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The First Cut is the Deepest:

Stock Market Re-entry Decision and Hot Stove Effect

Ozlem Arikan

1 Arie E. Gozluklu

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Gi H. Kim3 Hiroaki Sakaguchi

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This Version: 19th

of May, 2016

Abstract

This paper analyses how the first-time experience in the stock market affects the re-entry decision

of inexperienced market participants. Using a unique dataset of Finnish retail investors, we show

that the complete withdrawal from the stock market after an initial loss - the hot stove effect (HSE) -

is prevalent in the Finnish market. The HSE is especially pronounced after heavy losses in the first-

time investment. The HSE is stronger in the bear market after the burst of the dot-com bubble. While

the gender of the investor does not play a role, age significantly affects the re-entry decision: the older

the investor the more likely that she will withdraw from the market entirely after an initial loss. We

find that the HSE is not significantly weaker for Nokia shares, which is a well-recognized name for

Finnish market participants. Our findings have important implications for financial literacy.

KEYWORDS: hot stove effect, market participation, Finnish market, trader characteristics,

financial literacy, re-entry decision.

1,2,3,4

University of Warwick; Correspondence to Gi Kim at: [email protected]. We would like to thank the

participants of WBS Behavioural Science group meeting for valuable comments and suggestions.

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The First Cut is the Deepest:

Stock Market Re-entry Decision and Hot Stove Effect

Abstract

This paper analyses how the first-time experience in the stock market affects the re-entry decision

of inexperienced market participants. Using a unique dataset of Finnish retail investors, we show

that the complete withdrawal from the stock market after an initial loss - the hot stove effect (HSE) -

is prevalent in the Finnish market. The HSE is especially pronounced after heavy losses in the first-

time investment. The HSE is stronger in the bear market after the burst of the dot-com bubble. While

the gender of the investor does not play a role, age significantly affects the re-entry decision: the older

the investor the more likely that she will withdraw from the market entirely after an initial loss. We

find that the HSE is not significantly weaker for Nokia shares, which is a well-recognized name for

Finnish market participants. Our findings have important implications for financial literacy.

KEYWORDS: hot stove effect, market participation, Finnish market, trader characteristics,

financial literacy, re-entry decision.

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1. Introduction

Individuals are increasingly more responsible to make their own financial decision to smooth

their consumption over life-time. In the US, the retirement system moves toward defined contribution

plans where individuals are asked to make financial choices. Similarly, the UK government promotes

private pension schemes “to save money for later in life”. These changes in the pension system as a

result of the ageing population in the developed economies, shifts the financial decision making about

future consumption to naïve individual investors (van Rooij et al., 2011).

Most recent academic papers (Bali et al., 2009; Barberis, 2000; Campbell and Viceira, 2005;

Levi, 2015; Siegel, 2014) and advice from the finance industry agree that stocks are an important

investment vehicle for longer investment horizons.1 While there is no consensus on the optimal

weight one should invest in risky assets, even for long-term investors (Bodie, 1995; Pastor and

Stambaugh, 2012), there is a consensus that stocks -especially a well-diversified stock portfolio such

as the market index- as an asset class should be included in a wealth portfolio to exploit

diversification benefits.2 For example, Viceira (2001)’s model suggests that employed individuals

should invest more in stocks, in particular if their labor income is not correlated with stock returns.

However, there is an extant literature on limited participation puzzle (Allen and Gale, 1994; Brav et

al., 2002; Guvenen, 2009; Vissing-Jorgensen, 2002). Even those who participate in the stock market,

often enter the market only with a limited number of stocks (Calvet et al., 2007; Goetzmann and

Kumar, 2008).

Earlier studies so far mainly focus on first-time market participation (Guiso and Japelli, 2005;

Guiso et al., 2008; van Rooij et al., 2011) and investor attrition (Seru et al., 2010), which is related to

the exit decision. In particular, they show that such decisions are highly dependent on financial

literacy, especially when it comes to investment in stocks (van Rooij et al., 2011). Most of the

1 Target-date (life-cyle) funds offered by Fidelity or Vanguard suggest 90% stock allocation for long-term investors (Viceira, 2008, Pastor and

Stambaugh, 2012). 2 Stocks, in principle, should also provide hedge against inflation, since stocks are claims to productive capacity as oppose to bonds with nominal claims (Bodie, 1976; Schotman and Schweitzer, 2000; Kim and In, 2005).

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individual investors are also subject to behavioral biases (see, for example, the survey by Barberis and

Thaler, 2003) such as disposition effect -selling stocks too early after gains and holding losing

securities for too long-, insufficient (naïve) diversification strategies, and overconfidence resulting in

excessive trading especially among male market participants (Barber and Odean, 2001).

This paper has a different focus. To the best of our knowledge, it is the first empirical paper

that studies the stock market re-entry versus withdrawal decision of inexperienced naïve investors.

Exploiting the detailed Finnish dataset used in earlier studies (e.g., Seru et al., 2010; Grinblatt and

Keloharju, 2000, 2001a, 2001b), we first confirm that such individual investors are subject to a hot

stove effect (HSE), that is, individuals shy away from the stock market after an initial bad experience

as shown by the previous literature (e.g., Seru et al., 2010). In particular, we identify the HSE by

focusing on the differences in re-entry decisions among inexperienced investors after initial gains and

losses. If the HSE effect is dominant, we shall observe a higher withdrawal rate from the market

among inexperienced investors after an initial loss. However, if the prospect theory (Kahnemann and

Tversky, 1979) is a better description of the behavior of individual investors, then we shall observe a

more risk-seeking attitude, hence a higher re-entry ratio, after the initial loss. We then confirm that

the magnitude of the first investment, investor characteristics such as gender and age, bull versus bear

markets, or exiting with a well-known stock such as Nokia affect the re-entry decision. In the

robustness section, we also test whether our results are sensitive to the number of stocks in the initial

portfolio, to the location of the investor, Helsinki versus outside Helsinki, and to the exclusion of the

most focal firm (Nokia) in our sample.

