market timing of ctas an examination of...

We thank an anonymous referee for very helpful comments and suggestions. We thank CISDM for providingnecessary data and seminar participants at the University of Massachusetts Amherst and FinancialManagement Association Annual Meeting 2008 for their inputs, especially those from Ben Branch, BingLiang, and Mila Getmansky Sherman. All remaining errors are our own.

*Correspondence author, University of Washington Bothell, 18115 Campus Way NE, Bothell, WA 98011.Tel: +1 425 352 5394, e-mail: [email protected]

Received April 2008; Accepted December 2008

■ Hossein Kazemi is at University of Massachusetts Amherst, Amherst, Massachusetts.

■ Ying Li is Assistant Professor of Finance at University of Washington Bothell, Bothell, WA 98011.

The Journal of Futures Markets, Vol. 29, No. 11, 1067–1099 (2009)© 2009 Wiley Periodicals, Inc.Published online in Wiley InterScience (www.interscience.wiley.com).DOI: 10.1002/fut.20392

MARKET TIMING OF CTAS:AN EXAMINATION OF

SYSTEMATIC CTAS VS.DISCRETIONARY CTAS

HOSSEIN KAZEMIYING LI*

This study uses a set of return-based factors to explore market (return and volatil-ity) timing ability of commodity trading advisors (CTAs). Unlike previous research,we use return-based factors that are related to the futures markets in which mostCTAs trade. This leads to higher explanatory power for our multifactor model. Ourapproach allows us to test for the presence of market timing in multiple markets.Accordingly, we are able to identify the markets in which CTAs may have markettiming ability. We find that systematic CTAs are generally more skilled at market timing than discretionary CTAs, with the latter having slightly better over-all risk-adjusted performance during our study period: January 1994 to December2004. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:1067–1099, 2009

INTRODUCTION

The term managed futures represents an industry comprising professionalmoney managers known as commodity trading advisors (CTAs) who manage

1068 Kazemi and Li

Journal of Futures Markets DOI: 10.1002/fut

client assets, using global futures and options markets as an investment medi-um. These managers are usually self-regulated and registered,1 and they arecompensated through both management fees and incentive fees. CTAs cantake long or short positions in low transaction cost investment vehicles (i.e.,futures contracts) in an attempt to benefit from trends in commodity prices,exchanges rates, interest rates, and equity markets. Similar to many otheralternative investment vehicles, CTAs together with the managed futuresindustry have had a dramatic growth in the past 15 years. From Figure 1, wesee that the assets under management (thereafter AUM) for the managedfutures industry has grown from less than U.S.$10 billion in 1990 to more thanU.S.$130 billion by the end of 2004. AUM for CTAs increases from U.S.$6 billionto close to U.S.$90 billion during the same period.

Because of their similarities to hedge funds, CTAs are usually listed as astrategy of hedge funds with the style name “managed futures.” In the litera-ture, most CTAs are also referred to as “trend-followers”2 because many CTAsutilize their proprietary trading models to capture trends in futures prices.CTAs fall into two broad subsets based on their self-reported trading strategies:systematic CTAs, which base their trading on technical models, and discre-tionary CTAs, which base their trading on managers’ discretions.3

Market timing refers to the ability of money managers to anticipatechanges in market trends and to adjust their risk exposures accordingly.Previous research in the area of market timing, which focuses on mutual funds,

Asset Under Management

0.00

50.00

100.00

150.00

1990

1992

1994

1996

1998

2000

2002

2004

Year

US

$ B

illio

n

Managed FuturesIndustry

CTA

FIGURE 1Asset under management for managed futures industry and CTAs, 1990–2004. Data are from Benefits of

Managed Futures, 2005 Update, Published by CISDM, 2005.

1CTAs with a domicile in United States are usually registered with NFA or CFTC, those with a domicile inEngland are registered with FSA, and those with a domicile in Canada are registered with OSC, for example.2Fung and Hsieh (1997) identify a principal component in hedge funds returns from CTAs, whereas Fungand Hsieh (2001) study the return characteristics in trend-followers.3The portfolio benefits of CTAs have been studied by Billingsley and Chance (1996), Liang (2004), and Kat(2004). They show that CTAs improve risk-return characteristics of equity and fixed income portfolios as theyexhibit negative correlation with the overall securities markets during extreme negative market downturns.

Market Timing of CTAs 1069


generally finds no or negative timing ability when monthly return data are used(see Admati, Bhattacharya, Pfleiderer, & Ross 1986; Becker, Ferson, Myers, &Schill, 1999; Ferson & Schadt, 1996; Henriksson & Merton, 1981; Jensen,1972; Lehman & Modest, 1987; Merton, 1981). More recently, Busse (1999),Bollen and Busse (2001), and Jiang, Yao, and Yu (2007) use high-frequencyreturn data and portfolio holdings data and find evidence supporting the pres-ence of timing ability in mutual funds. Empirical evidence on timing ability forhedge funds is also mixed. For example, Fung, Xu, and Yau (2002) find negativetiming ability for a group of hedge fund managers who follow global macrostrategy. Chen (2007) finds timing ability in some styles of hedge funds in theirfocus markets (mainly U.S. and non-U.S. bond markets and currency markets).Chen and Liang (2007) find significant return and volatility timing ability forhedge funds that are self-classified as market timers.

This study investigates the market and volatility timing ability of CTAs, andexamines whether discretionary CTAs display different market timing skills fromsystematic CTAs. Because of empirical similarities between market timers andtrend-followers, anecdotal evidence indicates that trend-following CTAs pos-sess timing ability. The goal of this study is to formally test this hypothesis andto determine if CTAs display market timing ability in those markets that are thefocus of their trading strategy.

As previous studies have shown (e.g., Fung & Hsieh, 2001), one importantchallenge in testing for the presence of market timing ability is that modelsemploying traditional factors have low explanatory power for CTA returns, and,therefore, may not be able to detect the presence of market timing skills.Besides, the traditional indices that are based on equity and fixed-incomemarkets may not include important risk factors such as those related to variouscurrencies, commodities, or interest rates that are present in most CTA portfo-lios. Unlike previous studies, we use a set of futures-related factors that arebased on returns from the most heavily traded futures contracts. Our proposedfactors are found to possess much higher explanatory power for CTA returnsthan traditional factors.

Similar to previous market timing studies, we use Treynor and Mazuy(TM, 1966) and Henriksson and Merton (HM, 1981) models to test for thepresence of market return timing ability. To test for market return and volatili-ty timing, we use models proposed by Bollen and Busse (2001). Our tests areapplied to various CTA indices using both traditional factors as well as the pro-posed futures return-based factors. These CTA indices are constructed from ourfull sample of 900 CTAs with return history between January 1994 andDecember 2004, which includes reported return figures for both live and defunctCTAs. The inclusion of defunct funds and employing post-1993 returns shouldreduce the impact of survivorship bias on our results.

1070 Kazemi and Li


We find that CTAs exhibit market return timing and volatility timing ability.More importantly, we find that CTAs are generally able to time the futures mar-kets in which they claim to specialize. For example, the currency CTA index isfound to display market timing skill in Euro–Yen futures market. Similarly, thefinancial CTA index displays the same skill in currency and fixed income mar-kets, whereas the diversified CTA index displays market timing skill in multiplemarkets. On the other hand, the commodity CTA and stock CTA indicesdisplay negative timing ability in some commodity and equity markets.4 Theestimated coefficients of return timing are economically significant as well, for example, the systematic currency CTAs on average are able to generate 11.44%excess return when the returns from the Euro futures contracts increase by 1%.

We find that discretionary and systematic CTAs behave quite differentlyfrom each other. Our model has higher explanatory power for returns on sys-tematic CTA indices, where the estimated as are significantly less than zero.Our model’s explanatory power is lower when applied to discretionary CTAindices, and reports weaker timing ability for this class of CTAs. On the otherhand, our estimated as for discretionary CTA indices are positive significant(the as do not appear to be economically significant though).

Market timing and security selection abilities are believed to representmacro- and micro-forecasting abilities of funds managers. However, whenJensen’s a is used as a measure of security selection ability, it can assign nega-tive performance to a market timer. Results appearing in Jensen (1972), Admatiand Ross (1985), Dybvig and Ross (1985) demonstrate that this is the case.Mamaysky, Spiegel, and Zhang (2008) show that a fund’s a can actually bedecomposed into a portion due to successful market timing and another portion due to transaction cost. Therefore, a fund’s estimated a may becomenegative in the presence of significant market timing ability. We confirm thiswith our CTA sample: as relevant factors are added to the modeling processand more of the variability in CTAs’ returns is explained by positive market tim-ing ability, we observe that the intercept (a) changes from significant positive toinsignificant, and even to significant negative. However, as discussed below,unlike mutual funds, the estimated negative as of CTAs cannot be attributed totheir lack of security selection skills.

As most CTAs do not invest in individual securities, the concept of securityselection does not readily apply to them. The set of return-based factors thatwe use in this study covers a significant part of the investment universe consid-ered by most CTAs. Although in the context of hedge funds and mutual fundsthe estimated value of a is typically assumed to be driven by security selectionability or lack of it, we speculate that in the context of CTAs the estimated4We notice that the explanatory power for commodity CTA index is very low (negative adjusted R2 value) andthat there are very few commodity CTAs and stock index CTAs in the live CTA universe. (Table I, Panel A).



values of the as are affected by other factors. For example, CTAs and especiallydiscretionary CTAs may change their factor exposures more frequently thanthe monthly frequency that returns are reported to public databases. This lackof synchronization between trading frequency and observation frequency couldlead one to obtain a significant estimate for security selection ability or markettiming ability when none exists (see Goetzmann, Ingersoll, & Ivkovich, 2000).The other potential reason for obtaining negative estimated as is the presenceof fees and transaction costs. We use net-of-fees CTA returns and if markettiming abilities of CTAs are not sufficient to cover both asset management andincentive fees, the effect would appear as a negative value for our estimated as.Also, our futures return-based factors represent returns to rolling positions infutures contracts. When calculating the returns to these rolling strategies, wedo not account for transaction costs. This should have very little impact on theestimates of factor exposures and market timing ability, but will have an impacton the estimates of a.

For robustness test, we run the same timing test for individual CTAs, andobtain results that are similar to those reported for CTA indices. Between one-half and one-third individual CTAs possess significant timing ability and it isalmost always positive timing ability. On the other hand, most of these CTAspossess negative significant as. We then employ the Baquero, ter Horst, andVerbeek (2005) procedure to adjust our CTA indices for survivorship bias and apply the same timing test to CTA indices constructed with the adjustedCTA return series. The results appear to be weaker but still significant at con-ventional levels, showing that CTAs do exhibit market timing ability.

The study is organized as follows: section “Data” describes the CTA returndata and risk factors; section “Methodology” discusses our methodology; sec-tion “Empirical Results” presents empirical results from various market timingmodels with different factors; section “Robustness” conducts robustness testsand the last section concludes.

