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The Quarterly Review of Economics and Finance, Vol. 37, No. 3, Fall 1997, pages 725-737

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ISSN 1062-9769

The Individual Investor and the Weekend Effect:

A Reexamination with Intraday Data

RAYMOND M. BROOKS

Oregon State University

Hongshik Kim

Daewoo Research Institute, Korea

It is a well known empirical finding that returns, on average, are negative on Monday. Miller

(1988) suggests that this anomaly could be the result of individual investor trading activity.

Lakonishok and Maberly (1990) and Abraham and Ikenberry (1994) use odd-lot trading as a

proxy for individual investor trading patterns and jind evidence consistent with this individual

investor hypothesis. We reexamine investor trading activity using intraday trades and the size of

transactions to proxy for individual and institutional investors. We find that trading activity is

sig@icantly lower on Monday for large-size trades. Moreover, small-size trades have a higher

percentage of sell orders on Monday morning compared to other days of the week. If srndl-size

trades reflect individual investor activity and large-size trades reject institutional investors then

both types of investors play a role in the negative return on Monday. The individual traders

directly contribute through their trading and institutional traders indirectly contribute through

their withdrawalof liquidity.

are negative on Monday and posi-

These daily return patterns have sparked a

large set of theoretical and empirical investigations. Of particular interest is the

negative return on Monday, the weekend effect. Miller (1988) suggests that this

anomaly could be the result of individual investor trading patterns, the so-

called individual investor hypothesis. Two factors impact the individual inves-

tor. First, individuals reflecting on their current needs over the weekend, when

they are not distracted with other activities, initiate a higher percentage of

trades on Monday. Second, the information individuals receive during the week

from the brokerage community is biased toward buy recommendations (see

Groth, Lewellen, Schlarbaum, and Lease, 1979; Diefenbach, 1972; Dimson and

Fraletti, 1986). Over the weekend, small investors are less likely to receive rec-

ommendations from the brokerage community. Therefore, individuals initiate a

725

Harris (1986) finds that returns, on average,

tive the remaining days of the week. ’

726 QUARTERLY REVIEW OF ECONOMICS AND FINANCE

higher percentage of sell orders on Monday morning. This conjecture of indi-

vidual trading patterns is the link between the individual investor and the nega-

tive returns observed on Monday. Lakonishok and Maberly (1990) and

Abraham and Tkenberry (1994) use odd-lot trading as a proxy for individual

investor trading patterns and find evidence consistent with this individual inves-

tor hypothesis. In addition, Abraham and Ikenberry find that negative Monday

returns follow negative Friday returns. They conclude, “it [the weekend effect]

is substantially the consequence of information released in prior trading ses-

sions, particularly on Friday” (p. 276). They also conclude, based on their odd-

lot trading proxy, that “individuals exert substantially greater selling pressure

on Mondays following negative returns in prior trading sessions” (p. 276).

Lakonishok and Maberly (1990) look at proxies for both individual and

institutional traders. Odd-lot trading is their proxy for individual trading pat-

terns and large block trades their proxy for institutional trading patterns. Their

evidence is consistent with selling pressure on Monday, yet they state, “a more

powerful test could be performed if intraday trading data of various market par-

ticipants were made available” (p. 232). More recently, Sias and Starks (1995)

examine the weekend effect by indirectly investigating the role of institutional

investors. They partition their sample of firms by the level of institutional hold-

ings. They find the weekend effect is higher in firms with large institutional

holdings and conclude that the weekend effect is primarily driven by institu-

tional investors.

In the spirit of Lakonishok and Maberly (1990), we reexamine the individ-

ual investor hypothesis using intraday trading data for 276 randomly selected

firms. Our proxy for individual and institutional trading activity is the size of the

transaction. We use small-volume transactions as a proxy for individual investors

and large-volume transactions as a proxy for institutional investors. However,

our proxy is not without problems, as institutional trades may be broken into a

series of small trades. Furthermore, individual traders can act collectively

through mutual funds. Our use of small-size versus large-size trades is consistent

with Lakonishok and Maberly. We also classify trades as market initiated buys if

they are above the contemporaneous bid-ask spread midpoint and market initi-

ated sales if below the midpoint.

We find large-size trades are significantly lower on Monday morning and

consequently, small-size trades represent a larger percentage of trades. In addi-

tion, small-size trades have a greater percentage of sell orders on Monday versus

other days of the week. If small-size trades reflect individual investor activity and

large-size trades reflect institutional investors then both types of investors play a

role in the negative return on Monday. The individual traders directly contrib-

ute through their trading and institutional traders indirectly contribute through

their withdrawal of liquidity.