We confirm for our data set that the HSE is prevalent in the Finnish market after controlling

for market conditions, time-fixed effects and the duration of the first-time experience in the stock

market, and that the HSE is more pronounced after heavy losses. We also show that the HSE is

stronger in the bear market, in the gloomy period after the burst of the dot-com bubble. While the

gender of the investor is a determining factor for market re-entry, that is, females are less likely to

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come back in line with the findings of Barber and Odean (2001), it does not play a role in the context

of the HSE. We did not find a significant difference in male versus female re-entry decisions after

exiting with a loss. Age, on the other hand, significantly affects the re-entry decision after an initial

loss: the older the investor the more likely that (s)he will withdraw from the market entirely after an

initial loss. The HSE is not significantly weaker for the first-time holders of Nokia shares. Overall,

our results imply that the first-time investor does not exhibit risk-seeking behavior after an initial loss,

and shy away from the market, shutting down an important channel to transfer consumption across

time.

The rest of the paper is organized as follows. In Section 2, we discuss the related literature

and introduce the predictions regarding the HSE. In Section 3, we provide the empirical analysis

describing the data, our identification strategy, non-parametric univariate analysis and multivariate

logit results. In Section 4, we show the robustness results. We conclude in Section 5.

2. Related Literature and Predictions

It is common in financial economics to assume that the individuals have stable preferences

(aversion) towards risk which describe the individual decision making when facing risky choices (for

example, Holt and Laury, 2002). The psychology literature, on the other hand, provides an alternative

description based on experiential learning which formulizes Mark Twain’s observation3, namely the

hot stove effect (HSE). Denrell and March (2001) argue that as part of the adaptation process

individuals try to reproduce successful outcomes, and hence alternatives that have experienced

relatively good outcomes in the past are more likely to be sampled than are alternatives that have

experienced relatively poor outcomes (Holland 1975). This type of experience-based learning results

3 “We should be careful to get out of an experience only the wisdom that is in it—and stop there: lest we be like the cat that sits down on a hot

stove lid. She will never sit down on a hot stove lid again—and that is well; but also she will never sit down on a cold one.” Mark Twain (1897).

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in missing out potentially good outcomes and creates bias against risky choices. Fujikawa (2009)

provides experimental evidence on the existence of the HSE.

In the finance context, Strahilevitz et al. (2011) document that investors also make decisions

based on past experience, and find that they are reluctant to repurchase stocks previously sold for a

loss. Huang (2013) extends Strahilevitz et al. (2011)’s findings and show that investors are also

reluctant to buy similar stocks from the same industry after a loss. Seru et al. (2010) find that past

negative experience in the stock market also affects the exit decision from the market. In an IPO

setting, Kaustia and Knüpfer (2008) confirm that investors repeat behavior that has produced good

outcomes in the past and avoids behavior that has produced poor outcomes. They find that an increase

in the returns that an investor earns on past IPO investments has a positive impact on this investor’s

propensity to participate in future IPOs. On the other hand, Malmendier and Nagel (2009) show that

not only the personal experience in the stock market but also individual experience of macroeconomic

shocks related to the stock market affect stock market participation.

However, none of the earlier papers directly test how an initial experience in the stock market

affect the re-entry decision to the market. If the naïve investors behave according to experience-based

learning, we expect that their re-entry decision will be conditional on their first-time experience.

Therefore in line with the previous literature cited above, we predict the following:

Prediction 1: Investors with a limited first-time experience in the stock market are more likely

to withdraw from the market completely after an initial loss. In other words, the hot stove effect

(HSE) reduces the likelihood of stock market re-entry.

One can argue that not only an initial loss but also the magnitude of the realized loss may

impact investors’ behavior. For example, Strahilevitz et al. (2011) find that if a stock is originally sold

for a loss, the likelihood of repurchase of the same stock drops nearly linearly with the magnitude of

that loss. Similarly, Seru et al. (2010) suggest that magnitude of losses is important in investors’ exit

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decision.4 They find that an investor whose performance is one standard deviation worse than the

mean is about 15% less likely to continue trading in the market. In a savings context, Choi et al.

(2009) show that individual investors over-extrapolate from their personal return experience. They

find that within a given time period, investors who experience particularly rewarding outcomes from

saving in their 401(k)—a high average and/or a low variance rate of return—increase their 401(k)

savings rate more than investors who have less rewarding experiences with saving. Therefore, our

second prediction is as follows:

Prediction 2: The bigger the loss in the first-time stock market experience the less likely is

that the investor comes back to the market. In other words, the hotter the stove, the stronger is the

effect on market re-entry decision.

In both survey and experimental settings, De Bondt (1993) finds that non-professional

investors are overly optimistic in bull markets and overly pessimistic in bear markets. Using market

based data, Bange (2000) confirms that investors are positive feedback traders. She finds that small

investors increase their equity holdings following market run-ups, and decrease their holdings after

downturns. Amromin and Sharpe (2009) find that expected risk and returns are strongly influenced

by expected economic conditions. When investors believe that macroeconomic conditions are more

expansionary, they tend to expect both higher returns and lower volatility. Similarly, Malmendier and

Nagel (2009) examine whether people who live through different macroeconomic histories differ in

their level of risk taking. They find that individuals, who have experienced low stock market returns

throughout their lives, report lower willingness to take financial risk, are less likely to participate in

the stock market, invest a lower fraction of their liquid assets in stocks if they participate, and are

more pessimistic about future stock returns. However, they also find that the effects of economic

4 Seru et al. (2010) focus on learning via trading, and consider investors with some experience in the market, that is, those with at least seven

round-trip trades.

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downturns vanish over time; individuals are influenced more strongly by recent returns than distant

returns.

The literature above suggest that individual investors are more optimistic in the boom periods

than in the bear periods, and past macroeconomic conditions affect individuals’ decisions, but this

effect vanishes over time. Therefore, it is worthwhile to ask whether first-time investors who exit the

market with a loss in a bear period are less likely to come back.