DATA

CTA Data

Our data come from the CISDM database,5 with monthly, net-of-fees returns,AUM, and information on other fund characteristics, such as fund inceptiondate, self-declared strategy of the fund, as well as the name of the managementcompany. CISDM began keeping record of defunct funds in December 1993.Thus, to minimize survivorship biases, we include only CTAs that have return

5Formerly known as MAR, CISDM is one of the major hedge funds databases and provides most compre-hensive data for CTAs. Fung and Hsieh, Billingsley and Chance (1996) all use the CISDM database for CTAstudies.

1072 Kazemi and Li


series between January 1994 and December 2004. We also exclude CTAs thatdo not provide information on their self-declared strategies.6 Before applyingthe above filter, there are 1,082 CTAs, with return series between December1989 and December 2004. Among them, 681 funds had stopped reporting toCISDM by December 2004, hence are considered defunct funds. After apply-ing the filter, we were left with 278 live and 622 defunct CTA funds. The old-est CTA in our sample has an inception date of May 31, 1973. The total AUM7

for our sample is over $64 billion and accounts for 72.12% of the CTA universeas of December 2004. Panel A of Table I reports summary statistics for oursample.

Next, we construct equally weighted indices of discretionary and systematicCTAs and use these indices to study the market timing ability at the aggregatelevel. All live and defunct funds are included in the construction of theseindices, which should reduce the impact of survivorship bias (see Fung &Hsieh, 2000). Fung and Hsieh (1997) point out that CTAs have option-likepayoffs and that they perform rather well when market volatility spikes. Wereproduce their plot of CTA returns in five states of world equity market inFigure 2 and observe that systematic CTAs plot quite differently from discre-tionary CTAs against the five states: discretionary CTAs are more positively cor-related to equity indices, whereas systematic CTAs are negatively related to thesame indices, performing the best when there are sharp drops in equity markets(when volatility spikes) and below average under normal market conditions.

�15.00%

�10.00%

�5.00%

0.00%

5.00%

10.00%

15.00%A

vera

ge M

onth

ly R

etur

ns

MSCI World

Discretionary

Systematic1 2 3 4 5

FIGURE 2Plots the average return of 36 months in states 1–5. State 1 is the worst performing 36 months for

MSCI World equity index, state 5 is the best performing 36 months for MSCI World equity index. State2 is the second worst performing 36 months for MSCI World, state 4 is the second best performing 36

months for MSCI World, and state 3 is the rest 36 months.

6There is a field in the database with self-reported information on the specialization market of each CTA.7The total AUM is calculated based on all live CTAs reporting to the CISDM database as of December 2004.

TA

BL

E I

Sum

mar

y S

tati

stic

s

Tota

l AU

MAv

erag

e A

UM

Aver

age

Age

Num

ber

(U.S

.$ M

illi

on)

(U.S

.$ M

illi

on)

(Mon

th)

Pane

l A. A

UM

and

age

(li

ve C

TAs

by t

he e

nd o

f 200

4 on

ly)

All

CTA

s27

861

,376

.60

221.

6085

Com

mod

ity C

TAs

161,

389.

8086

.90

56C

urre

ncy

CTA

s45

9,19

9.80

204.

4087

Div

ersi

fied

CTA

s15

036

,753

.00

245.

0093

Fin

anci

al C

TAs

4513

,447

.00

298.

8066

Sto

ck in

dex

CTA

s22

671.

6030

.50

68

Mea

n (%

)S

t. D

ev. (

%)

Min

(%

)M

ax (

%)

r

Pane

l B. S

umm

ary

stat

isti

cs o

f CTA

indi

ces

All—

disc

retio

nary

1.19

1.54

�1.

819.

860.

10A

ll—sy

stem

atic

0.99

2.81

�4.

539.

38�

0.10

Cur

renc

y—di

scre

tiona

ry0.

901.

32�

2.99

4.62

0.32

Cur

renc

y—sy

stem

atic

0.75

2.47

�3.

1712

.02

0.07

Fin

anci

al—

disc

retio

nary

0.90

1.41

�4.

545.

44�

0.07

Fin

anci

al—

syst

emat

ic0.

893.

01�

6.72

9.68

0.10

Com

mod

ity—

disc

retio

nary

1.35

3.96

�6.

7027

.95

0.02

Com

mod

ity—

syst

emat

ic2.

7911

.26

�31

.96

60.2

4�

0.29

Sto

ck in

dex—

disc

retio

nary

1.37

2.52

�9.

9612

.48

�0.

08S

tock

inde

x—sy

stem

atic

0.84

1.90

�5.

415.

72�

0.04

Div

ersi

fied—

disc

retio

nary

1.07

2.55

�5.

319.

730.

16D

iver

sifie

d—sy

stem

atic

1.01

3.62

�6.

5310

.18

�0.

04

(Con

tinue

d)

TA

BL

E I

(C

onti

nued

)

Mea

n (%

)S

t. D

ev. (

%)

Min

(%

)M

ax (

%)

r

Pane

l C, D

, E: s

umm

ary

stat

isti

cs o

f fac

tors

Pane

l C: f

utur

es r

etur

n

JPY

�0.

253.

68�

9.81

16.8

20.

00E

UR

O0.

072.

80�

6.30

7.52

0.14

IBP

0.30

2.13

�6.

675.

68�

0.10

U.S

. int

eres

t0.

322.

67�

10.2

27.

360.

08G

erm

an g

ov. b

ond

0.30

1.50

�3.

783.

620.

08E

urod

olla

r�

1.72

6.77

�29

.70

21.1

10.

28G

old

�0.

213.

64�

9.69

15.6

4�

0.12

Cop

per

0.54

6.52

�18

.20

19.7

1�

0.12

Nat

ural

gas

0.53

14.3

1�

29.4

747

.00

0.07

Cru

de o

il1.

659.

08�

23.1

334

.17

0.01

Cor

n�

0.83

5.74

�15

.45

12.3

50.

08F

TS

E10

00.

014.

25�

13.7

28.

81�

0.01

NIK

KE

I225

�0.

326.

09�

14.4

914

.15

�0.

04S

&P

500

0.46

4.50

�15

.64

8.62

0.00

Pane

l D: P

TF

S fa

ctor

s

PT

FS

BD

0.80

16.0

0�

24.2

266

.22

0.14

PT

FS

FX

�0.

1219

.23

�29

.98

90.2

70.

04P

TF

SC

OM

�0.

5112

.48

�22

.94

65.0

0�

0.12

PT

FS

IR�

0.24

18.8

5�

24.6

598

.70

0.04

PT

FS

ST

K�

5.70

13.2

9�

30.1

946

.15

0.20

Pane

l E: t

radi

tion

al fa

ctor

s

TB

ill0.

330.

150.

070.

531.

00E

xmkt

0.63

4.53

�15

.99

8.16

0.05

SM

B0.

293.

72�

11.6

014

.62

�0.

03H

ML

0.20

4.47

�20

.79

14.9

20.

11U

ML

0.82

5.53

�25

.00

18.3

8�

0.07

Em

ergi

ng m

arke

t0.

416.

72�

28.9

113

.77

0.17

Con

vert

ible

0.82

3.63

�12

.54

12.6

90.

09Te

rm p

rem

ium

0.65

2.54

�8.

887.

730.

10H

igh

yiel

d0.

602.

07�

7.37

7.49

0.15

End

-of-

mon

th V

IX6.

031.

883.

0712

.78

0.24

U.S

.G

erm

anN

atur

alC

rude

JPY

Eur

oS

terl

ing

T-B

ond

Bon

dE

urod

olla

rG

old

Cop

per

Gas

Oil

Cor

nF

TS

E10

0N

ikke

iS

&P

500

Pane

l F. C

orre

lati

on m

atri

x: c

orre

lati

on b

etw

een

the

retu

rns

of 1

4 m

ost

heav

ily

trad

ed fu

ture

s

JPY

1.00

Eur

o0.

441.

00S

terli

ng0.

270.

631.

00U

.S. T

-bon

d0.

010.

130.

171.

00G

erm

an b

ond

�0.

030.

030.

050.

721.

00E

urod

olla

r�

0.07

�0.

17�

0.20

�0.

57�

0.42

1.00

Gol

d0.

260.

360.

240.

100.

08�

0.16

1.00

Cop

per

0.13

0.10

0.10

�0.

16�

0.08

0.15

0.20

1.00

Nat

ural

gas

�0.

010.

220.

160.

210.

130.

010.

140.

031.

00C

rude

oil

0.08

0.05

0.01

0.01

�0.

070.

070.

150.

150.

371.

00C

orn

0.04

�0.

020.

030.

11�

0.01

0.00

0.02

0.03

0.13

�0.

021.

00F

TS

E10

00.

09�

0.15

�0.

26�

0.12

�0.

030.

27�

0.09

0.23

0.00

�0.

050.

011.

00N

ikke

e122

50.

01�

0.22

�0.

12�

0.05

�0.

020.

160.

120.

240.

160.

190.

060.

381.

00S

&P

500

0.14

�0.

10�

0.06

�0.

06�

0.06

0.22

�0.

030.

310.

03�

0.04

0.11

0.78

0.46

1.00

Not

e.In

bol

d, s

igni

fican

t val

ues

(exc

ept d

iago

nal)

at th

e le

vel o

f sig

nific

ance

a�

0.05

0 (t

wo-

taile

d te

st).

All

the

sum

mar

y st

atis

tics

are

calc

ulat

ed b

ased

on

mon

thly

ret

urns

of

CTA

indi

ces

and

fact

ors.

rre

pres

ents

firs

t-or

der

corr

elat

ion

of t

hose

ret

urn

serie

s. T

he d

ata

are

from

the

CIS

DM

dat

abas

e,an

d th

e sa

mpl

e pe

riod

is fr

om J

anua

ry 1

994

to D

ecem

ber

2004

.

1076 Kazemi and Li


Based on their self-declared strategies, there are mainly four differenttypes of specialized markets for CTAs: currencies, commodities (physicals),financials (currencies and interest rates), and stock indexes. CTA managerswho specialize in more than one of these four markets are classified as follow-ing a diversified strategy. Diversified CTAs dominate the CTA universe in termsof the number of funds and AUM. Financial and currency CTAs come next andcommodity and stock index CTAs are the smallest groups. Panel A of Table Ipresents the distribution of the five categories of live CTAs. The distribution ofthe five categories8 for all live discretionary and systematic CTA indices is pro-vided in Panel B.

Factors

To explain fund returns, past studies have typically used the single-factor (mar-ket model), Fama–French three-factor, and Carhart (1997) four-factor models.However, none of these models seem to explain CTA returns well.9 Fung andHsieh (2001) propose a set of primitive trend-following strategy (PTFS) factorsand use them to explain CTA returns. The PTFS factors10 represent returnsfrom investing in a portfolio of look-back straddles written on interest rate, cur-rency, commodity, and stock index futures contracts. Fung and Hsieh (2001)show that the PTFS factors together with equity and bond market factors cansignificantly increase their model’s explanatory power for trend-following funds.

