Bas and Holding Period

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2/19Electronic copy of this paper is available at: http://ssrn.com/abstract=966026

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1 IntroductionAssets with higher spreads are allocated in equilibrium to portfolios with (the same or) longerexpected holding periods

Amihud and Mendelson (1986) Journal of Financial Economics 17,

Proposition 1, page 228

The purpose of this paper is to directly test the relation between bid-ask spreads andholding periods of individual investors for common stocks. Bid-ask spreads form an

important and predictable component of the profitability of a trading strategy and aretherefore expected to have an effect on the required returns on investment assets. Thehigher the bid-ask spread, the higher the liquidity premium required by an investor tohold an asset. An investor who trades more is likely to require a larger liquidity premiumto be willing to invest in an asset with relatively high transaction costs. Therefore, inequilibrium, assets with higher spreads should be allocated to portfolios with longerexpected holding periods as proposed in the seminal theoretical work by Amihud andMendelson (1986). Due to data constraints, the earlier empirical literature on spreads andtrading activity use aggregate volume based proxy variables to measure investorsholding periods.

To test the clientele hypothesis, we use comprehensive trading records of Finnishindividual investors which are uniquely suited to this purpose since they enable direct

measurement of holding periods. We run cross-sectional instrumental variablesregressions of average individual investors holding periods on lagged bid-ask spreadsand a host of other variables. Consistent with the clientele hypothesis, we find thatholding periods are positively related to bid-ask spreads.

This paper extends the empirical results of Atkins and Dyl (1997), who previously testthe Amihud and Mendelson (1988) proposition. Using data on Nasdaq and NYSE stocksand volume-based proxy for average holding periods, they find a positive relationship

between the bid-ask spread and average holding periods as predicted by the clientelehypothesis. Consistent with the results of Atkins and Dyl, we also find that holding


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periods depend positively on the company size, but are negatively related to stock pricevolatility. However, these are not the only stock characteristics that affect holding

periods. The book-to-market ratio is positively associated to holding periods, suggestingthat value stocks are held for longer periods than growth stocks. In addition, stocks thatare part of the HEX25 index are owned by the investors with shorter holding periods.

We also extend the Atkins and Dyl evidence by including investor attributes in themodels. Earlier theoretical and empirical studies show that overconfidence increasesinvestors trading (Odean 1998; Barber and Odean 2001). There is also both theoreticaland experimental evidence suggesting that men are more overconfident than women infinancial affairs (Prince 1993) and that overconfidence decreases with investmentexperience (Gervais and Odean 2001). The earlier literature has well established on theindividual investors level that men trade more than women. We extend the evidence to

the cross-section of stocks by finding that average holding periods are positively relatedto the proportion of female owners. Furthermore, we also find that investment experience(measured by the number of days an investor shows up in the sample) is positivelyrelated to holding periods.

Besides Amihud and Mendelson (1988), certain other theoretical articles also evince propositions on the relation between transaction costs and trading activity. In the model by Constantinides (1986) investors reduce trading volume in response to increases intransaction costs. Similarly, Vayanos (1998), using an overlapping generation framework,shows that transaction costs have a decidedly negative effect on turnover. Therefore, ourempirical results could also shed some light on the validity of these propositions.

The rich literature on bid-ask spreads provides further evidence of a relation betweentransaction costs and trading activity. An early work by Demsetz (1968) reports findingsthat spreads are inversely related to the number of transactions per day. Bentson andHagerman (1974) show that the number of shareholders is negatively associated with the

bid-ask spread. Finally, the results of Tinic (1972), Stoll (1989), and more recently ofMenyah and Paudyal (2000) and Bollen, Smith and Whaley (2004) provide evidence ofan inverse relation between spreads and trading volume. All of the above-mentionedstudies are conducted in quote-driven or hybrid markets, but the Helsinki Stock Exchangeis an order-driven market. Brockman and Chung (1999), however, show that tradingactivity is also related to the bid-ask spreads in an order-driven environment.