Prediction 3: First-time investors leaving the market with a loss in a bear period are more

likely to withdraw completely from the market. In other words, the HSE is stronger in a bear period.

An important question is how the investor characteristics such as gender and age affect

experience-based decision making in the context of HSE. Previous research which examines gender

differences in risk aversion generally finds that females are more risk averse than males (Halek and

Eisenhauer 2001). Eckel and Grossman (2008) review experimental evidence examining risk

preferences across genders, and finds that while most results reveal that females are more risk averse

than males, results are less clear in contextual settings. Schubert et al. (1999) argue that although

there is some evidence that women are more risk averse than men it is questionable whether such

stereotypic risk attitudes can be confirmed in real life. They posit that behavior in abstract gambling

experiments may not correspond to risk behavior in contextual decisions. They find that when the

context is an insurance choice or a financial investment choice, there are no differences between the

risk attitudes across genders. In the abstract gambling treatment, men are found to be more risk averse

than women in the loss frame and the opposite is found in the gain frame. Using market based data,

Atkinson et al. (2003) find that male- and female-managed funds do not differ significantly in terms

of performance, risk, and other fund characteristics. Their results suggest that differences in

investment behavior often attributed to gender may be related to investment knowledge and wealth

constraints.

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As previous research examining gender differences in risk preferences does not give clear-cut

results, to explore gender difference in our setting we test the following null hypothesis:

Hypothesis 1: Gender does not affect investors’ re-entry decisions after the first-time bad

experience in the stock market.

Previous literature which examines the relationship between age and risk aversion is mixed

(Ameriks and Zeldes, 2004). Halek and Eisenhauer (2001) find that on average, risk aversion

decreases by age until age 65 and increases thereafter. Bellante and Green (2004) examine relative

risk aversion in portfolio allocation decisions among the elderly population, and find that risk

aversion distinctly decreases as among elderly but find only a modest increase in risk aversion

between the ages of 70 and 90. Bakshi and Chen (1994) on the other hand find that as age increases

the risk premium also increases implying that on average older people are more risk averse than

younger people. However, they also find that as people age their demand for financial assets

increases: older people invest more in financial markets whereas in younger ages housing is a more

urgent need. The authors’ findings jointly imply that although older people invest in financial assets

more than younger ones, they invest less in risky assets such as stocks. Some studies confirm that as

people age they invest less in stocks (Agnew et al., 2003; Holden and Van Derhei, 2001). Others

suggest a different pattern: the share of financial wealth in equities increases over the working life and

then declines or stays flat after the retirement (Poterba and Samwick, 1997; Yoo, 1994). In contrast,

some studies find no evidence supporting a gradual reduction in portfolios comprised of stocks with

age (Ameriks and Zeldes, 2004). In sum, previous literature has mixed evidence on how age impacts

risk aversion and investment behavior. Therefore, our hypothesis, in null form, is as follows:

Hypothesis 2: Age does not affect investors’ re-entry decisions after the first-time bad

experience in the stock market.

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Frieder and Subrahmanyam (2005) find that individual investors prefer to invest in stocks

with easily recognized products. In line with this, Lou (2014) finds that advertising spending is

associated with individual investor buying and a contemporaneous rise in abnormal stock returns,

which is then reversed in subsequent years suggesting that investors' initial response to changes in

advertising is indeed biased and excessive. In addition, they find that the return pattern is significantly

stronger among firms producing consumer goods (e.g., Apple Computer) than those producing non-

consumer goods (e.g., US Steel), consistent with the intuition that advertising for consumer goods

(e.g., iPod) is more likely to attract consumer/investor attention. Similarly, Billett et al. (2014) find

that individual investors prefer more prestigious brands to less prestigious brands. However, to the

best of our knowledge, the repurchase decision of well-known brands after a bad initial experience is

still an empirical question. Therefore, we explore this question with the following null hypothesis:

Hypothesis 3: Investors are equally likely to re-enter if the first-time bad experience in the

stock market is with a well-known stock such as Nokia.

3. Empirical analysis

3.1. Data and Sample Construction

The transaction data used in this paper is well-established in the literature and identical to that

used in Seru et al. (2010). The data come from the Nordic Central Securities Depository (NCSD) and

cover all trading in all Finnish stocks over a nine-year period. The subset of the same dataset is used

in Grinblatt and Keloharju (2000, 2001a, 2001b).5 The original data contain 62,946,476 transactions

of nearly 1.3 million investors including individual investors and institutional investors spanning a

period from January 1995 to December 2003. We restrict the entries to transactions placed by

individual investors only, which reduce the number of transactions down to 4,965,147 by about 0.3

5 The detailed description on the data can be provided in these references.

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million retail investors. From this sample, we identify each investor’s first-time stock market

experience and her decision to re-enter the market subsequent to the first experience.

a. First-time stock market experience. The first-time stock market experience is measured

based on investor’s first ever stock investment since she opened the brokerage account in

our sample. In order to do this, we first remove the accounts that existed before the

beginning of our sample period because we do not know if they had been already involved

in any stock trade before. For every investor who opened the account after the beginning

of our sample period, we identify the time when she enters and exits the market. The entry

date is defined as the first date on which she purchases shares of any stock. We exclude

from the sample the investors who purchase multiple stocks on the entering date.6 The exit

date is then identified as the date on which a given investor sells all of her shares of the

stock, that is, she closes the position in full since her first-time entry. We do not consider

for our analysis any investor who never exits market. To make sure that this investor has

kept positions only in the stock they initially purchased, we require them to have not

involved in trades of any other stock between enter and exit date7. Our analysis focuses on

the investors entering the market with a single stock (as opposed to multiple different

stocks) as they represent better inexperienced naïve investors. For the same reason, we

exclude from our analysis the accounts of those investors who ever took a short position,

which is unlikely in case of naïve investors.

6 We consider in the robustness section the case of investors with multiple stocks. 7 Note that investors are allowed to add positions on the same single stock during 7 calendar days following the initial entry date or a period between the initial entry data and the exit date, whichever shorter

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b. Stock market re-entry. For each investor we define the re-entry date as the first date on

which she purchases any stock within one calendar year (that is, 365 days) since her exit. 8

It ensures that any other transaction does not occur between exit and re-entry date.