Following Fung and Hsieh (2001), we propose using factors based on returnsfrom investing in the most popular futures contracts11 to explain CTA returns andto detect their timing ability. Using returns from futures contracts as risk factorshas several advantages:

• A model that can detect market timing in multiple markets, allows us toidentify the markets in which a CTA has timing ability.

• Our proposed risk factors are based on heavily traded futures contracts andare highly liquid. Hence, we can avoid the problem that holding illiquid

8CTAs that specialize in four individual markets and diversified CTAs that specialize in more than one ofthose markets.9Fung and Hiseh (2001) point out that the explanatory power of traditional asset index factors for CTAs isquite low, with adjusted R2s no higher than in the teens and can even go below zero. The ineffectiveness is incontrast to other work on market timing of other money managers. For example, regression on HFR markettiming hedge fund index using Carhart four-factor model yields an adjusted R2 of higher than 0.50 in Chenand Liang (2007). This implies that traditional factors are not appropriate for explaining CTA returns.10For details about how the five PTFS factors are constructed, please refer to Fung and Hsieh (2001)Appendix.11We use return from investing in the most heavily traded 14 futures contracts. They are currency futures(futures of Euro, Japanese Yen, and British Pound), interest rate futures (futures of U.S. Treasury Bill,German government bond, and Eurodollar), commodity futures (futures of gold, copper, natural gas, crudeoil, and corn), and stock index futures (futures of S&P 500, FTSE 100, and NIKKEI225).



assets may lead one to obtain estimates of market timing when none exists(see Chen, Ferson, & Peters, 2008).

Panels C through E of Table I report the summary statistics of traditionalfactors, PTFS factors, as well as our proposed futures return-based factors.Panel F gives the correlation matrix for our proposed risk factors. With theexception of the correlation between the two stock index futures, FTSE100 andS&P 500, other estimates of correlations do not seem to lead to significantmulticollinearity problems.

METHODOLOGY

Based on their self-declared strategies, we use risk factors constructed from rel-evant futures contracts to analyze each style of CTAs. For example, we use riskfactors constructed from currency futures for currency CTAs, those constructedfrom commodities futures for commodity CTAs, and so on. For diversifiedCTAs, which account for about half of the CTA universe and trade in varioustypes of markets, we use a step-wise regression to select relevant risk factors.12

This factor-selection process ensures inclusion of relevant factors and helpsbuild a parsimonious model.

Apply Market Timing Models to CTAs

Multi-market timing in returns

Treynor and Mazuy (TM) (1966) and Henriksson and Merton (HM) (1981) arethe most widely used market timing models. In the TM model, managers withtiming ability adjust their risk exposure to the overall market according to theirforecast of the market return, leading to a convex relationship between themanager’s return and the market return. Admati et al. (1986) develop an empir-ical test based on the TM model by assuming a CAPM framework. The HMmodel, however, does not need the assumption that CAPM holds. Merton(1981) and Henriksson and Merton (1981) model the economic value of tim-ing as the payoff from a call option. A manager with timing ability chooseswhether to invest in the market or hold cash based on his/her forecast of theexcess market return being positive or negative.

Our study tests for market timing ability in CTAs using both TM and HMmodels, and expand them to model the presence of timing skills in multiplemarkets. One of the difficulties in modeling multiple market timing is that thenumber of parameters that need to be estimated could increase significantly.

12The step-wise regression is conducted manually targeting the highest adjusted R2 achieved from the regres-sion. We remove redundant pairs of linear and nonlinear excess returns of risk factors to preserve the spiritof both TM and HM timing tests.

1078 Kazemi and Li


To reduce the number of parameters, one may assume that there are no inter-actions between the timing of any two markets.13 Aragon (2007) develops amulti-market timing version of the HM model with two alternative specifica-tions and applies them to mutual funds. His first specification assumes thatmanagers choose markets with positive excess returns,14 where the timing signal can be written as Sj,t�1 � max(Fj,t�1, 0), in which Fj,t�1 represent the timet � 1 value of factor j. His second specification, however, states that the man-ager is able to detect the best-performing market (with positive excess returns)and maximize his/her risk exposure to it. Consistent with this specification, thetiming signal should be written as Sj,t�1 � max(max(F1,t�1, F2,t�1, . . .), 0). Weadopt both specifications in our study.

Suppose the return of a CTA is generated by a multi-factor model. In oursetup, the factors are buy-hold-roll returns from futures contracts. The CTAmanager adjusts his/her exposure to these factors based on his/her signal. Thefund manager observes the signal,Sj, with a noise term: Sj,t�1 � Fj,t�1 � ej,t�1 forthe TM model and Sj,t�1 � max(Fj,t�1, 0) � ej,t�1 or Sj,t�1 � max(max(F1,t�1,F2,t�1, . . .), 0) � ej,t�1 for the HM model specifications (I) and (II). The follow-ing equations are the bases for our empirical tests:

(1)

(2)

Equation (1) describes how a CTA manager determines his/her exposure, bj,to the observed signal, whereas Equation (2) describes the return process of thisCTA. The noise term ej,t�1 of the timing signal is assumed to be uncorrelated with�j,t�1 or vj,t�1, the error term in (1) and (2). The above expressions describe amulti-market timing scenario for the CTA manager, where the coefficient g eval-uates the market timing ability of the manager: Whether we use the TM model orthe HM model, a successful market-timing manager is able to increase exposureto factors with positive expected return and decrease exposure if the opposite istrue. This creates a convex relationship between the fund’s return and the better-performing factors (i.e., the estimated g should be positive).

Volatility timing

Fung and Hsieh (2001) show that the payoffs from trend-followers can bereplicated with a look-back straddle and present evidence for volatility timing

rp,t�1 � at�1 � aK

j�1bj,tFj,t�1 � vt�1.

bj,t � b0, j � gj,tSj,t�1 � yj,t�1.

13We are cautious about this assumption here, especially for the application in the TM model, as currenciesand physicals could assume independent return pattern, but stock indices and financials may not. Hence, wefocus on discussing results in the applicable cases. For the HM model, however, it should be of less concernto assume the independency.14Returns that are excess of risk-free rate.



in CTAs.15 Busse (1999) analytically shows that a fund manager who is able totime the market volatility reduces market exposure when he/she forecasts anincrease in market volatility. Busse (1999) applies the following model to agroup of mutual fund managers:

(3)

In Equation (3), Fj are risk factors and sm,t�1 is market volatility. Busse(1999) estimates sm,t�1 with conditional volatility models (EGARCH) and findsthat mutual fund managers have volatility timing ability. Chen and Liang(2007) use two proxies for market volatility, namely the implied volatility (VIX)and the realized market volatility and find significant evidence that certainhedge funds are able to time the stock market volatility, especially in bear mar-kets. They use the following model:

(4)

where sm,t are end-of-month VIX and is the overall mean for time series ofVIX.

Before we can test for volatility timing of CTAs, we need to select the mar-ket volatility benchmark that is used in the test. Should it be the volatility invarious futures markets or a proxy for the equity market volatility (VIX)? Stockmarket volatility is often used as a proxy for volatility in financial markets. Onthe other hand, because managers trade various futures contracts, it is intuitivethat these futures markets volatility be used as a benchmark for timing. Weinvestigate this issue empirically. To detect volatility timing ability in the spe-cialized markets, we use the following specification:

(5)

The volatility measure used in Equation (5) is realized monthly returnvolatility calculated from daily returns of relevant futures contracts. If CTAsare able to time the volatility, we expect to see negative significant coefficientsfor d.

No significant d was found when Equation (5) was estimated. That is, novolatility timing ability with regards to the relevant futures markets weredetected for CTAs. However, the estimated value of � in specification (4) turnsout to be significantly negative, indicating volatility timing with respect to the

rp,t�1 � at�1 � ak

j�1bjrj,t�1 � a

n

l�1dlrl,t�1(sl,t�1 � sl) � et�1.

sm

rp,t�1 � at�1 � ak

i�1biRi,t�1 � lrm,t�1(sm,t�1 � sm) � et�1

rp,t�1 � at�1 � aK

j�1bj,tFj,t�1 � lrm,t�1(sm,t�1 � sm) � et�1.

15A lot of these trend-followers are actually CTAs.

1080 Kazemi and Li


equity market volatility. Based on this finding, we use the end-of-month VIX toidentify CTAs’ volatility timing ability.

Combined return and volatility timing

Chen and Liang (2007) examine combined timing ability for hedge funds andfind evidence for both return and volatility timing. Following Busse (1999), weuse models (6), (7), and (8) to detect combined return and volatility timing forCTAs. Equation (6) is the TM model whereas Equations (7) and (8) are theHM model specifications (I) and (II).

(6)

(7)

(8)

where rp,t�1 is excess return on a CTA index, rj,t�1 represents excess return on afutures contract, and sm,t�1 is stock market volatility, measured by end-of-month VIX. If CTAs have both return and volatility timing ability, we expect tosee positive significant coefficients for g and D, negative significant coefficientsfor d.

EMPIRICAL RESULTS

Traditional Factors, PTFS Factors, and FuturesReturn-Based Factors

The results from regressing CTA indices against a single market factor,Fama–French factors, PTFS factors, and PTFS factors augmented with ourfutures-based factors are reported in Table II. We see that traditional factorsare barely able to explain CTAs returns, whereas PTFS factors work well forsystematic CTAs. However, even PTFS factors have difficulty explaining thereturns on the discretionary CTA index, where the adjusted R2 is 0.01.

As shown in Fung and Hsieh (2001), one cannot empirically distinguishbetween successful trend-followers and successful market timers. The returnfor a successful market timer resembles that from a standard straddle where

� drm,t�1(sm,t�1 � sm) � et�1

rp,t�1 � at�1 � ak


n

i�1Dimax(max(r1,t�1, r2,t�1, p , rn,t�1),0)

� drm,t�1(sm,t�1 � sm) � et�1.

rp,t�1 � at�1 � ak


n

i�1Dimax(ri,t�1, 0)

rp,t�1 � at�1 � ak


n

i�1giri,t�1

2 � drm,t�1(sm,t�1 � sm) � et�1.



TA

BL

E I

I

Exp

lana

tory

Pow

er o

f Va

riou

s Fa

ctor

Mod

els

Car

hart

Aug

men

ted

Sin

gle

Fact

orFo

ur-F

acto

rE

ight

-Fac

tor

FS

PT

FS

Fac

tor

Aug

men

ted

PT

FS

Mod

elD

ISS

YSD

ISS

YSD

ISS

YSD

ISS

YSD

ISS

YS

A0.

010.

010.

010.

010.

010.

000.

010.

010.

000.

00[5

.67]

[2.6

5][5

.32]

[2.0

5][5

.24]

[1.4

6][5

.15]

[5.4

9][0

.42]

[0.2

8]b

(Mkt

)0.