The effect of commissions on investors trading activity is examined by Garvey andMurphy (2004). They argue that individual investors trade less and hold shares for longerthan institutional investors because of the higher commission they have to pay for theirtrades.

Some studies also consider the impact of either the imposition or the abolition of

securities transaction tax (STT) on trading volume. For example, Umlauf (1993) findsevidence that the imposition of STT in Sweden reduced turnover on the Swedish stockmarket. In addition, Bhide (1993) suggests that the low overall transactions cots,including commissions, taxes and bid-ask spreads have increased liquidity in the UScompared to other countries.

The issue of how transaction costs affect investment decisions may also be relevant toholding period dependent capital gains tax, such as that currently prevailing in the U.S.stock markets. Under the U.S. tax code, capital gains from long-term (longer than oneyear holding period) investments are subject to a lower rate of taxation than those from


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short-term (less than one year holding period) investments. Empirical research shows thatthe holding period dependent capital gain tax reduces individual investors trading aroundannouncements of quarterly earnings and additions to S&P 500 index (Blouin, Raedy andShackelford 2003). On the other hand, Hurt and Seida (2004) find somewhat mixedevidence as to whether individual investors selling activity is affected by these taxationconsiderations.

The rest of the paper proceeds as follows: Section 2 introduces the data andcomputation of variables used in the study, and presents some descriptive results; Section3 specifies the cross-sectional models for holding periods and presents estimation resultson these models; finally, Section 4 concludes.


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2 Data and descriptive analysis

2.1 Data

This study uses the Finnish Central Securities Depository (FCSD) transaction data from1995 to 2003. The FCSD database is uniquely appropriate for this study because it allowsdirect computing of holding periods and covers on a daily basis all individual transactionsconducted in the shares listed on the Helsinki Stock Exchange (HSE). If a stock is listedon another exchange the data also reports these transactions. The key features of thesedata for computing holding periods are the investor specific ID-code, transaction day,transaction type (buy or sell), and stock identifier. Furthermore, the data report someinvestor attributes such as year of birth and gender, which enable richer modeling ofholding periods.

We have a full cross-section of firm-specific data available after the HSE list reform oflate 1998. Since we use the lagged bid-ask spread as an instrument for the spread of thefollowing year we have results for the time period from 2000 to 2003 (inclusive).

Some of the Finnish companies have two share classes, with different voting rights. Inthese cases we include both share classes in the sample, because Karhunen and Keloharju(2001) document that these share classes have different ownership clienteles.

2.2 Stock characteristics

Table 1 shows means and medians of various stock characteristics such as bid-askspread, market value of equity (size), volatility, and book-to-market ratio. Spread andvolatility are computed on the basis of the daily closing prices from the HEX. Marketvalues of equity are computed on the basis of FCSD data. The annual bid-ask spreads arecomputed as follows:


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( )n

Bid Ask Spread

t it it iT

+= =1 2/ Bid Ask n it it

, (1)

where Ask it and Bid it are daily closing ask and bid prices for stock i on day t, and n isthe number of trading days for stock i during year T. This measure is an average of thedaily percentage bid-ask spreads. Previous cross-sectional studies are sometimes forcedto use the spread only from the last trading day of December. However, this day is

probably one of the most unusual trading days of the year due to the turn-of-the-yeareffect in spreads (see Fortin, Grube, and Joy, 1989). Using an average spread avoids

potential biases associated with this phenomenon. We delete daily observations of spreads

of stocks that are negative or greater than 50 percent. This procedure eliminates someextreme observations (mainly some negative spreads) that are likely to be errors in thedata or from very illiquid penny stocks.

The market value of equity is used as a measure of the size of the stock. Volatility ofreturns is measured as the standard deviation of the firms daily returns. Book-to-marketratio is the book value of equity scaled by the market value of equity. Annual spreads,market values and book-to-market ratios are computed as an average of daily figures overthe trading days of a year. The number of share classes goes down from 167 to 154 peryear during the research period due to some mergers and acquisitions.

