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Anomalies in US equity markets: a re-examinationof the January effectSeyed Mehdian & Mark J. Perry

Available online: 07 Oct 2010

To cite this article: Seyed Mehdian & Mark J. Perry (2002): Anomalies in US equity markets: a re-examination of theJanuary effect, Applied Financial Economics, 12:2, 141-145

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Anomalies in US equity markets: a re-

examination of the January eVect

SEYED MEHDIAN and MARK J. PERRY*{

School of Management and * Department of Economics, University of Michigan-Flint,Flint, Michigan 48502, USAE-mails: seyed@¯int.umich.edu and * mjperry@umich.edu

This study investigates the January eVect in US equity markets using three marketindexes from 1964±1998: Dow Jones Composite, NYSE Composite and the SP500.Chow tests for structural stability indicate that the estimated parameters in anequation testing for monthly seasonal eVects in the stock market are not stableover time. In the 1964±1987 sample period it is found that January returns arepositive and signi®cant in all three stock market indexes. After 1987, Januaryreturns are positive but not statistically diVerent from zero. The results thereforeprovide no statistical support for the January eVect in US equity markets in the post-1987 market crash period.

I . INTRODUCTION

The e� cient market hypothesis (EMH) posits that stocks

are priced e� ciently to re¯ect all available information

about the intrinsic value of the security. An e� cient market

is also one where all unexploited pro®t opportunities areeliminated by pro®t seeking investors. A large number of

empirical studies in ®nance, however, have documented

several persistent and exploitable seasonal patterns in

equity markets, which have challenged the EMH. If stock

market anomalies exist, investors can generate abnormal

returns using trading rules to exploit the predictable behav-

iour of stock prices, an outcome that is inconsistent withthe EMH. One of the best-known stock market anomalies

is the January eVect (see RozeV and Kinney, 1976; Haugen

and Jorion, 1996), that occurs when stock returns in

January are signi®cantly higher than returns in other

months of the year. The January eVect has generally been

found: (1) to aVect small ®rms more than large ®rms and

(2) to occur mostly in the early part of the month (Banz,1981; Keim, 1983).

Two hypotheses have been put forward to explain the

January eVect. First, the `tax-loss selling’ hypothesis

(Reinganum, 1983) argues that returns in January are sig-

ni®cantly positive for those stocks with negative returns

during the previous year or towards the end of the year.

The hypothesis assumes that investors sell poorly perform-

ing stocks at the end of the year in order to realize capital

losses for tax purposes. Investors then buy stocks after the

®rst of the year to re-establish their portfolios and this

buying pressure creates the January eVect. However,

Gultekin and Gultekin (1983) study the January eVect in16 international stock markets with diVerent tax calendars

and report that the January eVect is present in ®fteen of the

countries studied.

A second explanation for the January eVect is the `insti-

tutional investor behaviour’ hypothesis (see Haugen and

Lakonishok, 1988). This hypothesis postulates that institu-tional investors, `warehouse’ money in a market index used

to measure their performance until the end of the year, and

then buy stocks after the ®rst of the year, which puts

upward pressure on security prices and creates the

January eVect.

Most previous studies of the January eVect make no

attempt to explicitly test for structural stability of the esti-mated coe� cients and therefore have assumed that the

data series used are intertemporally stable. This article

investigates the long-term stability of the January eVect

using Chow break point tests to see whether the eVect

changes over time. There is a further test to ®nd whether

Applied Financial Economics ISSN 0960±3107 print/ISSN 1466±4305 online # 2002 Taylor & Francis Ltd

http://www.tandf.co.uk/journalsDOI: 10.1080/0960310011008806 7

Applied Financial Economics, 2002, 12, 141±145

141

{ Corresponding author.

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this eVect occurs in the ®rst trading week of January orlater in this month.

In this paper it is documented that US equity returns in

three major stock indexes (Dow Jones Composite, NewYork Stock Exchange and S&P 500) are not structurally

stable over time and there is a signi®cant structural breakaround the 1987 stock market crash. Evidence is also foundthat while January stock returns are signi®cantly positivefrom 1964 to 1998, they are statistically insigni®cant after

1987. That is, the results reveal no signi®cant support forthe January eVect in the post-crash era in US equity mar-

kets. In addition, it is demonstrated that stock returns arenegative, though statistically insigni®cant, during ®rst®ve trading days of January as opposed to the rest of the

trading days in this month. This ®nding is inconsistent withthe results previously reported in the literature (Keim,1983).

This paper is organized as follows. Section II discussesthe stock market data and the methodology used. SectionIII presents the empirical results and Section IV contains

the summary and conclusions.