00�

0.08

0.00

�0.

04�

0.05

�0.

17[0

.16]

[�1.

51]

[0.0

1][�

0.64

][�

0.75

][�

1.45

]b

(SM

B)

�0.

03�

0.00

�0.

03�

0.01

[�0.

70]

[�0.

01]

[�0.

68]

[�0.

09]

b(H

ML)

�0.

020.

05�

0.04

0.09

[�0.

37]

[0.6

0][�

0.78

][0

.95]

b(U

MD

)�

0.01

0.07

�0.

020.

03D

(CU

R)

0.14

0.28

[�0.

34]

[1.0

7][�

0.65

][0

.39]

[1.3

4][1

.89]

b(E

mer

)0.

080.

09D

(FIN

)�

0.03

�0.

08[2

.64]

[1.6

5][�

0.55

][�

1.25

]b

(LT

)0.

110.

39D

(CO

M)

0.02

0.01

[2.0

7][4

.10]

[0.6

6][0

.28]

b(H

Y)

�0.

04�

0.30

D(S

TK

)0.

02�

0.03

[�0.

47]

[�1.

98]

[0.3

3][�

0.33

]b

(CV

)�

0.05

0.19

d(V

IX)

�0.

04�

0.06

[�0.

51]

[1.1

8][�

1.65

][�

1.98

]b

(BD

)0.

010.

030.

020.

05[1

.06]

[2.2

0][1

.88]

[3.6

3]b

(FX

)0.

010.

060.

010.

05[1

.55]

[5.4

6][1

.03]

[4.1

7]b

(CO

M)

0.01

0.05

0.01

0.05

[1.1

4][3

.00]

[1.2

5][3

.21]

b(I

R)

�0.

01�

0.00

�0.

000.

00[�

1.03

][�

0.03

][�

0.65

][0

.45]

b(S

TK

)�

0.00

0.03

�0.

000.

02[�

0.34

][2

.17]

[�0.

31]

[1.0

2]�

0.01

0.01

�0.

03�

0.00

0.02

0.11

0.01

0.37

0.18

0.53

Not

e.C

oeffi

cien

ts e

stim

ates

fro

m s

ingl

e- a

nd m

ulti-

fact

or m

odel

s w

ith t

radi

tiona

l fac

tors

, F

ung

and

Hsi

eh (

2001

) P

TF

S f

acto

rs,

and

PT

FS

fac

tors

aug

men

ted

with

our

fut

ures

ret

urn-

base

d fa

ctor

s ar

e pr

esen

ted

here

with

t-st

atis

tics

repo

rted

in b

rack

ets.

Mkt

, SM

B, H

ML,

UM

D a

re F

ama–

Fre

nch

(199

3) th

ree

fact

ors

and

Car

hart

(19

97)

mom

entu

m fa

ctor

. Em

er, L

T, H

Y,C

V a

re a

ugm

ente

d fa

ctor

s re

pres

entin

g re

turn

s fr

om M

SC

I em

ergi

ng m

arke

t in

dex,

ter

m p

rem

ium

, cr

edit

risk

prem

ium

, co

nver

tible

bon

d pr

emiu

m in

dex.

BD

, F

X,

CO

M,

IR,

ST

K r

epre

-se

nt F

ung

and

Hsi

eh (

2001

) P

TF

S f

acto

rs:

PT

FS

BD

, P

TF

SF

X,

PT

FS

CO

M,

PT

FS

IR,

and

PT

FS

ST

K,

resp

ectiv

ely.

Aug

men

ted

PT

FS

fac

tor

mod

el in

clud

es b

oth

PT

FS

fac

tors

and

our

futu

res

retu

rn-b

ased

fact

ors

as s

peci

fied

in H

M m

odel

spe

cific

atio

n II.

D( *

) ar

e es

timat

ed c

oeffi

cien

ts fo

r re

turn

tim

ing

abili

ty in

thes

e m

arke

ts u

nder

HM

Mod

el s

peci

ficat

ion

(II)

. d(V

IX)

are

vola

tility

tim

ing

coef

ficie

nts.

R2

1082 Kazemi and Li


the timer enters the straddle based on his/her forecast. The return for a suc-cessful trend-follower resembles that from a look-back straddle where thetrend-follower buys at the bottom and sells at the peak. As the length of hold-ing period shrinks, the return from a standard straddle approaches that from alook-back straddle with the same terms.

After the futures return-based factors are added to PTFS factors, theadjusted R2 increases significantly from 0.01 to 0.18, especially for discre-tionary CTAs. Our results support two arguments: (a) supplementing PTFSfactors with our futures return-based factors can improve model explanatorypower and (b) even after controlling for trend-following ability, CTAs show cer-tain market return and volatility timing ability.

Market Timing with Futures Return-Based Factors

HM model specification (II) provides an overall test of CTAs’ market timingability. The results are reported in Table III. As we noted in the previoussection, specification (II) of HM assumes that managers can time the best-performing markets in each category of futures markets; hence, with thisspecification we can come up with a parsimonious model without losing muchexplanatory power.

The timing coefficient estimates carry the expected signs for both discre-tionary and systematic CTA indices. The adjusted R2 are 0.16 and 0.29, respec-tively. Both discretionary CTAs and systematic CTAs show ability in timing thebest-performing markets among currency futures (represented by Euro,

TABLE III

Return Timing: All CTA Indices, HM Model Specification (II)

a D (CUR) D (COM) D (FIN) D (STK) d (VIX)

All �0.01 0.57 �0.00 0.07 0.03 �0.03 0.29[�1.58] [4.30] [�0.02] [1.02] [0.29] [�0.84]

DIS 0.00 0.20 0.01 0.02 0.02 �0.02 0.16[0.13] [2.25] [0.38] [0.34] [0.34] [�1.00]

SYS �0.01 0.66 �0.00 0.08 0.03 �0.03 0.29[�1.70] [4.27] [�0.09] [1.05] [0.26] [�0.79]

Note. This table shows the return timing results for each category of CTAs in their specialization markets using HM model specifi-cation (II), where managers are assumed to time the best-performing futures market(s); hence, the timing signal is max(max(F1,t�1,F2,t�1, . . .), 0). t-Statistics of the estimates are reported in brackets. DIS represents the equally weighted portfolio of all discretionaryCTAs, and SYS represents the equally weighted portfolio of systematic CTAs, in each category. CUR, COM, FIN, STK represent cur-rency, physicals, financials, and stock indices futures markets, respectively. D (*) are estimated coefficients for return timing ability inthese markets under HM Model specification (II).

(1)

(2)rp,t�1 � at�1 � aK

j�1bj,tFj, t�1 � vt�1.

bj,t � b0, j � gj,t Sj, t�1 � yj, t�1.

R2



Japanese Yen, and British Pounds). Not only are the timing coefficients statisti-cally significant, but their magnitudes are also of economic significance. Due to their ability to time the best-performing markets, on average CTAs wereable to generate an extra return of 0.57% anytime there was a 1% change in anycurrency futures market.

Table IV reports return timing results for CTA indices by category. Basedon their self-reported classification, we have five different CTA indices coveringcurrency, financial, commodity, stock index, and diversified.16 We see thatalmost all market timing regressions have higher explanatory power for system-atic CTAs than for discretionary CTAs. The difference in adjusted R2 is actuallysignificant except that for the diversified category.

Panel A reports timing coefficients estimated for the TM model and Panel Breports those for specification (I) of the HM model. Results reported here areconsistent with previously reported findings. For example, systematic currencyCTAs are found to have strong timing ability in Euro, Japanese Yen marketswhereas discretionary ones are able to time the British Pound market.Diversified CTAs are found to be able to time Euro and Germany governmentbond futures markets. It is interesting to note that stock index CTAs actuallypossess negative return timing ability for FTSE and SP500. The HM specifica-tion (I) actually locates significant timing ability for discretionary diversifiedCTAs in Euro, British Pound, corn markets as well.

Another interesting finding in Panel A and Panel B is that CTAs identifiedwith significant return timing ability (positive significant b) have negative sig-nificant abnormal performance (negative significant a). This is, consistent withthe findings of Mamaysky, Spiegel, and Zhang (2008) and others of a negativerelationship between a and b due to estimation process. This reflects the factthat after the portion of return explained by timing ability is accounted for, theleftover in performance is usually significantly negative. As we argue earlier, the negative a should not be interpreted as lack of security selection ability butas representing fees and other charges for market timers.

Combined Return Timing and Volatility Timing

Table V shows the abnormal performance (a) and volatility timing results fromapplying volatility timing model (4). Following Chen and Liang (2007), we usethe end-of-month implied volatility from VIX series as we previously reportedthat volatilities in individual futures markets do not seem to play a role inexplaining CTA returns. Most CTAs, whether discretionary or systematic,exhibit significant volatility timing ability. At the aggregate level, the estimated

16A stepwise regression is used to find effective factors for diversified CTAs to achieve parsimony.

TA

BL

E I

V

Ret

urn

Tim

ing:

Cat

egor

y C

TA I

ndic

es

Cur

renc

y C

TAs

ag

(Eur

o)g

(JP

Y)g

(IB

P)

Pane

l A. T

M m

odel

DIS

0.00

0.43

0.53

4.13

0.06

[1.4

0][0

.25]

[1.4

8)[2

.10)

SY

S�

0.01

11.8

12.

682.

090.

45[�

5.73

)[3

.84]

[4.2

5][0

.47]

Fina

ncia

l CTA

sa

g(E

uro)

g(J

PY)

g(I

BP

)g

(U.S

. T-B

ond)

g(G

er)

g(E

udol

lar)

DIS

0.00

1.17

0.25

0.14

0�

0.34

12.0

2�

0.04

0.02

[0.8

1][0

.84]

[0.5

6][0

.07]

[�0.

36]

[1.6

6][�

0.30

]S

YS

�0.

0211

.33

1.11

�0.

73�

0.91

28.0

00.

060.

42[�

4.32

][3

.42]

[1.8

0][�

0.13

][�

0.43

][3

.92]

[0.2

9]

Com

mod

ity

CTA

sa

g(G

old)

g(C

oppe

r)g

(Gas

)g

(Oil

)g

(Cor

n)

DIS

0.01

�0.

420.

190.

01�

0.17

0.36

�0.

01[0

.86]

[�1.

27]

[1.3

5][0

.10]

[�1.

18]

[1.8

9]S

YS

0.01

�0.

360.

160.

01�

0.14

0.32

�0.

04[0

.92]

[�1.

21]

[1.1

1][0

.06]

(�0.

954)

[1.7

5]

Sto

ck I

ndex

CTA

sa

g(F

TS

E)

g(N

IKK

EI)

g(S

P)

DIS

0.02

�2.

370.

28�

1.18

0.08

[5.2

1][�

2.74

][0

.32]

[�2.

27]

SY

S0.

000.

06�

0.06

�0.

130.

