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Quantitative Stock
Selection
Portable Alpha
Gambo Audu
Preston Brown
Xiaoxi Li
Vivek Sugavanam
Wee Tang Yee
Stock Selection Approach



Identify short-term technical factors and fundamental
value-oriented factors
Combine factors in effort to produce excess returns
relative to the market without extreme volatility
The potential securities were constrained:




Public US-based companies
Top 500 companies by market capitalization
For final screen, companies with stock prices lower than $5 were
removed
In-sample 1990-1999, out-of-sample 2000-2005
2
Final Screen Constituents

Final screen combined technical and valueoriented factors:
Current Yield/PE
 Dividend Payout Ratio
 Momentum
 Reversal
 Voom


Room for improvement is available
3
Current Yield/PE
Introduction


Definition: Trailing current dividend yield over P/E ratio
 We expect the factor to have a positive correlation with stock returns
 If the indicator is high, the dividend is relatively high while the stock
price is relatively low, which means the stock price may be
undervalued
 This ratio also shows how market participants evaluate the firm as
the P/E ratio reflect market expectations
FactSet code
FG_DIV_YLD(0) / FG_PE(0)

As fractiles increase we see
 Declining returns
 Higher standard deviation
 Decreasing success at beating the benchmark
• Consistent across up and down markets

Higher volatility spikes massive return in fractile 5 occasionally (e.g.
10/99- 1/00) over fractile 1
4
Current Yield /PE
Return and Volatility


The declining returns through
the in-sample period show that
implementing a long/short
trading strategy by buying
quintile 1 and shorting quintile 5
is profitable. The out-of-sample
test is less clear, but shows the
same possibility
Both in-sample and out-ofsample, quintile 5 has a higher
standard deviation than quintile
1
EW Current Yield /PE Factor
Monthly Return
2.00%
1.50%
1.00%
In-Sample
Out of Sample
0.50%
0.00%
1
2
3
4
5
-0.50%
Fractiles
Stdev. of EW Current Yield /PE Factor Monthly
Return
10.00%
8.00%
6.00%
4.00%
2.00%
0.00%
In-Sample
Out of Sample
1
2
3
Fractiles
4
5
5
Div. Payout Ratio
Introduction

Definition: Dividend per share over EPS.
 The payout ratio provides an idea of how well earnings support
dividend payments.
• More mature companies tend to have a higher payout ratio.
• Low payout ratio means firms retain large portions of earnings to
support long-term growth.

FactSet code
FG_DIV_PAYOUT

As fractiles increase we see
 Increasing returns
 Higher standard deviation
 Better success at beating the benchmark during up markets,
but not during down markets.
 Higher volatility leads to large returns in fractile 5 occasionally
(e.g. 10/99 and 5/00).
6
Div. Payout Ratio
Return and Volatility


Increasing returns during the
in-sample period show that
implementing a long/short
trading strategy by buying
quintile 5 and shorting quintile 1
is profitable. The out-of-sample
test confirms this possibility.
Both in-sample and out-ofsample, quintile 5 has a higher
standard deviation than quintile
1, suggesting caution in using
this factor.
EW Div. Payout Ratio Factor
Monthly Return
2.0%
1.5%
In-Sample
Out of Sample
1.0%
0.5%
0.0%
1
2
3
4
Fractiles
5
Stdev. of EW Div. Payout Ratio
Factor Monthly Return
8.0%
6.0%
In-Sample
Out of Sample
4.0%
2.0%
0.0%
1
2
3
4
5
Fractiles
7
Momentum Factor
Introduction

Definition: 12 month price change/Previous 1
year price

Based on long-term over-reaction from
investors
Formula: (CM_P(-1)-CM_P(-13))/CM_P(-13)
 As fractiles increase, returns and standard
deviation decrease
 No significant differences between in-sample
and out-of-sample returns

8
Momentum Factor
Return and Volatility

From 12/89 to 1/05,
declining returns through
fractiles suggest the
possibility of generating
returns through a longshort strategy across
high and low fractiles
Average Monthly Return (1990-2005) EW
2.0%
1.8%
1.6%
1.4%
1.2%
1.0%
0.8%
0.6%
0.4%
0.2%
0.0%
1
2
3
Fractile
4
5
Std Dev Monthly Data (1990-2005) EW
8.0%
7.0%
6.0%
5.0%
4.0%
3.0%
2.0%
1.0%
0.0%
1
2
3
4
5
Fractile
9
Reversal
Introduction

Definition: Price change over previous month
 We expect previous month returns to reverse
 Short-term momentum, not reversal takes place
• Stocks that gained in the previous month continue to gain
• Stocks that lost in the previous month continue to lose

FactSet code
FG_PRICE_CHANGE(-22,NOW)