The mean and median bid-ask spreads for the pooled sample are 4.09 percent and 2.76 percent respectively. In comparison, the mean and median spreads in the study by Atkinsand Dyl (1997) are 5.14 % and 3.75 % respectively for Nasdaq stocks, and 1.38% and1.03% for NYSE stocks. The trading costs of stocks in our sample, therefore, lie betweenthose in the two samples of the study by Atkins and Dyl.

Mean and median market values of equities in the pooled sample are 1,433 millioneuros and 102 million euros respectively. Again, it is interesting to compare these valuesto those in Atkins and Dyl (1997). In their sample, mean and median market values are132 million and 47 million dollars for the Nasdaq stocks and 1,405 million and 434million dollars for the NYSE stocks. Hence our sample seems to be somewhat moresimilar to their NYSE than the NASDAQ sample in terms of company size 2.

One striking feature of our sample is that the mean company size is more than tentimes greater than the median size. The great difference between the mean and medianvalues suggests that the size distribution in our sample is highly skewed. This skewness ismainly due to Nokia Corporation, which is a very markedly dominant stock on theFinnish market. The market values exhibit a declining trend over time, which is mainly

due to the bursting of the technology stock bubble.Mean and median volatilities, measured as standard deviations of daily returns, are

5.05 % and 3.16% respectively in the pooled sample. The volatility was particularly highin 2000, when the prices of technology stocks peaked.

Finally, mean and median book-to-market ratios have been at the bottom in 2000coinciding with the peak of the technology bubble. The cross-sectional distribution of

2 The euro to U.S. dollar exchange rate at the time of writing was 1.2331.


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book-to-market ratios also shows some skewness, as can be seen from the comparison between the average ratio of 1.14 and the median ratio of only 0.92 in the pooled sample.

2.3 Measuring holding periods

Our method of computing holding periods is different from that used by Atkins and Dyl(1997). Due to data limitations they use a volume based proxy for the average holding

period. More specifically, their measure of average holding period is the number ofoutstanding shares over the annual trading volume of a stock. Because they lackinformation on transactions and on the originators of these transactions, they are unableto directly compute holding periods for each investor.

We measure holding periods associated with sell transactions. Each sell transactionrealizes a holding period, which is the difference between the buy transaction date and thesell transaction date. We compute value-weighted average purchase days for each shareclass in each portfolio. For example, if an investor buys 50 shares in company A on day100 and 150 shares in the same company on day 200, the average purchase date of thesestocks is 175. Then, if the investor sells 170 shares in company A on day 300, the holding

period associated with this sell transaction is 125 days. The remaining 30 stocks in the portfolio still carry the average purchase date of 175. After obtaining the daily time-seriesof holding periods for each individual investor and share class, we compute equallyweighted average holding periods across investors. Finally, the average annual holding

period over the trading days is computed for each share class.Because we lack data on transactions prior to the beginning of 1995, we compute

holding periods for stocks acquired before 1995 using the beginning of the sample periodas the purchase date. This may create a somewhat downward bias on the level of holding

periods, but is unlikely to create a bias in the cross-sectional regression results. The othersource of noise affecting holding periods is created by initial public offerings (IPOs) thattake place during the sample period. To control for these issues, we include the time

period of how long a stock is included in the sample as one of the control variables in ourregressions.

Table 2 shows that mean and median holding periods of individual investors are 377and 322 trading days in the pooled sample. There is also an upward trend in holding

periods that may be partly due to the limited length of the data period, as discussed aboveand partly due to less active trading after the burst of the technology stock price bubble.