Once entry and exit dates are identified, we measure the average (raw) returns of stocks

purchased and sold for the periods between the two dates.9 In doing so, we use actual transaction

prices (adjusted for stock splits) if available, and otherwise we complement the dataset with the

closing prices from Thomson Reuters Datastream. Any intraday trades are netted to obtain a quantity-

weighted price for the daily transaction. In the case of multiple trades occurring for a given stock

between two dates, we assume that the amount of shares purchased first were sold first to compute the

quantity-weighted average returns of multiple trades (Grinblatt and Keloharju, 2000). We only focus

on investors who trade stocks that are available in Datastream, and who exit the market before the end

of 2002 (that is, one year prior to the end date of our transaction database). We do this to avoid the

potential bias where investors may re-enter in 2004 but are categorized as ‘no re-entry’ due to the

truncation of our sample at the end of 2003.

After clearing and merging, our final sample contains 11,543 individuals and 176 different,

publicly-listed Finnish stocks. Panel A of Table 1 presents some investor characteristics in our

sample, and Panel B shows the breakdown of the individual’s first investment by listing up the top 10

most-frequently traded stocks. The frequency of the exit and the re-entry by year is given in Panel C.

The summary statistics of variables used in our analysis are presented in Panel D.

As shown in Panel A, the male accounts are predominant taking 71% of total accounts in the

sample. This is in line with Barber and Odean (2001) documenting with the U.S. brokerage data that

men trade 45% more than women. The median age of investor is 38 and the bulk (84%) of investors is

8 We consider the one-year fixed window to reduce any noise that looking over longer horizon would introduce (e.g., see, Seru et al., 2010). We

also perform our tests without imposing the one-year window, though unreported but available upon request, and our results are largely

unchanged. 9 As a robustness check, we also employ risk-adjusted returns and our results are largely unchanged.

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aged between 20 and 65. While the elderly people over 65 years old take 7% of the accounts,

interestingly, we have significant number of accounts by underaged (below 20 years old) investors

constituting 9% of all individual accounts. Since in Finland a person must be 18 years old to set up a

brokerage account, it must be a guardian who opened the account, and made any transfer of shares

into or out of the account. Berkman et al. (2014) show the underage accounts make up for 2.5% of

total trading days attributable to the group of the underage investors.

As for the first-time investment performances, about 21% (2,451 accounts) experience a loss

as opposed to 78% (9,006 accounts) with a gain. Much higher frequency of a realized gain compared

to a loss is consistent with well-documented disposition effect, that is, the propensity of investors to

sell assets on which they have experienced gains, and to hold on to assets with which they have

experienced losses (see, Shefrin and Statman, 1985 and Odean, 1998 among others). More

specifically, the distribution of first investment returns is plotted in Figure 1(a), where we have the

bell-shaped return distribution with the mean (0.2956) and standard deviation (0.9554). The average

duration of first-time investments is 209 days (the minimum of 1 day and the maximum of 2,562

days) (Figure 1(b)). It is interesting to see about 10% of investors in our sample stay in the Finnish

stock market for a relatively short period (that is, less than 5 days).10

In Panel B we list the ten most popular stocks chosen by investors for their first-time

investment, and 36% of investors enter the stock market by buying Nokia shares. This is not

surprising since Nokia is by far the largest firm in the Finnish market accounting for 36% of the total

stock market capitalization on average during the sample period (ranging daily from 16% to a high of

64% at one point in 2000). It is also interesting to note that investors tend to herd on particular stocks

such that seven of ten investors in our sample choose one of top ten stocks from the large pool of 237

different stocks. It is shown in Panel C that 53% of investors (6,229 out of 11,543) do not re-enter

10 For the robustness, we conduct our analysis for the sample of investors whose holding periods are longer than five days. Our results remain unchanged.

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(within a year) and, a large number (2,213 accounts) of investors exit after 30th April in 2000, labeled

as 2000b, which coincides with the burst of dot-com bubble.

[Insert Table 1 here]

[Insert Figure 1 here]

3.2. Empirical Methodology and Result

In this chapter, we describe our empirical analysis and present the main results. For each of

the predictions derived in Section 2, we present the results of a non-parametric univariate analysis

exhibited in a series of figures, followed by a multivariate logit regression analysis including several

controls.

Prediction 1: The existence of the HSE

The hot stove effect (HSE) predicts that investors who have experienced a negative return on

a particular stock will not invest in any other stocks, let alone the same stock they sold for a loss. In

order to investigate this, we relate each investor’s stock market re-entry decision to her past stock

market experience. More specifically, each investor is first categorized according to her first-time

investment performance as either Loss, representing those people who earned a negative return, or

Gain, those who experienced a positive return. Then, we examine for each group the proportion of

investors withdrawing from the stock market by not being engaged in stock trades any more (within

one year) after their first-time investment gain (or loss). We call this proportion ‘the withdrawal rate’.

If the HSE exists in the stock market we should observe a significantly higher withdrawal rate for

Loss investors compared to Gain investors. The difference in withdrawal rates between two groups

can be considered as capturing how strongly individuals are subject to the HSE: the bigger the

difference, the stronger the HSE.

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As seen in the left panel of Figure 2(a), investors in a Loss group show much higher

likelihood of not returning to the market than their counterparts in a Gain group. About 72.8% of

accounts in Loss are not engaged in any trade for any stock at all within one year since they sold a

particular stock for a loss. Strikingly, we observe a much lower ratio of 48.9% for the Gain group. It

is previously documented with the U.S. retail investor data (e.g., Strahilevitz et al, 2011) that

investors tend to avoid repurchasing the same stock that they sold for a loss. Our finding is, however,

more comprehensive in that investor’s tendency to not reinvest after experiencing a loss is not

restricted to the case of a same stock, and is also carried over to the case of different stocks they never

invested in before. We argue that this finding is consistent with investors being subject to the HSE as

opposed to becoming more risk-seeking after a loss (a preference consistent with the Prospect theory).

