Transcript Slide 1

Performance Attribution
• These characteristics of returns are well known.
• Known “styles” of returns.
– don’t give credit to a passive value manager
for beating the S&P500 – that’s too easy!
• Evaluation now is relative to a “style” or
benchmark portfolio
– Growth
-- Value
– Small-Cap
-- Large-Cap
– Industry
-- International
– Momentum
-- Emerging markets
Finding Alpha
• Word of caution: finding historical alpha is easy!
– Suppose you could sell historical alpha?
– Measurement of alpha is difficult: the market
is very volatile: S&P500 = 20% per year,
individual stocks 50% per year.
– This has a significant effect on the reliability of
estimates of alpha.
Finding Skill
• High past returns:
– Risk?
– Return to active management: skill?
– Luck?
• Do returns persist?
– Yes if manager always takes positions with
known high returns: do a style correction.
– Yes because of momentum in stock returns.
Example: Carhart (1997)
• Realized returns from declared holdings or net
asset value corrected for distributions
• Regression of returns on
– the market
– small cap versus large cap factor (SMB)
– value versus growth factor (HML)
– momentum factor (PR1YR)
• Similar to a style-based evaluation of
performance.
Table III of Carhart 1997
Implications of these studies
• Mutual funds tend to generate negative alpha
when evaluated relative to sophisticated
benchmarks
• There is persistence in performance, but
– It is driven by momentum
– It is mostly due to luck
– Loads and fees chew up any gains
• There is persistence in poorly performing funds,
– These are the funds with large expense ratios
and large turnover
Spectacular growth in HF
Strategy composition
•HF do lots of different things.
•Strategy gobbledygook. Who knows what any of this means?
•Obscure strategies seems an important part of HF marketing
Returns
Not astronomical, but if beta really = 0, these aren’t bad returns! Is beta 0?
Hedge fund alphas and betas – lags and stale prices
Not zero!
Style
ER (%/mo)
a
b
a3
b3
Index
0.64
0.46
0.28
0.36
0.44
Std. errors
0.20
0.17
0.04
Short
-0.53
0.10
-0.94
0.13
-0.99
Emerg mkts
0.39
0.00
0.58
-0.07
0.69
Event
0.61
0.46
0.22
0.38
0.37
Global Macro
0.93
0.82
0.17
0.74
0.31
Long/Short Eqty
0.73
0.42
0.47
0.32
0.65
Bigger with lags
Smaller with lags
Really not zero.
“Alternative asset?”
Long-short doesn’t
mean zero beta!
rti  a  brts& p500  ti
rti  a3  b1rt sp  b2 rt sp1  b3rt sp2  b4 rt sp3   ti
b3  b1  b2  b3  b4
•Lags are important – stale prices or lookback option
•Betas are big!
Source: regressions using CFSB/Tremeont indices at hedgeindex.com, idea from Asness et al JPM
•Correlation with the market is obvious.
•Getting out in 2000-2003 was smart! (Mostly due to Global/Macro group)
Monthly returns on Global Macro HF and US market
•“Global macro” yet you see the correlation with US market
•Lagged market effect is clear in 1998. Is Nov/Dec 1998 unrelated to Oct?
•Dramatic stabilization / change of strategy in mid 2000
Monthly returns on Emerging Market HF and US market
•“Emerging markets diversify away from US investments, give us
access to a new asset class?”
•Names: yes. Betas: no. Names don’t mean much!
Option-like return example:
Merger “arbitrage”.
Price
Merger announced
Merger completed
Offer price
Buy
Merger fails
Time
•Cash offer. Borrow, buy target.
•Large chance of a small return if successful. (Leverage: a large return)
•Small chance of a large loss if unsuccessful.
•The strategy seems unrelated to the overall market, “beta zero”
•But…offer is more likely to be unsuccessful if the market falls!
•Payoff is like an index put!
Merger arb returns
Source: Mark Mitchell and Todd Pulvino, Journal of Finance
•Line: like the payoff of writing index puts!
•Source: Mitchell and Pulvino, using CFSB/Tremont merger-arb index
•News: 1) “occasional catastrophes’’ 2) catastrophes more likely in market declines
Hedge fund up/down betas
Style
b3
b up
b down
Index
0.44
0.08
0.77
Short
-0.99
-0.22
-1.82
Emerg mkts
0.69
0.08
1.16
Event
0.37
0.18
0.47
Global Macro
0.31
-0.08
0.66
Long/Short Eqty
0.65
0.19
1.18
rti  a3  b1rt sp  b2 rt sp1  b3rt sp2  b4 rt sp3   ti
b3  b1  b2  b3  b4
Example: if the market goes
up 10%, the HF index goes up
0.8%. But if the market goes
down 10%, the HF index goes
down 7.7%!
rti  a  bup (rt sp  0)  bdown (rt sp  0)  ti
(Includes 3 lags)
•Many near, or above 1. These are big betas!
•Many HF styles are much more sensitive to down markets = write puts =
“short volatility.”
•Source: my regressions using hegefundindex.com data; following Asness et al JPM
Implications of option-like payoffs
• Need option-return benchmarks for risk
management (investing in HF) and
compensation benchmarks.
rt  i   r  ...  
i
sp sp
i t
put index put
i
t
r
 ...  
i
t
Additional benchmarks matter too!
Style
Rm+
Rm-
Term+
Term-
Corp+
Corp-
Index
0.07
0.72
0.27
0.55
1.01
0.94
Conv arb
0.25
0.20
0.10
0.35
-0.22
1.03
Short
-0.32
-1.75
-0.05
0.19
0.62
-0.66
Emerg mkts
0.13
1.03
-0.44
0.20
0.73
2.48
Event
0.21
0.39
0.15
0.32
0.30
1.43
BondArb
0.08
0.12
-0.02
0.35
0.44
1.40
Global Macro
-0.09
0.66
0.66
1.17
2.29
1.53
Long/Short Eqty
0.16
1.05
0.09
0.22
0.38
0.84
•Term = long term gov’t bond return – t bill rate
•Corp = corporate bond return – long term gov’t
•Big betas, especially on corp (default spread)
•Often much more for bad news than for good news
•Market up/down has moderated since 1998, but term, corp up/down still strong
•Most HF strategies amount to “providing liquidity”, “disaster insurance” in some market
•Source: my regressions using hegefundindex.com data