33[1

.49]

[0.1

3][�

0.12

][�

0.40

]

Div

ersi

fied

CTA

sa

g(E

uro)

g(J

PY)

g(I

BP

)g

(Cor

n)g

(Ger

)g

(FT

SE

)

DIS

�0.

007.

180.

392.

950.

3710

.73

�0.

180.

31[�

1.02

][4

.17]

[0.8

2][0

.88]

[1.5

6][1

.56]

[�0.

19]

SY

S�

0.02

10.5

20.

736.

040.

8121

.32

1.71

0.33

[�3.

20]

[2.9

5][0

.75]

[0.9

9][1

.82]

[2.1

1][1

.50]

R2

R2

R2

R2

R2

Pane

l B. H

M m

odel

spe

cific

atio

n (I

)

Cur

renc

y C

TAs

aD

(E

uro)

D (

JPY

)D

(IB

P)

DIS

�0.

000.

030.

140.

440.

06[�

0.42

][0

.13]

[1.4

2][2

.23]

SY

S�

0.02

1.23

0.67

0.62

0.46

[�8.

23]

[3.8

3][3

.82]

[1.4

4]

Fina

ncia

l CTA

sa

D (

Eur

o)D

(JP

Y)D

(IB

P)

D (

T-B

ond)

D(G

er)

D(E

u$)

DIS

�0.

000.

170.

08�

0.01

0.19

0.55

�0.

050.

02[�

0.48

][1

.04]

[0.7

6][�

0.06

][0

.97]

[1.3

1][�

0.99

]S

YS

�0.

031.

110.

330.

460.

101.

220.

030.

40[�

4.63

][3

.22]

[1.9

9][0

.98]

[0.2

2][2

.17]

[0.2

4]

Com

mod

ity

CTA

sa

D (

Gol

d)D

(C

oppe

r)D

(G

as)

D (

Oil

)D

(C

orn)

DIS

0.01

�0.

440.

240.

01�

0.18

0.14

�0.

01[1

.49]

[�1.

40]

[1.1

0][0

.06]

[�1.

53]

[0.5

7]S

YS

0.04

0.28

�0.

780.

25�

0.46

0.03

�0.

03[1

.61]

[0.2

9][�

2.28

][1

.37]

[�1.

06]

[0.0

6]

Sto

ck I

ndex

CTA

sa

D (

FT

SE

)D

(N

IKK

EI)

D (

SP

)

DIS

0.02

�0.

23�

0.11

�0.

060.

04[4

.46]

[�1.

94]

[�0.

73]

[�1.

00]

SY

S0.

01�

0.03

�0.

090.

030.

34[1

.97]

[�0.

32]

[�1.

03]

[0.7

2]

Div

ersi

fied

CTA

sa

D (

Eur

o)D

(JP

Y)D

(IB

P)

D (

Cor

n)D

(G

er)

D (

FT

SE

)

DIS

�0.

020.

570.

191

0.66

0.19

0.53

0.15

0.29

[�3.

45]

[2.6

5][1

.30]

[2.0

4][2

.24]

[1.0

7][0

.95]

SY

S�

0.04

0.90

0.32

1.25

0.34

1.04

0.39

0.35

[�4.

92]

[2.1

3][1

.40]

[2.1

2][2

.57]

[1.8

6][2

.09]

Not

e.T

his

tabl

e sh

ows

the

retu

rn ti

min

g re

sults

for

each

cat

egor

y of

CTA

s in

thei

r sp

ecia

lizat

ion

mar

kets

usi

ng T

M m

odel

, HM

mod

el s

peci

ficat

ion

(I).

t-S

tatis

tics

of th

e es

timat

es a

re r

epor

ted

in b

rack

ets.

DIS

rep

-re

sent

s th

e eq

ually

wei

ghte

d po

rtfo

lio o

f all

disc

retio

nary

CTA

s, a

nd S

YS

rep

rese

nts

the

equa

lly w

eigh

ted

port

folio

of s

yste

mat

ic C

TAs,

in e

ach

cate

gory

. Eur

o, J

PY,

IBP,

cor

n, T

-bon

d, G

er, E

urod

olla

r, go

ld, c

oppe

r,ga

s, o

il, F

TS

E, N

IKK

EI,

SP

are

Eur

o, J

apan

ese

Yen,

Brit

ish

Pou

nd, c

orn,

U.S

. T-b

ond,

Ger

man

gov

ernm

ent b

ond,

Eur

odol

lar,

gold

, cop

per,

natu

ral g

as, c

rude

oil,

FT

SE

100

, NIK

KE

I 225

, and

S&

P50

0 fu

ture

s m

ar-

kets

for

TM

mod

el a

nd H

M s

peci

ficat

ion

(I).

g( *

),D

( *)

are

retu

rn ti

min

g co

effic

ient

s fo

r T

M m

odel

and

HM

mod

el, r

espe

ctiv

ely.

R2

R2

R2

R2

R2

1086 Kazemi and Li


coefficients are negative for both discretionary and systematic CTAs, whereas itis highly significant for systematic CTAs. However, we notice that currencyCTAs are an exception in that market volatility does not seem to play a role inaffecting their returns, leaving a significant amount of unexplained perform-ance in the intercept estimate (a).

Table VI reports results from the combined return and volatility timing mod-els (6), (7), (8). Based on the previous results, we pick the futures markets inwhich CTAs are found to be able to time to achieve parsimony. With this com-plete model, we see that the explanatory power improves at both aggregate leveland categorical level. With the TM model, we find in Panel A that discretionaryCTAs successfully time corn, German bond futures markets and that systematicCTAs successfully time Euro futures market and VIX. The futures return-based models perform well for discretionary diversified CTAs, which are found to time Euro markets successfully. Systematic diversified CTAs, on the other hand,time Euro, corn, Germany Bunds futures markets, and VIX successfully. Panel B

TABLE V

Volatility Timing

a d (VIX)

All CTAs DIS 0.01 �0.24 0.03[5.31] [�0.94]

SYS 0.00 �1.51 0.15[1.94] [�2.48]

Currency CTAs DIS 0.01 �0.16 �0.01[3.37] [�0.75]

SYS 0.01 0.42 �0.01[2.23] [0.78]

Financial CTAs DIS 0.01 0.48 �0.01[4.02] [1.25]

SYS 0.00 �1.49 0.19[1.27] [�2.25]

Commodity CTAs DIS 0.01 �0.94 �0.02[2.71] [�1.44]

SYS 0.02 �3.19 �0.00[2.77] [�1.78]

Stock index CTAs DIS 0.01 1.74 0.09[5.70] [2.72]

SYS 0.00 �0.24 0.34[1.96] [�0.70]

Diversified CTAs DIS 0.01 �0.73 0.12[2.61] [�1.25]

SYS 0.00 �2.14 0.17[1.44] [�2.79]

Note. Table V shows the abnormal performance (a) and volatility timing results as in volatilitytiming model (7). t-Statistics of the estimates are reported in brackets. VIX is the end-of-monthimplied volatility from CBOE VIX series. d (VIX) are volatility timing coefficients.

R2

TA

BL

E V

I

Com

bine

Ret

urn

and

Vola

tilit

y T

imin

g

All

CTA

sa

g(E

uro)

g(J

PY)

g(I

BP

)g

(Cor

n)g

(Ger

)g

(FT

SE

)d

(VIX

)

Pane

A. T

M m

odel

DIS

0.00

2.38

0.17

2.03

0.44

10.1

3�

0.16

�0.

300.

14[�

1.49

][1

.60]

[0.3

7][1

.01]

[2.5

8][2

.08]

[�0.

40]

[�1.

20]

SY

S�

0.01

10.6

01.

112.

210.

4511

.12

0.37

�1.

520.

41[�

3.11

][3

.31]

[1.5

8][0

.43]

[1.4

0][1

.50]

[0.5

3][�

2.26

]

Cur

renc

ya

g(E

uro)

g(J

PY)

g(I

BP

)d

(VIX

)

DIS

0.00

0.55

0.62

3.80

�0.

490.

06[1

.13]

[0.3

3][1

.76]

[1.9

3][�

1.68

]S

YS

�0.

0111

.93

2.77

1.78

�0.

460.

45[�

6.24

][3

.77]

[4.1

3][0

.38]

[�0.

61]

Fina

ncia

la

g(E

uro)

g(J

PY)

g(I

BP

)g

(T-B

ond)

g(G

er)

g(E

udol

lar)

d(V

IX)

DIS

0.00

0.95

0.06

0.74

�0.

5413

.74

0.04

1.11

0.06

[0.7

2][0

.71]

[0.1

1][0

.36]

[�0.

58]

[1.8

9][0

.31]

[1.7

4]S

YS

�0.

0211

.70

1.44

�1.

74�

0.57

25.1

4�

0.07

�1.

860.

45[�

4.20

][3

.52]

[2.3

1][�

0.31

][�

0.29

][3

.56]

[�0.

34]

[�1.

99]

Com

mod

ity

ag

(Gol

d)g

(Cop

per)

g(G

as)

g(O

il)

g(C

orn)

d(V

IX)

DIS

0.01

�1.

120.

470.

08�

0.51

1.11

�0.

90�

0.01

[1.3

4][�

0.89

][0

.65]

[0.7

8][�

3.16

][1

.46]

[�1.

36]

SY

S0.

030.

23�

2.37

0.16

�0.

900.

67�

3.87

�0.

04[2

.36]

[0.0

9][�

2.22

][0

.77]

[�1.

61]

[0.4

8][�

1.84

]

Sto

ck I

ndex

ag

(FT

SE

)g

(NIK

KE

I)g

(SP

)d

(VIX

)

DIS

0.02

�1.

840.

71�

1.08

2.41

0.15

[5.1

9][�

2.21

][0

.72]

[�2.

13]

[2.9

4]S

YS

0.00

�0.

051

�0.

15�

0.15

�0.

490.

33[1

.57]

[�0.

11]

[�0.

29]

[�0.

47]

[�0.

99]

R2

R2

R2

R2

R2

(Con

tinue

d)

TA

BL

E V

I (C

onti

nued

)

Div

ersi

fied

ag

(Eur

o)g

(JP

Y)g

(IB

P)

g(C

orn)

g(G

er)

g(F

TS

E)

d(V

IX)

DIS

�0.

006.

600.

613.

400.

3612

.43

0.32

�0.

450.

30[�

1.13

][3

.78]

[1.3

1][1

.04]

[1.4

9][1

.62]

[0.4

4][�

0.61

]S

YS

�0.

0111

.16

1.23

4.27

0.83

18.8

60.

93�

2.51

0.38

[�3.

42]

[3.1

4][1

.52]

[0.7

2][2

.02]

[1.9

5][0

.95]

[�2.

54]

Pane

l B. H

M s

peci

ficat

ion

(I)

All

CTA

sa

D (

Eur

o)D

(JP

Y)D

(IB

P)

D (

Cor

n)D

(G

er)

D (

FT

SE

)d

(VIX

)

DIS

�0.

