Price and earnings momentum in Australian stock returns

Price and earnings momentum in Australian stockreturns

Paul Schneider, Clive Gaunt

UQ Business School, The University of Queensland, Brisbane 4072, Qld, Australia

Abstract

There is no prior published Australian research on earnings momentum and onlyone prior unpublished work of limited depth and scope. We provide some of thefirst Australian evidence on earnings momentum and revisit price momentumwith the first Australian evidence of the behaviour of returns beyond 12 months.Price momentum is found to be a feature of this market, but there is some rever-sal of returns during the second year after portfolio formation, suggesting trendchasing behaviour. Earnings momentum is also present, but with weak continua-tion into the second year. Price momentum and earnings momentum are shownto provide independent explanatory power over future returns.

Key words: Price momentum; Earnings momentum; Anomalies

JEL classification: G110, G120, G140

doi: 10.1111/j.1467-629X.2010.00395.x

1. Introduction

Momentum refers to the tendency of stock prices to move in a future directionthat is consistent with some past movement. Some studies have attributedmomentum returns to higher systematic risk and biased statistical design (forexample MacDonald and Power, 1993; Power et al., 1991). However, morerecent research suggests momentum is not a statistical artefact but is primarilyattributed to the market’s sluggish response to digesting information, such thatthe gradual absorption of information is reflected in the drift of prices (Chan

The authors gratefully acknowledge the provision of the Fama French Australian factordata by Michael O’Brien as well as the extensive comments of Phil Gray on drafts of thispaper.

Received 1 April 2010; accepted 15 December 2010 by Robert Faff (Editor).

� 2011 The AuthorsAccounting and Finance � 2011 AFAANZ

Accounting and Finance 52 (2012) 495–517

et al., 1996; Chordia and Shivakumar, 2006). There is debate as to whethermomentum strategies derive exploitable returns. Although many studies docu-ment statistically significant momentum strategies, the strength of these strategiesis often driven by small stocks. Further, sceptics of momentum trading arguereturns are reduced to mere ‘paper profits’ once transaction costs and hindrancesto short-selling are considered.There are two forms of momentum which have been identified and studied:

price momentum which is the persistence of returns based on past pricechanges; and earnings momentum which is the continuation of returns basedon changes in past earnings or a change in expectations regarding futureearnings.The bulk of momentum research has focused on price momentum. How-

ever, prices should reflect the present value of expected earnings, and thus,it is logical to examine if price continuation is a result of a past change inexpected earnings. Therefore, to develop a better understanding of pricemomentum, it is important to examine price and earnings momentum simul-taneously. Earnings momentum strategies buy and sell stocks to exploit thecontinuation in prices that is associated with unexpected changes in a com-pany’s earnings. This may be the result of a company announcement indi-cating that recent or expected future earnings are different from thatanticipated by the market. Alternatively, it might be the result of analystrevisions to expected future earnings. Despite some reported evidence forthe strength of earnings momentum and the debate regarding its connectionwith price momentum, there have been few studies since Chan et al. (1996)to substantiate the existence of earnings momentum or to explore its rela-tionship to price momentum. One notable exception is that of Chordia andShivakumar (2006) who find price momentum to be captured by the system-atic component of earnings momentum. Australian research is particularlyuncommon.There are two main objectives of this study. First, to document the extent to

which earnings momentum is present in the Australian market. Only one priorunpublished study has looked at the Australian market, and that was a smallpart of a review of earnings momentum across eleven countries. Second, toinvestigate to what extent past returns and earnings surprise have separateexplanatory power. Again, we are aware of just one prior unpublished study ofAustralian data, and that provides a cursory examination of the issue. Further,international research is rare. Finally, unlike prior Australian research, we extendour analysis beyond the first 6 months post portfolio formation to garner furtherinsights into the nature of momentum behaviour.The remainder of this study is structured as follows. Section 2 discusses the rel-

evant Australian and international momentum literature. Section 3 describes theresearch methodology employed, including stock ranking, portfolio formation,holding period returns and risk adjustment. Section 4 describes the data sourcesutilised and the process used to construct the final samples. Section 5 reports the

496 P. Schneider, C. Gaunt/Accounting and Finance 52 (2012) 495–517


results and analysis. Finally, Section 6 offers some concluding comments andavenues for future research.

2. Prior research

2.1. Price momentum

Jegadeesh and Titman (1993) were amongst the first to investigate pricemomentum. They did so by examining J/K relative strength strategies whereportfolios are formed monthly on the basis of ranking stocks over the mostrecent J months. These stocks are assigned to respective decile portfolios forwhich subsequent performance is measured over the following K months. Fortheir sample of New York Stock Exchange and American Stock Exchangestocks from 1965 to 1989, the 12/3 zero-cost hedge portfolio strategy recordedthe best performance with returns of 1.31 per cent per month. When their sampleis broken into three size groups (small, medium and large), the hedge portfolioreturns to large stocks are smaller than the other two groups, but there is no sig-nificant size effect observable. There is a consensus amongst studies from theUnited States (US) that the price momentum anomaly is positive and statisticallysignificant across a broad time period and coverage of stocks (Conrad and Kaul,1989; and Lo and MacKinlay, 1999).Liu et al. (1999) and Bagella et al. (2000) find evidence of statistically signifi-

cant momentum strategies in the United Kingdom (UK). Liu et al. (1999) alsolook at the role of size and find the medium size hedge portfolio return to belarger than the small and big size groups. Using an approach similar toJegadeesh and Titman (1993), Rouwenhorst (1998) also finds price momentum ispresent in twelve European countries1 for the period 1978–1995. However,Hameed and Kusnadi (2002) find the evidence of price momentum in Asianmarkets is less convincing than that found in the US and developed Europeaneconomies.Hurn and Pavlov (2003) is one of the first Australian studies to directly

examine price momentum. Using the Australian Graduate School of Manage-ment Centre for Research in Finance (AGSM-CRIF) monthly return databasefor the period 1974–1998, they adopt an approach similar to that of Jegadeeshand Titman (1993). Hurn and Pavlov restrict their study to the largest 200stocks by market capitalisation, given the prevalence of small illiquid stocks inthe Australian market reduces the ability to implement strategies that includethese stocks. Their study finds that the 6/12 hedge portfolio (winner-loser)strategy records abnormal returns of 5–8 per cent per annum. Further, whenthey split their sample into two groups comprising the largest 50 stocks and

1 Austria, Belgium, Denmark, France, Germany, Italy, The Netherlands, Norway, Spain,Sweden, Switzerland and the UK.

P. Schneider, C. Gaunt/Accounting and Finance 52 (2012) 495–517 497


the next 150 stocks, they find stronger momentum amongst the largest 50stocks.Demir et al. (2004) examine momentum using daily returns and investigate

J/K strategies for 30, 60, 90 and 180 day ranking and holding periods, for theperiod 1990–2001. In the first half of the period, their sample is limited to thosestocks approved for short selling (tends to be larger stocks) and is expanded tostocks which form part of the All Ordinaries Index in the latter half of theperiod. They report strong momentum returns (up to 5.34 per cent per month)and some evidence that momentum is stronger amongst the smallest quintile ofstocks.Marshall and Cahan (2005), for the period 1990–2003, use monthly returns of