2.4 Investor attributes

To our knowledge this paper is one of the first to investigate investor attributes on a stocklevel. This is possible because the FSCD data covers all transactions of individualinvestors and includes various investors attributes. We analyze two demographic investorattributes: share of female owners and age. Table 2 shows that the share of female ownersfor a median stock is about 26 %. Female ownership has stayed fairly stable, althoughthere was a slight decrease in 2003. The next columns show that the average age of


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owners for an average stock is approximately 49 years. This average age rose from 47.8years in 2000 to 49.7 years in 2003. Therefore, an average investor became somewhatolder during the sample period.The last three columns of the table provide information on the market values of theindividual investors portfolios. The average size of the portfolio for a median stock isapproximately 148,700 euros when computed over the entire research period. However,the median market value of the portfolio is considerably smaller, being only 21,974euros 3. The large difference between mean and median figures is driven by the relativelysmall number of investors with extremely large portfolios. The market value of theaverage portfolio has fallen dramatically from 274,700 euros at the height of thetechnology stock bubble in 2000 to 119,900 euros in 2003. In percentage terms thistranslates into a loss of 56.4%.

2.5 Correlations

The Pearsons partial correlation coefficients, using sample time of a share class as acontrol variable, are shown in Table 3. The variables appearing in the table are later usedin the regression analysis. The first column shows how bid-ask spreads are associatedwith different stock characteristics and investor attributes. There seems to be very strongnegative correlation between spread and size of the company. It is hardly surprising thatsmall companies tend to have large bid-ask spreads and vice versa. However, the strongcorrelation may cause some multicollinearity in our regression analysis. Themulticollinearity problem increases the standard errors associated with regression

coefficients, resulting in lower significance levels, but does not bias the coefficientestimates. Furthermore, the spread appears also to be positively associated with the valuecharacteristic (book-to-market ratio) and volatility of the stock price.

Turning to investor attributes, holding period seems to be positively associated with bid-ask spread, providing initial support for the clientele hypothesis proposed by Amihudand Mendelson (1986). Share of female owners appears to be negatively correlated withspread. However, in an unreported analysis we regress the fraction of female owners onspread while controlling other stock characteristics and find an insignificant positivecoefficient estimate. Therefore, we must be cautious when interpreting these preliminarycorrelation results. What seems to be more interesting in Table 3, however, is the strong

positive correlations between holding period and fraction of female owners, and holding period and investors experience. We will study these relations more carefully in the

following section.

3 This is not reported in the table 2.


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3 Cross-sectional modeling of holding periods

3.1 Atkins and Dyl variables

The main focus of this paper is on the relation between holding period and bid-askspread. However, various other variables may also be associated with the holding periodand need therefore to be controlled for. Furthermore, these additional variables are oftenvery interesting determinants of holding periods per se . We start with those controlvariables that were found to be significant by Atkins and Dyl (1997), namely size of thecompany (measured as a market value of equity), volatility of stock price (measured as astandard deviation of daily returns), and yearly intercept dummies.

Atkins and Dyl argue that larger firms are more likely to be considered investmentgrade than smaller firms, leading to longer holding periods for larger firms. In addition,investors expectations are likely to be less diverse in large stocks, leading to less activetrading.

The positive relation between trading activity and volatility is well documented in theearlier literature (see Karpoff 1987 for an extensive review). If short-term tradingincreases volatility or volatility induces trading, we would expect an inverse relation

between holding periods and volatility. Volatility may also reflect the degree ofinformation asymmetry, which in turn is a key ingredient in the theoretical models oftrading (Kyle 1985).

Following Atkins and Dyl (1997), we include yearly intercept dummy variables in all

of the models. These dummies are used to capture time-specific effects on holding periods. One such time-specific effect in our research period may have been the burstingof the bubble in 2000.

We include a variable that measures the time span of a stock being a part of the sampleas an additional control variable in our regressions. The time variable is included in theanalysis for two reasons. The first reason is that we want to control for the fact thatholding periods must mechanically depend on the time span during which a stock has

been included in the sample. The right-trimmed holding periods should not bias theresults when testing the Amihud and Mendelson hypothesis on the relationship between


