historical backtesting vs. real-world positions
DESCRIPTION
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. Outline. The Asset Allocation Problem - PowerPoint PPT PresentationTRANSCRIPT
Historical Backtesting vs. Real-World Positions
SECOND EUBANK CONFERENCE: MODELING FINANCIAL MARKETS IN
A WORLD OF FIAT MONEY
John A. Dobelman
Rice UniversityOctober 18-19, 2010
George R. Brown School of Engineering STATISTICS
2
33
Outline
• The Asset Allocation Problem
• The Equity Portfolio
• Managing the Portfolio
• Conclusion and Future Work
Asset Allocation
4WSJ, October 19, 2030
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
– Powerball ($50M Jackpot):• 4,999,999,900% 1-Day return
6
LBSV Liberty Silver, OTC-BB
10/14/09 – 10/15/10
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– SBAH = BAH + DivMgt
– SBAH = BAH + DivMgt + Tender
– SBAH = BAH + DivMgt + Tender + Tax
– SBAH = BAH + DivMgt + Tender + Tax + Realloc8
S, :BAHS S BAH
Come a Long Way Since 1973
9
2
1 2
2
1
2
2 1
( , )
( ) ( )
1log( ) ( )
2
1log( ) ( )
2
rt
SX
SX
S GBM
CP C S
S
C S d Xe d
r td
t
r td d t
t
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 …, 199911
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 Ω=Ω– Departure 1 Ω=ΩE
– Departure 2 Ω=ΩE\Priv
– Departure 3 Ω=ΩS
– Departure 4 Ω=ΩIndex
– Departure 5 Ω→Your E-V portfolio, and – Departure 6 Ω→Your E-V portfolio, – Departure 7 Ω→ Some other portfolio P
16
ˆ, ˆ
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
22
Valuation Measures ABC DEF ABC DEF
Market Cap (intraday)5: 238.79M 2.10B Income Statement 68.60M 2.12BEnterprise Value (Oct 4, 2010)3: 185.34M 3.86B Revenue (ttm): 2.65 8.36Trailing P/E (ttm, intraday): 130.57 15.83 Revenue Per Share (ttm): 3.00% 8.00%Forward P/E (fye Dec 31, 2011)1: 29.48 13.71 Qtrly Revenue Growth (yoy): 50.14M 421.18MPEG Ratio (5 yr expected)1: 2.81 1.49 Gross Profit (ttm): 4.77M 508.55MPrice/Sales (ttm): 3.59 1.01 EBITDA (ttm): 1.83M 136.66MPrice/Book (mrq): 4.25 1.46 Net Income Avl to Common (ttm): 0.07 0.54Enterprise Value/Revenue (ttm)3: 2.7 1.82 Diluted EPS (ttm): N/A 74.40%Enterprise Value/EBITDA (ttm)3: 38.84 7.58 Qtrly Earnings Growth (yoy):
Financial Highlights Balance Sheet 60.77M 152.24MTotal Cash (mrq): 2.33 0.62
Fiscal Year Total Cash Per Share (mrq): 0 1.86BFiscal Year Ends: 31-Dec 31-Dec Total Debt (mrq): N/A 125.54Most Recent Quarter (mrq): 30-Jun-10 30-Jun-10 Total Debt/Equity (mrq): 2.51 1.03
Current Ratio (mrq): 2.21 5.97Profitability Book Value Per Share (mrq):Profit Margin (ttm): 2.67% 6.46%Operating Margin (ttm): 5.01% 16.10% Cash Flow Statement 9.40M 346.98M
Operating Cash Flow (ttm): 3.97M 263.52MManagement Effectiveness Levered Free Cash Flow (ttm):Return on Assets (ttm): 2.47% 2.50%Return on Equity (ttm): 3.40% 9.61%
Fundamental Analysis