000.

180.

100.

310.

140.

53�

0.01

�0.

350.

13[�

1.12

][0

.93]

[0.8

1][1

.40]

[2.3

7][1

.66]

[�0.

07]

[�1.

44]

SY

S�

0.03

1.00

0.38

0.76

0.20

0.45

0.13

�1.

680.

42[�

4.88

][2

.71]

[2.3

4][1

.51]

[2.0

4][1

.12]

[0.9

5][�

2.62

]

Cur

renc

ya

D (

Eur

o)D

(JP

Y)D

(IB

P)

d(V

IX)

DIS

�0.

000.

040.

160.

40�

0.51

0.06

[�0.

66]

[0.2

2][1

.69]

[1.9

5][�

1.77

]S

YS

�0.

031.

250.

700.

58�

0.53

0.46

[�8.

09]

[3.7

1][3

.58]

[1.2

4][�

0.68

]

Fina

ncia

la

D (

Eur

o)D

(JP

Y)D

(IB

P)

D (

T-B

ond)

D (

Ger

)D

(E

udol

lar)

d(V

IX)

DIS

�0.

000.

130.

060.

030.

180.

62�

0.05

0.45

0.02

[�0.

46]

[0.8

2][0

.60]

[0.1

3][0

.90]

[1.3

3][�

0.91

][1

.03]

SY

S�

0.03

1.23

0.38

0.32

0.13

0.96

0.02

�1.

440.

42[�

4.80

][3

.31]

[2.3

2][0

.66]

[0.3

0][1

.84]

[0.1

9][�

2.12

]

R2

R2

R2

R2

Com

mod

ity

aD

(G

old)

D (

Cop

per)

D (

Gas

)D

(O

il)

D (

Cor

n)d

(VIX

)

DIS

0.01

�0.

430.

22�

0.00

�0.

190.

13�

0.89

�0.

01[1

.50]

[�1.

36]

[1.0

2][�

0.01

][�

1.58

][0

.53]

[�1.

24]

SY

S0.

040.

33�

0.87

0.23

�0.

49�

0.01

�4.

01�

0.02

[1.7

1][0

.35]

[�2.

45]

[1.1

6][�

1.17

][�

0.02

][�

1.98

]

Sto

ck I

ndex

aD

(F

TS

E)

D (

NIK

KE

I)D

(S

P)

d(V

IX)

DIS

0.02

�0.

33�

0.26

0.03

2.49

0.15

[4.4

5][�

1.98

][�

2.09

][0

.16]

[3.1

5]S

YS

0.00

0.00

�0.

01�

0.13

�0.

530.

33[1

.72]

[0.0

3][�

0.16

][�

1.05

][�

1.11

]

Div

ersi

fied

aD

(E

uro)

D (

JPY)

D (

IBP

)D

(C

orn)

D (

Ger

)D

(F

TS

E)

d(V

IX)

DIS

�0.

020.

590.

220.

620.

190.

500.

13�

0.57

0.29

[�3.

40]

[2.6

9][1

.47]

[1.8

5][2

.28]

[1.0

0][0

.82]

[�0.

78]

SY

S�

0.04

0.99

0.44

1.05

0.35

0.91

0.28

�2.

700.

39[�

5.30

][2

.35]

[2.2

0][1

.77]

[2.7

2][1

.65]

[1.5

1][�

2.62

]

Not

e.T

his

tabl

e sh

ows

the

com

bine

d re

turn

and

vol

atili

ty ti

min

g re

sults

for

TM

mod

el, H

M m

odel

spe

cific

atio

n (I

) an

d (I

I) in

Equ

atio

ns (

6)–(

8). t

-Sta

tistic

s of

the

estim

ates

are

rep

orte

d in

bra

cket

s. D

IS r

epre

sent

sth

e eq

ually

wei

ghte

d po

rtfo

lio o

f all

disc

retio

nary

CTA

s, a

nd S

YS

rep

rese

nts

the

equa

lly w

eigh

ted

port

folio

of s

yste

mat

ic C

TAs,

in e

ach

cate

gory

. Eur

o, J

PY,

IBP,

cor

n, T

-bon

d, G

er, E

urod

olla

r, go

ld, c

oppe

r, ga

s, o

il,F

TS

E, N

IKK

EI,

SP

are

Eur

o, J

apan

ese

Yen,

Brit

ish

Pou

nd, c

orn,

U.S

. T-b

ond,

Ger

man

gov

ernm

ent b

ond,

Eur

odol

lar,

gold

, cop

per,

natu

ral g

as, c

rude

oil,

FT

SE

100

, NIK

KE

I 225

, and

S&

P50

0 fu

ture

s m

arke

ts fo

rT

M m

odel

and

HM

spe

cific

atio

n (I

). C

UR

, CO

M, F

IN, a

nd S

TK

are

cur

renc

y, c

omm

odity

, bon

d/in

tere

st r

ate,

and

sto

ck in

dex

futu

res

mar

kets

. VIX

is th

e en

d-of

-mon

th im

plie

d vo

latil

ity fr

om C

BO

E V

IX s

erie

s. g

( *),

D( *

) ar

e re

turn

tim

ing

coef

ficie

nts

for

TM

mod

el a

nd H

M m

odel

, res

pect

ivel

y, a

nd d

(VIX

) ar

e vo

latil

ity ti

min

g co

effic

ient

s.

R2

R2

R2

1090 Kazemi and Li


shows results from the HM model specification (I); the results are similar tothose from the TM model. From Table VI we notice that the stock index CTAsseem to behave differently from the other CTAs, where they present negativetiming ability as well as positive significant abnormal performance.

To examine the robustness of our results, we also use a 36-month windowrolling regression to estimate return and volatility timing coefficients for ourCTA categorical indices to examine the time variation of timing coefficients.The results are plotted in Figure 3 together with the t-statistics of those coeffi-cients. Consistent with our full-period regression, CTAs show both return andvolatility timing ability during January 1994 and December 2004. This is mostpronounced for currency, financial, and commodity CTAs, whereas stock indexCTAs is the only group that seems to present negative return timing.

ROBUSTNESS

Our findings so far point to positive market return and volatility timing abilityin most CTA managers. However, multiple reasons can lead to finding pseudo-timing in a sample. First, illiquid asset holdings are likely to lead to pseudo-timing. Second, as suggested by Jagannathan and Korajczyk (1986),option payoffs have a convex relationship with the market return, which maybe interpreted as timing ability when there is none if the fund manager haspositive positions in options. Third, as pointed out by Ferson and Schadt(1996), Christopherson, Ferson, and Glassman (1998), not considering publicinformation in performance evaluation may lead to pseudo-timing as well.Finally, as Goetzmann et al. (2000) and Ferson and Khang (2002) state, thehigh frequency at which a fund manager trades combined with the low fre-quency at which the returns are reported may also lead to spurious results thatthe manager possesses market timing ability.

We carry out robustness checks to address the above concerns. As CTAstrade mainly liquid futures contracts, the concern regarding liquidity is notlikely to affect our study. To address the second concern, we exclude CTAs thattrade options from our sample and run the same analysis to see whether theyexhibit timing ability to address the option-trading concern. For the third con-cern, because we have little evidence in the literature about public information’spredictability to various futures markets and we desire to keep parsimoniousregression models, we employ unconditional market timing models in thisstudy and leave for future research the effect of public information on infer-ence of CTAs’ market timing ability. As we do not have higher frequency returndata, we cannot address the last concern.

Table VII reports the regression results on all CTA indices from a com-bined timing model using various models. Altogether 740 CTAs indicate to the



�2

�1

0

1 4

(a)

7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94

1

2

3

4

5

6

7

8

Cur_DIS Cur_SYS t(Cur_DIS) t(Cur_SYS)

�1.5

�1

�0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Fin_DIS Fin_SYS t(Fin_DIS) t(Fin_SYS)

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94

(b)

�1

�0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

Com_DIS Com_SYS t(Com_DIS) t(Com_SYS)

1 4

(c)

7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94

FIGURE 3 (Continued)

1092 Kazemi and Li


Stk_DIS Stk_SYS t(Stk_DIS) t(Stk_SYS)

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94

(d)

�3

�2.5

�2

�1.5

�1

�0.5

0

0.5

1

1.5

2

Vix_DIS Vix_SYS t(Vix_DIS) t(Vix_SYS)

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94

(e)

�8

�6

�4

�2

0

2

4

6

8

10

12

FIGURE 3Time variation of timing coefficient. Timing variation in timing coefficient for the futures markets thatCTAs exhibit return timing ability are plotted. The timing coefficients are estimated from the combinedreturn and volatility timing HM model specification (II) as in (8). The figures display market timing with

respect to currencies, interest rate rates, commodity prices, stock indices, and volatility. The plots of t-statistics are also presented. DIS represents the equally weighted portfolio of all discretionary CTAs,and SYS represents the equally weighted portfolio of systematic CTAs, in each category. CUR, COM,

FIN, STK represent currency, physicals, financials, and stock indices futures markets, respectively. Therolling regression window is 36 months. The sample period is from January 1994 to December 2004.Panel A: Time variation of timing coefficient to currency futures markets. Panel B: Time variation of

timing coefficient to bond/interest rate futures markets. Panel C: Time variation of timing coefficient tocommodity futures markets. Panel D: Time variation of timing coefficient to stock index futures markets.

Panel E: Time variation of timing coefficient to market volatility (VIX).

database that they do not trade options. We run the timing test on the equallyweighted average index of all these 740 CTAs based on TM and HM specifica-tions. The explanatory power is reasonable, taking into consideration the poorexplanatory power from traditional factors. We again find significant return andvolatility timing ability for this group. Currency markets stay the most populararea for CTAs that exhibit return timing ability. Hence, our concern aboutpseudo-timing due to option trading is alleviated to a large extent.



