Divergent investment skills across investor types

before and after earnings announcements

August 2010

Henk Berkman Department of Accounting and Finance University of Auckland Business School

Auckland, New Zealand H.Berkman@auckland.ac.nz

Paul Koch*

School of Business University of Kansas

Lawrence, KS 66045-7585 pkoch@ku.edu

Joakim Westerholm

Department of Economics University of Sydney

Sydney, Australia J.Westerholm@econ.usyd.edu.au

Abstract

Using Finnish data, we find evidence suggesting that local individual and institutional investors benefit from private information just before earnings announcements. In contrast, after the announcement, institutional investors in general benefit from their ability to assimilate the newly released public information. We also find that investors are more successful trading before or after an announcement if they have profited more from similar trades around previous announcements. Wherever we find evidence of superior trading ability, it is limited to trading in small Finnish stocks that do not have ADRs traded in the U.S. This outcome suggests that these trading skills are manifested only where there is greater information asymmetry. JEL Classification: D82, G14, G19. Key Words: market efficiency, individuals, institutions, informed investors, local bias, home bias, familiarity, portfolio concentration, earnings announcements. *Corresponding author. This version is preliminary. Please do not quote without permission.

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Divergent investment skills across investor types before and after earnings announcements

Abstract

Using Finnish data, we find evidence suggesting that local individual and institutional investors benefit from private information just before earnings announcements. In contrast, after the announcement, institutional investors in general benefit from their ability to assimilate the newly released public information. We also find that investors are more successful trading before or after an announcement if they have profited more from similar trades around previous announcements. Wherever we find evidence of superior trading ability, it is limited to trading in small Finnish stocks that do not have ADRs traded in the U.S. This outcome suggests that these trading skills are manifested only where there is greater information asymmetry. JEL Classification: D82, G14, G19. Key Words: market efficiency, individuals, institutions, informed investors, local bias, home bias, familiarity, portfolio concentration, earnings announcements.

Introduction

Several recent papers examine whether certain types of investors systematically outperform other

types of investors. For example, prior work compares the performance across investors that are

foreign versus domestic, local versus non-local, institutional versus individual, female versus

male, and those with concentrated versus diversified portfolios.1 Throughout this literature, it is

implicitly assumed that any differences in investment skills across traders do not vary over time.

This study extends the literature by separately analyzing the performance of different

types of investors, based on trades made in the days just before or after earnings announcements.

We use data over the period, 1999 - 2004, from the Finnish Central Share Depository (FCSD),

which records all changes in daily shareholdings for every investor trading on the Helsinki Stock

Exchange (HEX).2 Our focus on earnings announcements, and our distinction between trading

skills just before or after the announcement, allows us to contribute to the literature in four ways.

First, our focus on earnings announcements enables a powerful event study approach that

concentrates on the performance of trading behavior around days when value-relevant

information is released and processed.3 This focus can help to mitigate concerns that the results

of prior research may be due to inadequate controls for differences in systematic risk, or to other

flaws in the research methodology.4

Second, we test whether different investor categories have divergent skills in analyzing

different kinds of value-relevant information. For example, differences in performance across 1 For example, see Barber and Odean (2000, 2001, and 2002), Coval and Muskowitz (1999, 2001), Grinblatt and Keloharju (2000, 2001), Ivkovic et al. (2008), Ivkovic and Weisbroner (2005), Massa and Siminov (2006), Seasholes and Zhu (2005), Stoffman (2007), Van Nieuwerburgh and Veldkamp (2005, 2008), and Zhu (2002). 2 Other studies that use this database include Grinblatt and Keloharju (2000, 2001a,b), Linnainmaa (2006, 2007, 2008), and Stoffman (2007). 3 See also Berkman et al. (2009), Griffin et al. (2008), Kaniel et al. (2008), LaPorta (1997), and Vieru et al. (2004, 2005). 4 For example, Ivkovic and Weisbenner (2005) find that living near a company can lead to better performance. In contrast, Seasholes and Zhu (2005) use the same database, but account for contemporaneous correlation of returns across household portfolios, and find that living near a company does not lead to better performance.

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investor types based on trades made before earnings announcements likely reflect differences in

access to private firm-specific information. In contrast, differences in returns based on trades

made immediately after the earnings release are likely due to differences in the ability to process

public information. Some types of investors may be more capable of generating abnormal returns

from access to superior private information before the announcement, while other types are

better able to assimilate public information immediately after the announcement.5

Third, our use of account-level data, enables us to examine whether different types of

investors have divergent trading capabilities that might persist across successive earnings

announcements. For example, we investigate whether a given investor’s performance from trades

made before or after a given earnings release is associated with that same investor’s performance

from similar trades made around previous announcements. Once again, such persistence could be

due to sustained differences across investor types in access to private information, or in the

ability to utilize public information. This kind of specialization would be consistent with prior

work which finds that: (i) investors who focus their attention on familiar assets tend to

outperform,6 and (ii) investors learn from their past success or mistakes.7

Fourth, we argue that the success of different investor types is likely to be limited to

trading around the earnings announcements of smaller firms that are subject to greater

information asymmetry. A limited number of the largest stocks in Finland account for the bulk of

that market’s total capitalization. Most of these largest stocks are cross-listed with ADRs traded

in the U.S., and are thus subject to greater trading volume, media coverage, and investor

scrutiny. Thus, we conjecture that the success of different types of investors who trade before or

after earnings announcements is limited to trading in the smaller Finnish stocks with no ADRs.

5 These implications are consistent with the theoretical models of Kim and Verrecchia (1991a,b, 1994, and 1997). 6 See Ivkovic et al. (2004), Massa and Simonov (2004), and Van Nieuwerburgh and Veldkamp (2005). 7 See Linnainmaa (2008), Nicolosi et al. (2008), and Van Niewurburgh and Veldkamp (2005, 2008).

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We analyze a sample of 2,068 earnings announcements made by 133 Finnish firms

during the period, 1999 through 2004. All earnings announcements in Finland are released prior

to the market’s close on the announcement day (0), with most being disclosed before the open.

