Long/Short Trading Strategy
Cam’s Crazies
Global Asset Allocation
February 2005
Agenda
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Methodology
Factors
UniVariate Models
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Book to Price
Dividend Yield
FY2 Earnings Yield to Growth
BiVariate Model
Scoring Model
Why Results Might Be Wrong
Follow-up Research
Conclusion
Methodology
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Objective: To develop a quantitative long/short model that
generates positive and consistent returns with no market
exposure (beta zero).
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Universe: For the sake of liquidity and availability of historical
data, we limited our screening universe to the 500 largest market
capitalization companies listed in the U.S.
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Rebalancing: To limit turnover and transaction expenses, we
resort and rebalance annually.
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Weights: For simplicity and prevention of outlier performance,
we chose an equal weight strategy, both in the number of stocks
‘per bucket’ and in the amount invested or shorted per stock.
Factors
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Book to Price
Earnings Yield (FY1/FY2)
FY1/FY2 Earnings Yield to Growth
Earnings Growth
Dividend Yield
3 Year EPS Growth
Factors: Book to Price
Go long stocks with high B2P ratios (low Price to Book) and short stocks with low B2P ratios.
Average annual return of 9% and .53 Sharpe ratio.
High volatility of returns (+48% and -23%). The screen did not perform well during the
1998-1999 valuation bubble, producing -21% and -11% returns, respectively.
Factors: Dividend Yield
Go long high dividend yield stocks short low/no dividend yield stocks. Some sector
weight and value vs. growth concerns.
Best screen: 16.7% beta-neutral average returns, 1.11 sharpe ratio, lowest
turnover of all factors. Alphas for fractiles 1 and 5 11.6% and -7.7%, respectively.
Most consistent results: fractile 1 was top performer all but 2 years. Bottom fractile nearly
always 4 or 5. Consecutive losses in 1998 and 1999, but positive returns in all other years.
Factors: FY2 Earnings Yield to Growth
Forward looking: uses fiscal earnings estimates for two years out scaled by consensus long
term growth expectations. Go long high EY to growth fractile, short low EY to growth fractile.
A beta neutral long/short strategy resulted in an average return of 10% with a range of
-11% to +37%. High Sharpe ratio of .78. There were four years of negative returns over
the 20-year time period. Consecutive losses in 1998 and 1999.
BiVariate Model
Factor 1 Dividend Yield – fundamental data
Factor 2 FY2 Earnings Yield to Growth – expectational data
Two sorts produced 25 fractiles with ~20 companies each – long top fractile, short bottom
Trouble sorting in later years damages credibility of data.
Dividend Yield
Average beta-weighted return of 29% and sharpe ratio of 1.58 are exceptionally high.
Returns in bubble years range from 32%-58%. 2004 only year of negative returns (-8%).
Fractile
Summary
-17.30
Earnings Yield
-2-310.91
13.26
-1-
7.96
17.47
20.48
20.70
32.61
-2-
9.80
11.96
15.79
17.93
25.26
-3-
6.40
9.71
11.38
14.72
21.11
-4-
2.45
7.40
11.01
12.10
15.66
-5-
2.38
-3.17
0.55
8.36
13.84
-415.77
-521.27
Scoring Model
Selected Book to Price, Dividend Yield, Earnings Yield (FY2) and Earnings Yield (FY2) to
growth. These factors combine both fundamental and expectational variables.
We subjectively chose scores, ranging from -5 to 5, for fractiles 1 and 5. Highest possible
Score is 12, lowest possible score is -9.
Beta-neutral average returns of 12%, sharpe ratio of .81; not as good as stand-alone
dividend yield.
Why Might Results Be Wrong?
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Model depends on FactSet – both the
accuracy of the data and Alpha Tester
program.
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Data could be subject to:
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errors
lags
survivorship bias
outliers
high minus low
sector bets
Follow-Up Research
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Detailed review of the accuracy of the FactSet
historical data and the Alpha Tester model.
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Suspicious beta screen
Model Specific:
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more frequent rebalancing
a larger universe of companies/ international markets
a more exact estimation of the impact of turnover,
management fees, and short sale restrictions
optimization of weights in Scoring model
ex-ante application of the model on a real time basis
Conclusion
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Results were very impressive
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Further research is needed to gain confidence in models
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How long will excess returns persist before identified and
competed away?
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That said, we can’t help but wonder …
What if we’re right??