As fractiles increase we see
 Decreasing returns
 Mildly increasing standard deviation
 Decreasing proportion of positive returns
 Decreasing proportion of benchmark-beating returns
• Consistent across up and down markets

Occasional volatility spikes (e.g. 1/99) when fifth fractile outperforms
massively
10
Reversal
Return and Volatility


From 12/89 to 1/05,
declining returns through
fractiles suggest the
possibility of generating
returns through a longshort strategy across high
and low fractiles
High standard deviation
on low fractiles are signs
of high occasional spikes
in last quintile returns
EW Monthly Return
3%
2%
1%
0%
-1%
1
2
3
4
5
NA
5
NA
Fractile
St. Dev. of EW Monthly Return
8%
6%
4%
2%
0%
1
2
3
4
Fractile
11
Voom (Volume x Momentum)
Introduction


Change in volume scaled by price magnitude and direction
 1 month price change * (10 day Avg Vol / 3 month Avg Vol)
Hypothesis was that large Voom could predict strong positive or
negative trends
 Reality was that Voom was much better at predicting sell-offs
• When Voom was high, stock price tended to drop in the following
month

Voom stayed consistent through both in and out of sample
periods, and across up and down markets
 Need to employ a long/short strategy to create a portfolio that is
market neutral and is best positioned to have consistent returns
regardless of market direction
12
Voom
Return and Volatility
Monthly Returns 1990-1999
2.00




Returns are negative for the
first quintile, and then grow
positive.
4th quintile performed well with
low volatility
Equal weighted portfolio is
more consistent through time
Suggests that 1st quintile can
be used as a short strategy,
and a blend of the 4th and 5th
quintiles can be used for a long
strategy
1.50
Equal Weighted
1.00
Value Weighted
0.50
0.00
1
2
3
4
5
Monthly Returns 2000-2005
1.00
0.80
0.60
0.40
0.20
Equal Weighted
0.00
Value Weighted
-0.20
-0.40
-0.60
1
2
3
4
5
13
The Weighted Factor
Introduction

Created from subjectively-weighted factors that were
determined to best describe portfolio. Weighted
factors include:



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
Momentum (Scored 4 for Quintile 1 & -2 for Quintile 5)
Reversal (5 for Quintile 1 & -5 for Quintile 5)
Voom (-4 for Quintile 1, 4 for Quintile 4 & 3 for Quintile
4)
Current Yield/PE (3 for Quintile 1 & -3 for Quintile 5)
Payout Ratio Score (5 for Quintile 5)
14
The Weighted Factor
2.00
1.50
1.00
0.50
0.00
1
2
3
4
5
-0.50
Fractile
Cumulative Returns(%) vs Market Returns (%) Weighted Factor
5000.00
4500.00
4000.00
3500.00
3000.00
2500.00
2000.00
1500.00
Fractile 1
Fractile 2
Fractile 3
Fractile 4
Fractile 5
Market
1000.00
500.00
0.00
ec
-8
9
ec
-9
D 0
ec
-9
D 1
ec
-9
D 2
ec
-9
D 3
ec
-9
D 4
ec
-9
D 5
ec
-9
D 6
ec
-9
D 7
ec
-9
D 8
ec
-9
D 9
ec
-0
D 0
ec
-0
D 1
ec
-0
D 2
ec
-0
D 3
ec
-0
D 4
ec
-0
5

Observed trend shows that
annual returns decrease
uniformly from Q1 to
Q5,indicating that a longshort investing strategy
would be effective
Cumulative Returns for Q1 >
5000% over time period (in
and out of sample); cum.
returns for Q5 < 100% over
same period (mkt returns >
500% over the same period
2.50
D

Monthly Returns (%) - EW, Weighted Factor
D
Returns
15
The Weighted Factor
Volatility and Sharpe Ratio
Standard Deviation(%) - EW, Weighted Factor

Q5 has higher s than Q1,
despite the fact that returns
for Q5 are lower than for Q1
6.00
5.00
4.00
3.00
2.00
1.00

This fact is validated by the
comparing Sharpe Ratios –
Q1 SR > 0.35, Q5 SR < 0
0.00
1
2
3
4
5
Fractile
Sharpe Ratio - EW, Weighted Factor
0.50
0.40
0.30
0.20
0.10
0.00
1
2
3
4
5
-0.10
-0.20
Fractile
16
Conclusion




Reversal and the weighted score formed the best
factors with monthly F1-F5 returns of over 2%
Investors should guard against volatility spikes with
options
Transactions costs may be high for some factors
Next steps



Incorporate forward-looking factors (e.g. FY2 P/E)
Optimize weights on weighted score
Examine interaction and macro variables as factors
17