We compare the performance of trading conducted in the two days before or after earnings

announcements, across the following investor groups depicted in Figure 1: foreign versus

domestic investors, institutions versus individual retail investors, financial institutions versus

non-financial corporations, and local versus non-local investors. We control for the firm’s size

and market-to-book ratio, as well as account-specific attributes such as the previous year’s

portfolio alpha, portfolio concentration, and the performance of trades made before or after prior

announcements. We measure the performance of trades made before an announcement (on days

-2 or -1) using the announcement-period return, defined as the cumulative abnormal return in the

first two days after the earnings release (CAR(0,+1)). Likewise, we measure the performance of

trades made after the announcement (on days 0 or +1) using the post-announcement period

return, defined as the cumulative abnormal return over the next three months (CAR(+2,+60)).

Our results can be summarized as follows. First, consider the pre-announcement trading

skills of different investor types. We document that trades made before the announcement by

local investors (i.e., with the same postcode as the announcing firm) earn announcement-period

returns that average approximately 2 percent greater than similar trades by non-local investors. In

addition, we find that financial institutions tend to outperform other investor types when they

trade before earnings announcements. However, this evidence of outperformance is limited to

trading before the announcements of smaller firms without ADRs. These results suggest that

local investors and financial institutions have greater access to private information about small

firms with greater information asymmetry, during the pre-announcement period.

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Second, consider the post-announcement trading ability of different investor types.

Relative to individual investors, foreign investors and domestic financial institutions display

superior investment skills based on their trades made after the earnings announcement. This

evidence suggests that these investor categories are better equipped to interpret the public

information released at the time of the announcement. Once again, these post-announcement

trading skills are limited to investments in small firms without ADRs.

Third, consider how the attributes of investors affect their trading skills around earnings

announcements. We find that recent overall trading skills (measured by portfolio alpha over the

previous year) do not help investors succeed in trading before or after earnings announcements.

Similarly, portfolio concentration does not affect the profitability of traders’ activity before

earnings announcements. On the other hand, we find that post-announcement trading in small

stocks is more profitable if the portfolio is more diversified (i.e., with a lower Herfendahl index),

while the post-announcement trading in large stocks is more profitable if the portfolio is less

diversified. Finally, we find that investors are more successful trading before or after the

announcements of small stocks, if they have profited more from similar trading activity around

recent earnings announcements during the previous year.

In additional robustness tests, we provide corroborating evidence … (Work yet to do.)

The remainder of this study is organized as follows. Section 1 reviews the literature and

develops our hypotheses. In section 2 we describe the data and research design. Section 3 gives

the results and section 4 provides robustness tests. Section 5 summarizes and concludes.

1. Literature review and hypotheses

This paper draws upon two major research areas. We first discuss previous work on differences

in performance across investor groups. Second, we review the relevant theoretical and empirical

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literature on trading around earnings announcements. Finally we synthesize this prior work to

develop our hypotheses.

1.1 The relative performance across different investor categories.

A substantial body of work examines the trading performance of individuals versus institutional

investors. This work finds that, on average, individuals tend to lose while institutions profit from

their trading.8 Some researchers argue that individuals underperform because their irrational

trading drives prices away from fundamental value.9 Another explanation is that well-informed

institutions buy undervalued stocks from individuals and sell overvalued stocks to individuals.10

A second major literature compares the preferences and performance of local versus non-

local investors. This work finds that local investors tend to concentrate their holdings in familiar

stocks (e.g., with headquarters in the same geographic vicinity). However, the evidence on the

trading skills of local investors is mixed. Some studies suggest that individuals and financial

institutions are able to exploit local knowledge and thereby achieve superior performance,

whereas others find no such evidence that local investors outperform non-local investors.11

A related body of work finds similar evidence of home bias, indicating that domestic

investors prefer to hold domestic stocks. However, the evidence regarding trading skills is again

8 For studies of the performance of individual investors, see Barber and Odean (2000, 2001, 2008), Barber et al. (2006), Campbell et al. (2007), Goetzmann and Kumar (2008), Grinblatt and Keloharju (2000, 2001), Hvidkjaer (2006), Kaniel, Saar, and Titman (2008), Kaniel, Liu, Saar, and Titman (2008), Kumar (2007, 2008), Linnainmaa (2007), Odean (1999), Schlarbaum et al. (1978a, 1978b), and Stoffman (2007). For studies of mutual fund performance, see Carhart (1997), Chan et al. (2000), Coval and Moskowitz (2001), Daniel et al. (1997), Grinblatt and Titman (1989, 1993), and Wermers (2000). For studies of pension fund performance, see Christopherson et al. (1998), Coggin et al. (1993), Delguercio and Tkac (2002), Ferson and Khang (2002), and Lakonishok et al. (1992). 9 See Barber et al. (2006) and Hvidkjaer (2006). 10 See Campbell et al. (2007), Kaniel, Saar, and Titman (2008), Kaniel et al. (2008), and Stoffman (2007). A related set of studies provides mixed evidence on the relative performance across different financial institutions that pursue active versus passive strategies (Carhart, 1997, Grinblatt et al., 1995, Wermers, 1999, 2000, and 2003, Daniel et al., 1997, and Chan et al., 2002). 11 For evidence that local investors outperform non-local investors, see Coval and Moskowitz (1999, 2001), Hau (2001), Huberman (2001), Ivkovic and Weisbenner (2005), Ivkovic et al. (2008), Massa and Siminov (2006), Miller and Shantikumar (2008). Contrasting evidence is presented in See Seasholes and Zhu (2005), and Zhu (2002).

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less uniform. While some studies find that domestic investors outperform foreign investors

(Brennan and Cao, 1997, Choe, 1999, Hau, 2001, and Dvorak, 2005), others find evidence

suggesting that foreigners have an advantage in sophistication (Froot and Ramadorai, 2001, and

Grinblatt and Keloharju, 2000).

Our study contributes to the literature by separately examining the trading performance of

individual investors and several types of institutional investors in Finland, through their trading

around the earnings announcements of local versus nonlocal stocks. We also compare the trading

skills of foreign versus domestic investors in the pre- and post announcement period.

In our research we distinguish between small and large stocks. The large stocks in our

sample include 24 of the largest Finnish firms that are cross-listed and have ADRs traded in the

U.S. These 24 firms account for XX percent of the Finnish market’s total market capitalization,

and the lion’s share of total trading volume.12 For these large firms, we expect the relative

information advantage for domestic and local Finnish investors to be reduced, because they are

exposed to more media attention and analyst coverage, and their financial statements are subject

to more stringent disclosure requirements (Bailey et al., 2006, and Baker et al., 2002).