To check the robustness of our results further, we investigate the timingability at individual fund level. We report a number of significant timing abili-ties for individual CTAs. The results are reported in Table VIII for CTA cate-gories that are identified to exhibit significant timing ability at index level only,including currency CTAs, financial CTAs, and diversified CTAs. To have a rea-sonably long time series for timing test against multiple factors, we requireeach individual CTA to have at least 36 months of return history. This restric-tion reduces the number of qualified funds: The number of currency CTAs isreduced from 44 to 32, that of financial CTAs is reduced from 154 to 113, andthat of diversified CTAs is reduced from 452 to 337. We observe that the tim-ing ability in different markets (currency CTAs to currency futures markets,financial CTAs to currency and bond/interest rate futures markets, diversifiedCTAs to all the futures markets) are significant for more than half of the indi-vidual CTAs. We further observe that if a CTA does exhibit significant timingability, it is highly probable that (�95%) that he/she displays positive market

TABLE VII

Robustness Test: CTAs Indices as Equally Weighted Average of All Nonopiton Trading CTAs

a g (Euro) g (JPY) g (IBP) g (Corn) g (Ger) g (FTSE) d (VIX)

Panel A: TM model

DIS 0.00 5.13 1.68 0.57 0.19 15.65 0.22 �0.69 0.15[�0.13] [2.17] [2.34] [0.17] [0.71] [1.96] [0.35] [�1.51]

SYS �0.01 11.51 1.28 2.29 0.52 11.36 0.43 �1.61 0.42[�3.32] [3.37] [1.70] [0.41] [1.53] [1.46] [0.56] [�2.26]

Panel B: HM model specification (I)

a D (Euro) D (JPY) D (IBP) D (Corn) D (Ger) D (FTSE) d (VIX)

DIS �0.01 0.34 0.48 0.28 0.07 0.69 0.16 �0.78 0.13[�1.83] [1.16] [2.29] [0.85] [0.72] [1.27] [0.96] [�1.64]

SYS �0.03 1.09 0.43 0.794 0.22 0.44 0.16 �1.80 0.43[�4.95] [2.78] [2.48] [1.47] [2.10] [1.05] [1.02] [�2.63]

Panel C: HM model specification (II)

a D (CUR) D (COM) D (FIN) D (STK) d (VIX)

DIS �0.00 0.48 0.01 �0.04 0.10 �0.90 0.12[�0.49] [3.45] [0.59] [�0.74] [0.78] [�1.87]

SYS �0.02 0.75 0.05 0.03 �0.06 �1.87 0.34[�2.51] [3.36] [2.28] [0.43] [�0.74] [�3.11]

Note. This table shows the timing ability of 740 CTAs that do not trade options using TM model, HM specification (I) and (II). t-Statistics of the estimates are reported in brackets. DIS represents the equally weighted portfolio of all discretionary CTAs that donot trade options, and SYS represents the equally weighted portfolio of all systematic CTAs that do not trade options. Euro, JPY, IBP,corn, Ger, FTSE are Euro, Japanese Yen, British Pound, corn, German government bond and FTSE 100 futures markets for TMmodel and HM specification (I). CUR, COM, FIN, and STK are currency, commodity, bond/interest rate, and stock index futures mar-kets. VIX is the end-of-month implied volatility from CBOE VIX series. g (*), D (*) are return timing coefficients for TM model and HMmodel, respectively, and d (VIX) are volatility timing coefficients.

R2

R2

R2

1094 Kazemi and Li


timing ability. On the other hand, most of these CTAs exhibit negative signifi-cant abnormal performance.

This finding that CTA funds have a combination of positive b and nega-tive a is similar to the results reported by Kon (1983), Henriksson (1984), andMamaysky, Spiegel, and Zhang (2008), and others with regard to mutualfunds.

In tests that are not reported here, we also detect significant timing abilityfor some individual funds in the category of physicals CTAs and stock index

TABLE VIII

Individual Funds Timing

Live Defunct Live Live Defunct DefunctAll (ALL) (ALL) (DIS) (SYS) (DIS) (SYS)

Currency CTAs# CTAs 131 44 87 9 35 21 66# Qualified 89 32 58 8 24 14 44�t (a)��1.65 46 17 29 1 16 3 26T (a)�1.65 6 3 3 1 2 1 2�t (g-cur) ��1.65 47 17 30 1 16 3 27T (g-cur)�1.65 47 17 30 1 16 3 27

Financial CTAs# CTAs 154 42 112 6 36 20 92# Qualified 113 39 74 5 34 16 58�t (a)��1.65 45 23 22 2 21 2 20T (a)�1.65 2 0 2 0 0 0 2�t (g-cur) ��1.65 42 21 21 0 21 3 18T (g-cur)�1.65 41 21 20 0 21 3 17�t (g-fin)}�1.65 7 0 7 0 0 1 6T(g-fin)}�1.65 6 0 6 0 0 0 6

Diversified CTAs# CTAs 452 150 302 11 139 49 253# Qualified 337 134 203 11 123 35 168�t(a)��1.65 130 61 69 1 60 5 64T (a)�1.65 2 1 1 0 1 0 1�t (g-cur)��1.65 136 64 72 7 57 10 62T (g-cur)�1.65 132 63 69 7 56 9 60�t(g-fin)}�1.65 84 33 51 2 31 4 47T (g-fin)�1.65 75 28 47 1 27 4 43�t(g-com)��1.65 60 40 20 2 38 1 19T(g-com)�1.65 57 40 17 2 38 1 16�t(g-stk)��1.65 22 8 14 1 7 2 12T (g-stk)�1.65 12 3 9 1 2 0 9

Note. This table reports the number of CTAs that belong to the categories in which market timing has been detected at index level(either successful or unsuccessful). The qualified CTAs have at least 36 months of return history. t(a) is t-statistic for abnormal per-formance (a), t(g-cur) is t-statistic for timing coefficients on the three currency futures markets (Euro, Japanese Yen, and BritishPound), t(g-fin) is t-statistic for timing coefficients on the three interest rate futures (U.S. T-bond, German government bond,Eurodollar), t(g-com) is t-statistic for timing coefficients on the five physicals futures (gold, copper, natural gas, crude oil, and corn),and t(g-stk) is t-statistic for timing coefficients on the three stock index futures (FTSE100, Nikkei 1225, and S&P 500).



CTAs. Even though no significant timing ability is found for these categories atindex level, individual funds are able to time the markets. The findings are ingeneral consistent with what have been reported in Table VIII and discussedabove.

It has been widely documented that the attrition rate for hedge fundsand CTAs is quite high—up to 20% based on counting of funds that drop outof databases (e.g., see Fung & Hiseh. 2000; Liang,1999 for more details). Aspointed out by Baquero et al. (2005) even when data on defunct hedge fundsare used to study performance persistence, the reported results may still besusceptible to look-ahead bias. Note that the impact of look-ahead bias is notlikely to be as severe on our results because we do not need to look ahead intesting for the presence of market timing skill. Further, CTAs invest in veryliquid instruments and therefore the average length of redemption noticeperiod for a typical CTA is smaller than that of a typical hedge fund, andCTAs are not expected to suffer significant negative returns during liquida-tion process.17 Nevertheless, in order to address the concern that survivor-ship bias may still generate misleading results for CTA timing ability, weapply the adjustment procedure proposed in Baquero et al. (2005) and reportthe results in Table IX. Though the results are somewhat weaker, our findingswith regard to CTAs’ market timing ability when the adjusted return series areused is consistent with our findings when the raw returns are used. Withrespect to both upward survivorship bias and downward bias from labeling alldefunct funds as real failed ones, we suggest that the real market timing abil-ity of CTAs may fall somewhere between the results reported in Tables III–Vand Table IX.

CONCLUSION

This study makes several contributions to the literature of CTA performance. Itis the first study to examine the return and volatility timing ability for CTAs asa separate group. Second, with risk factors based on returns from the mostheavily traded futures contracts, we are able to present multi-factor modelswith increased explanatory power in order to test for the presence of markettiming ability. Third, using the multi-market model of market timing, we areable to estimate market timing ability of CTAs in markets that are focus of theirtrading strategies. Fourth, we show that discretionary CTAs behave differentlyfrom systematic CTAs, having higher a (measuring selection ability) and less

17Kazemi and Li (2006) and Liang and Park (2008) argue that the real failure rate for hedge funds is muchlower than the attrition rate employed by Baquero et al. (2005). The reason is that a large percentage of theso-called defunct funds may stop reporting to a database for reasons other than liquidation.

TA

BL

E I

X

Tim

ing

Abi

lity

Test

Wit

h S

urvi

vors

hip

Bia

s-A

djus

ted

Ret

urn

Ser

ies

Cur

renc

y C

TAs

ag

(Eur

o)g

(JP

Y)g

(IB

P)

VIX

Pane

l A. T

M m

odel

DIS

0.00

2.09

0.88

2.42

�0.

040.

11[2

.98]

[1.6

2][2

.23]

[1.2

1][�

2.54

]S

YS

0.00

11.4

40.

483.

640.

020.

25[�

1.00

][4

.76]

[0.6

5][0

.97]

[0.5

9]

Fina

ncia

l CTA

sa

g(E

uro)

g(J

PY)

g(I

BP

)g

(U.S

. T-B

ond)

g(G

er)

g(E

udol

lar)

VIX

DIS

0.00

1.38

0.24

0.58

0.64

8.96

�0.

230.

010.

11[3

.54]

[1.0

5][0

.59]

[0.2

9][0

.64]

[2.1

3][�

1.95

][0

.33]

SY

S�

0.01

14.5

31.

40�

1.11

0.20

21.0

50.

020.

020.

33[�

2.42

][4

.45]

[1.4

2][�

0.22

][0

.08]

[2.0

2][0

.08]

[0.5

8]

Com

mod

ity

CTA

sa

g(G

old)

g(C

oppe

r)g

(Gas

)g

(Oil

)g

(Cor

n)V

IX

DIS

0.01

�1.

020.

460.

06�

0.39

0.37

�0.

030.

01[2

.03]

[�0.

74]

[0.9

1][0

.53]

[�1.

49]

[0.5

4][�

0.49

]S

YS

0.01

1.41

0.06

�0.

05�

0.32

0.73

�0.

01�

0.01

[0.9

9][0

.84]

[0.1

0][�

0.39

][�

0.99

][0

.86]

[�0.

18]

Sto

ck I

ndex

CTA

sa

g(F

TS

E)

g(N

IKK

EI)

g(S

P)

VIX

DIS

0.02

�2.

05�

1.64

�0.

360.

060.

11[7

.38]

[�2.

02]

[�3.

20]

[�0.

37]

[1.4

5]S

YS

0.01

�0.

710.

63�

0.02

0.01

0.09

[2.9

2][�

0.67

][1

.18]

[�0.

02]

[0.3

3]

Pane

l B. H

M m

odel

spe

cific

atio

n (I

)

Cur

renc

y C

TAs

aD

(E

uro)

D (

JPY)

D (

IBP

)V

IX

DIS

0.00

0.15

0.22

0.24

�0.

040.

08[1

.21]

[1.0

4][2

.35]

1.29

[�2.

53]

SY

S�

0.01

1.09

0.26

0.73

0.01

0.25

[�2.

87]

[4.0

3][1

.50]

[2.0

8][0

.43]

R2

R2

Aut

hor

Pro

of

Fina

ncia

l CTA

sa

D (

Eur

o)D

(JP

Y)D

(IB

P)

D (

T-B

ond)

D (

Ger

)D

(E

udol

lar)

VIX

DIS

0.01

0.16

0.07

0.06

0.22

0.35

�0.

090.

010.

12[2

.36]

[1.0

4][0

.72]

[0.3

1][1

.98]

[1.2

3][�

1.81

][0

.46]

SY

S�

0.02

1.29

0.48

0.43

0.14

0.55

�0.

010.

030.

30[�

2.98

][3

.41]

[2.0

7][0

.89]

[0.5

0][0

.75]

[�0.