Australian Stock Exchange (ASX) listed stocks to investigate the profitability ofmomentum strategies based on how close stocks are to their 52-week high. Theyreport a monthly average return of 2.14 per cent to the Winner-Loser hedgeportfolio where the sample is limited to those stocks approved for short selling.They also find a monotonic relationship between size and momentum returnswith the smallest size quintile earning 4.76 per cent per month and the largest sizequintile earning 0.64 per cent per month.Durand et al. (2006) also perform a Jegadeesh and Titman type replication

using monthly returns for all available Australian listed stocks over the period1980–2001. However, rather than momentum, they find that past losers signifi-cantly outperform past winners. This result is clearly at odds with other Austra-lian and international research.In a follow-up study to their 2003 work, Hurn and Pavlov (2008) extend their

sample period to 2004 and include all ASX listed companies. When including abroader range of stocks, the strength of momentum is maintained. However,momentum is shown to be stronger in smaller stocks (those outside the largest200).Noting the inconsistencies in prior Australian research, Brailsford and O’Brien

(2008) explicitly investigate the link between stock size and price momentum.They find a strategy, which buys the winners and sells the losers of the previous6 months, is profitable for larger stocks only (for approximately the largest 500stocks). Amongst those strategies, it is strongest for stocks ranked from 201 to500 in market capitalisation. The authors attribute much of the inconsistency inprior Australian momentum findings to momentum’s interaction with size.Although their study finds a positive momentum effect, such a strategy requirescontinual short positions in the loser portfolios which are unlikely to be sustain-able, given the relatively high trading costs associated with trading small stocksand short-selling.In the most recent Australian research, O’Brien et al. (2009) consider the inter-

action between size, book-to-market and price momentum. Most relevant to ourstudy is their finding of a relationship between returns and momentum aftercontrolling for size. In the large- and middle-size portfolios, winners beat losers,but in the smallest size portfolio losers significantly outperform winners.



2.2. Earnings momentum

The study of the reaction of the market to earnings information began withthe seminal work of Ball and Brown (1968), who documented a puzzling postearnings announcement drift, and this has been confirmed in numerous subse-quent studies. Bernard and Thomas (1989) show that annual abnormal returnsof 18 per cent are possible using a strategy that exploits this drift. More impor-tantly, they find that a large portion of that drift is delayed until the subsequentquarter’s earnings announcement. This suggests not only a sluggish response bythe market to earnings information but also a need by the market for repetitionand reinforcement of that information. Givoly and Lakonishok (1979) tell a sim-ilar story when they examine the markets response to analyst revisions of earn-ings. Hew et al. (1996) and Booth et al. (1996) have extended the earningsmomentum evidence to developed European markets.The only study to examine earnings momentum in Australia is an unpublished

one by Hong et al. (2004) who examine the strength of earnings momentumstrategies based on analyst forecast revisions for eleven countries for the period1987–2000. Their study, which forms quintile portfolios, finds statistically signifi-cant returns from Australian earnings momentum strategies with 3/6 and 6/6strategies delivering hedge portfolio returns of 1.03 per cent and 0.92 per centper month, respectively. They find significant earnings momentum profits areearned in Australia, Canada, France, Germany, Hong Kong and the UK, butnot in Japan, Korea, Malaysia, Singapore and Taiwan.

2.3. Price and earnings momentum

Chan et al. (1996) is the seminal momentum paper that simultaneously investi-gates the continuation of stock returns based on both past price performanceand earnings surprise. They find that momentum strategies based on stocks’prior 6 month returns and analyst earnings forecast revisions produce spreads(winner–loser) of 8.8 per cent and 7.7 per cent, respectively over the following6 months. Chan et al. (1996) show that both price and earnings momentum haveincremental predictive ability over each other and thus do not subsume eachother. They also report that momentum is evident across both large and small-size stocks but is weaker in the large-size sub-sample.Hong et al. (2004) examine price and earnings momentum across eleven coun-

tries. They find price and earnings momentum in six of the eleven countries, andnote that price momentum is only evident in countries where earnings momen-tum is profitable, suggesting a significant link between the two.Chordia and Shivakumar (2006) extend the work of Chan et al. (1996) but

seek to find a relationship between price momentum and the systematic compo-nent of earnings momentum. They find that while earnings momentum and pricemomentum provide independent explanatory power for future returns, thesystematic component of earnings momentum subsumes the ability of price



momentum to explain future returns. They conclude that price momentum is anoisy proxy for earnings momentum, a view that is consistent with Hong et al.(2004) who find that price momentum occurs only in countries where earningsmomentum is present.

3. Method

The objective of this study is to examine the returns to price and earningsmomentum using Australian data and to investigate to what extent past returnsand earnings surprise have separate explanatory power. An important contribu-tion of this study is to examine return behaviour of portfolios beyond the first6 months post portfolio formation, which is where nearly all prior Australianresearch concludes. Return behaviour beyond 6 and 12 months can offerinsights into the nature of momentum behaviour. For example, Chan et al.(1996) find portfolio returns in the second year after portfolio formation to berelatively stable, suggesting the momentum returns in the first year are sus-tained and not because of trend chasers or exploitation by technical analysts(chartists).The profitability of price and earnings momentum is assessed by measuring

the returns to portfolios which are formed on the basis of past performance.Winner and loser portfolios are formed on the basis of past returns or earningssurprise which are measured over a ranking period of J months. Consistent withthe majority of prior literature, the hedge portfolio of buying the winner and sell-ing the loser portfolio is examined. Also examined is the performance of thelong-only portfolio which simply involves buying the winner portfolio. Examin-ing the long-only portfolio provides a viable and realistic alternative to the hedgeportfolio, which may be impractical to implement owing to its reliance oncontinual short positions. The returns to these portfolios are measured on a rawand market-adjusted basis over a holding period of K months which gives rise tovarious J/K strategies.Following Chan et al. (1996), Chordia and Shivakumar (2006) and Hong

et al. (2004), dual-sort portfolios are created to examine if price and earningsmomentum have separate and incremental predicative ability by measuring theimpact of one portfolio ranking measure, while controlling for the other. Toexamine momentum at the stock level rather than the portfolio level, monthlycross-sectional regressions, similar to that of Fama and MacBeth (1973),examine the direct effect of past returns and earnings surprise for futurereturns.