In a broader sense, our result is also in a similar vein with Malmendier and Nagel (2011) that

individuals express a lower willingness to take financial risk, and are less likely to participate in the

stock market, especially when they have experienced low real stock-market returns in their lives.


While our univariate results hint at the existence of the HSE, one would argue that there exist

other characteristics, both individual- and market-level, that could drive our results. In order to

control for various factors that may affect investor’s stock market re-entry decision, we carry out

multivariate analyses by employing the logit regression framework. Our baseline regression is as

follows:

Withdrawali,t+1 = β0 + β1Lossi,t + β2MktReti,t + β3MktVoli,t + β4Yeari,t + β5Durationi,t + 𝜖𝑖,𝑡 (1)

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The dependent variable is Withdrawal equal to one if an individual neither purchase nor sell any stock

within one year since unwinding her first investment in full, and otherwise zero. Loss is a dummy

variable, of our main interest, equal to one if the first investment position was unwound with a loss,

and otherwise zero. MktRet and MktVol is return and volatility (a standard deviation of daily return)

respectively of OMX Helsinki Index, which are measured for one month period after the investor’s

exit. Year is a year dummy variable with year 1995 a reference level, representing the year in which

an investor exit the market. Due to the dramatic changes in stock market conditions during the year of

2000, two time dummies, 2000a and 2000b are used respectively for the year before and after the

burst of dot-com bubbles (that is, 30th of April). Duration is the length of time in days between entry

and exit date. The latter is an important control to account for a potential disposition effect. To make

our baseline model as parsimonious as possible, we choose to include in the baseline regression in

Equation (1) only the basic set of control variables: stock market conditions when investors exit the

market summarized by its return and volatility, and the holding period of first investments. We also

include year dummies to ensure to remove time trends or any aggregate effects, such as time-varying

aggregate risk aversion.


Table 2 shows main results of our baseline regression. Consistent with our Prediction 1, it is

shown that the coefficient on Loss is positive and statistically highly significant with p-value virtually

zero. Being other things equal, the odds of investors withdrawing (vs. non-withdrawing) from stock

markets increase by more than two times (= 𝑒0.9963 = 2.70) when their first experience is negative.

The coefficients on other variables also show the expected signs. It is interesting to find the positive

coefficient on Duration, which suggests that the longer investors are engaged in their first-time stock

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investing, the more likely they withdraw from the market. The effect of duration seems robust as it

carries a highly significant coefficient throughout the analyses regardless of the model specification.

It is also interesting to see that stock market conditions at the time of exit are shown to be

relevant to investor’s re-entry decision. For example, the negative, and statistically highly significant,

coefficient on MktVol suggests the likelihood of withdrawal is low if the exit was made earlier during

a turbulent time. In a smilar vein, the coefficients on time dummies for a volatile period of 1999-2000

(Year1999, Year2000a, and Year2000b) are negative with a relatively large magnitude and all highly

significant. This can be interpreted as investors attributing their investment perfomance to luck rather

than to skill in case of a loss, also known as self-serving bias (Heider, 1944, Campbell and Sedikides,

1999; Myers, 2015). Therefore, a negative experience would be less likely to deter them from

reinvesting in stock markets.

Overall, our main results from the baseline regression support our conjecture that once an

investor had a negative experience with a particular single stock, she shows a tendency to cease

trading any of stocks alltogher, let alone the stock they traded earlier.

Prediction 2: The HSE and the Magnitude of Loss

As with the analogy in Mark Twain’s cat, the hotter the stove the cat has sat on, the stronger is

the cat’s unwillingness to sit on any of stoves again, hot or cold. In other words, the worse is the

experience from stock market participation in the first trial the less likely it is that the investor comes

back to the market. To investigate the relation between the HSE and the painfulness of past

investment experience, we first plot in Figure 3 the withdrawal rates by the magnitue of loss or gain.

In doing so, investors are sorted into deciles based on the realized returns (scaled by their volatilites)

they earned for their first-time stock market participation. We adjust the returns with volatilites to

accounts for a preference for volatile stocks. We also control for the duration of the initial investment

18

to capture the disposition effect.11 Due to the relatively small number of accounts who sold a stock for

a loss in our sample, only two of ten deciles (that Decile 1 and Decile 2) are assigned to those in

Loss, Decile 3 is a mixture of accouts in Loss and Gain, and other six (Decile 4 to Decile 10) goes to

accounts in Gain. For instance, Decile 1 represents the group of investors whose Vol-AdjustedReturn

was lower than -5.5% of returns, Decile 2 between -5.5% and -.5%, , and Decile 3 between -.5% and

1.4%. Likewise, Decile 4 between 1.4% and 2.8% while Decile 10 higher than 25.6%.

Interestingly, Figure 3 reveals the U-shape patterns in withdrawal rates across ten investor

groups. The rate is highest for Decile 1 (that is, the group with lowest returns) and decreases with the

returns until Decile 6 and then increase from Decile 7 to Decile 10. Considering the deciles separately

for Loss and Gain, we first see that withdrawal rates are overall higher for Loss than for Gain, which

is in line with our finding in previous section. Second, the rate decreases monotonically with the

magnitude of returns for Loss deciles. This is consistent with our conjecture that the more painful

investors’ first experience the more likely they shun the stock market later. Even though the

experience is limited to a single stock, the pain is severe enough to keep them away from the universe

of stocks. Third, the increasing, though not monotonically, pattern in withdrawal rates with the

magnitude for Gain deciles appears at odds with the HSE, according to which the rate should be

lower for high return deciles. In particular, it is very puzzling to see the highest withdrawal rates

among investors (that is, Decile 9 and Decile 10) having reaped extremely high returns (45% at

minimum) from their earlier investment. Earning a hefty return as high as 40-50% at minimum (for a

80 day holding period on average in the sample) is a very rare event. Presumably, once having

experienced the positive rare event in the market, investors, especially if naïve and unsophisticated,

may become averse to the possibility of another rare event where they end up with a huge negative

return from next investments.