1.2 Trading around earnings announcements

Our research is closely related to Kim and Verrechia (1997), who introduce a model of rational

trade with two types of informed investors: those with private information gathered in

anticipation of a public disclosure, and those in possession of private information that is useful in

conjunction with the announcement itself. We appeal to the implications of their model by

directly investigating the differential performance of different types of informed investors who

12 For example, Nokia is the largest Finnish company, and comprises roughly 35% of the total Finnish stock market capitalization during the sample period.

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might actively trade on private information just ahead of an announcement, versus those who

might actively trade on private or public information immediately following the announcement.

A number of other researchers focus on major news events to obtain information about

the performance of various types of investors in the period around the release of value-relevant

information. For example, Kaniel, Liu, Saar, and Titman (2008) investigate the trading and

return patterns of individual U.S. investors before and after earnings announcements. They find

that intense individual buying (selling) ahead of the announcement tends to be associated with

positive (negative) abnormal returns in the subsequent months. They also find that, after the

announcement, individuals trade in the opposite direction of pre-announcement returns and the

earnings surprise. Vieru et al. (2004) also find that the most active individual investors in Finland

tend to follow a contrarian strategy, especially selling after good news announcements. However

Vieru et al. (2005) find little evidence that trading by individual investors leads to superior post-

announcement returns. Campbell et al. (2007) examine the trading behavior of U.S. institutions,

and find that institutional trade flows predict firm-level earnings surprises and the post-earnings

announcement drift. Zhu (2002) finds no systematic differences in buying and selling imbalances

across local and remote investors, during the weeks before earnings announcements. He

concludes that local investors do not possess superior information that enables them to

consistently forecast earnings surprises. Finally, Griffin et al. (2008) explore the trading behavior

of institutions on the days around major news events, and find little evidence to support the view

that institutions possess an information advantage. They conclude that the relative success of

institutional investors likely stems from a superior ability to process public information.

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1.3 Development of hypotheses

We synthesize these two major bodies of research by further exploring the divergent

performance of trades made by different investor categories on the days before and after earnings

announcements. The above literature suggests the following four hypotheses that posit divergent

capabilities for different types of investors, to exploit private information before the

announcement, or to assimilate newly released public information after the announcement:

H1: If local investors and domestic financial institutions have better access to private firm-specific information before earnings announcements, then their pre-announcement trading activity (on days -2 and -1) should lead to superior performance immediately after the earnings release (on days 0 and +1), relative to similar trades by other investor groups. H2: If foreign investors and domestic institutions are sophisticated investors who are more capable of assimilating new public information than individual investors, then their trading activity immediately after the earnings release (on days 0 and +1) should outperform similar trades by individual investors in the post-announcement period (on days +2 through +60). H3: If investors were more successful trading before or after a stock’s recent earnings announcements, then they are more likely to be successful when they enter similar trades before or after the stock’s current earnings announcement. H4: If the trading skills posited in H1-H3 are due to exploitation of information asymmetries, then these hypotheses should be more likely to apply to the smallest Finnish firms that are not cross-listed through ADRs in the U.S.

2. Sample selection, variable construction, and research design

2.1 Sample selection, investor groups, and firm characteristics

Trading in Finland is conducted on the Helsinki Stock Exchange (HEX) through an electronic

limit order book with strict price and time priority (see Hedvall et al., 1997). The identity of the

broker behind each limit order is displayed to members of the exchange.

This study is concerned with measuring share price performance following trades by

investors of different types, made on the two days before or the two days after earnings

announcements. We obtain information necessary for this analysis from the Finnish Central

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Share Depository (FCSD). To trade on the HEX, investors must register with the FCSD.

Investors are then given a unique FCSD account, even if they trade through multiple brokers.

The FCSD database records the shareholdings of all registered trading accounts in Finland, and

documents daily changes in shareholdings for each investor registered. Foreign investors are

partially exempt from registration. They may choose to trade through an account that is

registered in the name of a ‘nominee’ financial institution. As a result, nominee accounts listed

as ‘foreign’ by the FCSD include the trading activity of multiple foreign investors.13

Our sample from the FCSD database includes the transactions of nearly half a million

individuals and firms around Finnish earnings announcements during the period, January 1, 1999

through December 31, 2004. The database classifies each investor into one of 37 categories, and

two main ownership types (nominee account or individual account). All transactions placed each

day by every investor are recorded as either purchases or sales. As a result, we can analyze daily

changes in ownership in Finnish stocks across several well-defined investor groups.14

We obtain earnings announcement dates from Bloomberg. For the sample period, we

have 2,527 earnings announcements from 162 different firms. After merging with Datastream

(based on ISIN), we retain 2,243 earnings announcements from 135 firms. We merge these

earnings announcements with the FCDS database, where we require at least one transaction on

the earnings announcement date for inclusion in our sample. This final requirement leaves 2,068

earnings announcements from 133 different firms in the final sample.15

13 The same is also true of American Depository Receipts (ADRs). For example, if an individual in the U.S. trades shares of Nokia’s ADR on the NYSE, this will be classified in the FCSD database as a trade by the institution that serves as a nominee for the ADR. Grinblatt and Keloharju (2000) give a detailed description of the FCSD database. 14 Unfortunately, we do not know the time of day for trades, nor can we determine whether traders acted as initiator. 15 Some firms have more than 1 share class. Since returns for different share classes of the same firm are highly correlated, we only include the share class with the highest trading volume. Inclusion of all share classes yields similar results.

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Alignment of event dates around the precise time of the earnings release is essential in

this study, since we investigate possible divergent skills in using private or public information

immediately before versus after the information is made public. We checked the announcement

dates from Bloomberg against the official source, at http://www.nasdaqomxnordic.com/news/,

which reports the date and time of all Finnish earnings announcements since January of 2003,

and we found no discrepancies over this portion of our sample period. This outcome suggests

that our information about the timing of Finnish earnings announcements is accurate.

Finally, most firms announce earnings before the start of trading, but the earnings release

sometimes occurs after the market opens. As a result, while the closing price on day 0

universally incorporates the initial market reaction to all Finnish earnings announcements, our

trading-based abnormal return measures on day 0 might be affected by some trades that occur

before the announcement, earlier in the day. We return to this issue in our robustness tests.

2.2 Investor groups

In this study we separate investors in Finnish stocks into several categories using information

compiled in the FCSD database. Figure 1 provides an overview of our separate investor types,

while Table 1 presents descriptive statistics for the different account categories. In Panel A of

Table 1, we summarize the account characteristics and trading activity by each investor class on

the four days around all earnings announcements in our sample period. This Panel provides the

summary statistics for trading in all stocks, as well as for small and large stocks separately.