23
Variable ABC DEF Variable ABC DEF
Shares Outstanding 25.8 280.2 F1 Sales 61.55 2280.60 Market Cap 506.2 3,936.2 F2 Sales 78.43 2294.00 Avg $M Volume 3.13 9.72 LTG #N/A 11.50 Closing Price 19.6 14.1 EPS NTM 0.45 0.57 Act. Price 19.6 14.1 FY1 EPS 0.41 0.51 Exchange NYSE NYSE FY2 EPS 0.47 0.60 Ind Name Software & ProgrammingPersonal Services FY3 EPS 0.59 #N/A F0 Sales Hist 2.2 7.7 F1 CFOPS 10.58 #N/A F0 Sales IBES Act 62.1 2,285.3 F1 FCF 9.03 291.71Working Capital Ann 27.6 -169.6 F0/F1 Sales Growth -0.87 -0.33Working Capital Qtr 28.1 -73.2 F1/F2 Sales Growth 27.75 1.73 LTD $M 0.0 1,779.8 Price to Book 17.75 2.50 BV PS 1.1 5.6 Price to Sales 0.32 0.01 EV $M 506.2 5,716.1 Price to Free Cash Flow 40.73 20.16 DA LTM 0.05 0.44 TTM PE 43.30 16.45 DA Annual 0.05 0.35 NTM PE 43.60 24.65 DA PS 0.05 0.44 F1 PE 47.85 27.55 CAPX LTM 0.08 0.54 F2 PE 41.74 23.42 CAPX Annual 0.04 0.36 F0 Yield 2.31 6.08 CAPX PS 0.08 0.54 F1 Yield 2.09 3.63 EBIT $M Ann 6.83 260.30 F2 Yield 2.40 4.27 EBIT $M Qtr 9.61 325.35 EPS NTM/TTM 6.57 123.57 EBITDA $M 8.10 356.99 F0/F1 -9.52 -40.27 EPS LTM 0.42 0.25 F1/F2 14.63 17.65 F0 EPS 0.45 0.85 F2/F3 25.53 #N/A FCF $M Ann. 12.43 195.26 EBIT/Tang Capital 24.77 -153.47 FCF $M LTM -29.48 204.01 EBIT/Tang Capital TTM 34.13 -444.77 ROE 95.45 4.63 EBIT/EV 1.35 4.55 Dividend Yield 40.77 1.14 EBIT/EV TTM 1.90 5.69
Fundamental Approach
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25
Outliers/Outliars
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
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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
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Graham Portfolio
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Company Name and Archeology Dates Changes
AMERICAN CAN CO 1960-1988Commercial Credit/ PRIMERICA CORP
NEW/Travelers/Citigroup Inc.Commercial Credit/ PRIMERICA CORP
NEW/Travelers/Citigroup Inc. 1988-2009 Citigroup owns all of travelersAMERICAN TELEPHONE & TELEG CO 1960-2005 SOUTHWESTERN BELL CORP
SOUTHWESTERN BELL CORP/ATT 2009 Renamed the entirety ATTANACONDA CO 1960-1970 ATLANTIC RICHFIELD CO
ATLANTIC RICHFIELD CO 1970-2000 BRITISH PETROLEUM LTD/BP PLCBRITISH PETROLEUM LTD/BP PLC 2000-2009 Anaconda holds the EPA liability
SWIFT & CO/Esmark 1960-1984 Sold to Beatrice Beatrice Foods 1984-1986 Private with KKR 3/31/1986
WOOLWORTH F W CO/Venator Group/Foot Locker 1960-2009 Woolworths purchases KinneyWoolworths forms VenatorKinney becomes the base for Foot LockerVenator falls completely under Foot locker
Price (10/10/10) Last SplitC 4.17 8/28/2000T 28.28 5/20/1998BP 41.42 10/4/1999FL 15.64 6/1/1990
Graham BAH Results
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CAGR from 1971Thru 12/31/2009: 4.13%Thru 10/10/2010: 4.09%