11

We thank an anonymous referee for suggesting these controls.

19

In order to look into this relation, we re-run our baseline regression in Equation (1) with the

addition of new variables, Loss×Magnitude(Loss). Magnitude(Loss) is a continuous variable

measuring the absolute value of negative realized returns on the first investment. Our main finding

from the univariate analysis carries through to the multivariate regression analysis. As presented in

Table 3, the coefficient on Loss×Magnitude is positive and statistically significant (with the p-value

being 0.0232), meaning that the larger the losses on the first investment the less likely that investors

re-enter the market. As an illustration, being other thigs equal, the odds of an investor withdrawing

from the market increase by 1.72 times (= 𝑒0.5468 = 1.72) when the loss is heavier by the magnitude

of 1 (that is, realized return decreases by 100 percentage point).



Prediction 3: The HSE and Bull vs. Bear Markets

As we notice earlier in Table 2, the time of exit seems to influence an investor’s re-entry

decision: people withdraw less if they exit in a boom period (that is, 1999 and 2000a as in Figure

4(a)). Now we want to give a closer look at how investor’s re-entry decision would be affected by the

time when she exits the market, especially with an investment loss. Our conjecture is that HSE is

stronger (weaker) for those who exit the market in bear (bull) periods. We examine how withdrawal

rates differ by year of exit as presented in Figure 4(b). The most important observation from the graph

is that withdrawal rates do not show much differences between Loss and Gain groups until the year of

2000a. But the gap becomes significantly wider for the later periods (that is, 2000b, 2001, and 2002).

In other words, investors are more likely to withdraw from the market if it is in the early 2000’s when

they realized losses than it is in the late 1990’s.

20

It is important to note that there is a dramatic change in stock market conditions in Finland

(see Figure 4(a)) before and after 30th of April in 2000, which coincides with the burst of dot-com

bubble in the US. To see a clearer picture, we divide investors into two groups with one having exited

in a bull period (that is, before 30th of April in 2000), and the other in a bear period (that is, after 30th

of April in 2000). Then we compare the withdrawal rates for two groups as in Figure 4(c). It is clearly

seen that the HSE is pronounced among exit-in-bear investors: as high as 80% of them with a loss (vs.

45% with a gain) never come back to stock markets again. This comes with a big contrast to the case

of exit-in-bull investors whose withdrawal rate after a loss is merely 50%.

Why would the HSE be less pronounced for those investors who leave stock markets in the

bull period? Our explanation is that witnessing the stock market run-ups during the period of 1997-

1999 would lead to an optimistic view on the stock market, even though the investors themselves had

a negative experience. This optimism would diminish the pain from the earlier (negative) experience,

and make investors less likely to withdraw from the market. On the other hand, investors form a

pessimistic view on the market in the bear period, which reinforces their tendency to shun the market

after the negative experience.

We modify the baseline logit regression specification by introducing the interaction term

between Loss and bear (vs. bull) market dummy variable, Exit-in-Bear, being equal to one if an

investor fully unwinds the first investment before 30th of April in 2000, and otherwise zero. Table 4

presents a clear result where Loss×Exit-in-Bear has a positive coefficient of 0.5282, which is also

statistically highly significant. Its economic magnitude is also important: the withdrawal rate after

experiencing a loss is 1.7 times (= 𝑒0.5282 = 1.70) higher for exit-in-bear investors than their exit-in-

bull counterparts. Overall, our finding is consistent with Prediction 3 stating the HSE are stronger

(weaker) for those who exit the market in bear (bull) periods.


21


Prediction 4a: The HSE and Investor Gender

Gender is known to be one of the important factors in affecting stock market entry decisions

(e.g., Barber and Odean, 2001, Croson and Gneezy, 2009), so it is conceivable that the gender also

affects the re-entry decision. The preliminary result in Figure 2(b), however, shows that gender

appears to have no significant influence on the HSE. We observe a higher withdrawal rate after Loss

by about 25 percentage points than after Gain regardless of investor’s gender. Focusing only on a

Loss group, we have a higher withdrawal rate for women than for men, which is in line with the

finding of Barber and Odean (2001) that men trade more excessively than women.

To test the effect of gender, we include in our baseline regression the additional variable,

Female, that is, the dummy variable equals to one if women and zero otherwise. As shown in Panel A

of Table 5, Female is seen to have a positive and statistically significant coefficient, indicating

women are less likely to return to the stock market than men regardless of the performance of their

initial investment. This result is in line with the literature of women trading less excessively. The

coefficient on the interaction term, Loss×Female, however, is not statistically significant at all, which

confirms that investor gender has no significant influence on the HSE.

Prediction 4b: The HSE and Investor Age

Investor’s attitude for risk-taking may change over their life cycle (e.g., Bakshi and Chen,

1994) and therefore one might expect that the HSE varies with age. Figure 5(a) presents the

withdrawal rates for Loss and Gain by different age groups, and the differences in rates between Loss

and Gain are plotted for each group in Figure 5(b). Figure 5(a) shows that withdrawal rates for Loss

investors increase with investor age whereas the rates for Gain are largely unchanged for different age

groups. Figure 5(b) provides a clearer picture for which the withdrawal rate of investors over the age

22

of 65 after Loss is about 30 percentage points higher than the rate after Gain. This difference,

however, becomes smaller for the young below 20 years old and is only 20 percentage points. This

result would imply that the older are more prone to the HSE. It is very interesting to see the youngest

group (that is, the age of 20-) is least subject to the HSE. This may be because the underaged accounts

in Finland are maintained by guardians of the underage, who are supposedly informed and financially

more sophisticated (Berkman et al., 2014).