3.1. Price momentum

At the beginning of each month from January 1990 to December 2006(204 months), each stock is ranked on its past return as measured by its buy-and-hold return over the previous 6 months. The 10 per cent of stocks with the



best past returns are placed in a winner portfolio and the worst 10 per cent per-formers are placed in the loser portfolio. A long position is taken in the winnersand a short position is taken in the losers, with these positions being held for theK-month holding period of 3–24 months. Chan et al. (1996) and Bernard andThomas (1990) suggest that the continuation of returns based on past returnsand earnings performance is unlikely to continue beyond three (US) reportingquarters. However, given the lower frequency of earnings announcements inAustralia (semi-annual), we extend the holding periods beyond 12 months tocapture the effect of a second earnings announcement post portfolio formationand to assess the behaviour of momentum returns in the medium-term. Value-weighted returns are calculated for the winner and loser portfolios. These arethen combined to produce the buy-and-hold return of the hedge portfolio. Theprocess is repeated each month by rolling the ranking and holding periods for-ward by 1 month such that the strategy is repeated each month from February1990 to December 2006.


The implementation of the earnings momentum strategy is very similar to thatof price momentum except stocks are ranked on a proxy of earnings surprisemeasured over a J month ranking period.2

Chan et al. (1996) employ change in consensus analyst forecast scaled byprice as their earnings surprise metric. However, they note that scaling by pricepenalises high price-earnings ratio stocks. In unreported results, they use per-centage change in median forecast, and find their results robust to the differentmetric. To avoid the interaction of price to earnings and forecast revisionshighlighted by Chan et al. (1996), our earnings surprise metric is calculated bythe percentage change in the consensus analyst EPS forecasts (REV).3 REVmeasures the revision of the analysts’ consensus median earnings forecasts overthe 6-month ranking period as the ratio of the current forecast to a priorforecast.

2 For comparability, we have used decile portfolios for both price momentum (R6) andearnings momentum (REV6). However, as an anonymous referee has highlighted, theREV6 extreme decile portfolios may have a small number of observations in some cases.In unreported results, we have re-run our REV6 tests using quintiles rather than deciles.These results show a similar picture to the one presented with the decile portfolios andwould not change the conclusions offered here.

3 The use of Standardised Unexpected Earnings (SUE) is common in prior US research,where its use benefits from the availability of quarterly earnings data. In Australia, earn-ings are reported semi-annually. We choose to use consensus analyst EPS revisions asthey are generally available with high frequency (monthly), are forward looking andtherefore strongly linked to valuation, and unlike SUE does not require a model ofexpected earnings.



REVit ¼fitfit�6

ð1Þ

where:fit is the consensus (median) I/B/E/S estimate in month t of firm i’s EPS for the

current fiscal year;fit)6 is the consensus (median) I/B/E/S estimate 6 months prior to the current

month for firm i’s EPS for the current fiscal year.

3.3. Measurement of portfolio returns

To examine the profitability and persistence of price and earnings momentum,portfolio value-weighted buy-and-hold raw returns are calculated for the Kholding periods of 3, 6, 9, 12, 15, 18, 21 and 24 months. Monthly portfolioreturns are averaged over the 204 iterations to produce an aggregate raw returnfor each portfolio. The raw returns analysis is only insightful to the extent itallows the relative assessment of price and earnings momentum. To assess theperformance of the momentum portfolios relative to the broader market,market-adjusted returns are also calculated. For long-only portfolios, market-adjusted returns are calculated using Buy-and-Hold Abnormal Returns(BHARs) which is the multiplicative compound stock return less the buy-and-hold return of the benchmark.

BHARp;vw ¼YK

t¼1ð1þ rp;vwÞ �

YK

t¼1ð1þ rm;vwÞ ð2Þ

where:rp,vw is the value-weighted buy-and-hold return for portfolio p during the hold-

ing period K;rm,vw is the value-weighted market buy-and-hold return during the holding per-

iod K. This is computed using all the stocks that qualify each month for inclu-sion in the ten price momentum portfolios.We employ a monthly rolling approach to forming portfolios, similar to that

employed by Jegadeesh and Titman (1993). In other words, each ranking andholding period strategy is performed on data of the previously examined rankingand holding periods by rolling forward their respective time brackets each by1 month. Each J/K strategy is tested at the beginning of each of the 204 monthsbetween January 1990 and December 2006. Rolling portfolios does not bias thepoint estimates but does induce autocorrelation in the standard errors of themean returns of the portfolios examined. We account for the autocorrelation byadjusting the standard errors of the portfolios by using the Heteroscedasticityand Autocorrelation Consistent (HAC) standard errors as carried out in thestudy by Newey and West (1987).



3.4. Tests of momentum strategy independence

We form dual-sort portfolios and examine to what extent past returns andearnings surprise have separate explanatory power for future returns. Thedual-sort portfolios provide a suitable setting to examine the individual effectof one portfolio ranking variable while controlling for the other. At the begin-ning of each month, sample stocks are sorted on the basis of their past6-month returns and assigned to one of three4 past returns portfolios which allhave an equal number of stocks. Next, the stocks are ranked again indepen-dently on their earnings surprise over the same 6-month ranking period andplaced into one of three equally sized earnings surprise portfolios. Using thisprocedure, each stock is assigned to one of nine portfolios. Buy-and-holdreturns are measured for each of these portfolios and averaged over the 204iterations.

3.5. Fama–MacBeth cross-sectional regressions

The raw returns and market-adjusted returns analysis is conducted at the port-folio level. It is also possible to examine the relationship between past and futurereturns and earnings surprise at the individual stock level using Fama and Mac-Beth (1973) cross-sectional regressions. First, each stock’s 6-month buy-and-holdreturn is regressed on log(size), R6 and REV6 to assess the association betweenpast and future performance. Second, we adjust each stock’s 6-month returnusing the Fama French three factors then four factors by including PMN. ThePMN factor is calculated as the mean return on the 30 per cent of stocks withthe largest value of REV6 minus the 30 per cent of stocks with the smallest valueof REV6. Portfolios are re-formed monthly in calculating the PMN factor. Theregression is run for each of the 204 sample period months, thus providing atime-series of slope estimates. The Fama–MacBeth regression estimates aresimply the time-series average of these slope estimates.

4. Data

The price momentum strategy considers stocks listed on AGSM-CRIF, whilethe earnings momentum strategy considers only those AGSM-CRIF listedstocks that also have analyst following listed in the I/B/E/S database. Theanalysis that simultaneously considers price and earnings momentum uses theearnings momentum sample. The period studied is January 1990 to December2006.

4 In this dual-sort procedure, allocating stocks to decile portfolios on past return thenearnings surprise would result in 100 portfolios, many comprising only a handful ofstocks. The use of tertiles here ensures an adequate sample size.



4.1. AGSM-CRIF sample

Monthly returns for ranking and holding periods are sourced from theAGSM-CRIF price-relative files which incorporate dividends and capitalisationchanges. Stocks with a monthly closing price of <$0.20 are excluded in order toalleviate concerns over problems with illiquidity and market microstructure thatare associated with micro-cap stocks, and which prevent institutional investorsusing these stocks in momentum strategies. Panel A of Table 1 shows that theaverage number of firms assessed per month for price momentum is 680 with theaverage firm having a market capitalisation of $633m. Comparison of the yearlymean and median market capitalisation figures reveal the familiar skewness inthe Australian equity market.

