Table 1 Hedge funds and CTA funds with assets over US1 billion
Fund Additional
Assets on 12r31r97 Monthly data
similar funds Amount
Source Ž
. US million
1 –
2071 Tass
9006–9801 2
2 1500
Tass 8608–9801
3 –
1000 Republic
9301–9801 4
– 1098
MAR 8802–9801
5 2
1050 Tass
9206–9801 6
1 1130
MAR 9201–9801
7 2
1000 Republic
9101–9712 8
2 1042
Tass 9207–9801
9 1
1093 Tass
9002–9801 10
2 1382
Tass 8207–9801
11 –
5600 MAR
8601–9801 11
– 1326
MAR 8408–9801
12 –
1600 Republic
8603–9802 13
– 4700
Republic 9402–9801
14 –
1020 MAR
9301–9801 15
– 4000
MAR 9001–9802
16 –
4000 MAR
9201–9708 17
– 5369
MAR 8701–9801
18 –
1824 MAR
9201–9801 19
– 2772
MAR 9703–9802
20 –
1210 MAR
9104–9801 21
– 1596
MAR 9201–9801
22 –
1815 MAR
9309–9801 23
2 1571
Tass 9401–9801
24 –
1100 Republic
8801–9708 25
– 1100
Republic 8611–9802
26 –
1000 Republic
9611–9711 27
– 1490
MAR 8705–9801
Total 63,938
Ž procedure described above. In total, these 27 large funds 41 if we count duplicate
. funds managed US64 billion at the end of 1997. They accounted for at least
one-third of the US100–200 billion of assets managed by the industry. Thus, the market impact of all hedge funds can be approximated by the activities of these 27
large funds. The Appendix A lists an additional three large hedge funds that had since been dissolved.
3. Style classification
Ž .
Following Fung and Hsieh 1997a , we applied principal component analysis on the monthly returns for 27 large funds to identify their styles. Table 2 reports
Table 2 Principal component analysis
Percentage of total variation explained by principal components Ž .
Ž . Ž .
Ž . Ž .
Period No. of funds
No. 1 No. 2
No. 3 No. 4
No. 5 1992–1994
20 31
15 10
10 6
1993–1995 23
32 13
9 7
6 1994–1996
24 31
14 9
6 6
1995–1997 23
32 14
9 7
7 1992–1997
17 31
10 8
8 7
Excluding quota, quasar, quantum emerging, and quantum industrial 1992–1997
14 25
11 9
9 7
the results for several different time periods. The first and second principal components consistently explained more than 33 and 10, respectively, of the
total cross-sectional variation. The remaining principal components each explained less than 10. To check if the results were dominated by the Quantum group of
funds, we removed four funds operated by Soros Management leaving only the Quantum Fund. The results did not change substantially.
To identify the styles associated with these principal components, we grouped funds according to their correlation to the various principal components.
Twelve funds had returns strongly correlated to the first principal component. They are typically known as GlobalrMacro funds in the industry, including
Jaguar, Quantum, and the standard marquee names in the group. We therefore labeled their style ‘‘GlobalrMacro’’. The 12 funds managed roughly US30
billion of assets at yearend 1997.
Three funds had returns strongly correlated to the second principal component. Their returns and the second principal component were correlated to the Trend-
Ž .
Following style in Fung and Hsieh 1997b . For this reason, we labeled their style ‘‘Trend-Following’’. They managed roughly US4 billion of assets at the end of
1997. One fund managing US2.1 billion fund at the end of 1997 invested primarily
in emerging market securities, so we labeled its style ‘‘Emerging Market’’. Ž
The remaining 14 funds managing assets of US22.4 billion at the end of .
1997 had no strong return correlation with the first two principal components, nor emerging market indices. We labeled them as ‘‘Unclassified’’ funds. They repre-
sented various ‘‘niche’’ styles according to classifications by various hedge fund consultants such as fixed income arbitrage.
These results are similar to the principal component analysis in Fung and Hsieh Ž
. 1997a . There, the first five components explained less than 45 of the cross-sec-
tional variation in 409 funds. Here, the first two components explained roughly 40 of the cross-section variation in 27 large funds. The latter sample contained
fewer principal components because large funds are predominantly GlobalrMacro funds. The other styles, such as those used by value traders and distressed
securities traders, tend to have smaller funds and are therefore under represented in the large fund sample here.
A comment regarding the conceptual difference between ‘‘herding’’ and ‘‘style’’ helps to clarify the subsequent analysis. ‘‘Herding’’ means that some funds mimic
other funds’ trades without having the same information. In contrast, two funds with the same ‘‘style’’ having the same information and employing similar trading
strategy are likely to have the same trades. However, herding necessarily implies a lead-lag relationship between the trades of leaders and followers, whereas funds
with the same style can have the same trades without any lead-lag relationship between them.
While it makes sense to distinguish between ‘‘herding’’ and ‘‘style’’ conceptu- ally, there may be no practical way to tell them apart using monthly returns.
Hedge funds managers are considered nimble.
2
Unless a continuous record of all trades is available, it would be impossible to test for lead-lag relationships between
the trades of different funds. For the purpose of this paper, we follow Fung and Ž
. Hsieh 1997a,b and use the word ‘‘style’’ to describe funds with correlated
returns without references to causality. Returning to the principal component analysis, the most striking characteristic,
Ž .
both here and in Fung and Hsieh 1997a , is that the majority of hedge funds had Ž
low return correlation with the major styles or with each other. Herding i.e., .
persistent imitation of positions , if present at all, is restricted to a subset of hedge funds. The more interesting issue from a market impact stand point is to identify
the infrequent episodes of ‘‘style convergence’’ where different investment styles converge onto similar positions.
3
4. Hedge funds and market events