First consider the relative proportions of the different account types that traded in all

stocks, in the top frame of Panel A in Table 1. In total, there are almost half a million accounts

that traded around Finnish earnings announcements during our sample period. Only 46 of these

accounts are held by nominees, representing a multitude of different foreign investors. In

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contrast, all domestic Finnish accounts represent one trader each. The large majority of these

domestic Finnish traders (94 percent) are individuals, while the remaining 6 percent represent

corporations. Of the individual accounts, 57 percent are held by males and 37 percent by

females.16 Among the remaining 6 percent of total accounts that are held by domestic

corporations, non-financial corporations make up the bulk (4.94 percent), and financial

institutions hold much of the rest (.4 percent).

Second consider the trading activity by each investor class that traded in all stocks, in the

top frame of Panel A. Foreign investors comprise the most active investor group in terms of both

the proportion of total trades (40 percent), and the total value of trading volume in euros (73

percent). Individuals represent the second most active investor group in terms of number of

trades (38 percent), but their activity makes up only 3 percent of total volume, reflecting the

much smaller average trade size of individual investors.17 Domestic financial institutions are the

third most active group in terms of trading activity, with 11 percent of all trades, but second

overall in trading volume, with 15 percent of the total. Nonfinancial corporations rank just

behind financial institutions, also with 11 percent of trades, but with only 8 percent of volume.

Lastly, the number of trades and trading volume originating from other non-profit and state

organizations represents an insignificant proportion of total trading activity. Thus, in the

remainder of our study we exclude these other institutions from the analysis.

Third consider the relative account proportions and trading activity in small versus large

stocks, separately, in the second and third frames of Panel A in Table 1. The proportion of total

accounts in each investor category that trades small versus large stocks is similar. However,

some investor types account for more of the trading activity in small or large stocks. For

16 There is no information regarding gender for approximately 6 percent of the individual accounts. 17 Note that the number of trades by males is nearly five times greater than that of females, even though the number of female account holders is more than half the number of male account holders (see Barber and Odean, 2001).

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example, foreign investors conduct 54 percent of all trades in large stocks (comprising 75% of

the volume), but only 16 percent of trading in small stocks (and 51% of volume). In contrast,

individual investors conduct 62 percent of all trading in small stocks (but make up only 11% of

volume), again largely due to the activity of males. Domestic financial corporations show similar

tendencies to trade large and small stocks around earnings announcements. We provide more

details of about the separate subsamples or large versus small stocks in Table 2.

Panels B and C of Table 1 summarize the analogous characteristics and behavior across

investor categories over the two days before and the two days after earnings announcements,

respectively. These Panels reveal that roughly 16% of all Finnish investors trade actively in the

two days before or after earnings announcements. Together, these earnings announcement trades

(in Panels B and C combined) represent approximately 7% of all trades during the period. This

evidence indicates abnormally high trading activity in the four days around earnings

announcements, since these earnings announcement periods constitute only 6.3% of all trading

days during the year (16 / 252). Finally, on the days before and after earnings announcements,

the relative proportions of trading activity attributed to each investor type are consistent with the

results for trading on all four days in Panel A.

2.3 Firm Characteristics

Table 2 provides descriptive statistics regarding the characteristics of all Finnish firms with

earnings announcements in our sample. For each firm characteristic, we first take the mean value

across all earnings announcements made by a given firm, and we then take the cross-sectional

average of these means across the 133 firms in the sample. In Panel A we present the average

characteristics across all firms and earnings announcements in the sample. Panels B and C

provide the analogous information for the subsamples of large and small firms, respectively.

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First consider the average firm characteristics across all firms and earnings

announcements, in Panel A of Table 2. The top 2 rows in Panel A provide the average firm size

and market-to-book ratios measured at the start of the year before each earnings announcement.

The remaining rows give descriptive statistics for the market-to-book ratio, the percent of sample

accounts located in the same geographical area (i.e., postcode) as the firm, and the cumulative

abnormal return (CAR) measures in the days after earnings announcements.

Panel A reveals that the total assets of the stocks in our sample vary from zero to 24

billion euro (for Nokia), and average 1.3 billion euro. The market-to-book ratio ranges from -4 to

over 40, and is 2.95 on average. The cumulative abnormal return (CAR) on days (0, +1) range

from – 22 percent to +22 percent, and are close to zero on average. The analogous CAR over

days (+2, +60) is slightly negative on average, and ranges from -54 percent to 55 percent.

Panels B and C of Table 2 reproduce the analogous information for the subsamples of

firms that have ADRs traded in the U.S. versus those that do not have ADRs. Results indicate

that the firms without ADRs are smaller, have higher market-to-book ratios, and have slightly

smaller CARs over the two post-announcement periods covering days (0,+1) and (+2,+60).

2.2 Variable construction and research design

We focus on the performance of pre-announcement trading and event-period trading by different

trader categories, around the quarterly earnings announcements of Finnish stocks during the

period, 1999 to 2004. To measure performance, we first define the daily abnormal return for

every stock as the actual return minus the market return, where the market return is defined as

the equally weighted average return across all 133 sample stocks on the same day.

Our analysis of pre-announcement trading skills then relates the net purchases of different

groups of investors that were made during the pre-announcement period (over days -2 and -1), to

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the cumulative abnormal return during the announcement period (over days 0 and +1). Similarly,

our analysis of announcement-period trading skills relates the net purchases of different investor

types that were made during the announcement period (over days 0 and +1), to the cumulative

abnormal return during the post-announcement period (over days +2 through +60).

We separately analyze the performance of pre-announcement trading by each investor

(i.e., account), according to whether the account is a net buyer or seller of a stock over the two

days before the jth earnings announcement. If the ith account is a net purchaser of a stock (i.e., the

number of shares bought exceeds the number of shares sold) over days -2 and -1, then the

‘announcement-period return’ is defined as the stock’s cumulative abnormal return on the two

days after the announcement (CAR(0,+1)ij). On the other hand, if the ith account is a net seller of

the stock (i.e., shares sold exceed shares bought) over days -2 and -1, then the ‘announcement-

period return’ for this account is the stock’s cumulative abnormal return over days 0 and +1,

multiplied by negative one (i.e., -CAR(0,1)ij).