4.2. I/B/E/S sample

The consensus EPS forecast measure, which is compiled in the middle of eachmonth, is drawn from the I/B/E/S Summary History File. Every month, eachstock’s EPS forecast for the current fiscal year period and next fiscal year end areused to calculate the earnings surprise metric REV. Analyst EPS forecasts forAustralian firms for the period July 1989 to December 2006 are collected. Similarto the procedure in collecting returns data, the last 6 months of 1989 are col-lected in order to calculate REV metrics for the first 6 months of 1990. Panel Bof Table 1 shows that the average number of companies eligible each month forearnings momentum is 236, which is considerably higher than the Australiansample of Hong et al. (2004) international earnings momentum study which con-tained an average of 157 stocks per month. Unsurprisingly, the average size andnumber of stocks included in the I/B/E/S Summary History File increases overthe 17-year sample period. The average number of estimates that contribute toeach consensus forecast decreases steadily over the sample period as the numberof stocks (which will include greater coverage of smaller companies) increase.The average REV6 metric for each stock-month observation is reasonably stableover the 17-year period.

5. Results and analysis

5.1. Price momentum

Table 2 shows that for the period 1990–2006, there was a positive associationbetween portfolios most recent 6-month returns (R6) and future returns. Table 2illustrates a monotonic increase in holding period raw returns when moving fromloser to winner past returns portfolios. Consistent with prior research, themomentum effect is strongest around the 9–12 month point after portfolio for-mation. The hedge portfolio return peaks at 9 months with 12.54 per cent andretreats slightly to 12.42 per cent at 12 months. From that point forward, the



Table

1

AGSM-C

RIF

andI/B/E/S

samplesbyyear

Year

PanelA

PanelB

Average

number

of

stocksper

month

Number

of

stock

observations

per

year

Median

market

capitalisation

($m)

Average

market

capitalisation

($m)

Average

number

ofstocks

per

month

Number

of

stock

observations

per

year

Median

market

capitalisation

($m)

Mean

market

capitalisation

($m)

Average

number

of

earnings

estimates

per

stock

each

month

Mean

REV6

ofeach

stock

per

month

1990

642

7706

17294

116

1393

384

1203

8.0

0.92

1991

528

6339

21391

118

1413

325

1347

7.6

0.91

1992

535

6418

26431

141

1688

301

1217

7.8

1.01

1993

596

7146

31472

155

1856

371

1392

8.3

1.03

1994

717

8604

33429

179

2142

403

1460

8.1

1.00

1995

675

8103

36477

228

2731

259

1197

6.6

0.96

1996

706

8468

41540

243

2911

286

1265

6.2

0.92

1997

697

8364

46612

231

2771

342

1506

6.8

0.96

1998

617

7409

49677

239

2872

327

1472

8.0

0.99

1999

653

7835

48700

265

3181

307

1728

7.3

1.06

2000

761

9136

46641

277

3322

326

1852

6.5

1.00

2001

663

7961

54758

315

3782

242

1761

6.3

0.97

2002

632

7580

60818

303

3635

265

1826

5.8

1.04

2003

631

7566

68790

264

3172

360

1943

5.2

0.99

2004

756

9075

76806

291

3490

399

2207

5.0

1.05

2005

823

9881

80918

317

3806

422

2535

4.1

1.06

2006

928

11,130

851007

339

4070

461

2970

4.3

1.10

Average:

680

Total:138,721

Average:

633

236

Total:48,235

1670

6.5

Pan

elA

summarises

theAGSM-C

RIF

sample

from

1January

1990

to31Decem

ber

2006(204

months).Theaverage

andmedianmarket

capitalisationof

samplefirm

sisin

millionsofdollars.Pan

elBreportstheaveragenumber

ofstock-m

onth

observationsoftheAGSM-C

RIF

-I/B/E/S

merged

sample.Mean

andmedianfirm

market

capitalisationisin

millionsofdollarsan

disobtained

from

AGSM-C

RIF

astheproduct

ofshares

onissuean

dclosingprice.The

averagenumber

ofearnings

estimates

that

contribute

totheconsensusforecastiscalculatedfrom

theI/B/E/S

SummaryHistory

File.REV6iscalculatedas

thepercentage

chan

gein

theI/B/E/S

EPSconsensusforecastforthelast6months.



Table

2

Raw

andmarket-adjusted

returnsto

Price

Momentum

Strategiesbased

onR6

Portfolio

Meanmarket

capitalisation

($m)

Medianmarket

capitalisation

($m)

R6

Holdingperiod(m

onths)

36

912

1518

21

24

2ndyear

P1(loser)

200

29)36.31

)1.37

0.32

2.53

5.43

9.85

13.81

17.08

21.27

15.16

P2

457

42)17.04

0.60

1.35

3.49

7.50

10.80

14.35

16.94

19.07

11.34

P3

613

52)8.45

2.19

4.33

7.39

10.44

13.50

15.96

18.36

20.69

9.59

P4

662

51)2.70

3.03

5.87

8.56

12.09

15.39

17.55

20.57

23.88

10.64

P5

806

632.33

2.65

5.93

9.45

12.77

15.68

18.38

21.71

24.35

10.62

P6

933

757.76

2.88

5.79

9.53

13.83

17.17

20.33

23.53

26.77

11.56

P7

968

8114.10

3.07

7.06

10.44

13.57

16.78

20.16

23.66

26.70

11.93

P8

835

8123.24

3.67

7.45

11.11

14.64

18.47

21.53

25.14

28.34

12.62

P9

630

6739.10

3.95

7.99

11.22

14.22

17.03

20.88

24.11

27.27

11.89

P10(w

inner)

232

43113.85

6.56

11.80

15.07

17.85

20.11

23.13

25.64

27.19

9.52

P10-P1(hedge)

7.93

11.47

12.54

12.42

10.26

9.32

8.56

5.92

)5.64

t-statistic

8.56**

9.36**

8.11**

7.30**

5.76**

4.71**

3.92**

2.52*

)3.48**

Market

Return

2.93

6.09

9.33

12.93

16.21

19.42

22.60

25.49

11.39

Long-only

(P10)

6.56

11.80

15.07

17.85

20.11

23.13

25.64

27.19

9.52

Long-only

BHAR

3.63

5.71

5.74

4.92

3.89

3.71

3.04

1.70

)1.87

t-statistic

5.38**

6.09**

4.64**

3.31**

2.45*

2.12

1.53

0.79

)1.22

Thistablepresentstherawandmarket-adjusted

returnsto

various6/Kprice

momentum

strategies.Value-weigh

tedcumulative

rawreturns(%

)arepresented

foreach

ofthetenpastreturnsportfolios.Attheendofeach

month

from

January1990

toDecem

ber

2006,

stocksareranked

ontheirpast6-month

buy-

and-hold

return

(R6).R6isin

%.Stocksareassign

edto

tenequally-w

eigh

tedportfoliosan

dvalue-weigh

tedreturnsarecomputedforeach

portfolio.The

bottom

10%

isassign

edto

theP1(loser)portfolioandthetop10%

totheP10

(winner)portfolio.Thehedge

portfolio(P10-P1)

isthezero-costportfoliothat

buys

thewinner

portfolio(P10)an

dsellstheloserportfolio(P1).Thelong-only

strategy

buys

thetop10%

ofpastperform

ers(P10)an

dholdstheposition

forK

months.Allholdingperiodreturnsarevalue-weigh

ted.Thelong-only

BHAR

isthemarket-adjusted

return,calculatedas

thevalue-weigh

tedreturn

from

P10

less

theholdingperiodreturn

forthevalue-weigh

tedbenchmarkforthesampleperiod.TheJ/K

strategy

isim

plementedas

carriedoutin

thestudy

byJegadeesh

andTitman(1993)

withoverlappingportfoliosconstructed.In

each

month,each

portfoliocomprisesstockswiththepriorJ

)1months.If

a

stock’sreturn

ismissingduringtheholdingperioditisreplacedwithazero

return.Thet-statistics

presentedarebased

ontheHACstan

darderrors.*an

d**

indicates

statisticalsign

ificance

atthe5an

d1%

levels,respectively.