Original DOW 30 from 1971Components unchanged 1956-19765 gone, only 14 continuously traded
Original DOW 30 from 1/3/2000Thru 10/10/2010: 4.59%
Indexes from 1/3/2000 thru 10/10/10DJIAK -0.29% DOXIK 2.04% SP50 -2.03% SP50.R -0.24%
10-Yr S&P 500 Returns
30
10-Year, SPX Ex-Div
IRR%
Pro
port
ion
-10 -5 0 5 10 15 20
0.00
0.04
0.08
-10 -5 0 5 10 15 20
0.0
0.4
0.8
Cumulative Probability
IRR%
Pro
port
ion
10-Year, SPX Div
IRR%
Pro
port
ion
-10 -5 0 5 10 15 20
0.00
0.04
0.08
-10 -5 0 5 10 15 20
0.0
0.4
0.8
Cumulative Probability
IRR%
Pro
port
ion
30-Yr S&P 500 Returns
31
30-Year, SPX Ex-Div
IRR%
Pro
port
ion
-10 -5 0 5 10 15 20
0.00
0.10
0.20
0.30
-10 -5 0 5 10 15 20
0.0
0.4
0.8
Cumulative Probability
IRR%
Pro
port
ion
30-Year, SPX Div
IRR%
Pro
port
ion
-10 -5 0 5 10 15 20
0.00
0.10
0.20
0.30
-10 -5 0 5 10 15 20
0.0
0.4
0.8
Cumulative Probability
IRR%
Pro
port
ion
Horizon Dependence
32
0 20 40 60 80
05
1015
20
IRR of SP500 Ex-Div
Horizon in Years
IRR
%
°°°°°°°°
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°
°°°°°°°°°°°°°°°°°°°°°°°°°°°
0 20 40 60 80
05
1015
20
IRR of SP500
Horizon in Years
IRR
%
°°°°°°°°°°°°°°°
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°
0 20 40 60 80
05
1015
20
IRR of GE Ex-Div
Horizon in Years
IRR
%
°°°°
°°°
°°°
°°°
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°
°°°°°°°°°°°°°°°°°°°°°°
0 20 40 60 80
05
1015
20
IRR of GE
Horizon in Years
IRR
%
°°°°
°°°
°°°°°
°°°°°°°°°°°°°°°°°°°°°°°°°°
°°°°°°°°°°°°°°°°°°°°°°°°
°°°°°°°°°°°°°°°°°°
0 20 40 60 80
05
1015
20
IRR of SP500 Eq-Wt, ExDiv
Horizon in Years
IRR
% °°°°°°
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°
0 20 40 60 80
05
1015
20
IRR of SP500 Eq-Wt
Horizon in Years
IRR
%
°°°°°°
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°
Benchmark Summaries
33
Ja Mr Ma Jl Se Nv
-40
040
80
1-Year, SPX Ex-DivIR
R%
Ja Mr Ma Jl Se Nv
-40
040
80
5-Year, SPX Ex-Div
IRR
%
Ja Mr Ma Jl Se Nv
-15
-55
15
10-Year, SPX Ex-Div
IRR
%
Ja Mr Ma Jl Se Nv
-15
-55
15
30-Year, SPX Ex-DivIR
R%
Benchmarks
• 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
SP500: Horizon Returns Equity Index Benchmarks: 1999-2009Jan 1926 - Dec 2009 132 Fiscal Periods (Ja-Dc), 11 Years (1/4/99 Thru 10/15/10)
Index Min Q1 Med Q3 Max5 Years: -19.5 5.0 10.79 16.1 36.010 Years: -6.2 6.8 11.03 15.6 21.520 Years: 1.3 8.3 11.71 14.1 18.430 Years: 7.4 10.3 11.01 12.5 14.750 Years: 7.2 10.5 11.19 12.1 14.0
Benchmarks
35
Benchmarks
36
Equity Index Benchmarks: 1999-2009132 Fiscal Periods (Ja-Dc), 11 Years (1/4/99 Thru 10/15/10)
Index Avg% Avg L3Y Worst TotNegS&P 500 0.37 -2.9 -46.34 -932.3Russell 2000 5.48 -0.8 -46.37 -757.8Russell 3000 1.22 -2.3 -46.64 -921.4Universe Return 7.72 3.5 -50.41 -802.1DOW 30 2.54 -1.2 -43.43 -660.0DOW 30 Ex-Div 0.19 -3.9 -45.13 -743.4S&P 500 Ex-Div -1.43 -5.0 -47.70 -1004.9