We follow a similar procedure to analyze the announcement period trading skills of

different investor categories, for trades made during the two-day earnings announcement period,

by relating account-specific trading on days 0 and +1 to the cumulative abnormal return over

days +2 through +60. Once again, if the ith account is a net purchaser of a stock during the

announcement period, then the ‘post-announcement return’ for the ith account and the jth earnings

announcement is the abnormal return cumulated over days +2 through +60 (CAR(+2,+60)ij). If

the ith account is a net seller of a stock during the announcement period, then this cumulative

abnormal return is multiplied by negative one.

In our first set of tests, we compare each cumulative abnormal return measure,

CAR(0,+1) or CAR(+2,+60), across four different investor categories: foreign investors,

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domestic financial institutions, domestic nonfinancial corporations, and domestic individuals.

We specify a dummy regression model to analyze the comparative performance of either

CAR(0,+1) or CAR(+2,+60), across investor types, as follows:

[CAR(0,+1)ij ,CAR(+2,+60)ij ] = b1 + b2 Foreignij + b3 Non-Fin Corpij + b4 Financial Instij + εij, (1)

where CAR(0,+1)ij = announcement-period cumulative abnormal return of the ith account over the 2 days following the jth earnings announcement,

CAR(+2,+60)ij = post-announcement cumulative abnormal return of the ith account over the 2 months following the jth earnings announcement,

Foreignij = 1 if ith account is a foreign account, and 0 otherwise, Non-fin corpij = 1 if ith account is a non-financial corporation, and 0 otherwise, Financial Instij = 1 if ith account is a financial corporation, and 0 otherwise.

Note that the category of individual investors represents the omitted group. Therefore, the

intercept (b1) reflects the average cumulative abnormal return (CAR(0,+1)ij or CAR(+2,+60)ij)

for the category of individual investors. Then each respective dummy coefficient (b2 - b4)

represents the difference between the average abnormal performance (CAR(0,+1)ij or

CAR(+2,+60)ij) for each remaining investor type relative to that of individual investors.

The second set of tests uses a dummy regression model that is analogous to the first,

except that it controls for account-specific behavior and firm-specific characteristics. These

controls are attained by including additional variables that reflect: (i) whether investor i has the

same postcode as firm j, (ii) the Herfendahl index representing the degree of diversification of

account i, (iii) the alpha of account i during the previous year, (iv) the market capitalization of

firm j, (v) the market-to-book ratio of firm j, and (vi) the average CAR earned by the ith account

on all similar trades made before or after earnings announcements of this firm in the prior year:

[CAR(0,+1)ij ,CAR(+2,+60)ij ] = b1 + b2 (Non-Fin Corpij) + b3 (Financial Instij) +

b4 (Same-Postcodeij) + b5 (Herfendahlij) + b6 (Account Alphaij) + b7 (MarketCapij) +

b8 ( Market / Bookij ) + b9 (Previous EA Performanceij) + εij . (2)

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Note that this model excludes the dummy variable that identifies foreign accounts, since

we do not have the information regarding these characteristics for foreign accounts. These

additional variables enable us to analyze the differential performance across investor types,

controlling for divergent behavior by local versus nonlocal investors (Same-Postcode), or due to

differences in portfolio diversification across accounts (Herfendahl), or due to previous account

performance on trades over the previous year (Account Alpha), or due to investment in firms of

different size or market-to-book ratios, or due to previous account performance on similar trades

before or after earnings announcements during the previous year (Previous EA Performance).

We first estimate regression model (1) or (2) across all earnings announcements in each

of the 20 quarters in our five-year sample period. These cross-sectional regression coefficients

are then averaged over the 20 quarterly cross-sectional regressions, and the corresponding t-

statistics are based on the standard errors of the time-series mean regression coefficient. These

standard errors do not suffer from any potential bias associated with cross-sectional clustering of

events (see Bernard, 1987, Fama and MacBeth, 1973, and La Porta et al., 1997).

3. Empirical Results

3.1 Comparative performance across investor types, for trades made before or after earnings announcements

3.1.1 Performance of trades made on the two days before earnings announcements

The results for regression model (1) are provided in Table 3. First consider the relative

performance across different investor types, based on trades made during the two days before

earnings announcements (CAR(0,+1)), in Panel A of Table 3. Once again, the intercept of (1)

represents the average CAR(0,+1) for the group of individual investors, while the remaining

coefficients (b2 – b4) reflect the differential performance of the other three investor types, relative

to that of individual investors.

17

Most of the regression coefficients provided in Panel A are not significantly different

from zero. In fact, the only significant coefficients in this Panel appear in the analysis of the

subsample of small firms without ADRs, and then only the coefficients for domestic nonfinancial

corporations and domestic financial institutions (b3 and b4, respectively) are significantly

different from zero. These significant positive coefficients indicate that these two investor

categories tend to systematically outperform individual investors over the earnings

announcement period (days 0 and +1), based on trades made just before the announcement.

On the other hand, when we add the intercept (b1) to each respective coefficient to obtain

the average CAR(0,+1) for the other three investor types, the coefficient sums provided in Panel

A indicate that only domestic financial institutions earn average announcement period returns

that are significantly greater than zero (b1 + b4 = 0.653 percent, t-statistic = 2.93). These results

suggest a tendency for domestic financial institutions to display significant trading skills just

ahead of the earnings announcements of small firms. Such skills could reflect access to value-

relevant private information about such small firms in the days ahead of the earnings release.

3.1.2 Performance of trades made on the two days after earnings announcements

Second consider the relative performance across different investor types, based on trades

made during the two days after earnings announcements (CAR(+2,+60)), in Panel B of Table 3.

Once again, we find the most significant results for the subsample of small stocks without ADRs.

Now all four regression coefficients are significantly different from zero, as the intercept is

significantly negative while the remaining three coefficients are significantly positive. This

outcome indicates that foreign investors, domestic nonfinancial corporations, and domestic

financial institutions all tend to outperform individual investors when they trade immediately

following an earnings release.

18

On the other hand, once again when we add the intercept to each respective coefficient,

we find that only domestic financial institutions obtain an average post-announcement CAR that

is significantly positive, albeit at the .10 level (b1 + b4 = 0.964 percent, t-statistic = 1.70).

Together, the results in Table 1 suggest some tendency for outperformance by domestic financial

institutions based on trades made just before or after earnings announcements.

