.36
0.08
0.54
6.83
260.30
9.61
325.35
8.10
356.99
0.42
0.25
0.45
0.85
12.43
195.26
-29.48
204.01
95.45
4.63
40.77
1.14
Variable
F1 Sales
F2 Sales
LTG
EPS NTM
FY1 EPS
FY2 EPS
FY3 EPS
F1 CFOPS
F1 FCF
F0/F1 Sales Growth
F1/F2 Sales Growth
Price to Book
Price to Sales
Price to Free Cash Flow
TTM PE
NTM PE
F1 PE
F2 PE
F0 Yield
F1 Yield
F2 Yield
EPS NTM/TTM
F0/F1
F1/F2
F2/F3
EBIT/Tang Capital
EBIT/Tang Capital TTM
EBIT/EV
EBIT/EV TTM
ABC
DEF
61.55
78.43
#N/A
0.45
0.41
0.47
0.59
10.58
9.03
-0.87
27.75
17.75
0.32
40.73
43.30
43.60
47.85
41.74
2.31
2.09
2.40
6.57
-9.52
14.63
25.53
24.77
34.13
1.35
1.90
2280.60
2294.00
11.50
0.57
0.51
0.60
#N/A
#N/A
291.71
-0.33
1.73
2.50
0.01
20.16
16.45
24.65
27.55
23.42
6.08
3.63
4.27
123.57
-40.27
17.65
#N/A
-153.47
-444.77
4.55
5.69
23
Fundamental Approach
24
Outliers/Outliars
25
BAH with the Greats
• Benjamin Graham
• Criteria for Defensive Investor 12/31/70
– Size: 100M sales (326M today)
– Financial Strength: CR 2:1, LTD<WC
– Positive EPS in last 10 years
– 20 years of uninterrupted dividends
– Min 33% EPS growth in 10 years
– PE < 15 for last 3 years average EPS
– P/BV < 15-22
26
Graham Portfolio
• As of 12/31/1970, this was the portoflio
– AC, American Can
– T, AT&T
– A, Anaconda
– SWX, Swift
– Z, F.W. Woolworth
• Bring up to the present
– 1/4/1971 - End
27
Graham Portfolio
Company Name and Archeology
AMERICAN CAN CO
Commercial Credit/ PRIMERICA CORP
NEW/Travelers/Citigroup Inc.
AMERICAN TELEPHONE & TELEG CO
SOUTHWESTERN BELL CORP/ATT
ANACONDA CO
ATLANTIC RICHFIELD CO
BRITISH PETROLEUM LTD/BP PLC
SWIFT & CO/Esmark
Beatrice Foods
WOOLWORTH F W CO/Venator Group/Foot Locker
Price (10/10/10)
C
4.17
T
28.28
BP
41.42
FL
15.64
Dates
Changes
1960-1988
Commercial Credit/ PRIMERICA CORP
NEW/Travelers/Citigroup Inc.
