GLOBAL ASSET ALLOCATION AND STOCK

SELECTION

ASSIGNMENT # 1SMALL CAP LONG-SHORT STRATEGY

FIRST-YEAR BRAVESDaniel Grundman, Kader Hidra, Damian Olesnycky,

Jason Trujillo, Alex Volzhin

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: S&P 500

In-sample period: Jan, 1995 – Dec, 2004

Out-of-sample period: Jan-Dec, 2005

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)

Strategy Based on1-Month Return

1-Month Return 1-Month Alpha

Strategy Based on6-Month Return

6-Month Return 6-Month Alpha

Current Price to 52-Week High

Price to 52-Week High Return Price to 52-Week High Alpha

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.

Book to Market Price

Book to Price Return Book to Price Alpha

Price to EarningsP/E Return P/E Alpha

Revision RatioRevision Ratio Return Revision Ratio Alpha

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%

Bivariate Model: 1-Month Return & Price to 52-Week

High

Price to 52 Week High and 1 Month Return

-3

-2

-1

0

1

2

3

4

5

6

1 2 3 4 5Mon

thly

Ret

urn

Price to 52 Week High and 1 Month ReturnAlpha

-3

-2

-1

0

1

2

3

4

1 2 3 4 5

Beta for Bivarate P to 52High & 1 Month Return

Model

1 2 3 4 5 NA

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

Beta (Price to 52 Week High)

Beta

Fractile

Bivariate Model: 6-Month Return & Price to 52-Week

High

P-52 High and 6 Month Return Model

-2

-1

0

1

2

3

4

1 2 3 4 5

P-52 High and 6 Month Model Alpha

-3

-2

-1

0

1

2

3

4

1 2 3 4 5

Multivariate Model

Multivariate Model Return Multivariate Model Alpha

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.

In-Sample Two-Factor Model:1-Month Return & Price to 52-Week High with Scoring

1 2 3 4 5 NA

-4

-3

-2

-1

0

1

2

3

4

Alpha (Total Quintile Score)

Alpha

Fractile

1 2 3 4 5 NA

-4

-3

-2

-1

0

1

2

3

4

Alpha (Total Quintile Score)

Alpha

FractileIn-Sample Model w/ Scoring Return In-Sample Model w/ Scoring Alpha

Beta for Bivarate 52-P and 1- Month Return Scoring

Model

1 2 3 4 5 NA

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.0

2.1

2.2

2.3

Beta (Total Quintile Score)

Beta

Fractile

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%

Out-of-Sample Two-Factor Model: 1-Month Return & Price to 52-Week High w/o Scoring

1 2 3 4 5 NA

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

Alpha (Price to 52 Week High)

Alpha

FractileOut-of-Sample Model Return Out-of-Sample Model Alpha

Out-of-Sample Two-Factor Model Beta: 1-Month Return & Price to 52-Week High

without Scoring

1 2 3 4 5 NA

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

3.8Beta (Price to 52 Week High)

Beta

Fractile

Out-of-Sample Two-Factor Model: 1-Month Return &

Price to 52-Week High with Scoring

Out-of-Sample Model w/ Scoring Return Out-of-Sample Model w/ Scoring Alpha

Out-of-Sample Two-Factor Scoring Model Beta: 1-Month Return & P to 52-W High

with

1 2 3 4 5 NA

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

3.4

3.6

Beta (Total Quintile Score)

Beta

Fractile

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.

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.

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%

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%.

Long/Short DistributionsPositively Skewed After Scoring

Long/Short Returns DistributionP52-1Month In-Sample

0

5

10

15

20

25

30

35

-35% -30% -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% More

P52 - 1 Month P52 - 1 Month Scoring

Long/Short Returns DistributionP52-1Month Out-Of-Sample

0

1

2

3

4

5

-35% -30% -25% -20% -15% -10%

Returns RangeP52-1Month without scoring OOS P52-1Month with scoring OOS

Concerns Transaction Costs

Short Selling Constraints

Execution

Volatility/Exit Signals

Fact Set

Concerns

Monthly rebalancing

• Many months have >50% change in fractile components.

Large number of securities

• ~60 Stocks per fractile per month

Transaction Costs

Concerns

Dealing only with small cap securities.

May be limited opportunity to short sell some securities.

Short Selling Constraints

Concerns

How to execute as an actual trading strategy.

• When to run model?

• When do you make trades?

Execution

Concerns

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.

Volatility and Exit Signals

Concerns

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.

Fact Set

LimitationsPrimary 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

SummaryWe 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.