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bid-ask spreads and holding periods. However, to reduce noise in our regressions the useof the sample time variable is warranted 4. The second reason for including the sampletime is that holding periods tend to be shorter following IPOs due to trading strategieslike flipping. This would also imply an inverse relationship between time and holding

period variables.Each model is estimated using the GMM technique. Since holding period and spread

are endogenously determined, we use a lagged spread as an instrument for a currentspread. All other explanatory variables are used as instrument for themselves. This GMMtechnique is equivalent to the two-stage least squares (2SLS) method 5 used by Atkins andDyl (1997). The lagged bid-ask spread is used as an instrument for the spread of the nextyear, whereas all the other explanatory variables act as the instruments for themselves.All other except dummy variables are in logarithmic terms, because there is skewness

present in their sample distributions.The preliminary diagnostic tests show that the disturbance terms of the models tend to

be heterogeneous. Therefore, unlike in Atkins and Dyl, the covariance matrix is estimatedusing the sandwich technique which controls for the unknown type of heterogeneity. Thismethod of covariance matrix estimation was proposed by White (1980).

In the second column of Table 4, we present the results of the Atkins and Dyl model.Holding periods of individual investors are positively related to bid-ask spreads. Thisrelation is relatively strong with the highly significant t-value of 12.27. The resultsuggests that stocks with higher spreads (and associated larger gross expected returns)attract investors with longer holding periods, supporting the proposition of Amihud andMendelson (1986).

The coefficient estimates for size and volatility are also significant at the 1% levelwith the expected signs. The size is positively related to holding periods with a t-value of7.82, indicating that large companies are held for longer than small firms. There is also a

pronounced negative association between holding periods and stock price volatility,which is consistent with the previously documented volume-volatility relation. In thiscase the t-value is staggering -14.57.

3.2 Additional stock characteristics

In the next step we extend the model of Atkins and Dyl (1997) by including additionalstock characteristic variables which may also be related to holding periods. Thesevariables are book-to-market ratio, dividend yield, and dummy variable for the

membership in the HEX25 index.Value and growth are one of the most popular styles in equity investment. Theevidence shows that in the long run the value strategy has outperformed the growthstrategy (see Chan and Lakonishok 2004 for a review and recent evidence). However,

4 We also control the issue of sample time dependent holding periods by running regressionswithout the age-variable but including a given stock in a sample only after having at least one ortwo years of data being available for it. This procedure does very little to change the results.5 See for example Hayashi (2000).


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during some frothy periods growth stocks tend to generate superior returns compared tovalue stocks. An obvious recent example of such period is the technology stock boomaround the turn of the millennium, when growth stocks far outstripped value stocks.Based on this evidence, we would expect value stocks to be favored by long-terminvestors, who are characterized by long holding periods. Growth stocks, on the otherhand, are likely to attract more speculative short-term trades, who hold stocks for shorter

periods. The primary measure of value vs. growth characteristic in this study is the book-to-market ratio, which is the most commonly used value measure in the earlier literature.

The other value characteristic that we consider is the dividend yield. The dividendyield is computed as dividends over share price. If there is more than one payout in a yearwe computed an average figure across these events. The motivation for trying outincluding the dividend yield in the models is that some investors hold stocks for income

purposes - they may be willing to consume dividends but leave capital untouched. Suchinvestors may be attracted by high dividend paying stocks and are characterized by longholding periods.

Stocks that are part of the market index may be more liquid than other stocks. InFinland the most prominent selective market in index is the HEX25 index 6, which iscomposed of stocks with the highest trading volume. Therefore, by construction, mostheavily traded stocks end up in the index. However, index membership itself is also likelyto enhance the liquidity of the stock. The index stocks may attract extra attention frominvestors, and the number of option contracts and index funds is based on the HEX25index, which may improve the liquidity and market depth of those stocks that are part ofthe index. These aspects predict that the index stocks may be held for shorter periods. Onthe other hand, the index stocks may be long-term core holdings in the portfolio, whichwould tend to make holding periods longer. To control for these issues, we include anintercept dummy variable in the models which equals 1 if a share class has been part ofthe HEX25 index during the year and otherwise 0.