1988-2009
1960-2005
2009
1960-1970
1970-2000
2000-2009
1960-1984
1984-1986
1960-2009
Citigroup owns all of travelers
SOUTHWESTERN BELL CORP
Renamed the entirety ATT
ATLANTIC RICHFIELD CO
BRITISH PETROLEUM LTD/BP PLC
Anaconda holds the EPA liability
Sold to Beatrice
Private with KKR 3/31/1986
Woolworths purchases Kinney
Woolworths forms Venator
Kinney becomes the base for Foot Locker
Venator falls completely under Foot locker
Last Split
8/28/2000
5/20/1998
10/4/1999
6/1/1990
28
Graham BAH Results
CAGR from 1971
Thru 12/31/2009:
Thru 10/10/2010:
4.13%
4.09%
Original DOW 30 from 1971
Components unchanged 1956-1976
5 gone, only 14 continuously traded
Original DOW 30 from 1/3/2000
Thru 10/10/2010:
4.59%
Indexes from 1/3/2000 thru 10/10/10
DJIAK
-0.29%
DOXIK
2.04%
SP50
-2.03%
SP50.R
-0.24%
29
10-Yr S&P 500 Returns
10-Year, SPX Div
0.00
-10
-5
0
5
10
15
20
-10
-5
0
5
10
15
20
Cumulative Probability
Cumulative Probability
0.4
0.0
0.4
Proportion
0.8
IRR%
0.8
IRR%
0.0
Proportion
0.04
Proportion
0.08
0.04
0.00
Proportion
0.08
10-Year, SPX Ex-Div
-10
-5
0
5
IRR%
10
15
20
-10
-5
0
5
IRR%
10
15
20
30
30-Yr S&P 500 Returns
30-Year, SPX Div
0.30
0.20
0.00
-10
-5
0
5
10
15
20
-10
-5
0
5
10
15
20
Cumulative Probability
Cumulative Probability
0.4
0.0
0.4
Proportion
0.8
IRR%
0.8
IRR%
0.0
Proportion
0.10
Proportion
0.20
0.10
0.00
Proportion
0.30
30-Year, SPX Ex-Div
-10
-5
0
5
IRR%
10
15
20
-10
-5
0
5
IRR%
10
15
20
31
Horizon Dependence
60
80
20
20
40
60
5
80
0
20
40
IRR of SP500 Eq-Wt
60
80
80
15
10
°
°°°° ° °°°°°°°°°°°°°°
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°
° °°
°°°°°
°°°°°
0
5
IRR %
10
°°
° °
°°°°° °°°° °°°°
°°°°°° °°°°°°°°°°
°°°°° °°° °°°°°°°°°°°°°°°°°°°
°°° °°
°
° °°°
°
°
° °
°
°
5
IRR %
°°°
°
0
40
Horizon in Years
60
20
IRR of GE
20
IRR of SP500
15
Horizon in Years
20
Horizon in Years
0
20
10
15
0
Horizon in Years
°
°°
°°°°°°°°°°°°°°°°°°°°°°°°°°°°° °°°°°°°°°°°°°°°°°°°°°°°
°°°°°°°°°°°°
°°°
°°°°°
°°°°°
0
°
°°° °° °
°° °° °°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°
°°°°°
°°°°°
0
0
40
15
20
5
10
°°
° °°°°°°°°°°°°
°°°° °°°°°°°°°°
°
°°°° ° °°°°°°°°°°°°°°°°°°°°°
°°° °°
°°°° °°°°
°
°
°
° °
°
°
IRR %
15
10
°°°
°
5
IRR %
15
10
0
5
IRR %
°
°
°°
°°°° °°°°°°°°°°°°
°°
°°° °°
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°° °°°°°°°°°
°
°°
°°°°°
0
IRR %
IRR of SP500 Eq-Wt, ExDiv
20
IRR of GE Ex-Div
20
IRR of SP500 Ex-Div
0
20
40
Horizon in Years
60
80
0
20
40
60
Horizon in Years
32
80
Benchmark Summaries
40
-40
0
IRR%
0
-40
IRR%
40
80
5-Year, SPX Ex-Div
80
1-Year, SPX Ex-Div
Ja
Mr Ma
Jl
Se
Nv
Ja
Se
Nv
15
-15
-5
5
IRR%
5
-5
-15
IRR%