II . DATA AND METHODOLOGY

The analysis is conducted using daily closing values from

three major US stock market indexes: the Dow JonesComposite (DJCOMP), the New York Stock Exchange

Composite (NYSE), and the Standard & Poors 500(SP500), from 4 June 1964 to 8 August 1998 (8301 obser-vations). Returns for each stock index are computed as:

Rit ˆ log…Iit=Iit¡1† ¤ 100 …1†

where Rit is the daily percentage return of stock index i onday t, and Iit and Iit¡1 are closing values on day t and t ¡ 1for the same index. Monthly dummy variables (D1 to D12)

are created and the January eVect is tested using the fol-lowing equation:

Rit ˆ ¬1iD1 ‡ ¬2iD2 ‡ ¬3iD3‡; . . . ; ‡¬10iD10 ‡ ¬11iD11

‡ ¬12iD12 ‡ "t …2†

In Equation 2 Ri t is the daily return of index i as de®ned

earlier, D1 through D12 are dummy variables for eachmonth of the year such that D1 ˆ 1 if day t falls inJanuary and D2 ¡ D12 ˆ 0 otherwise; D2 ˆ 1 if day t is in

February and D1 ˆ D3 ¡ D12 ˆ 0 otherwise, and so on.The ¬s are coe� cients to be estimated, and "t is a randomerror term. The estimated coe� cient ¬1i will be signi®-

cantly positive for those stock indexes that exhibit aJanuary eVect.

III . EMPIRICAL RESULTS

Equation 2 is estimated for each stock index using OLS,

and the estimated coe� cients with their t-statistics arereported in Table 1. As can be seen, there is a statistically

signi®cant, positive January eVect in all three stock indexes

over the 4 June 1964 to 8 August 1998 sample period (atthe 1% level for the DJCOMP and at the 5% level for the

NYSE and the SP500). The returns during April are also

positive and signi®cant at the 5% level for all indexes.These results are generally consistent with the ®ndings pre-

viously reported in the literature, cited earlier, that mean

stock returns in January are positive and signi®cant.As mentioned before, most previous studies of stock

market anomalies do not test whether the estimated coe� -

cients are stable over time. In this paper, a series of Chowbreakpoint tests are conducted of the null hypothesis that

the estimated parameters reported in Table 1 are stable

over the entire sample period. Table 2, Panel A displaysthe F-values from Chow tests using 1 October, 1987 (a

period right before the stock market crash) as the breakpoint. The evidence indicates that the estimated parameters

are structurally unstable for all indexes at the 10% level or

higher. Equation 2 is then re-estimated over the postcrashperiod only to test for the structurally stability of the esti-

142 S. Mehdian and M. J. Perry

Table 1. Regression results for January eVect: 6/4/1964 to 8/14/1998

DJCOMP NYSE SP500

JAN 0.0853 0.0805 0.0780(2.60)*** (2.52)** (2.31)**

FEB 0.0275 0.0347 0.0317(0.79) (1.03) (0.89)

MAR 0.0191 0.0346 0.0343(0.60) (1.11) (1.05)

APR 0.0680 0.0683 0.0782(2.05)** (2.11)** (2.29)**

MAY 70.0092 0.0064 0.0058(-0.28) (0.20) (0.17)

JUN 0.0017 0.0180 0.0172(0.05) (0.58) (0.53)

JUL 0.0465 0.0316 0.0359(1.42) (0.99) (1.07)

AUG 70.0012 0.0153 0.0124(70.04) (0.50) (0.38)

SEP 70.0088 70.0039 70.0078(70.26) (70.12) (70.23)

OCT 0.0010 0.0027 0.0084(0.03) (0.09) (0.26)

NOV 0.0298 0.0354 0.0344(0.88) (1.06) (0.98)

DEC 0.0489 0.0482 0.0487(1.47) (1.49) (1.43)

Notes: T-statistics are in parentheses. *** indicates statistical sig-ni®cance at the 0.01 level, ** at the 0.05 level.

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mated coe� cients using 1 January1994 as the breakpoint.

The F-values in panel B of this table are insigni®cant, pro-

viding evidence that the parameters are structurally stablein the post 1987 stock market crash period.1

Given the results of the Chow breakpoint tests, the data

set is divided into two subsample periods: the pre-1987

crash period, the post-1987 crash. Equation 2, is then re-estimated for these two subsample periods and the ®ndings

are reported in Tables 3 and 4.

The evidence in Table 3 indicates that the January eVect

is clearly present in the pre-crash 1964±1987 sample period

for all three stock indexes, since returns in January are

signi®cantly positive at the 5% level of signi®cance. The

results presented in Table 4, on the other hand, suggest thatthere is no January eVect in the post-crash 1987±1998 per-

iod, since January returns are positive but insigni®cant for

each stock index.