loser portfolio (15.16 per cent) significantly outperforms the winner portfolio(9.52 per cent) such that the hedge portfolio return at 24 months is just 5.92per cent. Furthermore, the value-weighted market return (11.39 per cent) alsooutperforms the winner portfolio (9.52 per cent) over that final 12 months.While the winner portfolio initially performs well and demonstrates a drift inprices that is associated with past returns, the portfolio’s subsequent underper-formance does raise questions about whether fundamentals or exuberance aredriving returns in the first year. Similarly, the loser portfolio’s outperformancein the second year (15.16 per cent) suggests excessive pessimism may be atwork in the first year after portfolio formation. DeLong et al. (1990) discussthe possibility that momentum profits may at least in part be driven by trendchasers. The evidence in Table 2 hints at this possibility. Chan et al. (1996) dis-credit the view that the source of momentum profits may be the tendency of atleast some investors to chase past trends. They argue that if trend chasing byinvestors is the cause of momentum profits, the resulting temporary price driftsfor these stocks should reverse, yet find no evidence of this. That is, they findthat returns are similar across all portfolios in the second year after portfolioformation.The long-only strategy of taking a buy-and-hold position in portfolio P10

(winner) delivers market-adjusted returns of 5.74 per cent for 9 months but thenprogressively declines over the following 15 months because of the underperfor-mance of the winner portfolio over that period.Brailsford and O’Brien (2008) report value-weighted decile portfolio returns

for Australian stocks with a 6-month holding period that show no clear pricemomentum effect. Our results are not consistent with Brailsford and O’Brien(2008), but are generally consistent with Hong et al. (2004) who do report anAustralian price momentum effect with quintile portfolios and a 6-month hold-ing period but use equal-weighted rather than value-weighted returns. Ourresults are also generally consistent with the US study of Chan et al. (1996) whoreport strong price momentum across decile portfolios for 6 and 12 months. Noprior Australian study reports price momentum returns for all portfolios beyond12 months.5 In their US study, Chan et al. (1996) report no difference in returnsduring the second year across all ten portfolios. In contrast, we find some evi-dence of outperformance of the loser portfolio and underperformance of thewinner portfolio in year two.Table 2 also shows that the extreme portfolios contain on average much smal-

ler companies. This is a familiar observation from other portfolio studies ofextreme performance. For example, Brailsford and O’Brien (2008) find stocks inextreme price momentum portfolios are smaller and have higher standard devia-tions of returns than average past performing stocks.

5 Hurn and Pavlov (2003) do consider holding periods up to 36 months, but they onlyreport the hedge portfolio return and only for the top 200 stocks.




Table 3 indicates a positive association between portfolios’ most recent 6-month change in analyst forecast earnings (REV6) and future returns. It illus-trates a near monotonic increase in holding period raw returns when movingfrom loser to winner portfolios. This is evident starting as early as the 3-monthmark, post portfolio formation, and continuing through to the 24-month mark.Looking at the hedge portfolio return, it rises rapidly to 10.85 per cent at month12, and then is stable over the next year or so, but is at its highest point (11.45per cent) at month 24. The last column of Table 3 shows that the winner portfo-lio (13.33 per cent) underperforms the loser portfolio (14.80 per cent) during thesecond year after portfolio formation. However, both portfolios outperformthe market portfolio (11.39 per cent) during the second year. Noticeable in thereturns, across the ten portfolios during the second year, is some weak evidenceof a continuing positive relationship between earnings momentum and return.Table 3 also shows that the winner portfolio outperforms the market portfolioover all holding periods through to 24 months, and while the outperformanceis larger in the first year than the second year, there is still non-trivial outper-formance during the second year.The only prior Australian study of earnings momentum, (Hong et al., 2004)

reports a strong earnings momentum effect across quintile portfolios based onanalyst forecast revisions with a 1-, 3- and 6-month holding period. Our resultsare not readily comparable as we use deciles rather than quintiles but in generalboth our raw returns for each portfolio and the hedge portfolio return is higherthan reported by Hong et al. (2004).There is no prior Australian research that examines holding periods beyond

12 months, and most extend only to 6 months. Chan et al. (1996) report a strongearnings momentum effect across decile portfolios around analyst forecast revi-sions particularly in the first 6 months, and a smaller effect in the second6 months. In the second year, there is little difference across the eight middleportfolios but these portfolios are outperformed by the highest earnings portfolioand underperformed by the lowest earnings portfolio. While our results showsome weak correlation in year two between earnings momentum and returns, amarked exception is the lowest earnings momentum portfolio with a relativelystrong performance. The highest earnings momentum portfolio also produces astrong relative performance.

5.3. Separate explanatory power of past returns and earnings surprise

While both price momentum and earnings momentum are evident in thereturns at the end of 2 years, the behaviour of the returns during that 2-year per-iod is noteworthy. The loser price momentum portfolio underperforms the lowearnings momentum portfolio in the first year but turns the tables in the secondyear with a strong performance, resulting in similar 24-month returns for each.



Tab

le3

Raw

andmarket-adjusted

returnsto

Earnings

Momentum

Strategiesbased

onREV6

Portfolio

Meanmarket

capitalisation

($m)

Medianmarket

capitalisation

($m)

Average

number

ofestimates

Average

REV6

Holdingperiod(m

onths)

36

912

1518

2124

2nd

year

P1(low)

431

143

4.5

0.47

0.22

1.68

3.48

7.35

11.91

16.66

19.87

22.02

14.80

P2

917

239

6.2

0.80

1.66

2.76

4.99

9.14

12.30

15.90

19.24

22.64

12.70

P3

1352

329

6.9

0.89

1.88

4.46

7.09

9.43

12.16

16.18

18.79

22.08

12.39

P4

1992

499

7.8

0.94

2.04

4.44

6.94

10.51

13.53

16.64

20.15

23.00

12.12

P5

2519

600

8.1

0.97

3.61

7.05

9.82

13.35

17.40

19.84

23.39

26.48

11.96

P6

2407

546

7.0

0.99

3.50

7.30

11.01

14.96

18.51

21.83

25.97

28.27

11.95

P7

2611

500

7.0

1.01

4.70

9.01

13.04

18.10

21.72

25.55

29.42

32.68

13.18

P8

2338

480

7.1

1.03

3.94

8.06

12.59

16.87

21.27

25.66

29.49

32.83

13.71

P9

1753

409

6.6

1.09

3.41

7.92

12.38

16.15

19.76

24.41

28.52

31.93

14.07

P10

(high)