Jl
30-Year, SPX Ex-Div
15
10-Year, SPX Ex-Div
Mr Ma
Ja
Mr Ma
Jl
Se
Nv
Ja
Mr Ma
Jl
Se
Nv
33
Benchmarks
SP500: Horizon Returns
Equity
Jan 1926 - Dec 2009
Index
5 Years:
10 Years:
20 Years:
30 Years:
50 Years:
Min
-19.5
-6.2
1.3
7.4
7.2
Q1
5.0
6.8
8.3
10.3
10.5
Med
10.79
11.03
11.71
11.01
11.19
Q3
16.1
15.6
14.1
12.5
12.1
132 Fiscal
Max
36.0
21.5
18.4
14.7
14.0
• 50-Year Real Returns of 7% (Siegel, 2002)
– 1802 – 1870 (Schwert)
– 1871 – 1925 (Cowles)
– 1926 – 2001 (CRSP, all NYSE/AMEX/NASD)
– Post WWII 1946 – 2001
• Most inflation has been during this period
34
Benchmarks
35
Benchmarks
Equity Index Benchmarks: 1999-2009
132 Fiscal Periods (Ja-Dc), 11 Years (1/4/99 Thru 10/15/10)
Index
S&P 500
Russell 2000
Russell 3000
Universe Return
DOW 30
DOW 30 Ex-Div
S&P 500 Ex-Div
Avg%
Avg L3Y
0.37
-2.9
5.48
-0.8
1.22
-2.3
7.72
3.5
2.54
-1.2
0.19
-3.9
-1.43
-5.0
Worst
-46.34
-46.37
-46.64
-50.41
-43.43
-45.13
-47.70
TotNeg
-932.3
-757.8
-921.4
-802.1
-660.0
-743.4
-1004.9
36
Return by Period
Benchmarks by Fiscal Period [DRD 10-16-10]
1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009
Index
S&P 500
19990104
20000103
20010102
20020102
20030102
20040102
20050103
20060103
20070103
20080102
20090102
Jan
1.05
15.40
-6.32
-8.81
-19.98
24.16
11.22
7.50
13.81
4.10
-34.06
22.57
Feb
0.45
12.10
-1.40
-17.20
-22.47
34.43
6.92
9.83
14.93
-1.67
-39.37
36.86
Mar
0.83
12.99
-8.97
-7.56
-24.39
40.36
6.55
8.70
10.74
-3.35
-46.34
63.14
Apr
0.53
17.27
-25.68
0.57
-23.79
34.20
5.44
12.45
12.92
-2.10
-39.23
48.40
May
0.20
5.77
-12.74
-13.03
-14.12
22.94
5.76
15.10
16.02
-3.28
-36.07
38.09
Jun
0.48
13.33
-11.96
-14.18
-4.94
18.40
9.20
8.95
21.75
-7.05
-29.95
15.92
Jul
-1.40
6.60
-14.99
-22.21
3.28
16.92
7.71
8.34
21.37
-15.26
-26.18
13.55
Aug
0.13
9.64
-14.42
-26.17
12.83
14.34
14.45
4.81
17.51
-12.23
-19.51
14.04
Sep
-0.20
15.60
-24.54
-19.95
19.07
11.37
12.45
9.37
14.50
-15.17
-19.98
10.45
Oct
0.93
13.27
-25.87
-17.11
22.29
13.06
10.56
10.84
18.35
-23.34
-8.90
13.57
Nov
0.65
6.18
-22.74
-15.60
18.76
9.83
8.30
15.91
12.37
-34.38
11.06
15.03
Dec
0.86
-4.82
-12.24
-17.21
16.23
13.30
8.10
12.54
8.02
-39.46
39.34
7.91
Russell 2000
19990104
20000103
20010102
20020102
20030102
20040102
20050103
20060103
20070103
20080102
20090102
5.15
15.11
-1.43
6.86
-18.30
44.72
17.49
8.08
16.46
-4.23
-31.83
25.56
4.70
19.85
2.28
-4.34
-21.38
58.41
10.10
18.48
11.13
-8.46
-38.33
38.67
5.83
51.20
-18.53
2.49
-23.56
66.60
8.59
17.63
7.83
-12.19
-46.37
78.86
6.67
36.91
-16.17
15.40
-25.85
63.37
3.92
26.57
8.21
-10.20
-38.58
61.59
5.58
15.82
-4.23
5.59
-20.76
41.95
4.58
32.53
8.54
-9.45
-32.11