Column 5 in Table 4 shows that holding periods depend positively on the bid-askspread, the t -statistic being 5.42. The evidence suggests that value stocks are held forlonger than growth stocks. However, the dividend yield seems not be a significantdeterminant of a holding period, and is therefore discarded from the final models.

The market index dummy variable enters with the negative coefficient estimate that isstatistically significant at the 1% level. This implies that the index stocks tend to be heldfor shorter periods than the non-index stocks.

3.3 Overconfidence

Theoretical models demonstrate that overconfident investors trade excessively. Forexample, Odean (1998) proposes that investors who overestimate their knowledge of thevalue of a stock trade more than rational investors. Experimental psychological studiessuggest that overconfidence is most pronounced in difficult tasks, in forecasts with low

probability, and in activities with slow and ambiguous feedback (Fischoff, Slovic, and

6 In Finland all the stocks are included in the general market index.


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Lichtenstein 1977; Griffin and Tversky 1992). Trading on stock markets is a very typicalexample of this.

Psychological research shows that men tend to be more overconfident than women(Lundeberg, Fox and Puncochar 1994). The gender based difference in overconfidenceseems to be particularly great in money matters (Prince 1993). Building on this

psychological premise Barber and Odean (2001) use gender as a proxy foroverconfidence and find in their sample of U.S. households that men trade 45% morethan women.

While it is well documented that on the individual investors level men trade morethan women, there is not a single paper studying whether different ownership clienteles interms of gender matter at the stock level. There seems, however, to be a sufficient amountof variation in the share of female owners across stocks for this to be a meaningful

exercise. Given the individual investor level evidence we would expect the averageholding period to be longer for those stocks with a relatively large proportion of(presumably less overconfident) female owners.

Gender is not the only possible proxy variable for the overconfidence. Theoreticalresearch shows that growing experience generally reduces overconfidence (Odean2001) 7. The essence is that with more experience, a trader comes to better recognize hisown ability. The natural measure for investment experience is the number of days aninvestor shows up in the sample. Using this measure, average experience of individualinvestors is computed for each share class and year.

The last column of Table 4 shows how holding periods are affected by some of theinvestor attributes. The share of female owners turns out be one of the most significantdeterminants of the holding period. The results demonstrate that female ownership is

positively related to the length of holding period. Even when the number of other factorsis controlled for the t -statistic of 7.77 is still rather impressive. Furthermore, the resultsalso indicate that the average experience of investors tends to lengthen the holding

period. Taken together these results suggest that overconfidence matters even on the shareclass level.

In addition, we also controlled for another two investor attributes: age and portfoliosize. Some of the Finnish studies find that young investors tend to trade more than theirolder counterparts (Perttunen and Tyynel 2003). Therefore, in the models, we also tryout including the average age of investors, which is measured separately for each shareclass and year. The correlation of the average portfolio size with the market value of thecompany is far from perfect. Therefore, controlling for portfolio size is a meaningfulexercise. The results showed, however, that age and portfolio size were not related toholding periods, ceteris paribus . Therefore, these variables were discarded from the

reported models.

7 Overconfidence may actually grow in the early stages of a career as an investor takes too muchcredit for his success. This is more likely when the market is booming.


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4 ConclusionIt is well established that the cross-sectional variation in stock liquidity is reflected incross-sectional differences in expected returns. The argument of Amihud and Mendelson(1986) is that stocks with varying levels of liquidity attract different investor clienteles, interms of holding periods, so that in the equilibrium there are no differences in (net)expected returns. Using a new measure of holding periods this paper empirically testswhether individual investors behave in the manner predicted by Amihud and Mendelson.We find that the holding periods of individual investors are in a strong positive relation tothe magnitudes of bid-ask spread. This result suggests that individual investors arerational enough in taking the transaction costs into account when making their investmentdecisions.