11]

[0.5

9]

Com

mod

ity

CTA

sa

D (

Gol

d)D

(C

oppe

r)D

(G

as)

D (

Oil

)D

(C

orn)

VIX

DIS

0.01

�0.

380.

200.

01�

0.15

0.07

�0.

010.

01[1

.70]

[�1.

27]

[1.3

0][0

.13]

[�1.

22]

[0.3

5][�

0.23

]S

YS

0.00

0.07

0.13

�0.

050.

030.

09�

0.02

�0.

02[0

.39]

[0.2

0][0

.67]

[�0.

49]

[0.1

9][0

.64]

[�0.

26]

Sto

ck I

ndex

CTA

sa

D (

FT

SE

)D

(N

IKK

EI)

D (

SP

)V

IX

DIS

0.03

�0.

42�

0.39

�0.

170.

060.

12[6

.77]

[2.0

7][�

3.12

][�

0.80

][1

.48]

SY

S0.

01�

0.09

0.10

�0.

240.

030.

10[2

.87]

[�0.

42]

[0.8

0][�

1.12

][0

.85]

Pane

l C. D

iver

sifie

d C

TAs,

TM

, and

HM

mod

el s

peci

ficat

ion

(I)

TM

Mod

elH

M M

odel

Dis

cret

iona

ry C

TAs

Sys

tem

atic

CTA

sD

iscr

etio

nary

CTA

sS

yste

mat

ic C

TAs

a0.

00[�

0.71

]�

0.01

[�2.

49]

�0.

01[�

1.79

]�

0.03

[�3.

26]

g(E

uro)

7.62

[4.3

7]12

.51

[4.5

9]0.

74[3

.46]

1.15

[3.2

3]g

(JP

Y)

0.72

[1.1

4]0.

75[0

.72]

0.29

[1.8

8]0.

47[1

.82]

g(G

old)

�0.

59[�

0.81

]0.

04[0

.24]

g(C

orn)

0.30

[0.7

5]0.

13[1

.24]

0.27

[1.5

4]g

(Ger

)19

.61

[1.8

7]g

(Eud

olla

r)0.

20[1

.05]

0.01

[0.1

5]g

(U.S

. T-b

ond)

�3.

04[�

1.93

]�

0.15

[�0.

61]

g(S

P50

0)1.

09[1

.54]

0.20

[1.3

0]g

(Gas

)0.

03[0

.34]

g(C

oppe

r)0.

50[1

.84]

0.82

[1.8

7]0.

12[1

.40]

0.24

[1.6

5]g

(FT

SE

)2.

13[1

.83]

VIX

�0.

03[�

0.94

]�

0.04

[�0.

96]

�0.

03[�

0.79

]�

0.04

[�0.

70]

0.44

0.36

0.40

0.32

Not

e.T

his

tabl

e sh

ows

the

com

bine

d re

turn

and

vol

atili

ty t

imin

g re

sults

for

TM

mod

el,

HM

mod

el s

peci

ficat

ion

(I)

and

(II)

in E

quat

ions

(6)

–(8)

usi

ng s

urvi

vors

hip

bias

adj

ustin

g pr

oced

ure

prop

osed

in B

aque

ro

et a

l. (2

005)

. t-S

tatis

tics

of th

e es

timat

es a

re r

epor

ted

in b

rack

ets.

DIS

rep

rese

nts

the

equa

lly w

eigh

ted

port

folio

of a

ll di

scre

tiona

ry C

TAs,

and

SY

S r

epre

sent

s th

e eq

ually

wei

ghte

d po

rtfo

lio o

f all

syst

emat

ic C

TAs,

in e

ach

cate

gory

. Eur

o, J

PY,

IBP,

cor

n, T

-bon

d, G

er, E

urod

olla

r, go

ld, c

oppe

r, ga

s, o

il, F

TS

E, N

IKK

EI,

SP

are

Eur

o, J

apan

ese

Yen,

Brit

ish

Pou

nd, c

orn,

U.S

. T-b

ond,

Ger

man

bon

ds, E

urod

olla

r, go

ld, c

oppe

r, na

tu-

ral g

as, c

rude

oil,

FT

SE

100

, NIK

KE

I 225

, and

S&

P50

0 fu

ture

s m

arke

ts fo

r T

M m

odel

and

HM

spe

cific

atio

n (I

). C

UR

, CO

M, F

IN, a

nd S

TK

are

cur

renc

y, c

omm

odity

, bon

d/in

tere

st r

ate,

and

sto

ck in

dex

futu

res

mar

-ke

ts. V

IX is

the

end-

of-m

onth

impl

ied

vola

tility

from

CB

OE

VIX

ser

ies.

g( *

),D

( *)

are

retu

rn ti

min

g co

effic

ient

s fo

r T

M m

odel

and

HM

mod

el, r

espe

ctiv

ely,

and

d(V

IX)

are

vola

tility

tim

ing

coef

ficie

nts.

R2

1098 Kazemi and Li


market timing ability. Finally, from our tests on individual CTAs, we find evi-dence that CTAs generate their returns mostly from successful market timing,and confirm the negative relationship between market timing (b) and selection(a) that is usually reported when market timing of hedge funds is estimated.

BIBLIOGRAPHY

Admati, A., Bhattacharya, S., Pfleiderer, P., & Ross, S. (1986). On timing and selectiv-ity. Journal of Finance, 41, 715–730.

Admati, A. R., & Ross, S. A. (1985). Measuring investment performance in a rationalexpectations equilibrium model. Journal of Business, 58, 1–26.

Aragon, G. O. (2007). Timing multiple markets; Theory and evidence from balancedmutual funds (working paper). Arizona State University.

Baquero, G., ter Horst, J., & Verbeek, M. (2005). Survival, look-ahead bias and the per-sistence in hedge fund performance. Journal of Financial and QuantitativeAnalysis, 40, 493–517.

Becker, C., Ferson, W., Myers, D., & Schill, M. (1999). Conditional market timing withbenchmark investors. Journal of Financial Economics, 52, 119–148.

Billingsley, R., & Chance, D. M. (1996). Benefits and limitations of diversificationamong commodity trading advisors. Journal of Portfolio Management, 23, 65–80.

Bollen, N., & Busse, J. (2001). On the timing ability of mutual fund managers. Journalof Finance, 56, 1075–1094.

Busse, J. A. (1999). Volatility timing in mutual funds: Evidence from daily returns.Review of Financial Studies, 12, 1009–1041.

Carhart, M. M. (1997). On persistence in mutual fund performance. Journal ofFinance, 52, 57–82.

Chen, Y. (2007). Timing ability in the focus market of hedge funds. Journal ofInvestment Management, 5, 66–98.

Chen, Y., Ferson, W., & Peters, H. (2008). Measuring the timing ability of fixed incomemutual funds (working paper). University of Southern California.

Chen, Y., & Liang, B. (2007). Do market timing hedge funds time the market? Journalof Financial and Quantitative Analysis, 42, 827–856.

Christopherson, J., Ferson, W., & Glassman, D. (1998). Conditioning manager alphason economic information: Another look at the persistence of performance. Reviewof Financial Studies, 11, 111–142.

Dybvig, P. H., & Ross, S. A. (1985). Differential information and performance meas-urement using a security market line. Journal of Finance, 40, 383–399.

Fama, E., & French, K. (1993). Common risk factors in the returns on bonds andstocks. Journal of Financial Economics, 23, 3–56.

Ferson, W., & Khang, K. (2002). Conditional performance measurement using portfo-lio weights: Evidence from pension funds. Journal of Financial Economics, 65,249–282.

Ferson, W., & Schadt, R. (1996). Measuring fund strategy and performance in chang-ing economic conditions. Journal of Finance, 51, 425–462.

Fung, H.-G., Xu, E., & Yau, J. (2002). Global hedge funds: Risk, return, and markettiming. Financial Analysts Journal, 58, Issue 6, 19–30.



Fung, W., & Hsieh, D. (1997). Empirical characteristics of dynamic trading strategies:The case of hedge funds. Review of Financial Studies, 10, 275–302.

Fung, W., & Hsieh, D. (2000). Performance characteristics of hedge funds and CTAfunds: Natural versus spurious biases. Journal of Financial and QuantitativeAnalysis, 35, 291–307.

Fung, W., & Hsieh, D. (2001). The risk in hedge fund strategies: Theory and evidencefrom trend-followers. Review of Financial Studies, 14, 313–341.

Goetzmann, W., Ingersoll, J., & Ivkovich, Z. (2000). Monthly measurement of dailytimers. Journal of Financial and Quantitative Analysis, 35, 257–290.

Henriksson, R. (1984). Market timing and mutual fund performance: An empiricalinvestigation. Journal of Business,57, 73–96.

Henriksson, R., & Merton, R. (1981). On market timing and investment performanceII. Statistical procedures for evaluating forecasting skills. Journal of Business, 54,513–533.

Jagannathan, R., & Korajczyk, R. (1986). Assessing the market timing performance ofmanaged portfolios. Journal of Business, 59, 217–235.

Jensen, M. (1972). Optimal utilization of market forecasts and the evaluation of investment performance. Mathematical methods in finance. G. P. Szego, KarlShell, eds., North Holland Publishing Company.

Jiang, G., Yao, T., & Yu, T. (2007). Do mutual funds time the market? Evidence fromportfolio holdings. Journal of Financial Economics, 86, 724–758.

Kat, H. (2004). Managed futures and hedge funds: A match made in heaven. In G. Gregoriou, V. Karavas, F. Lhabitant, & F. Rouah (Eds.), Commodity tradingadvisors. New Jersey: Wiley.

Kazemi, H., & Li, Y. (2006). Leverage and survival pattern in hedge funds (workingpaper). University of Massachusetts Amherst.

Kon, S. (1983). The market timing performance of mutual fund managers. Journal ofBusiness, 56, 323–348.

Lehman, B. N., & Modest, D. M. (1987). Mutual fund performance evaluations: Acomparison of benchmarks and benchmarks comparisons. Journal of Finance, 42,233–365.

Liang, B. (1999). On the performance of hedge funds. Financial Analysis Journal, 55,72–85.

Liang, B. (2004). Alternative investments: CTAs, hedge funds, and fund-of-funds.Journal of Investment Management, 2, 76–93, fourth quarter (2004).

Liang, B., & Park, H. (2008). Predicting hedge fund failure: A comparison of risk meas-ures. Journal of Financial and Quantitative Analysis, forthcoming.

Mamaysky, H., Spiegel, I. M., & Zhang, H. (2008). Estimating the dynamics of mutualfund alphas and betas. Review of Financial Studies, 21, 233–264.

Merton, R. (1981). On market timing and investment performance I: An equilibriumtheory of value of market forecasts. Journal of Business, 54, 363–407.

Treynor, J., & Mazuy, K. (1966). Can mutual funds outguess the market? HarvardBusiness Review, 44, 131–136.

market timing of ctas an examination of...

Documents