49.12
5.39
14.01
3.25
-1.54
-5.03
31.03
10.26
19.41
17.28
-11.19
-27.68
24.55
3.19
15.15
-3.94
-11.93
1.82
31.08
11.74
13.97
17.47
-19.39
-23.87
18.40
4.96
14.25
-0.24
-19.37
22.05
19.04
26.24
2.20
14.12
-6.71
-20.91
17.39
4.88
27.15
-12.29
-15.40
36.67
11.51
22.44
9.20
11.20
-9.04
-23.42
13.45
6.08
24.49
-20.32
-6.16
37.83
18.22
15.51
8.38
17.14
-17.46
-11.58
17.88
6.54
15.98
-10.89
-10.61
39.64
11.90
10.85
18.34
6.99
-31.44
7.75
26.59
6.84
1.92
2.33
-11.09
37.32
17.36
8.50
14.51
-0.55
-39.40
43.57
20.69
37
Avg
0.37
10.28
-15.16
-14.87
-1.44
21.11
8.89
10.36
15.19
-12.76
-20.77
24.96
5.48
20.99
-6.68
-4.17
5.04
34.60
12.52
15.77
11.32
-14.93
-20.28
32.73
QPM
• Anomalies Research
• Ripe with Outperformance Goal
– Market Outperformance: 433,000
– "Seeking Alpha“: 922,000
• Poor performance of mutual funds
• Quantitative Portfolio Management
– Matching market index
– Outperforming market index
– “Beat the Index”
38
QPM
•
•
•
•
Characterized by lots of data
Long look-back periods
Backtesting
Pitfalls
– Bad data
– Biases
– Datamining
– Transaction costs
39
Statistical QPM
• Lots and lots of quantitative funds
– Good job prospects, BTW: E.g.,
• Quantitative Portfolio Analyst - Asset Manager for
a Leading Hedge Fund
– Diversification and expansion has seen them create a
traditional asset management fund.
– New York; Up to $200k + standard benefits and excellent
bonus potential
• Options Strategy
• Public Domain
• Simugram
40
Time Value Option Sales
4
1995
2000
2005
1990
1995
2000
Time
Cash, 45% Draw, 1-Strike
Cash, 65% Draw, 1-Strike
2005
0
-1
-2
-3
-3
-2
-1
$M
0
1
Time
1
1990
$M
2
-2
0
$M
2
-2
0
$M
4
6
Cash, 35% Draw, 1-Strike
6
Cash, 10% Draw, 1-Strike
1990
1995
2000
Time
2005
1990
1995
2000
Time
2005
41
MaxMedian Rule
Performance
60
Value of Initial Dollar at Time
50
40
T-Bill
S&P 500
30
MaxMedian
#REF!
20
10
0
0
10
20
30
40
50
Yea rs Since Purchase
42
Simugram
43
Simugram
2001
2002
2003
2004
2005
2006
SPX
-10.5%
-23.8%
22.3%
9.3%
3.8%
11.8%
OEX
Simugram
-12.4%
-15.0%
-24.5%
-23.3%
19.9%
24.3%
4.6%
13.7%
22.2%
15.7%
44
Fundamentals QPM
•
•
•
•
Graham-Dodd on Steroids
Exploit available data
Try and sell for OPM
Examples:
– O'Shaughnessy
– Greenblatt
– Homegrown
• What happens in real life
45
James P. O'Shaughnessy
• c1920: Ignatius Aloysius O'Shaughnessy
– $110 million, I A O'Shaughnessy Foundation
– Avoided 20’s stocks, fed his own companies
– 66 years > $10M > $5.4B (at 10%)
• 1960: Jim O'Shaughnessy's Investment
Horizon began
• 1986: BA Econ, University of Minnesota
– Began work at the family's VC firm
• 1988: O'Shaughnessy Capital Mgmt, Inc.
