Portable alphaand beta forlong/shortequity manager

While stock-picking strategies are, in principle,

meant to exploit evidence of predictability in individual

stock-specific risk, equity managers, as a

result of their bottom-up security selection decisions,

often end up making discretionary, and

most of the time unintended, bets on market,

sector and style returns as much as they make

bets on individual stock returns.

These unintended bets are unfortunate as they

can have a dramatic impact either way on the

portfolio return, and their presence introduces an

undesirable element of luck in the performancegenerating

process. Consider, for example, the

case of long/short equity managers. The vast

majority favour stock-picking as a way to generate

abnormal return. Long/short managers do

not actively manage their market exposure, and

most of them end up having a net long bias.

This can be seen from the correlation of HFR

Equity Hedge (a prominent index for long/short

hedge fund managers) with the S&P500, which

turns out to be equal to 0.63 based on monthly

data over the period 1990-2000. This is due to the

fact that these managers, most of them being

originally long-only mutual fund managers, typically

feel more comfortable at detecting undervalued

stocks than overvalued stocks.

This long bias, which is not the result of an active

bet on a bullish market trend but merely the result of

a lack of perceived opportunities on the short

selling side, has undoubtedly explained a large fraction

of the performance of these managers in the

extended bull market periods of the 1990s.

On the other hand, it has very significantly hurt

their performance in the past few years of market

downturns. Similarly, long/short managers - even

those who target market neutrality - have unintended

time-varying residual exposure to a variety

of sectors or investment styles (growth or value,

small cap or large cap) resulting from their bottomup

stock-picking decisions. Since very few managers

are both market- and factor-neutral, it is not obvious

to extract from their performance anything but a

very noisy signal on their pure stock-picking ability.

Active management of asset allocation decisions

actually can take two forms. The first possible

form of an active asset-allocation strategy

involves adding market- and/or sector-timing

alpha benefits to original stock-picking-based

decisions. Long/short managers can also choose

to use market-, sector- or style-timing as an alternative

way to generate alpha in the absence of

confidence in their ability to have consistent performance

through stock-picking.

In the paper entitled 'Portable Alpha and

Portable Beta Strategies in the Euro Zone - Implementing

Active Asset Allocation Decisions using

Equity Index Options and Futures', forthcoming

in the Journal of Portfolio Management, we show

how to construct a pure overlay portfolio that is

designed to capture excess return through tactical

asset- and factor-allocation decisions on the European

markets, using active management of betas

to generate (portable) alphas. We focus on pure

active allocation decisions implemented through

trading in index derivatives markets so as to

study the performance of an overlay strategy

that is not impacted by stock-picking decisions,

which should remain the sole focus of attention

from bottom-up managers.

In the following table, extracted from the paper

to which the reader is referred for further details,

can be found we show, among others, the results

obtained in the case of an absolute return

strategy (benchmark: 1 month Libor) implementing

a tactical asset allocation (long or short

bets on the DJ EuroStoxx50 index using futures

contracts) with a gross leverage fluctuating from

1 to 2 (net leverage from 0 to 1). The differences

observed between the two experiments lie in the

fees (monthly management fees: 0.075%, monthly

administration fees: 0.045%) taken into account

in the second back test.

FROM 07/2000 TO 06/2003

BENCHMARK = LIBOR 1 MONTH

Ref Ptf LiborBack Test 1 Back Test 2

Cumulative Return 12.12% 27.52% 22.98%

Annualised Return 3.82% 8.28% 7.07%

Annualised Std Deviation 0.25% 5.55% 5.57%

Sharpe - 0.80 0.58

% Negative Returns - 13.89% 16.67%

Worst Monthly Drawdown - -3.39% -3.04%

The economic significance of the timing strategy

can be seen from the overperformance of the

portfolio. For example, in the the above-mentioned

absolute-return strategy with a benchmark

100% invested in cash, the annual performance is

a solid 8.28% (respectively 7.07% fees included)

for a 5.55% (respectively 5.57% fees included)

standard deviation. We could favorably compare

such a performance with that of a typical marketneutral

hedge fund.

The second possible form of an active assetallocation

strategy involves implementing an

option-based portfolio strategy, of which the sole

objective is to modify the asset-allocation risk

profile in the portfolio. In particular, options on

equity indices can be used to truncate return distributions

with the aim of eliminating the few

worst (and best) outliers generated from managers'

forecast errors. In the paper we refer to, we

show how suitably designed option strategies can

be used to enhance the performance of a markettiming

strategy, the objective being to design a

programme that would consistently add value

during the trendless periods, which are typically

not favorable to timing strategies.

There are actually a number of reasons why

this is the case. First, it is obviously easier to predict

significant market moves, as opposed to

small changes in trends that can easily be confused

with noise. Besides, if the market experiences

a series of short-term reversals within the

one-month time frame, the model's prediction,

based on the previous month's data, will fail to

forecast the right direction. Finally, even if the

model yields correct predictions, they are of little

use if the spread of the risk-asset return over the

risk-free rate is small. All these reasons explain

why even a well-designed TAA strategy usually

performs poorly (only slightly better than the

risk-free rate) in periods of low volatility.