In sum, while evidence is found of a January seasonal

before the 1987 market crash, there is no evidence of theJanuary eVect in the post market crash era. This means

that the ®ndings do not support either the `tax loss-selling’hypothesis or `institutional investor behaviour’ hypothesis

in US equity markets after the 1987 stock market crash.

Finally, following Keim (1983), an investigation is con-

ducted to see whether the January eVect occurs in the ®rst

®ve trading days of January. To accomplish this, one must®rst decompose the mean returns in January into two com-

Anomalies in US equity markets 143

1 These results are not sensitive to the exact breakpoint chosen. Although not reported here, six other breakpoint dates also resulted ininsigni®cant F-values.

Table 2. Tests of intertemporal stability

A. 6/4/1964 to 8/14/1998

NASDAQ 2.07**NYSE 1.77**SP500 1.66*

(Break point: 10/1/1987)

B. 11/1/1987 to 8/14/1998

DJCOMP 0.68NYSE 0.72SP500 0.72

(Break point: 1/1/1994)

Notes: ** indicates statistical signi®cance at the 0.05 level and * atthe 0.10 level. F-statistics are based on the null hypothesis that theslope coe� cients and the overall regressions are structurally stableover the sample period, against the alternative hypothesis thatthey are not stable.

Table 3. Regression Results for the January eVect: 6/4/1964 to 10/01/1987

DJCOMP NYSE SP500

JAN 0.0887 0.0865 0.0796(2.39)** (2.34)** (2.09)**

FEB 70.0118 70.0019 70.0045(70.30) (70.05) (70.11)

MAR 0.0227 0.0420 0.0448(0.63) (1.17) (1.21)

APR 0.0472 0.0587 0.0656(1.26) (1.57) (1.70)*

MAY 70.0487 70.0449 70.0487(71.31) (71.22) (71.29)

JUN 0.0022 0.0222 0.0194(0.06) (0.62) (0.53)

JUL 0.0185 0.0010 0.0011(0.50) (0.03) (0.03)

AUG 0.0328 0.0465 0.0469(0.93) (1.33) (1.30)

SEP 70.0158 70.0177 70.0216(70.42) (70.48) (70.56)

OCT 0.0492 0.0554 0.0583(1.37) (1.55) (1.59)

NOV 0.0327 0.0431 0.0383(0.84) (1.12) (0.96)

DEC 0.0177 0.0106 0.0132(0.47) (0.28) (0.34)

Notes: T-statistics are in parentheses. *** indicates statistical sig-ni®cance at the 0.01 level, ** at the 0.05 level and * at the 0.01level.

Table 4. Regression results for January eVect: 11/01/1987 to 8/14/1998

DJCOMP NYSE SP500

JAN 0.0780 0.0680 0.0744(1.41) (1.32) (1.31)

FEB 0.1098 0.1115 0.1079(1.87)* (2.05)** (1.79)*

MAR 0.0117 0.0192 0.0129(0.22) (0.39) (0.24)

APR 0.1117 0.0885 0.1049(1.99)** (1.70)* (1.82)*

MAY 0.0737 0.1145 0.1201(1.33) (2.22)** (2.12)**

JUN 0.0004 0.0090 0.0125(0.01) (0.18) (0.23)

JUL 0.1065 0.0971 0.1104(1.91)* (1.88)* (1.94)*

AUG 70.0770 70.0542 70.0648(71.42) (71.08) (71.17)

SEP 0.0078 0.0291 0.0249(0.13) (0.53) (0.41)

OCT 0.0274 0.0169 0.0275(0.49) (0.33) (0.48)

NOV 0.0241 0.0198 0.0266(0.43) (0.38) (0.46)

DEC 0.1132 0.1258 0.1219(2.04)** (2.43)** (2.14)**

Notes: T-statistics are in parentheses. *** indicates statistical sig-ni®cance at the 0.01 level, ** at the 0.05 level and * at the 0.01level.

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ponents: (a) the mean return for the ®rst ®ve trading days

of the month (®rst trading week) and (b) the mean return

for the last 25 trading days of the month (second to ®fthtrading weeks). Then, diVerence-of-means tests are con-

ducted based on the null hypothesis that the mean returns

during the ®rst trading week of January are equal to the

mean returns during the rest of January.

Table 5 provides means, standard deviations, and t-

values from the diVerence-of-means tests for the entire

sample (1964±1998) and the two sub-sample periods. Ascan be seen in Panels A and B, mean returns during the

®rst trading week of January are higher than the returns

during the rest of the month, but the diVerence-of-means

tests show that this diVerence is not statistically signi®cant.