3.2 Comparative performance across investor types, for trades made before or after earnings announcements, controlling for previous investor behavior and firm characteristics

3.2.1 Performance of trades made on the two days before earnings announcements

The results for regression model (2) are provided in Table 4. Now the analysis is limited to the

performance of domestic traders, for which we have information on: (i) their location, (ii)

portfolio concentration, (iii) performance over the previous year, and (iv) performance before or

after the previous four earnings announcements. First consider the relative performance across

different investor types, based on trades made during the two days before earnings

announcements (CAR(0,+1)), in Panel A of Table 4.

As in Table 3, in Panel A of Table 4 we find the coefficient of the dummy variable for

domestic financial institutions is significantly positive for the sample of all stocks, although this

significance only applies to the subsample of small stocks without ADRs. This result once again

indicates that financial institutions tend to outperform individual investors based on trades made

just before the earnings announcements of small firms.

Next consider the results for the control variables. We find a significant positive

coefficient on the dummy variable that indicates whether the investor resides in the same

postcode as the firm. Panel A of Table 4 reveals that this coefficient is significantly positive for

all stocks, but once again this significance only applies to the subsample of small stocks. This

19

outcome reveals that local investors of all types earn significantly higher CARs on trades made

before the earnings announcements of small firms, relative to nonlocal investors in those firms.

The magnitude of this coefficient is both economically and statistically significant, indicating a

2.159 percent higher CAR(0,+1) for a pre-announcement trade by a local investor relative to a

nonlocal investor (t-statistic = 3.92). These results contribute to the literature that provides mixed

evidence on the relative performance of local versus nonlocal investors in other settings.18

The only other control variable in Panel A of Table 4 that reveals a significant coefficient

is the variable representing the average performance across all similar trades made before

earnings announcements during the previous year. Yet again, this variable reveals a significant

coefficient only for the subsample of small stocks without ADRs. This result implies that

investors display greater performance in their pre-announcement trading activity when they have

been more successful at similar trades before the firm’s earnings announcements during the

previous year. This evidence suggests that investors learn, or that investors who are more

familiar with a given stock can translate that experience and familiarity into incremental

performance.

3.2.2 Performance of trades made on the two days after earnings announcements

Second consider the relative performance across different investor types, based on trades made

during the two days after earnings announcements (CAR(+2,+60)), in Panel B of Table 4. Once

again, as in Table 3, we find significant dummy variables for both domestic non-financial

corporations and financial institutions for the sample of all firms, but this significance is due to

the subsample of small firms with no ADRs. This result indicates that both types of domestic

institutional investors tend to outperform individual investors when they trade after the earnings 18 For other evidence that local investors outperform non-local investors, see Coval and Moskowitz (1999, 2001), Hau (2001), Huberman (2001), Ivkovic and Weisbenner (2005), Ivkovic et al. (2008), Massa and Siminov (2006), Miller and Shantikumar (2008). Contrasting evidence is presented in See Seasholes and Zhu (2005), and Zhu (2002).

20

announcements of small firms, when we control for investor performance and firm

characteristics.

Next consider the control variables in Panel B of Table 4. First, it is important to note that

the coefficient of the “same postcode” dummy variable is not significantly different from zero in

this Panel. Thus, the ability of local investors to outperform based on pre-announcement trades,

previously documented in Panel A of Table 4, does not extend to post-announcement trades in

Panel B. This result emphasizes that the apparent information advantage of local investors only

applies to trading ahead of earnings announcements, not after. Second, consider the coefficient of

the investor’s Herfendahl index. Panel B indicates a significant coefficient on the investor’s

Herfendahl index that takes opposite signs for the subsamples of small and large firms,

respectively. These results imply that post-announcement trading in small stocks is more

profitable if the portfolio is more diversified (i.e., with a lower Herfendahl index), while the

post-announcement trading in large stocks is more profitable if the portfolio is less diversified

(with a high Herfendahl index). Finally, we once again find that investors are more successful

trading after the earnings announcements of small stocks, if they have profited more from similar

trading activity after that firm’s earnings announcements during the previous year.

5. Summary and conclusions

This study analyzes the divergent performance of different types of investors, based on trades

made in the days just before or after earnings announcements. We use data over the period, 1999

- 2004, from the Finnish Central Share Depository (FCSD), which records all changes in daily

shareholdings for every investor trading on the Helsinki Stock Exchange (HEX). Our focus on

earnings announcements, and our distinction between trading skills just before or after the

announcement, allows us to contribute to the literature in four ways.

21

First, our focus on earnings announcements represents a powerful event study approach

that concentrates on the performance of trading behavior around days when value-relevant

information is released and processed. Second, we find that some types of investors are more

capable of generating abnormal returns from access to superior private information before the

announcement, while other types are better able to assimilate public information immediately

after the announcement. Third, we find that different types of investors have divergent trading

capabilities that persist across successive earnings announcements. Such persistence could be

due to sustained differences across investor types in access to private information, or in the

ability to utilize public information. This kind of specialization would be consistent with prior

work which finds that: (i) investors who focus their attention on familiar assets tend to

outperform, and (ii) investors learn from their past success or mistakes. Fourth, we find that the

success of different investor types is limited to trading around the earnings announcements of

smaller firms that are subject to greater information asymmetry.

Our results support the view that local investors of all types are able to outperform

nonlocal investors, based on trades made in the two days before earnings announcements. On the

other hand, this ability of local investors does not extend to trades made on the two days after the

announcement. In contrast, more sophisticated financial institutions display superior

performance based on trades made on the two days after announcements. Furthermore, wherever

we find tendencies to outperform before or after earnings announcements, these tendencies are

limited to trading the shares of smaller stocks with no ADRs, where the advantages of trading

based on superior information are likely to reflect greater information asymmetries.

This work has implications for regulations on disclosure, and the implications of

disclosure for firm performance. The conventional wisdom is that disclosure reduces the cost of

22

capital by substituting a public announcement for private, pre-announcement information (for

example, see Lambert et al., 2008, 2009, and Diamond and Verrecchia, 1991). However, this

wisdom may only apply to an environment in which pre-announcement private information

exists. To the extent that event-period private information may also exist, it is possible that

disclosure may actually increase the cost of capital, at least temporarily around the time of an

announcement. In this light, our empirical evidence that distinguishes pre-announcement effects

from event-period effects, and separates the performance of different types of investors before

versus after the release of value-relevant information, can further our understanding of how

disclosure can impact the firm’s cost of capital. This impact, in turn, links disclosure alternatives

to other economic benefits (for example, see Coval and Moskowitz, 1999, 2001).