Next we add the age variables to the baseline regression in Equation (1) to conduct

multivariate analysis. In Panel B of Table 5, Age is a categorical variable with four levels (0-19, 20-

39, 40-64, and 65+). The bin consisting of accounts at age 0-19 is the reference level. It is clearly seen

that the magnitude of coefficient on the interation term, Loss×Age, increases monotonically with the

age, indicating the oldest (youngest) is most (least) likely to withdraw from the market after

experiencing a loss. The tendency of the oldest, in particular, not returning to the market is also

statistically significant. As an illustration, investors with age of 65+ are 1.7 times (= 𝑒0.5390 =

1.71) more likely to withdraw from stock markets than underaged investors after they exited the

market with an investment loss. This result is not likely to be driven by the death of investors because

we examine their re-entry within one year since their exit. Overall these results suggest that older

people tend to be more affected by the HSE.



Prediction 5: The HSE and Brand Stocks

Billett et al. (2014) find that individual investors prefer more prestigious brands to less

prestigious brands. If we observe the same pattern in the context of the stock market, investors may

be less scarred by a negative experience if it is with brand stocks, and less likely to withdraw from the

23

market. In this vein, we predict that the HSE would be weaker if investor’s past experience involves

brand stocks. In examining this relation, we use the Nokia stock as representing the brand stock in

Finland. This is conceivable because of the unique feature of Finnish stock market, unlike others, that

Nokia was undoubtedly the most famous brand especially during our sample period. Nokia was by far

the largest firm in the Finnish market accounting for 36% of the total stock market capitalization, and

36% of individual investors initiated their stock trading by buying Nokia. As shown in Table 6 with a

new dummy variable, Nokia, equal to one if the first investment was on Nokia and otherwise zero,

the variable of our interest, Loss× Nokia, is shown to have a coefficient of expected negative sign.

Though the significance is low, it is still interesting to see that investor’s re-entry decision is

somehow linked to the brand awareness of the stock. Note that the variable Nokia itself, however,

carries a highly significant negative coefficient.


3.3. Robustness

3.3.1. Investor location

According to Viceira (2001)’s model, especially those households whose labor income is

negatively correlated with stock market returns should invest in the market. Given that the majority of

the companies in the Finnish stock market are located in Helsinki, one can argue that the investor

location could a good proxy for the correlation between labor income and stock market returns. So,

the labor income of individuals living in Helsinki is presumably more correlated with stock market

returns. Therefore, according to the model, the Finnish stock market should be more appealing for the

people living outside Helsinki. In this section, we want to check whether our results would be

different depending on where the investor is located. Based on this conjecture, we introduce a new

24

dummy variable, Helsinki, being equal to one for investor living in Helsinki and zero otherwise. Table

7 reveals two interesting results: (i) the HSE is still prevalent after controllong for investor location,

and (ii) investors living in Helsinki are less subject to the HSE (based on the coefficient on Loss×

Helsinki being negative and marginally significant). To mitigate the concern on the selection bias, we

also employ the matching methodology (see Appendix 2) and compare two groups of investors in

Figure 6(a). Our main result carries through, and we see that investors living outside Helsinki are

more subject to the HSE, this is precisely the group of investors who should consider the stock market

for their retirement consumption.



3.3.2. Nokia effects

Given that Nokia is a predominant entry in the sample, one may argue that our results could

be firm-specific and may not hold in general. Therefore in this section we check the robustness of our

results for the sample excluding the Nokia shares. In Table 8, we exclude all accounts who invested in

Nokia for their first investments, and re-run the baseline regression in Equation (1). The main results

remain unchanged and thus it is unlikely that our findings are driven by a particular stock.


3.3.3. Multiple-stock entries

25

In the main analysis, we focus on investors who once enter and exit the market by trading

only a single stock. Even though we intend to understand the re-entry decision of naïve investors

(e.g., household investors), one criticism may be that our results cannot be generalised to a broader

class of investors, e.g., financially more sophisticated market participants. Hence in this section we

perform a robustness test for our results and include investors entering and exiting the stock market

by trading a portfolio of stocks (as opposed to a single stock).12 In doing so, we define a new dummy

variable, Multiple, being equal to one for a multiple-stock entry investor and zero for a single stock

one. Our main results are unchanged after enlarging our sample. Interestingly, we have a positive

coefficient on the interaction variable, Loss× Multiple, suggesting that investors who trade a portfolio

of stocks are more subject to the HSE than those who trade a single stock. This may be due to the fact

that investment failure comes more painful when investors would think that trading a portfolio is a

less risky and correct way of investment. To mitigate the selection bias, we construct a matched

sample (see Appendix 2), and compare investors for the two cases (that is, trading a single stock vs. a

portfolio of multiple stocks) in Figure 6(b). Matching sample results confirm that the multiple stock

investors are more subject to the HSE.


4. Conclusion

In this paper, we document that complete withdrawal from the stock market is more likely after an

initial first-time loss. Using a detailed dataset which contains all individual transactions of the market

participants from the Finnish stock market, we show that the HSE is strong even after controlling for

market conditions, time-fixed effects and the duration of the first-time investment. The magnitude of

the first-time loss exacerbates the HSE. Moreover, the broad market performance (bear vs. bull)

12

For the detail on the sample construction for the multiple stock investors, see Appendix 1.

26

during the stock market exit has a strong impact on the re-entry decision. While the gender does not

play a role in the context of the HSE, investor age is positively correlated with the HSE, that is, the

older the investor the more likely that she will withdraw from the market entirely after an initial loss.

Our results do not depend on the number of stocks in the initial portfolio, investor location or the first

stock being Nokia.

There is scant evidence on the re-entry decision of inexperienced investors. Our paper is an initial

attempt to fill the gap. The existence of the HSE among the first-time investors, and thus the fact that

inexperienced investors largely do not exhibit gambling behavior has important implications on

incentives for stock market participation, and contributes to the discussion on financial literacy; if the

re-entry decision heavily depends on the performance of the limited first-time experience in the stock

market, then investors should be informed about risks involved in investing stocks in the short-run,

and encouraged to consider the stock market for long-run investment horizon. Neither trading

excessively nor withdrawing completely allow the stock markets to serve as a vehicle to transfer

consumption to the period when is most valued, that is, during the retirement period.