Return by Period
37
Benchmarks by Fiscal Period [DRD 10-16-10]1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009
Index Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec AvgS&P 500 1.05 0.45 0.83 0.53 0.20 0.48 -1.40 0.13 -0.20 0.93 0.65 0.86 0.37
19990104 15.40 12.10 12.99 17.27 5.77 13.33 6.60 9.64 15.60 13.27 6.18 -4.82 10.28
20000103 -6.32 -1.40 -8.97 -25.68 -12.74 -11.96 -14.99 -14.42 -24.54 -25.87 -22.74 -12.24 -15.16
20010102 -8.81 -17.20 -7.56 0.57 -13.03 -14.18 -22.21 -26.17 -19.95 -17.11 -15.60 -17.21 -14.87
20020102 -19.98 -22.47 -24.39 -23.79 -14.12 -4.94 3.28 12.83 19.07 22.29 18.76 16.23 -1.44
20030102 24.16 34.43 40.36 34.20 22.94 18.40 16.92 14.34 11.37 13.06 9.83 13.30 21.11
20040102 11.22 6.92 6.55 5.44 5.76 9.20 7.71 14.45 12.45 10.56 8.30 8.10 8.89
20050103 7.50 9.83 8.70 12.45 15.10 8.95 8.34 4.81 9.37 10.84 15.91 12.54 10.36
20060103 13.81 14.93 10.74 12.92 16.02 21.75 21.37 17.51 14.50 18.35 12.37 8.02 15.19
20070103 4.10 -1.67 -3.35 -2.10 -3.28 -7.05 -15.26 -12.23 -15.17 -23.34 -34.38 -39.46 -12.76
20080102 -34.06 -39.37 -46.34 -39.23 -36.07 -29.95 -26.18 -19.51 -19.98 -8.90 11.06 39.34 -20.77
20090102 22.57 36.86 63.14 48.40 38.09 15.92 13.55 14.04 10.45 13.57 15.03 7.91 24.96
Russell 2000 5.15 4.70 5.83 6.67 5.58 5.39 3.19 4.96 4.88 6.08 6.54 6.84 5.4819990104 15.11 19.85 51.20 36.91 15.82 14.01 15.15 14.25 27.15 24.49 15.98 1.92 20.99
20000103 -1.43 2.28 -18.53 -16.17 -4.23 3.25 -3.94 -0.24 -12.29 -20.32 -10.89 2.33 -6.68
20010102 6.86 -4.34 2.49 15.40 5.59 -1.54 -11.93 -19.37 -15.40 -6.16 -10.61 -11.09 -4.17
20020102 -18.30 -21.38 -23.56 -25.85 -20.76 -5.03 1.82 22.05 36.67 37.83 39.64 37.32 5.04
20030102 44.72 58.41 66.60 63.37 41.95 31.03 31.08 19.04 11.51 18.22 11.90 17.36 34.60
20040102 17.49 10.10 8.59 3.92 4.58 10.26 11.74 26.24 22.44 15.51 10.85 8.50 12.52
20050103 8.08 18.48 17.63 26.57 32.53 19.41 13.97 2.20 9.20 8.38 18.34 14.51 15.77
20060103 16.46 11.13 7.83 8.21 8.54 17.28 17.47 14.12 11.20 17.14 6.99 -0.55 11.32
20070103 -4.23 -8.46 -12.19 -10.20 -9.45 -11.19 -19.39 -6.71 -9.04 -17.46 -31.44 -39.40 -14.93
20080102 -31.83 -38.33 -46.37 -38.58 -32.11 -27.68 -23.87 -20.91 -23.42 -11.58 7.75 43.57 -20.28
20090102 25.56 38.67 78.86 61.59 49.12 24.55 18.40 17.39 13.45 17.88 26.59 20.69 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
• Simugram40
41
Time Value Option Sales
Cash, 10% Draw, 1-Strike
Time
$M
1990 1995 2000 2005
-20
24
6
Cash, 35% Draw, 1-Strike
Time
$M
1990 1995 2000 2005
-20
24
6
Cash, 45% Draw, 1-Strike
Time
$M
1990 1995 2000 2005
-3-2
-10
1
Cash, 65% Draw, 1-Strike
Time
$M
1990 1995 2000 2005
-3-2
-10
1
MaxMedian Rule
42
Performance
0
10
20
30
40
50
60
0 10 20 30 40 50
Years Since Purchase
Valu
e o
f In
itia
l D
ollar
at
Tim
e
T-Bill
S&P 500
MaxMedian
#REF!