For the strategy to perform well in periods of

low volatility, one of the best techniques consists

of implementing short positions in options.

Assume the DJ EuroStoxx50 index is at a (normalised)

100 level. Let us further assume we sell a

call option with a 110 strike and a put option with

a 90 strike price. Such a strategy, which is known

as a 'top strangle', allows an investor to take a

short position on volatility. If the market goes

through a calm period so that the index price

remains within the 90-110 range, none of the

options will be exercised and the option portfolio

will generate a profit due to the time-decay. Intuitively,

the profit comes from the loss in value of

unexercised options as they come close to maturity.

While this portfolio of short options should

add performance in trendless periods when TAA

strategies generally do not outperfom dramatically,

it remains, on the other hand, the risk of one

option being exercised in case of a large change

in the index value. Should this happen, the profitability

of the underlying TAA strategy would

be significantly impacted.

In an attempt to add the benefits of risk reduction

to the benefits of return enhancement, we

can notably choose to hedge the risk associated to

the short option positions by adding long positions

in further out-of-the-money options. To get

back to the previous example, we would buy a

call option with say a 120 strike price and a put

option with say a 80 strike price. Such a strategy

is known as a 'bottom strangle'. If these options

are chosen to be of longer maturity (eg, 45-90

days versus 30-35 days), then the net theta of the

option portfolio would be positive and the

strategy would still profit from the time decay,

while adding a protection to the underlying TAA

position in case the index goes below 80 or above

120 in our example.

To demonstrate the interest of this approach,

we have implemented an option overlay strategy

by selecting options on the DJ EuroStoxx50 index

with strike prices symmetrically distributed

around the at-the-money level. The decision rule

that we have systematically applied is as follows:

• For each month m, short-term (i.e.,

expiring in month m+1) call and put options

to be sold have been chosen so that the strike

price is the closest to current index price +50

(case of a call option) or current index price -

50 (case of a put option). For each month m,

longer-term (ie, expiring in month m+2) call

and put options to be purchased have been

chosen so that the strike price is the closest

to the current index price +100 (case of a call

option) or current index price -100 (case of a

put option).

• The quantities in the top and bottom

strangle strategies have been optimised so as

to maximise the net theta of the overall position,

while at the same time satisfying a euroneutrality

constraint.

We have then selected the scale of the option

overlay strategy so as to make it commensurate

with the scale of the underlying TAA strategy.

For example, the average leverage of the TAA

portfolio is 1.17 over the period. We have

designed the size of the option overlay strategy

so that the leverage is equal to that number, up to

rounding errors. We have obtained theta and delta

estimates by using the Black-Scholes model after

extracting an implied volatility estimate from

options settlement prices. We have then performed

a systematic rebalancing at the beginning

of each month, i.e. positions at the beginning of

month m systematically closed out at the beginning

of month m+1 using the settlement prices.

The following table, extracted from the paper

to which the reader is referred for more details,

contains an overview of the results obtained

through this experiment. The previous experiment

(see 'back test 2') corresponds to the results

shown above right in column 'TAA w/o Options.

Both experiments with and without options

include management and administration fees over

a 36-month period.

IMPACT OF ADDING AN OPTION

OVERLAY STRATEGY

FROM 07/2000 TO 06/2003

Bnchmrk LiborTAA w/optionsTAA w/o Options

Cumulative Return 12.12% 27.51% 22.98%

Annualised Return 3.82% 8.28% 7.07%

Annualised Std Deviation 0.25% 5.58% 5.57%

Sharpe NA 0.80 0.58

Downside Risk (3.00%) NA 4.78% 4.46%

Sortino (3.00%) NA 1.10 0.91

% Negative Returns NA 13.89% 16.67%

As can be seen from this second table, the TAA

performance can be further improved by using an

option overlay portfolio as a portable beta

vehicle. In particular, we show that suitably

designed option strategies would have added 121

basis points in terms of average returns, without

increasing the level of risk (annualised volatility:

5.58% versus 5.57%; downside deviation [3%]:

4.78% versus 4.46%; negative months: 13.89%

versus 16.67%).

We can therefore draw the conclusion that the

option overlay strategy acts as a real return

enhancer while reducing the negative outliers

(peak-to-valley: -3.18% versus -3.54% without

options; worst monthly drawdown: -2.95% versus

-3.04% without options).

Noël Amenc, Philippe

Malaise and Lionel

Martellini are

professors in finance at

EDHEC Graduate

School of Business.

Daphné Sfeir is a senior

research engineer at

EDHEC Risk and Asset

Management Research

Center.The author for correspondence is

Philippe Malaise (e-mail address:

philippe.malaise@edhec.edu).The authors

wish to thank Elizabeth Regan and Brendan

Bradley for very useful comments.This

research has been sponsored by Eurex.

Key Points

Bottom-up security selection decisions often

have dramatic and unintended impacts on

portfolio returns

Suitably designed option strategies can be used

to enhance the performance of a markettiming

strategy

The option overlay strategy acts as a real

return enhancer while reducing the negative

outliers