711

245

4.8

1.83

4.60

8.96

13.85

18.20

22.32

26.93

30.78

33.47

13.33

P10-P1(hedge)

4.38

7.28

10.36

10.85

10.40

10.27

10.90

11.45

)1.47

t-statistic

5.18**

6.28**

7.76**

6.43**

5.51**

4.72**

4.34**

4.36**

)0.95

Market

Return

2.93

6.09

9.33

12.93

16.21

19.42

22.60

25.49

11.39

Long-only

(P10)

4.60

8.96

13.85

18.20

22.32

26.93

30.78

33.47

13.33

Long-only

BHAR

1.67

2.87

4.52

5.28

6.10

7.51

8.17

7.98

1.94

t-statistic

2.67*

3.19**

3.86**

4.07**

4.08**

4.49**

4.19**

4.03**

1.76*

Thistablepresentstheraw

andmarket-adjusted

returnsto

various6/K

earnings

momentum

strategies.Value-weigh

tedcumulativeraw

returns(%

)are

pre-

sentedforeach

ofthetenearnings

surprise

portfolios.Attheendofeach

month

from

January1990

toDecem

ber

2006,

stocksareranked

onthebasisofthe

percentage

chan

gein

theirmonthly

analystearnings

per

shareforecast

duringthemost

recent6months(R

EV6).Thebottom

10%

isassign

edto

theP1

(loser)

portfolioan

dthetop10%

totheP10

(winner)portfolio.Stocksareassign

edto

tenequally

weigh

tedportfoliosan

dvalue-weigh

tedreturnsarecom-

putedforeach

portfolio.Thehedge

portfolio(P10-P1)

isthezero-cost

portfoliothat

buys

thewinner

portfolio(P10)andsellstheloserportfolio(P1).The

long-only

strategybuys

the10%

ofstockswiththehighestearnings

surprise

asmeasuredbyREV6an

dholdsthepositionforK

months.Thelong-only

BHAR

isthemarket-adjusted

return

tothewinner

portfolio(P10),calculatedas

thevalue-weigh

tedreturn

from

P10less

theholdingperiodreturn

forthe

value-weigh

tedbenchmarkforthesampleperiod.Allholdingperiodreturnsarevalue-weigh

ted.TheJ/K

strategy

isim

plementedas

carriedoutin

studyby

Jegad

eesh

andTitman(1993)

withoverlap

pingportfolios.In

each

month,each

portfoliocomprisesstockswiththepriorJ

)1months.Ifastock’sreturn

is

missingduringtheholdingperiod,itisreplacedwithazero

return.Thet-statistics

presentedarebasedontheHACstan

dard

errors.*and**indicates

statis-

ticalsign

ificance

atthe5and1%

levels,respectively.



In the first year, the winner price portfolio and high earnings portfolio have sim-ilar returns, but in the second year, the winner price portfolio significantly und-erperforms the high earnings portfolio and the market. Looking across allportfolios what is noticeable is the outperformance of the highest four earningsportfolios over the highest four price portfolios in both the first and secondyear, such that there is a wide margin between the two groups at the end of2 years. Also noteworthy is the outperformance of the highest four earningsportfolios over the market portfolio during the second year. Further, only oneof the earnings portfolios (P4) is outperformed by a price portfolio (P4) overthe 2 years. Recall, that the earnings portfolios comprise fewer and larger stocksthan the corresponding price portfolios. Size does not therefore provide a readyexplanation for the return differences, as the earnings portfolios with the largerstocks have on average outperformed the price portfolios that comprise smallerstocks.The differing behaviour of the price momentum portfolios and the earnings

momentum portfolios, particularly during the second year, suggests that pricemomentum or earnings momentum does not subsume the other. That is, whilethere may be common elements to each, both price and earnings momentumhave unique drivers. To explore this further, (dual-sort) portfolios are createdbased on both price momentum and earnings momentum, and used to examinethe incremental return from changing one, while holding the other constant.Then, stock level Fama–MacBeth regressions are performed to provide a morerigorous statistical assessment of the incremental contribution of price momen-tum and earnings momentum.Table 4 shows the separate effect of ranking on earnings surprise (REV6) when

controlling for past returns (R6). It is important to note that in order to ensure alarge-enough sample size in each portfolio, each stock is placed independently inone of three price momentum and one of three earnings momentum portfolios,rather than decile portfolios as was done with the earlier single sort. Table 4highlights the impact of changing the level of earnings momentum while holdingprice momentum constant. Portfolios 1–3 represent the one-third of stocks withthe lowest price momentum (i, losers), portfolios 4–6 represent the one-third ofstocks with the middle price momentum (ii), and portfolios 7–9 represent theone-third of stocks with the highest price momentum (iii, winners). As expected,the mean R6 values for portfolios 1–3 are materially negative and significantlypositive for portfolios 7–9. Also, as expected, the mean REV6 values are similaracross each of the three groups of portfolios. For example, for portfolios 1–3,REV6 is 0.71, 0.98 and 1.51 compared with values of 0.74, 0.98 and 1.25 acrossportfolios 7–9. The stock size and number of analyst metrics indicate, not sur-prisingly, that the middle portfolio whether sorting on price (ii) or earnings (B)momentum, tends to have the larger stocks with greater analyst coverage. This isnot surprising as we reasonably expect portfolios with the more extreme behav-iour (portfolio i/A and iii/C) to have a greater concentration of smaller stockswith lower analyst coverage. Further, while portfolio ii/B tends to comprise



larger stocks than portfolios i/A and iii/C, portfolio iii/C tends to comprise largerstocks than portfolio i/A, probably reflecting the negative impact of poor pastperformance on market capitalisation at the time of portfolio formation. Thus,size is a variable that has not been controlled in this dual-sort procedure andmay play some role in the underlying returns. Turning to the portfolio returns,the following features are noteworthy. At the 12-month mark after portfolioformation, the hedge portfolio (C minus A) return is positive in all three pricemomentum groups, but only weakly so in the lowest price momentum group(i). Further, within the two largest price momentum groups (ii and iii), therelationship between earnings momentum and return is positive and monotonic,but not so in the lowest price momentum group (i). During the second year after

Table 4

Raw returns to R6-REV6 dual-sort value-weighted momentum portfolios with a focus on changing

REV6 while holding R6 constant

Portfolio # 1 2 3 4 5 6 7 8 9

K holding period (months)

R6 i (loser) i i ii ii ii iii iii iii (winner)

REV6 A (low) B C A B C A B C (high)