– Consulting to Institutional Investors
46
O'Shaughnessy (CONT’D)
•
•
•
•
1995: Compustat (Standard & Poor's)
1996: Cornerstone Growth and Value Funds
1997: "What Works on Wall Street“, RBC
2000: Sold Cornerstone to Hennessey
– $200M Assets as of 6/30/00
• 2001: Sold O'Shaughnessy Capital to BSC
– About $500M
• 2005: Updated WWOWS
47
O'Shaughnessy (CONT’D)
• 3Q2007: O'Shaughnessy Asset Mgmt, LLC
– Unwound in BSCM sale to JPM
– Taking $8B out BSAM's $44B
• Strategy
– Benchmark: RUT2000
– No regard for sector
– Growth: EPS Growth, 52W Price Increase, P/S
– Value: Div Yield, LTM P/S , LTM P/CF
48
O'Shaughnessy (CONT’D)
• Dreyfus Premier Alpha Growth Fund
– 1,600 companies
– 300 largest-market-cap
– 130 after P/E, 52W Price Incr, then by P/S
– Quarterly validation
– Dumping rules
•
•
•
•
loss of 50% of market value
takeover that doesn't meet the screens' criteria
allegations of fraud
bankruptcy
49
O'Shaughnessy-esgue
• Recall 11-year benchmarks:
Equity Index Benchmarks: 1999-2009
132 Fiscal Periods (Ja-Dc), 11 Years (1/4/99 Thru 10/15/10)
Index
S&P 500
Russell 2000
Russell 3000
Universe Return
DOW 30
DOW 30 Ex-Div
S&P 500 Ex-Div
Avg%
Avg L3Y
0.37
-2.9
5.48
-0.8
1.22
-2.3
7.72
3.5
2.54
-1.2
0.19
-3.9
-1.43
-5.0
Worst
-46.34
-46.37
-46.64
-50.41
-43.43
-45.13
-47.70
TotNeg
-932.3
-757.8
-921.4
-802.1
-660.0
-743.4
-1004.9
50
30
40
Best Factor Collation IRR,
132 Fiscal Periods (Ja-Dc), 11 Years: 1999-2009
Filter: P/S Top 25%
20
12-Period Average IRR
P/S Backtest
0
20
40
60
80
100
Number of Securities
-60 -55 -50 -45
Max Drawdown
Worst Drawdown by Portfolio Size
0
20
40
60
80
100
51
Number of Securities
Starting and Stopping Times
•
•
•
•
Backtested 1950 thru 1996 (47 years)
Selected best factors (e.g., P/S)
Started his funds
Got lucky? In 4 years up about 166% vs.
75% for the market
• Got lucky? Sold to Hennessey at peak
• Got bad rap, 2000-2002 worse than
market
52
HFCGX
• Hennessy Cornerstone Growth HFCGX
– 10 Year return:
– Expenses:
•
•
•
•
•
3.29%
1.36%
“Net”:
1.93%
DOW:
1.7%
SP500:
0.74%
Dow Div: 4%
SP500 Div: 2.5%
53
HFCGX
54
Rules of the Game
•
•
•
•
How much can your investors stomach
Restrictions on redemptions
Success begets loyalty
Success depends on starting time
55
Another Example
• 10 year horizon
• Backtested 99-06, 12 fiscal periods
(without drawdown constraint)
• Selected best factors (Screen#1)
• Out-of-sample returns were still good;
(encouraging)
• Drawdowns horrible
• Developed Screen#2 with drawdown
constraint
56
Real Example, CONT’D
• In 99-06 backtest, best return factor had
drawdowns of:
– Screen#1
– Screen#2
31%
20%
• In 99-09 OOS sample testing, best return
factors remained the same (and equal
IRR’s of 33-34%), but with drawdowns of:
– Screen#1
– Screen#2
79.9%
39%
57
Out of Sample
0
20
40
60
80
20
30
40
50
Best Factor Collation IRR,
132 Fiscal Periods (Ja-Dc), 11 Years: 1999-2009
Filter: Factor1 Top 30%, Factor2 Top 35%, Factor3>0
12-Period Average IRR
50
40
30
20
12-Period Average IRR
Best Factor Collation IRR,
132 Fiscal Periods (Ja-Dc), 11 Years: 1999-2009