Consistent with the earlier empirical study by Atkins and Dyl (1997), company size isfound to be positively related to holding periods, whereas the association with volatilityand holding periods is negative. However, holding periods are also related to variousother stock characteristics and investor attributes which were not documented by Atkinsand Dyl. Of these stock characteristics the most important ones are the value vs. growthfeature of a stock and whether or not a stock is part of the market index. Morespecifically, the book-to-market ratio is positively related to holding periods, suggestingthat the value stocks are held for longer periods than the growth stocks. On the otherhand, if a stock is included in the HEX25 index then the holding periods tend to beshorter than for stocks which are not included in the index.

Finally, holding periods depend on overconfidence related investor attributes such asgender and investment experience. We find that average holding periods tend to get

longer with the share of female owners and average experience of investors. Thesefindings suggest that overconfidence not only shortens holding periods on the individualinvestors level but also in the more aggregate stock level.


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ReferencesAmihud Y & Mendelson H (1986) Asset pricing and the bid-ask spread. Journal of Financial

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Blouin J L, Raedy J S & Shackelford D A (2003) Capital gains taxes and equity trading: Empiricalevidence. Journal of Accounting Research 41: 611-651.Bollen N P B, Smith T & Whaley R (2004) Modeling the bid/ask spread: measuring the inventory-

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extreme confidence. Journal of Experimental Psychology 3: 552-564.Fortin R D, Grube R C & Joy O M (1989) Seasonality in Nasdaq dealer spreads. Journal of

Financial and Quantitative Analysis 24: 395-407.

Garvey R & Murphy A (2004) Commissions matter: the trading behavior of institutional andindividual active traders. Journal of Behavioral Finance 5: 214-221.

Gervais S & Odean T (2001) Learning to be overconfident. Review of Financial Studies 14: 1-27.Griffin D & Tversky A (1992) The weighing of evidence and the determinants of confidence.

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types: a study of Finlands unique data set. Journal of Financial Economics 55: 43-67.Hayashi F (2000) Econometrics. Princeton University Press.Hurtt D N & Seida J A (2004) Do holding period tax incentives affect earnings release period

selling activity of individual investors? Journal of American Taxation Association 26.


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Karhunen J & Keloharju M (2001) Shareownership in Finland 2000. Finnish Journal of BusinessEconomics 50: 188-226.

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Kyle A (1985) Continuous auctions and insider trading. Econometrica 53: 1315-1335.Lundeberg M A, Fox P W & Punccohar J (1994) Highly confident but wrong: Gender differences

and similarities in confidence judgements. Journal of Educational Psychology 86: 114-121.Menyah K & Paydyal K (2000) The components of bid-ask spreads on the London Stock

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Finance 53: 1887-1934.Prince M (1993) Women, men, and money styles. Journal of Economic Psychology 14: 175-182.Shleifer A & Vishny R (1997) The limits of arbitrage. Journal of Finance 52: 35-55.Stoll H R (1989) Inferring the components of the bid-ask spread: Theory and empirical tests.

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Table 1. Means, medians and standard deviations of spread, size, volatility and book-to-market ratio.

Year N Bid-ask spread (%) Size (million euros) Volatility (%)

Mean Med. Std. Mean Med. Std. Mean Med. Std. Mean

2000 167 4.19 2.87 4.38 2193 123 18661 10.12 7.44 11.33 0.96

2001 161 4.51 3.39 4.21 1312 107 10265 4.11 3.23 4.46 1.25

2002 154 3.88 2.52 4.66 1169 89 7122 2.88 2.36 1.81 1.16

2003 154 3.74 2.17 4.65 997 91 5690 2.72 2.29 2.04 1.21

00-03 636 4.09 2.76 4.48 1433 102 11741 5.05 3.16 7.06 1.14

The average bid-ask spread is computed as follows:

n

Bid Ask Bid Ask

Spread t it it it it

iT

= +

= 1 2/)(

n

,

where Ask it is the closing ask price for stock i on day t, and Bid it is the closing bid price for stock i on day t, and n is the number of trading days

measured as the market value of a share class. Volatility is computed as a standard deviation of daily stock returns over a year. The reported trading volum

euros. Book-to-market is the book value of equity divided by the market value of the equity.