Panel A: ranking variables

R6 )20.37 )13.61 )16.04 4.78 5.67 6.33 38.15 31.46 37.61

REV6 0.71 0.98 1.51 0.79 0.98 1.20 0.74 0.98 1.25

Panel B: size

894 1673 1176 228 2969 2095 793 2158 1724

Panel C: number of analysts

6.2 7.0 5.6 6.2 7.9 6.7 5.1 7.0 6.4

Panel D: value-weighted raw returns

3 0.14 3.02 1.09 2.82 3.02 3.35 3.65 3.81 4.54

6 1.36 5.32 2.65 5.57 6.77 6.82 7.04 7.57 9.67

9 3.51 8.02 5.55 7.98 10.41 11.15 10.32 10.84 14.64

12 7.53 10.70 8.95 10.11 14.58 16.02 13.30 14.93 18.52

15 10.61 14.68 12.94 13.07 18.14 19.81 16.51 18.24 22.23

18 14.27 17.89 16.75 15.79 20.97 24.00 20.62 21.12 26.40

21 16.85 20.56 20.70 18.71 24.68 27.87 24.49 25.20 29.95

24 20.29 23.22 24.59 21.38 27.52 31.42 27.05 27.67 32.74

2nd year 12.76 12.52 15.64 11.27 12.94 15.40 13.75 12.74 14.22

This table presents the 6-month buy-and-hold returns (%) to each of the nine past returns-analyst

revision value-weighted momentum portfolios during the period January 1990 to December 2006.

The portfolios are formed by first ranking stocks on their R6 and then independently by their REV6.

The positions in each portfolio are held for 3–24 months. R6 is the buy-and-hold return on stocks

for the most recent 6 months. REV6 is the percentage change in the I/B/E/S consensus forecast over

the most recent 6 months. Panel A lists the magnitude of each of the ranking variables for each of

the nine dual-sort portfolios. R6 is presented in percentage. Panel B lists the average market capitali-

sation of the stocks (in millions of dollars) entering each of the nine dual-sort portfolios. Panel C lists

the average number of analysts that contribute to the I/B/E/S consensus forecast for each portfolio

per month. Panel D lists the value-weighted raw returns to each of the nine dual-sort portfolios.



portfolio formation, a weaker relationship between earnings momentum andreturns endures with positive hedge portfolio returns (C minus A).Table 5 presents the same data as Table 4 but is re-ordered to highlight the

control of earnings momentum (REV6) while changing price momentum (R6).Portfolios 1–3 comprise the lowest earnings momentum (A) portfolios (meanREV6 range of 0.71–0.79), while portfolio 7–9 comprise the highest earningsmomentum (C) portfolios (mean REV6 range of 1.2–1.51). Within each earningsmomentum group, the mean R6 value moves from large negative to large posi-tive, as expected, and these R6 values are comparable across the groups. Forexample, for portfolios 1–3, R6 is )20.37 per cent, 4.78 per cent and 38.15 per

Table 5

Raw returns to R6-REV6 dual-sort value-weighted momentum portfolios with a focus on changing

R6 while holding REV6 constant

Portfolio # 1 2 3 4 5 6 7 8 9

K holding period (months)

R6 i ii iii i ii iii i ii iii

REV6 A (low) A A B B B C C C (high)

Panel A: ranking variables

R6 )20.37 4.78 38.15 )13.61 5.67 31.46 )16.04 6.33 37.61

REV6 0.71 0.79 0.74 0.98 0.98 0.98 1.51 1.20 1.25

Panel B: size

894 1228 793 1673 2969 2158 1176 2095 1724

Panel C: number of analysts

6.2 6.2 5.1 7.0 7.9 7.0 5.6 6.7 6.4

Panel D: value-weighted raw returns

3 0.14 2.82 3.65 3.02 3.02 3.81 1.09 3.35 4.54

6 1.36 5.57 7.04 5.32 6.77 7.57 2.65 6.82 9.67

9 3.51 7.98 10.32 8.02 10.41 10.84 5.55 11.15 14.64

12 7.53 10.11 13.30 10.70 14.58 14.93 8.95 16.02 18.52

15 10.61 13.07 16.51 14.68 18.14 18.24 12.94 19.81 22.23

18 14.27 15.79 20.62 17.89 20.97 21.12 16.75 24.00 26.40

21 16.85 18.71 24.49 20.56 24.68 25.20 20.70 27.87 29.95

24 20.29 21.38 27.05 23.22 27.52 27.67 24.59 31.42 32.74

2nd year 12.76 11.27 13.75 12.52 12.94 12.74 15.64 15.40 14.22

This table presents the 6-month buy-and-hold returns (%) to each of the nine past returns-analyst

revision value-weighted momentum portfolios during the period January 1990 to December 2006.

The portfolios are formed by first ranking stocks on their R6 and then independently by their REV6.

The positions in each portfolio are held for 3–24 months. R6 is the buy-and-hold return on stocks

for the most recent 6 months. REV6 is the percentage change in the I/B/E/S consensus forecast over

the most recent 6 months. Panel A lists the magnitude of each of the ranking variables for each of

the nine dual-sort portfolios. R6 is presented in percentage. Panel B lists the average market capitali-

sation of the stocks (in millions of dollars) entering each of the nine dual-sort portfolios. Panel C lists

the average number of analysts that contribute to the I/B/E/S consensus forecast for each portfolio

per month. Panel D lists the value-weighted raw returns to each of the nine dual-sort portfolios.



cent compared with values of )16.04 per cent, 6.33 per cent and 37.61 per centacross portfolios 7–9. At the 12-month mark, there is a distinct positive relation-ship between price momentum and returns, within each earnings momentumgroup and the hedge portfolio return (iii minus i) is a large positive value in eachcase. However, during the second year, there is no discernable relationshipbetween price momentum and returns.By finding that returns to earnings momentum vary while holding price

momentum constant (and vice versa), these results indicate that both pricemomentum and earnings momentum deliver incremental returns. This suggeststhat the simultaneous use of both price and earnings momentum shouldimprove on the returns that are achieved with each in isolation. Stocks that areranked highest by both price and earnings momentum (portfolio 9 in Table 4)deliver a return of 18.52 per cent after 12 months and 32.74 per cent after24 months. This 12-month return exceeds that recorded by the price momentumwinner portfolio (17.85 per cent) and the earnings momentum high portfolio(18.2 per cent). And, while it outperforms the price momentum winner portfolio(27.19 per cent) over 24 months, it is slightly less than the earnings momentumhigh portfolio (33.47 per cent). However, it is important to recognise that thecombined price and earnings momentum portfolio faces a handicap by beingdrawn from the top one-third of each group of stocks rather than the extremetop ten per cent in the case of the price momentum winner portfolio or earningsmomentum high portfolio. This must serve to dampen the resulting return, butprobably also to increase the size of stocks and perhaps reduce the riskiness ofstocks in the combined portfolio. The increase in stock size is marked with port-folio 9 in Table 4 having a mean size of $1,724m compared to just $232m forthe winner portfolio in Table 2 and $711m for the high earnings portfolio inTable 3.The analysis presented up to this point has been at the portfolio level without