Simugram
43
Simugram
44
SPX OEX Simugram2001 -10.5% -12.4% -15.0%2002 -23.8% -24.5% -23.3%2003 22.3% 19.9% 24.3%2004 9.3% 4.6% 13.7%2005 3.8% 22.2%2006 11.8% 15.7%
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)
47
• 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
O'Shaughnessy (CONT’D)
48
• 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
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:
50
Equity Index Benchmarks: 1999-2009132 Fiscal Periods (Ja-Dc), 11 Years (1/4/99 Thru 10/15/10)
Index Avg% Avg L3Y Worst TotNegS&P 500 0.37 -2.9 -46.34 -932.3Russell 2000 5.48 -0.8 -46.37 -757.8Russell 3000 1.22 -2.3 -46.64 -921.4Universe Return 7.72 3.5 -50.41 -802.1DOW 30 2.54 -1.2 -43.43 -660.0DOW 30 Ex-Div 0.19 -3.9 -45.13 -743.4S&P 500 Ex-Div -1.43 -5.0 -47.70 -1004.9
P/S Backtest
51
0 20 40 60 80 100
20
30
40
Number of Securities
12
-Pe
rio
d A
vera
ge
IRR
Best Factor Collation IRR, 132 Fiscal Periods (Ja-Dc), 11 Years: 1999-2009
Filter: P/S Top 25%
0 20 40 60 80 100
-60
-55
-50
-45
Number of Securities
Ma
x D
raw
do
wn
Worst Drawdown by Portfolio Size
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
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HFCGX
• Hennessy Cornerstone Growth HFCGX– 10 Year return: 3.29%– Expenses: 1.36%
• “Net”: 1.93%• DOW: 1.7%• SP500: 0.74%• Dow Div: 4%• SP500 Div: 2.5%
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HFCGX
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Rules of the Game
• How much can your investors stomach
• Restrictions on redemptions
• Success begets loyalty
• Success depends on starting time
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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
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Real Example, CONT’D
• In 99-06 backtest, best return factor had drawdowns of:– Screen#1 31%– Screen#2 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 79.9%– Screen#2 39%
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Out of Sample
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0 20 40 60 80 100
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4050
Number of Securities
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RR
Best Factor Collation IRR, 132 Fiscal Periods (Ja-Dc), 11 Years: 1999-2009
Filter: Factor1<xx, Factor4>yy, Factor5<>zz
0 20 40 60 80 100
-80
-60
-40
Number of Securities
Max
Dra
wdo
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Worst Drawdown by Portfolio Size
0 20 40 60 80 100
2030
4050
Number of Securities
12-P
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Best Factor Collation IRR, 132 Fiscal Periods (Ja-Dc), 11 Years: 1999-2009
Filter: Factor1 Top 30%, Factor2 Top 35%, Factor3>0
0 20 40 60 80 100