Additionally, Panel C shows that in the postcrash period,the mean returns for the ®rst trading week of January are

actually negative, and are not signi®cantly diVerent from

the rest of the month. Consequently, the ®ndings reveal nosigni®cant support for the results reported previously in theliterature that the January eVect substantially occurs in the®rst ®ve trading days of January.

IV. SUMMARY AND CONCLUSION

This study re-examines the January eVect in US equitymarkets from 1964±1998 using three major market indexes(DJCOMP, NYSE, and SP500). Over the full sample per-iod, it is found that a signi®cantly positive January eVectexists in all three stock market indexes consistent with pre-vious literature on stock market anomalies. However, it isdocumented that the estimated parameters in equationstesting for monthly seasonal eVects are not structurallystable over the full sample period and there is a statisticallysigni®cant intertemporal break around the time of the 1987stock market crash. The January eVect is then examinedseparately in the precrash period and postcrash periods. Inthe precrash period, evidence of the January eVect is foundin each of the three stock market indexes. In the postcrashperiod, however, January returns are found to be positivebut statistically insigni®cant, indicating that the JanuaryeVect does not exist in the postcrash period.Consequently, the results do not provide any statisticalsupport for either the `tax-loss selling’ or `institutionalinvestor behaviour’ eVects in the US stock market after1987.

Furthermore, in contrast to the previous literature, thispaper shows that stock returns during the ®rst week ofJanuary are not statistically diVerent from the returns dur-ing the rest of the month. The ®ndings, in general, indicatethat the January eVect can no longer be considered one ofthe several well-documented seasonal anomalies in the USstock market. Since seasonal anomalies representunexploited pro®t opportunities and violate market e� -ciency, the disappearance of the January eVect may implythat US stock markets are gradually becoming more`weakly e� cient’ in the postcrash period. The absence ofa January eVect in the post crash era may be due to thesigni®cant growth in the derivative markets for equities andincreased trading by institutional investors who processinformation faster and at lower transaction costs(Kamara, 1997).

REFERENCES

Banz, R. W. (1981) The relationship between return and marketvalue of common stock, Journal of Financial Economics, 9, 3±18.

Gultekin, M. and Bulent Gultekin, N. (1983) Stock market sea-sonality: international evidence, Journal of FinancialEconomics, 12, 469±81.

Haugen, R. A. and Jorion, P. (1996) The January eVect: still thereafter all these years, Financial Analysts Journal, 52, 27±31.

144 S. Mehdian and M. J. Perry

Table 5. January returns by week of the month

DiVerence ofWeek 1 Weeks 2-5 means test

A. 6/4/64±8/14/98DJCOMP

Mean 0.1733 0.0730 0.08Standard dev. 1.0599 0.8157

NYSEMean 0.1083 0.0766 0.27Standard dev. 1.0501 0.7810

SP500Mean 0.0777 0.0779 0.01Standard dev. 1.1252 0.8247

Observations 84 606

B. 6/4/64±10/1/87DJCOMP

Mean 0.2500 0.0654 1.55Standard dev. 0.8542 0.7911

NYSEMean 0.2130 0.0682 1.24Standard dev. 0.8432 0.7751

SP500Mean 0.1866 0.0642 1.02Standard dev. 0.8612 0.7981

Observations 59 409

C. 11/1/87±8/14/98DJCOMP

Mean 70.0076 0.0889 70.32Standard dev. 1.4390 0.8665

NYSEMean 70.1387 0.0942 70.80Standard dev. 1.4150 0.7950

SP500Mean 70.1790 0.1065 70.75Standard dev. 1.5770 0.8787

Observations 25 197

Notes: T-statistics are based on the null hypothesis that meanreturns during the ®rst trading week of the month is equal tomean returns during the rest of the month.

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Haugen, R. and LaKonishok, J. (1988) The Incredible JanuaryEVect (Homewood, IL: Dow Jones Irwin).

Kamara, A. (1997) New evidence on the Monday seasonal instock returns, Journal of Business, 70, 63±84.

Keim, D. B. (1983) Size-related anomalies and stock return sea-sonality: further empirical evidence, Journal of FinancialEconomics, 12, 13±32.

Reinganum, M. R. (1983) The anomalous stock market behaviorof small ®rms in January: empirical tests for tax-lossselling eVects, Journal of Financial Economics, 12, 89±104

RozeV, M. and Kinney, W. (1976) Capital market seasonality: thecase of stock returns, Journal of Financial Economics, 3, 379±402.

Anomalies in US equity markets 145

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