full statistical control over the variables of interest. Table 6 presents the resultsof performing Fama–MacBeth regressions at the individual stock level withsize, R6 and REV6 as explanatory variables for the subsequent 6-month return.In Panel A, the dependent variable is the raw (non-risk adjusted) return. InModel 1, past returns, R6, by itself possesses strong explanatory power forfuture returns. In Model 2, REV6, by itself, while statistically significant, is notas strong as R6. This suggests the percentage change in analyst EPS forecasts isnot as important in explaining future returns as is past returns. In Model 3when all three variables are included, the bulk of the explanatory power forfuture returns is retained by R6. Nonetheless, REV6 still maintains its strongstatistical significance suggesting it has explanatory power for future returnsthat is separate from past returns performance. In Panel B, the dependent vari-able is now the subsequent 6-month return adjusted using the Fama Frenchthree-factor model. It is not surprising after this adjustment to find the size var-iable is no longer statistically significant in all three Models (4–6). While R6loses some of its power in Model 4 compared to Model 1, REV6 becomes a



little stronger in Model 5 compared to Model 2. Similarly, in Model 6, R6 is alittle weaker and REV6 a little stronger than in Model 3. However, the overallpicture has not changed with both R6 and REV6 statistically significant and R6remaining the stronger of the two variables. Panel C employs four-factor risk-adjusted returns as the dependent variable, with the fourth factor being PMN –constructed monthly as the mean return on the 30 per cent of stocks with thelargest value of REV6 minus the 30 per cent of stocks with the smallest valueof REV6. Chordia and Shivakumar (2006) perform a similar analysis and findthat the use of the PMN factor greatly reduces the significance of the pricemomentum variables, while the earnings momentum variable retains a highlevel of significance. While, in Panel C below, there is some reduction in the sig-nificance of R6 because of the inclusion of PMN, R6 remains highly significantwhile REV6 is now only significant at 5 per cent rather than the 1 per cent levelpreviously.This evidence is consistent with the single sorts in Tables 2 and 3 at the

6-month mark where there is greater variation in return on the price momentumportfolios than on the earnings momentum portfolios. It is also consistent withthe return behaviour in the dual-sort portfolios of Tables 4 and 5 where at the6-month mark there is much greater variation in the returns to the price momen-tum portfolios when holding earnings momentum constant, than there is in thereturns to the earnings momentum portfolios when holding price momentumconstant.

Table 6

Monthly cross-sectional Fama-MacBeth regressions of returns of prior 6 month return and prior

earnings surprises

Model Intercept Log_Size R6 REV6 Adj R2

Panel A: dependent variable is raw (non-risk adjusted) return

1 0.116 (7.75) )0.009 ()5.39) 0.157 (8.61) 0.050

2 0.0625 (3.58) )0.006 ()3.76) 0.048 (3.54) 0.026

3 0.092 (5.25) )0.009 ()5.44) 0.149 (8.08) 0.025 (2.99) 0.059

Panel B: dependent variable is return adjusted using Fama French 3 factors

4 )0.008 ()0.47) 0.000 (0.02) 0.117 (7.24) 0.046

5 )0.060 ()3.12) 0.001 (0.60) 0.051 (5.53) 0.023

6 )0.036 ()1.79) )0.000 ()0.09) 0.112 (6.98) 0.030 (3.38) 0.053

Panel C: dependent variable is return adjusted using Fama French 3 factors plus PMN factor

7 0.018 (0.91) )0.001 ()0.53) 0.094 (5.34) 0.039

8 )0.017 ()0.85) )0.001 ()0.27) 0.035 (4.05) 0.019

9 )0.001 ()0.06) )0.001 ()0.57) 0.092 (5.16) 0.020 (2.33) 0.044

Cross-sectional regressions are estimated each month from January 1990 to December 2006 of indi-

vidual stock returns over the next 6 months on the natural log of market capitalisation (log_Size),

compound return over the prior 6 months (R6) and percentage change in the consensus analyst EPS

forecast over the most recent 6 months (REV6). The t-statistics presented are based on the standard

errors corrected for first order correlation.



6. Concluding comments

While there are a number of prior Australian studies of price momentum,there is a lack of consistency in reported results amongst those studies, and incomparison with US and other international research. Prior Australian studiesalso generally limit their analysis to the first 6–12 months after portfolio forma-tion. There are no prior published studies of Australian earnings momentum,and only one unpublished work where Australia is just one of eleven countriesexamined, and in necessarily limited depth. Our study provides the first compre-hensive examination of Australian earnings momentum and its interaction withprice momentum. Our analysis extends into the second year after portfolio for-mation, which offers additional insights into the character of the momentumeffect.We do find a price momentum effect which, consistent with prior US work, is

strongest around the 6–12-month mark, post portfolio formation. However,inconsistent with that work is a reversal in the extreme decile portfolios duringthe second year after portfolio formation which hints at some part of the initialmomentum returns being because of trend chasing behaviour. Employing mar-ket-adjusted returns, it becomes evident that the strength of the momentumeffect is due substantially to the short side, limiting its exploitability. A strongearnings momentum effect is also evident, particularly in the first year after port-folio formation. There is also some weak evidence of it continuing into the sec-ond year. Again, the strength of the earnings momentum effect is again duesubstantially to the short side. Of most interest in these initial results is the con-trasting performance of the price and earnings momentum portfolios during thesecond year. While the price momentum portfolios display evidence of a reversal,the earnings momentum portfolios show a tendency for continuation of themomentum. This result is broadly consistent with the findings of Chan et al.(1996). This behaviour is consistent with a view that the initial price momentumis at least partially affected by trend chasing behaviour of noise traders, whereasthe initial earnings momentum is driven by informed investors acting on substan-tive changes in future expected earnings.Further investigation reveals that the price momentum and earnings momen-

tum effects are not subsumed by the other. Dual-sort portfolios are createdallowing for price or earnings momentum to be held constant while changing theother. The relationship between each momentum type and return survives thisprocedure, indicating each has an independent effect on returns. However, theprice momentum effect is evidently stronger in the first 12 months, and theearnings momentum effect stronger in the second 12 months. Stock levelFama–MacBeth regressions confirm the independent role of price and earningsmomentum in returns.While our research confirms the existence and independent effects of price

momentum and earnings momentum, this literature is characterised by troublinginconsistencies. Brailsford and O’Brien (2008) notes the inconsistent findings of



prior Australian research then finds no price momentum when all Australianstocks are studied. In contrast, we do find price momentum in a similar settingbut across a somewhat different time period. More troubling is the lack of con-sistency between these Australian results and prior US research. We find evi-dence of reversal in the extreme price portfolios, which is not evident in Chanet al. (1996) US study. Why Australian data provides contrary evidence is puz-zling and requires further research. Prior research has moved little beyond estab-lishing the existence of a momentum effect. Future research needs to deepen ourunderstanding of what underpins that effect. Ultimately, this should help explainand resolve the notable inconsistencies between and within markets.

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Price and earnings momentum in Australian stock returns

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