Filter: Factor1<xx, Factor4>yy, Factor5<>zz
100
0
20
40
60
80
100
Number of Securities
Worst Drawdown by Portfolio Size
Worst Drawdown by Portfolio Size
-40
-60
-50
Max Drawdown
-40
-60
-80
Max Drawdown
-30
Number of Securities
0
20
40
60
80
Number of Securities
100
0
20
40
60
80
Number of Securities
100
58
Keeping in the Game
Filter: Factor1<xx, Factor4>yy, Factor5<>zz
Name
Factor6
year_1999
year_2000
year_2001
year_2002
year_2003
year_2004
year_2005
year_2006
year_2007
year_2008
year_2009
Jan
32.45
8.54
69.46
10.14
10.85
87.31
51.23
18.75
88.82
31.90
-56.92
171.56
Feb
36.25
125.07
32.97
13.10
-21.50
110.91
46.83
32.92
66.65
25.33
-62.17
247.59
Mar
30.09
117.67
32.27
-5.09
-29.21
212.25
13.51
37.45
60.05
12.07
-79.87
430.59
Apr
23.82
30.38
8.09
33.96
-26.52
127.27
-5.61
57.80
44.98
6.93
-75.81
495.48
May
25.77
5.12
44.11
53.18
-15.67
74.51
-1.33
44.54
38.69
0.81
-55.24
308.04
Jun
21.05
3.15
48.27
55.60
5.68
78.52
4.66
37.60
49.83
-28.50
-52.48
144.14
Jul
30.21
17.01
51.57
32.33
46.32
90.81
-3.48
42.02
68.81
-15.07
-36.41
112.84
Aug
35.00
23.12
42.48
-0.23
99.41
124.89
33.33
4.91
77.16
-5.08
-17.52
64.76
Sep
34.63
6.87
54.47
15.12
118.69
62.32
23.15
21.87
86.74
-20.57
3.94
52.32
Oct
43.61
9.29
39.66
49.87
238.18
105.99
29.80
10.40
89.90
-38.38
24.28
38.34
Nov
42.33
14.12
60.71
39.21
143.15
79.04
15.44
41.28
50.98
-59.26
140.75
50.90
Dec
AvgRHS
41.40
33.05
20.95
31.77
25.65
42.47
58.17
29.61
63.81
52.77
97.06 104.24
28.29
19.65
60.38
34.16
38.62
63.44
-70.23 -13.34
242.93
-2.04
62.92 181.62
Jun
29.91
-14.68
94.34
55.64
16.68
63.70
31.96
45.62
55.58
-0.92
-27.23
67.31
Jul
28.28
9.03
71.22
32.34
30.25
73.04
31.51
41.81
54.49
-15.46
-17.39
38.23
Aug
29.46
10.31
56.24
31.20
77.92
52.13
31.51
22.77
45.04
13.43
-16.03
25.39
Sep
26.33
3.71
32.93
32.27
87.24
28.36
50.10
48.99
48.14
-8.21
-15.52
16.19
Oct
31.48
-4.25
8.92
44.02
159.00
73.63
43.21
13.57
59.66
-17.96
9.83
27.49
Nov
32.65
-20.38
46.77
19.32
76.70
73.96
38.33
23.45
48.36
-32.95
144.49
21.61
Dec
AvgRHS
37.86
34.06
1.29
9.67
40.81
40.76
12.40
43.78
100.23
47.34
34.15
83.14
22.31
35.40
22.97
33.22
43.98
41.55
-34.77
1.56
329.60
20.84
22.87 121.39
Filter: Factor1 Top 30%, Factor2 Top 35%, Factor3>0
Name
Factor7
year_1999
year_2000
year_2001
year_2002
year_2003
year_2004
year_2005
year_2006
year_2007
year_2008
year_2009
Jan
39.50
14.57
-3.63
125.97
13.47
92.33
55.39
12.85
36.42
37.40
-25.54
192.09
Feb
34.35
9.38
16.39
25.58
-5.92
126.84
43.42
23.75
23.63
28.03
-32.47
297.56
Mar
39.72
29.58
41.96
27.28
0.34
118.86
30.54
22.02
31.59
28.44
-39.91
376.27
Apr
41.27
69.51
28.25
55.97
10.52
155.65
16.34
29.02
23.50
15.28
-35.29
237.65
May
37.91
7.95
54.91
63.36
1.62
105.08
30.21
91.88
28.17
6.42
-24.43
134.08
59
SYWTBAMM?
• Survival
• This is not an easy undertaking
• Survival depends on your starting time and
redemption restrictions
• Young funds can have the least restrictive
redemption requirements
60
A Special Thanks To:
•
•
•
•
•
Eubank Benefactors
Profs. J.R. Thompson and E.E. Williams
K.B. Ensor, Chair of CoFES
TRU – Dept. of Statistics
Collaborators
61