23

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Figure 1. Classification of Trader Accounts All Accounts Domestic Foreign Individuals Corporate Institutions Male Female Financial Non-Financial

Local Non-Local

Table 1.  Trading Activity by Different Types of Investors Around Finnish Earnings Announcements

Panel A: Trading Activity on the Four Days Around Earnings Announcements (Days ‐2, ‐1, 0, and +1)All Stocks # accounts % # trades % avg trade size Total Trading %Foreign 46 0.01% 8739192 40.43% 156517 73.01%Dom Financial Inst 1936 0.40% 2338673 10.82% 120695 15.07%        Foreign & Dom Inst 1982 0.41% 11077865 51.26% 148955 88.08%Dom Non‐Financial 23695 4.94% 2334626 10.80% 66405 8.28%Non Profit 2988 0.62% 76054 0.35% 39258 0.16%State 276 0.06% 17916 0.08% 135709 0.13%Male 273742 57.09% 6661589 30.82% 8002 2.85%Female 176816 36.88% 1445026 6.69% 6624 0.51%        All Individuals 451135 94.08% 8115013 37.55% 7779 3.37%                Total: 479499 100.00% 21613076 100.00% 533209 100.00%

Small Stocks (no ADRs) # accounts % # trades % avg trade size Total Trading %

Foreign 39 0.01% 1193104 15.88% 73421 50.51%Dom Financial Inst 1641 0.50% 706424 9.40% 53217 21.68%        Foreign & Dom Inst 1680 0.51% 1899528 25.28% 65907 72.19%Dom Non‐Financial 16944 5.14% 893984 11.90% 30386 15.66%Non Profit 1674 0.51% 38367 0.51% 26004 0.58%State 254 0.08% 10268 0.14% 74280 0.44%Male 200239 60.80% 3879274 51.63% 4127 9.23%Female 108556 32.96% 791472 10.53% 4173 1.90%        All Individuals 308795 93.76% 4675795 62.24% 4173 11.25%                Total: 329347 100.00% 7512893 100.00% 265607 100.00%

Large Stocks (ADRs) # accounts % # trades % avg trade size Total Trading %Foreign 43 0.01% 7546088 53.52% 169655 75.31%Dom Financial Inst 1592 0.49% 1632249 11.58% 149899 14.39%        Foreign & Dom Inst 1635 0.51% 9178337 65.09% 166142 89.70%Dom Non‐Financial 17319 5.36% 1440642 10.22% 88757 7.52%Non Profit 2149 0.67% 37687 0.27% 52752 0.12%State 106 0.03% 7648 0.05% 218181 0.10%Male 187766 58.14% 2782315 19.73% 13404 2.19%Female 113983 35.29% 653554 4.64% 9592 0.37%

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        All Individuals 301749 93.43% 3435869 24.37% 12681 2.56%                Total: 322958 100.00% 14100183 100.00% 702239 100.00%

Panel B:  Trading Activity on the Two Days Before Earnings Announcements (Days ‐2 and ‐1)Account Type # accounts % # trades % avg trade size Total Trading %Foreign 37 0.05% 285301 41.89% 151595 73.88%Dom Financial Inst 707 0.90% 75034 11.02% 124251 15.93%        Foreign & Dom Inst 744 0.95% 360335 52.90% 145902 89.81%Dom Non‐Financial 5792 7.41% 69045 10.14% 58463 6.90%Non Profit 338 0.43% 2141 0.31% 43858 0.16%State 36 0.05% 457 0.07% 16718 0.01%Male 53721 68.71% 206567 30.33% 7578 2.67%Female 17556 22.45% 42597 6.25% 6207 0.45%        All Individuals 71357 91.26% 249380 36.61% 7345 3.13%                Total: 78187 100.00% 681142 152.90% 408671 100.00%

Panel C:  Trading Activity on the Two Days After Earnings Announcements (Days 0 and +1)Account Type # accounts % # trades % avg trade size Total Trading %Foreign 38 0.03% 523708 39.46% 165491 75.32%Dom Financial Inst 840 0.75% 132221 9.96% 118304 13.59%        Foreign & Dom Inst 878 0.79% 655929 49.42% 155979 88.92%Dom Non‐Financial 7657 6.87% 143077 10.78% 57747 7.18%Non Profit 402 0.36% 3862 0.29% 47672 0.16%State 43 0.04% 1106 0.08% 15265 0.01%Male 75599 67.83% 439386 33.10% 8425 3.22%Female 26882 24.12% 83988 6.33% 6973 0.51%        All Individuals 102597 92.05% 523875 39.47% 8192 3.73%               Total: 111461 100.00% 1327348 1.494165 419877 100.00%

30

Table 2.  Descriptive Statistics for Firm Characteristics, Location,                 and Cumulative Abnormal Returns after Earnings Announcements

Panel A.  All Stocks

  mean STD T‐Stat Median Min Max N   

Total Assets 1344 3684 15.12 157 0 23920 2066Market / Book 2.95 4.95 27.08 1.87 ‐4.42 41.67 2066% Same Postcode .011 .038 13.23 .000 .000 .596 2066CAR(0,+1) ‐.003 .070 ‐2.07 ‐.003 ‐.216 .215 2066CAR(+2,+60) ‐.012 .176 ‐3.13 ‐.014 ‐.543 .554 2066

Panel B.  Small Stocks (Without ADRs)

  mean STD T‐Stat Median Min Max N   

Total Assets 380 983 14.43 81 0 7827 1398Market / Book 3.05 5.21 24.19 2.00 ‐4.42 41.67 1705% Same Postcode .011 .041 11.33 .000 .000 .596 1705CAR(0, +1) ‐.004 .071 ‐2.10 ‐.003 ‐.216 .215 1705CAR(+2, +60) ‐.014 .185 ‐3.08 ‐.016 ‐.543 .554 1705

Panel C.  Large Stocks (With ADRs)

  mean STD T‐Stat Median Min Max N  

Total Assets 5557 6850 14.51 2495 352 23920 361Market / Book 2.48 3.48 13.55 1.35 0.34 41.67 361% Same Postcode .010 .014 13.05 .004 .000 .073 361CAR(0,+1) ‐.001 .061 ‐.31 .001 ‐.202 .215 361CAR(+2,+60) ‐.004 .123 ‐.64 ‐.002 ‐.440 .469 361

31

Table 3. Trading Performance of Different Investor Types Around Earnings Announcements

Panel A. Performance of Trades Made on the Two Days Before Earnings Announcements

This table provides estimates for the following dummy regression model:[CAR(0,+1)ij ,CAR(+2,+60)ij ] = b1 + b2Foreignij + b3Non-Fin Corpij + b4Financial Instij + εij (1)where the omitted group (intercept, b1) represents individual accounts.