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Table 2. Means, medians and standard deviations of investor attributes.

Year N Holding period in days Fraction of female owners (%) Age Portf

Mean Med. Std. Mean Med. Std. Mean Med. Std. Mean

2000 167 285.7 253.5 200.3 28.6 26.0 8.9 47.8 48.0 4.5 274.7

2001 161 366.9 314.6 241.1 28.6 26.0 9.2 48.6 48.6 4.6 177.2

2002 154 412.7 341.5 259.7 28.1 25.9 9.0 49.2 49.3 4.4 156.3

2003 154 450.9 391.5 297.7 27.9 25.6 8.9 49.7 49.7 4.3 144.6

00-03 636 377.0 321.7 258.0 28.3 25.9 9.0 48.8 49.0 4.5 189.8

Holding period is computed as the number of days between the average buy transaction date and sell transaction date. Holding period in an investor categ

obtained by calculating the equally weighted average of individual holding periods. The portfolio size is measured as the market value of a portfolio.


18/19


19/19

19

Table 4. Cross-sectional determinants of individual investors holding periods.

1 2 3 4 5 6 7

N 651 651 651 651 651 651 651

Adj. R 2 0.551 0.575 0.563 0.588 0.646 0.603 0.652

Constant -0.427 0.290 -0.718 -0.001 1.717 -0.196 1.484

(-0.81) (0.52) (-1.34) (-0.00) (3.36) (-0.39) (2.95)

Spread 0.544 0.521 0.482 0.461 0.387 0.409 0.357

(12.27) (12.19) (10.21) (10.23) (8.66) (8.91) (7.81)

MV 0.159 0.174 0.174 0.189 0.121 0.184 0.122

(7.82) (8.91) (8.64) (9.69) (5.63) (9.72) (5.66)

Volatility -0.831 -0.766 -0.764 -0.700 -0.591 -0.599 -0.531

(-14.57) (-12.83) (-12.53) (-10.95) (-9.77) (-8.57) (-8.13)

Time 0.733 0.633 0.784 0.684 0.603 0.620 0.566

(7.33) (6.22) (7.82) (6.76) (7.14) (6.51) (6.68)

BV/MV 0.143 0.142 0.111 0.108 0.090

(5.42) (5.42) (4.64) (4.40) (3.84)

Index -0.260 -0.253 -0.229 -0.277 -0.246

(-4.12) (-4.13) (-4.17) (-4.49) (-4.42)

Female 0.640 0.601

(8.56) (7.77)

Exper 0.717 0.475

(4.36) (3.07)

This table reports number of observations, coefficient estimates, t-values (in parentheses) and adjusted R 2s of

pooled cross-sectional GMM regressions. Holding period is measured as the number of days between theaverage buy transaction date and sell transaction date. The bid-ask spread is defined as follows:

n Bid Ask

Bid Ask

Spread t it it it it

iT

= +

= 1 2/)(

n

,

where Ask it is the closing ask price for stock i on day t, and Bid it is the closing bid price for stock i on day t. N is

the number of trading days in a year T. Size iT is the average market value of firm i during a year T, Volatility iT

is the standard deviation of the daily return of firm i in year T, Time is the length of time a share class i has been

in the sample in year T, BV/MV is the book value of equity scaled by the market value of equity, Index is a

dummy variable that obtains value of 1 if the share class is included in the HEX25 index during a year, Female

is the share of female owners, and Exper is the length of time an investor has been in the sample. The models

include yearly intercept variables, but for shake of brevity they are omitted from the table. All other than

dummy variables are expressed in logarithmic terms. Holding periods and bid-ask spreads are assumed to beendogenously determined. All other variables are treated as exogenous. The spread of the previous year is used

as an instrument for the current spread. The covariance matrix is estimated using the sandwich technique, which

controls for heterogeneity. The sample period is 1999-2003. We do not have results for 1999 because the lagged

value of spread is used as one of the instruments.

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Bas and Holding Period