The increased selling activity of small-size transactions is consistent with the

individual investor hypothesis and the findings of Lakonishok and Maberly

(1990) and Abraham and Ikenberry (1994). The absence of large-size trades is

THE INDIVIDUAL INVESTOR AND THE WEEKEND EFFECT

727

consistent with the findings of Sias and Starks (1995), where firms with greater

institutional holdings have more pronounced negative returns on Monday. The

next section describes the sample, data, and procedures. Section II presents

some return characteristics of our sample. Section III presents our results. We

conclude with a brief summary in the final section.

I. SAMPLE, DATA, AND PROCEDURES

Abraham and Ikenberry (1994) use an intraday index to investigate the weekend

effect. This has merit in that it avoids some of the problems of the market

microstructure such as the bid and ask quoting convention and the discrete

l/&h prices. However, using an index prohibits investigating trading patterns

for individual stocks and therefore individual traders. Lakonishok and Maberly

(1990) use odd-lot trading and block trading volume of the NYSE to examine

the weekend effect. This approach also has merit in that it attempts to separate

individual and institutional trading patterns. But it ignores all the round lot

trades smaller than 10,000 shares. We reexamine the weekend effect using intra-

day data for 276 NYSE and AMEX firms. We use the firm as its own control for

trading activity on Monday versus other days of the week. This provides a differ-

ent view of the weekend effect and adds a new dimension to the examination of

the weekend puzzle.

We randomly select 276* firms with intraday trading data on the 1989 NYSE

and AMEX Trades and Quotes Transaction File prepared by the Institute for

the Study of Security Markets (ISSM). The intraday data from the ISSM tape

include time-stamped transactions, bid and ask quotes, the size of the trade,

opening quotes and prices, and closing prices. Our classification of trades begins

with a partition of buys and sells. A buy transaction is from the perspective of

the trade initiator and is defined as a transaction above the contemporaneous

bid-ask spread midpoint. A sale is a transaction below the bid-ask spread mid-

point.3 Trades at the bid-ask spread midpoint are eliminated from comparisons

relying on the type of trade but are used for other comparisons such as intraday

and interday trading volume.4 The 276 firms selected have over six million

trades during 1989.

The second classification of trades is based on the size of the trade. Trades

are classified into groups starting from one to five round lots (100 to 500 shares)

for the smallest-volume transaction group to trades of 100 round lots (10,000

shares) or greater for the largest group. The other groups are trades from six to

ten round lots, trades from 11 to 50 round lots, and trades from 5 1 to 99 round

lots.

Three sets of observable prices are used for determining the returns: trans-

action prices, bid quotes, and ask quotes. Transaction prices for daily returns

have inherent problems. For example, a transaction price could be from a mar-

ket sale or a market buy. If clustering at the bid or ask occurs for a specific

728 QUARTERLY REVIEW OF ECONOMICS AND FINANCE

trading time (i.e., Monday morning) then a calculated return could be under-

stated or overstateds5 Therefore, we also calculate returns using quotes.

The sample is also partitioned into ten portfolios based on the outstanding

equity value of a firm on December 31, 1988. Eight of the ten portfolios, on

average, have negative returns on Monday. In general, the smaller the equity

value of a firm, the more negative the return on Monday.

The size of the order imbalance, orders awaiting execution, provides infor-

mation about price pressure. However, our data only contain the depth of the

highest bid and lowest ask. Missing is the depth of the market at the next best

bid and ask quotes. In addition, the depth of a quote is not consistently updated

on this data set. As a result, the depth of the quote may be stale. Therefore, we

use the difference in the volume of executed buys and sales during a specific

time period (usually one hour of trading) to proxy for price pressure. Our proxy

for order imbalance is selling percentage. Selling percentage is selling volume

divided by total volume (excluding trades at the bid-ask spread midpoint):

selling pressure = selling volume / total volume

(1)

We examine the selling percentage across different sizes of transactions and

different trading periods during the day. We propose that if individual investor

selling decisions are influencing the negative returns on Monday, then selling

percentage from small-volume trades should be higher on Monday compared to

the remainder of the week. The alternative, failing to detect a significant change

in selling percentage for small-volume trades, would indicate that individual

investors are not influencing returns on Monday. The same logic is applied to

large-volume trades and institutional investors.