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Investor Mean Coefficient MeanCategory Coefficient CAR(0,+1) Sum CAR(0,+1)

Individuals (Int) b1 .122 .31Foreign Investors b2 -.289 -.74 (b1 + b2) -.167 -3.84 ***

Dom Non-Fin Corp b3 -.010 -.06 (b1 + b3) .111 .39Financial Inst b4 221 90 (b1 + b4) 343 1 46

All Firms

Panel A. Performance of Trades Made on the Two Days Before Earnings Announcements

T-Stat T-Stat

Financial Inst b4 .221 .90 (b1 + b4) .343 1.46

Individuals (Int) b1 -.287 -1.11Foreign Investors b2 .205 .67 (b1 + b2) -.082 -.43

Dom Non-Fin Corp b3 .378 2.75 ** (b1 + b3) .091 .44Financial Inst b4 .940 3.75 *** (b1 + b4) .653 2.93 ***

Small Firms without ADRs

Large Firms with ADRsIndividuals (Int) b1 .593 .68

Foreign Investors b2 -.824 -.97 (b1 + b2) -.231 -1.72 *Dom Non-Fin Corp b3 -.461 -1.27 (b1 + b3) .132 .24

Financial Inst b4 -.499 -.78 (b1 + b4) .095 .31

Panel B Performance of Trades Made on the Two Days After Earnings Announcements

Large Firms with ADRs

Investor Mean Coefficient MeanCategory Coefficient CAR(+2,+60) Sum CAR(+2,+60)

Individuals (Int) b1 -1.725 -1.41Foreign Investors b2 2.206 1.62 (b1 + b2) .481 1.37

Dom Non-Fin Corp b3 .903 1.88 * (b1 + b3) -.821 -.86( )

All Firms

Panel B. Performance of Trades Made on the Two Days After Earnings Announcements

T-StatT-Stat

Financial Inst b4 2.546 2.43 ** (b1 + b4) .821 1.54

Individuals (Int) b1 -5.578 -3.77 ***Foreign Investors b2 6.066 3.39 *** (b1 + b2) .488 .73

Dom Non-Fin Corp b3 2.388 3.58 *** (b1 + b3) -3.190 -2.70 **Financial Inst b4 6.542 4.25 *** (b1 + b4) .964 1.70 *

Small Firms without ADRs

Individuals (Int) b1 2.037 1.84 *Foreign Investors b2 -1.562 -1.29 (b1 + b2) .475 1.16

Dom Non-Fin Corp b3 -.557 -1.41 (b1 + b3) 1.480 1.60Financial Inst b4 -1.352 -1.34 (b1 + b4) .685 .82

* indicates significance at the .10 level; ** at the .05 level; and *** at the .01 level.

Large Firms with ADRs

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Controlling for Characteristics of Investor Types and FirmsTable 4. Trading Performance of Different Investor Types Around Earnings Announcements,

This table provides estimates of the following expanded dummy regression model:[CAR(0,+1)ij , CAR(+2,+60)ij ]  =  b1 +  b2 (Non‐Fin Corpij)  +  b3 (Financial Instij)     + 

b4 (Same Postcodeij)  +  b5 ( Herfendahlij )     +  b6 (Account Alphaij)  +  b7 (MarketCapij) +b8 ( Market / Bookij )   +  b9 (Prior EA Performanceij)  +  εij .                                                  (2)

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Investor Mean Mean MeanCategory Coeff CAR(0,+1) CAR(0,+1) CAR(0,+1)

I di id l (I t t) b 099 32 327 94 297 69

Large FirmsT-StatT-Stat

Panel A. Performance of Trades Made on the Two Days Before Earnings Announcements

T-StatSmall FirmsAll Firms

This table provides estimates of the following expanded dummy regression model:[CAR(0,+1)ij , CAR(+2,+60)ij ]  =  b1 +  b2 (Non‐Fin Corpij)  +  b3 (Financial Instij)     + 

b4 (Same Postcodeij)  +  b5 ( Herfendahlij )     +  b6 (Account Alphaij)  +  b7 (MarketCapij) +b8 ( Market / Bookij )   +  b9 (Prior EA Performanceij)  +  εij .                                                  (2)

Individuals (Intercept) b1 -.099 -.32 .327 .94 -.297 -.69

Dom Non-Fin Corp b2 .044 .33 .007 .04 .060 .28

Financial Inst b3 .376 1.84 * .534 2.81 ** .280 .90

Same Postcode b4 .659 1.95 * 2.159 3.92 *** -.086 -.23

Herfendahl b5 012 05 002 00 021 07Herfendahl b5 .012 .05 .002 .00 .021 .07

Account Alpha b6 .022 .11 .006 .02 .030 .17

MarketCap b7 -.513 -.84 .888 1.51 -1.011 -.82

Market / Book b8 .722 1.39 .348 .39 1.792 1.25

Prior EA Perf b9 .239 .83 .648 2.04 ** -.196 -.45

Investor Mean Mean MeanCategory Coeff CAR(+2,+60) CAR(+2,+60) CAR(+2,+60)

Individuals (Intercept) b1 -1.244 -1.28 -5.646 -3.05 *** 1.733 1.88 *

All Firms

T-StatT-Stat

Large Firms

T-Stat

Panel B. Performance of Trades Made on the Two Days After Earnings Announcements

Small Firms( p )

Dom Non-Fin Corp b2 .638 2.16 ** 1.312 2.61 ** -.010 -.03

Financial Inst b3 1.763 2.11 ** 4.041 3.04 *** -.051 -.06

Same Postcode b4 -.120 -.18 .187 .15 .840 1.13

Herfendahl b5 -.436 -.47 -3.295 -2.57 ** 1.044 2.32 **

Account Alpha b6 .386 1.00 .934 1.48 -.184 -.66

MarketCap b7 4.959 2.30 ** -4.407 -1.55 1.381 .40

Market / Book b8 -2.561 -1.46 -5.027 -1.78 * 2.421 .69

Prior EA Perf b9 1.430 2.07 ** 2.058 2.59 ** .430 .71

* indicates significance at the .10 level; ** at the .05 level; and *** at the .01 level.

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