GLOBAL ASSET ALLOCATION AND STOCK SELECTION

1
GLOBAL ASSET ALLOCATION AND STOCK SELECTION

• ASSIGNMENT 1
• SMALL CAP LONG-SHORT STRATEGY
• FIRST-YEAR BRAVES
• Daniel Grundman, Kader Hidra, Damian
  Olesnycky,Jason Trujillo, Alex Volzhin

2
Methodology

• Goal to identify long-short strategy for trading
  US small cap stocks using Fact Set.
• Universe Definition US stocks with market cap
  from 300M to 2B.
• Strategy Buy 1st quintile, Short 5th quintile.
• Benchmark SP 500
• In-sample period Jan, 1995 Dec, 2004
• Out-of-sample period Jan-Dec, 2005

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Factors

• We tested many factors but settled on three
• One-month return
• Six-month return
• Current price to 52-week high
• Additionally, we tried various combinations of
  these factors (two-factor and tree-factor models)

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Strategy Based on1-Month Return
1-Month Return
1-Month Alpha
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Strategy Based on6-Month Return
6-Month Return
6-Month Alpha
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Current Price to 52-Week High
Price to 52-Week High Alpha
Price to 52-Week High Return
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Other Explored Factors

• In addition to the previous 3 factors, we tried
  several other metrics
• Book to Market Price
• Price to Earnings
• Dividend Yield
• Return on Equity
• Revision Ratio
• However, we found all of them to be of little
  value.

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Book to Market Price
Book to Price Return
Book to Price Alpha
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Price to Earnings
P/E Return
P/E Alpha
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Revision Ratio
Revision Ratio Return
Revision Ratio Alpha
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Returns

• Our one-factor models delivered good returns
• 1-Month Returns Model 6.98
• 6-Month Returns Model 4.26
• Price to 52-Week High 3.55
• However, two-factor models were even better
• 1-Month Return Price to 52-Week High 6.95
• 6-Month Return Price to 52-Week High 4.55

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Bivariate Model 1-Month Return Price to
52-Week High
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Beta for Bivarate P to 52High 1 Month Return
Model
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Bivariate Model 6-Month Return Price to
52-Week High
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Multivariate Model
Multivariate Model Return
Multivariate Model Alpha
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Scoring

• We used scoring for bi-variate model (1-month
  return and price to 52-week high)
• For 1-month return
• 1st quintile 5, 5th quintile -5
• Price to 52-week high
• 1st quintile 3, 5th quintile -3
• More weight on 1-month return because
  single-factor model based on 1-month return is
  superior to that based on price to 52-week high.

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In-Sample Two-Factor Model1-Month Return
Price to 52-Week High with Scoring
In-Sample Model w/ Scoring Return
In-Sample Model w/ Scoring Alpha
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Beta for Bivarate 52-P and 1- Month Return
Scoring Model
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Out-of-Sample Testing

• We used the period from January, 2005 to
  December, 2005 for the out-of-sample testing of
  our best model (two-factor 1-month return
  current price to 52-week high).
• Annualized Returns -
• Benchmark Return 0.4
• Our model without scoring 11.79
• Our model with scoring 12.07

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Out-of-Sample Two-Factor Model 1-Month Return
Price to 52-Week High w/o Scoring
Out-of-Sample Model Return
Out-of-Sample Model Alpha
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Out-of-Sample Two-Factor Model Beta 1-Month
Return Price to 52-Week High without Scoring
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Out-of-Sample Two-Factor Model 1-Month Return
Price to 52-Week High with Scoring
Out-of-Sample Model w/ Scoring Alpha
Out-of-Sample Model w/ Scoring Return
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Out-of-Sample Two-Factor Scoring Model Beta
1-Month Return P to 52-W High with
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In-Sample Results (1/2)
Heat Map In-Sample WITHOUT Scoring

• Quintile 1 has NOT the highest average return.
• Only 3/10 years have the highest returns.
• Here we are concerned by 2003 when we actually
  got the lowest returns in Quintile 1.
• The spread would have crushed us!
• Quintile 5 has the lowest average return.
• 5/10 years have the lowest returns.
• Here we are concerned by 2003 when we actually
  got the highest returns in Quintile 5.

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In-Sample Results (2/2)
Heat Map In-Sample WITH Scoring

• The scoring screen alleviates our concerns
• Fractile 1 has the highest average return.
• 8/10 years have the highest returns.
• The scoring eliminates the 2003 crush!
• Fractile 5 has the lowest average return.
• 10/10 years have the lowest returns.

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Out-of-Sample Results (1/2)
Heat Map Out of Sample WITHOUT Scoring

• Quintile 1 has the highest average return.
• Only 3/12 months have the highest returns.
• Here we are concerned by these 2 months where we
  actually got the lowest returns in quintile 1.
• Quintile 5 has the lowest average return.
• 8/12 months have the lowest returns.
• Here we are concerned by these 2 months where we
  actually got the highest returns in quintile 5.
• The Long/Short spread is satisfactory 36

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Out-of-Sample Results (2/2)
Heat Map Out of Sample WITH Scoring

• The scoring screen alleviates our concerns
• Quintile 1 has the highest average return and
  outperform the unscored screen by far!
• Quintile 1 has the highest average return. 10/12
  months have the highest returns.
• Quintile 5 has the lowest average return and
  underperformed the unscored screen by far!
• Quintile 5 has the lowest average return. 9/12
  months have the lowest returns.
• The Long/Short spread is satisfactory 147.

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Long/Short DistributionsPositively Skewed After
Scoring
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Concerns

• Transaction Costs
• Short Selling Constraints
• Execution
• Volatility/Exit Signals
• Fact Set

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Concerns
Transaction Costs

• Monthly rebalancing
• Many months have gt50 change in fractile
  components.
• Large number of securities
• 60 Stocks per fractile per month

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Concerns
Short Selling Constraints

• Dealing only with small cap securities.
• May be limited opportunity to short sell some
  securities.

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Concerns
Execution

• How to execute as an actual trading strategy.
• When to run model?
• When do you make trades?

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Concerns
Volatility and Exit Signals

• Portfolios are not Beta neutral and overall betas
  are usually above 1.
• No parameters set for liquidating portfolios.
• In sample we had several very bad months.
• Given the high volatility of small caps, there
  is the potential for very large losses.

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Concerns
Fact Set

• Limited knowledge of the tool.
• Results seem almost too good.
• In practice we would run tests to verify that
  what we believe is happening is actually
  happening.

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Limitations

• Primary limitation is the fund size for which
  this is compatible.
• Relatively few securities
• Low market capitalizations
• Solution Change screen
• Wider market cap range
• Low trading volume requirement

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Summary

• We find the results of our analysis to be very
  compelling.
• The big challenge is efficient and proper
  execution.
• Proper study of transaction costs is required.
• We would also recommend a further review of the
  data before moving forward.