We choose dollar volume as our primary measure of volume, instead of the

number of transactions, to avoid giving extra weight to a series of small buys

(sales) over a large sale (purchase). However, we did conduct the same tests with

number of trades as the volume measure and found very similar results.

II. RETURN CHARACTERISTICS

OF 1989 SAMPLE

Our first investigation characterizes returns for our sample. This is especially

important because we use a much smaller time period for returns than prior

studies. The sample mean returns are a simple average of the 276 firm daily

returns. The sample results are very similar to the short time series of Harris

(1986) and the longer time series of Abraham and Ikenberry (1994). For the

unconditional returns, Monday has a significant negative return of -0.250% and

compares favorably with the finding of both Harris (1986), -0.2 1 l%, and Abra-

ham and Ikenberry (1994), -0.116%. Returns from our sample, the CRSP

equally weighted index for 1989, Harris (1986), and Abraham and Ikenberry

( 1994) are presented in Panel A of Table 1.

THE INDIVIDUAL INVESTOR AND THE WEEKEND EFFECT 729

Table 1. Mean Weekday Returns

Return %

(t-statistic)

Study

Mon

Tue

Wed

Thu

Fri

Panel A: Unconditional Mean Returns

1989 Sample

-0.250

(-3.85)

-0.029

(-0.79)

0.064

(1.39)

0.138

(1.17)

0.010

(0.54)

0.125

0.013

0.089

(4.23)

(0.46)

(4.38)

1989 CRSP equally-weighted

-0.089

(-1.54)

0.183

0.109

0.134

(17.02)

(1.77)

(1.27)

Harris

-0.202

(-1.31)

0.146

0.170

0.195

(1.23)

(1.79)

(1.95)

Abraham & Ikenberry

-0.116

(-4.56)

0.143

0.112

0.214

(7.15)

(5.89)

(11.46)

Panel B: Conditional Returns, Positive

1989 Sample Firm’s Prior

-0.275

(-2.69)

-0.081

(-1.45)

0.383

(2.97)

0.169

(7.53)

0.119

(2.78)

0.608

(10.92)

0.302

(13.91)

0.072

0.128

(1.84)

(4.39)

1989 Sample CRSP Prior

0.427

(8.64)

0.577

0.162

(15.12)

(1.67)

Abraham & Ikenberry

0.113

(4.81)

0.280

0.382

(12.86)

(18.74)

Panel C: Conditional Returns, Negative

1989 Sample Firm’s Prior

-0.211

(-4.25)

0.041

(1.03)

-0.286

(-6.15)

(-0.137

(-4.94)

0.134

(3.55)

-0.602

(-9.07)

-0.040

(-1.19)

-0.085

0.032

(-2.26)

(1.23)

1989 Sample CRSP Prior

-0.731

(-11.39)

-0.738

-0.153

(-12.89)

(-1.74)

Abraham & Ikenberry

0.607

(-11.02)

-0.156

-0.061

(-4.87)

(-1.85)

Notes: 1989 Sample mean returns are for a sample of 276 NYSE firms during the year 1989. The reported mean

retllrn is a simple average of the 276 average weekday return for each firm. 1989 CRSP equally-weighted is

the index for all NYSE and AMEX stocks. Harris mean returns are for an NYSE equally-weighted portfolio

for the period December 1981 to January 1983. Abraham and Ikenberry mean returns are for CRSP

equally-weighted index returns from 1963 to 1991. Returns are calculated from closing prices. t-statistics

are in parenthesis and are based on the null hypothesis that the mean daily return is equal to zero.

For Panels B and C, conditional mean returns for 1989 Sample are partitioned based on the individual

firm’s prior return and on the prior day’s CRSP return. Abraham and Ikenbeny conditional mean returns

are based on the prior day’s CRSP return.

We also examine conditional returns in the spirit of Abraham and Ikenberry

(1994). When the prior day’s return (CRSP index) is negative, Abraham and

Ikenbeny find returns are negative, regardless of the day of the week. When the

prior day’s return is positive the day’s return is positive, including Monday’s

return. This serial correlation of index returns suggests that general market con-

ditions spill over into the following day’s trading. We partition our sample of

firm observations into two subsamples based on the individual firm’s prior

return (negative or positive). Our sample does not have an individual firm spill-

over effect; individual firm returns are not serially correlated. We find negative

returns on Monday following both negative and positive firm returns on Friday.

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