-60
-50
-40
-30
Number of Securities
Max
Dra
wdo
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Worst Drawdown by Portfolio Size
Keeping in the Game
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Filter: Factor1<xx, Factor4>yy, Factor5<>zz
Name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec AvgRHSFactor6 32.45 36.25 30.09 23.82 25.77 21.05 30.21 35.00 34.63 43.61 42.33 41.40 33.05
year_1999 8.54 125.07 117.67 30.38 5.12 3.15 17.01 23.12 6.87 9.29 14.12 20.95 31.77year_2000 69.46 32.97 32.27 8.09 44.11 48.27 51.57 42.48 54.47 39.66 60.71 25.65 42.47year_2001 10.14 13.10 -5.09 33.96 53.18 55.60 32.33 -0.23 15.12 49.87 39.21 58.17 29.61year_2002 10.85 -21.50 -29.21 -26.52 -15.67 5.68 46.32 99.41 118.69 238.18 143.15 63.81 52.77year_2003 87.31 110.91 212.25 127.27 74.51 78.52 90.81 124.89 62.32 105.99 79.04 97.06 104.24year_2004 51.23 46.83 13.51 -5.61 -1.33 4.66 -3.48 33.33 23.15 29.80 15.44 28.29 19.65year_2005 18.75 32.92 37.45 57.80 44.54 37.60 42.02 4.91 21.87 10.40 41.28 60.38 34.16year_2006 88.82 66.65 60.05 44.98 38.69 49.83 68.81 77.16 86.74 89.90 50.98 38.62 63.44year_2007 31.90 25.33 12.07 6.93 0.81 -28.50 -15.07 -5.08 -20.57 -38.38 -59.26 -70.23 -13.34year_2008 -56.92 -62.17 -79.87 -75.81 -55.24 -52.48 -36.41 -17.52 3.94 24.28 140.75 242.93 -2.04year_2009 171.56 247.59 430.59 495.48 308.04 144.14 112.84 64.76 52.32 38.34 50.90 62.92 181.62
Filter: Factor1 Top 30%, Factor2 Top 35%, Factor3>0
Name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec AvgRHSFactor7 39.50 34.35 39.72 41.27 37.91 29.91 28.28 29.46 26.33 31.48 32.65 37.86 34.06
year_1999 14.57 9.38 29.58 69.51 7.95 -14.68 9.03 10.31 3.71 -4.25 -20.38 1.29 9.67year_2000 -3.63 16.39 41.96 28.25 54.91 94.34 71.22 56.24 32.93 8.92 46.77 40.81 40.76year_2001 125.97 25.58 27.28 55.97 63.36 55.64 32.34 31.20 32.27 44.02 19.32 12.40 43.78year_2002 13.47 -5.92 0.34 10.52 1.62 16.68 30.25 77.92 87.24 159.00 76.70 100.23 47.34year_2003 92.33 126.84 118.86 155.65 105.08 63.70 73.04 52.13 28.36 73.63 73.96 34.15 83.14year_2004 55.39 43.42 30.54 16.34 30.21 31.96 31.51 31.51 50.10 43.21 38.33 22.31 35.40year_2005 12.85 23.75 22.02 29.02 91.88 45.62 41.81 22.77 48.99 13.57 23.45 22.97 33.22year_2006 36.42 23.63 31.59 23.50 28.17 55.58 54.49 45.04 48.14 59.66 48.36 43.98 41.55year_2007 37.40 28.03 28.44 15.28 6.42 -0.92 -15.46 13.43 -8.21 -17.96 -32.95 -34.77 1.56year_2008 -25.54 -32.47 -39.91 -35.29 -24.43 -27.23 -17.39 -16.03 -15.52 9.83 144.49 329.60 20.84year_2009 192.09 297.56 376.27 237.65 134.08 67.31 38.23 25.39 16.19 27.49 21.61 22.87 121.39
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
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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