27

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Appendix:

1. The entry and exit date for multiple stock investors

In this section, we describe how to measure the entry and exit date for investors who initiate their

trading with more than a single stock. In doing so, we define the initial investment window for a given

investor as a period between the first buying trade and the first selling trade (excluding the first

selling date) or 7 calendar days since the first buying trade, whichever shorter (see Figure A.1). The

exit date is defined as the date on which a given investor had no position on any stocks in a portfolio

for the first time since the first entry. The reentry date is defined as the date on which a given investor

bought one or more stock for the first time since the exit. Figure A.1 illustrates the method:

Figure A.1. Illustration of entry and exit dates for multiple stock investors

33

2. Construction of matched investor samples: (i) Helsinki vs. Non-Helsinki and (ii)

Single Stock vs. Multiple Stocks

We matched 2,828 accounts living in Helsinki (Helsinki subset) with 8,715 accounts living

outside Helsinki (non-Helsinki subset), and matched 1,105 accounts who bought multiple stocks

during the initial investment window with 11,543 accounts who bought a single stock during the

window. In order to construct those matched samples, we used the one-to-one matching method

similar to Huang and Stoll (1996). For an account i in the Helsinki (Multiple-stocks) subset and an

account j in the non-Helsinki (Single-stock) subset, the distance between i and j is calculated as:

Distance(i,j) = ∑ [2(𝑥𝑖

𝑘− 𝑥𝑗𝑘)

𝑥𝑖𝑘− 𝑥𝑗

𝑘 ]2

𝑘 , where 𝑥𝑘 is standardized variable to be matched (2)

Variables were standardised by subtracting the minimum value and being divided by the range of

the variable in the subset such that the standardised variables were in the range between 0 and 1. All

matched pairs were kept regardless the matched distance between them. The procedure is as follows:

1. Randomly sample an account i from Helsinki (Multiple-stocks) subset.

2. Identify gender and exit-year (and Helsinki for matching the single-stock subset with

the multiple-stocks subset) of account i.

3. Extract accounts in the non-Helsinki (Single-stock) subset who have the same gender

and exit-year as account i (candidate accounts).

4. Calculate the distances based on Age and Days between the window-end and the exit

between account i and each candidate account according to Formula 1.

5. Find an account j in the candidate accounts whose distance to account i is the smallest.

6. Exclude account i from the Helsinki (Multiple-stocks) subset and account j from the

non-Helsinki (Single-stock) subset.

34

7. Repeat step 1 to 6 until all accounts in the Helsinki (Multiple-stock) subset are

matched to accounts in the non-Helsinki (Single-stock) subset.

In order to minimize the influence of the order of sampling, we iterated the procedure by 100 time

and chose the best matched samples on the basis of aggregated distances of each matched pairs. Note

that the range of aggregated distances of 100 iterations was [57.48, 61.12] for Helsinki-Non Helsinki

matching, and [27.42:28.15] for Single-Multiple matching.

35

Figure 1. First-time Stock Market Investment

(a) Realized raw return

This graph presents the distribution of realized returns on first-time investment. The y-axis represents the

number of investors and the x-axis is the rate of return. The width of each bar is .05. The red dotted lines

represent the boundaries between return deciles.

(b) Duration

This graph plots the distribution of durations of first-time stock investment with the y-axis representing the

number of investors, and the x-axis being the number of days between enter and exit date. The width of each

bar is 5 days.

36

Figure 2. Investment Profit and Investor Withdrawal Rate

(a) Investment profit and investor withdrawal rate: whole sample

This graph presents the withdrawal rate of investors since their first-time investment. The y-axis represents

the withdrawal rate, and the x-axis is the profit on the first-time stock investment. The bars represent the

withdrawal rate and error bars are corresponding 95% confidence intervals computed by the bootstrap

(1,000 resamples).

(b) Investment profit and investor withdrawal rate: subsample by gender

This graph presents the withdrawal rate of male investors vs. female investors since their first-time

investment. The y-axis represents the withdrawal rate, and the x-axis is the profit on the first-time stock

investment. The bars represent the withdrawal rate and error bars are corresponding 95% confidence

intervals computed by the bootstrap (1,000 resamples).

37

Figure 3. Investor Withdrawal Rate and Magnitude of Investment Profit

This graph presents the withdrawal rate of investors for different magnitudes of investment profits. The y-

axis represents the withdrawal rate predicted by a logit model, and the x-axis is the deciles of return

portfolio. The portfolio number in x-axis is in order of the volatility adjusted returns after controlling for the

duration of initial holding period: Decile 1: Less than -5.5, Decile 2: -5.5~-.5, Decile 3: -.5~1.4, Decile 4:

1.4~2.8, Decile 5: 2.8~4.5, Decile 6: 4.5~6.5, Docile 7: 6.5~9.2, Decile 8: 9.2~14.1, Decile 9: 14.1~.25.6,

Decile 10: More than 25.6. The bars represent the proportion of accounts who did not reenter the market.

Error bars are corresponding 95% confidence intervals.

38

Figure 4. Investor Withdrawal Rate and Bull vs. Bear Markets

(a) Finnish stock market prices

This graph plots the movement of Finnish stock market index price from January 1995 to January 2003. The

y-axis is HEX closing price and x-axis is the date.

(b) Investor withdrawal and the exit year

The bars represent the withdrawal rate for different years. For each year, two bars are plotted for Loss and Gain

separately. Error bars are corresponding 95% confidence intervals computed by the bootstrap (1,000 resamples).

39

(c) Investor withdrawal and the exit period: bull vs. bear

The bars represent the withdrawal rate. For each group of investors (Exit-in-bull or Exit-in-bear), two bars

are separately plotted for Loss and Gain. Error bars are corresponding 95% confidence intervals computed

by the bootstrap (1,000 resamples).

40

Figure 5. Investor Withdrawal Rate and Investor Age

(a) Investor withdrawal rate for different age group

The bars represent the withdrawal rate. For each age group, two bars are separately plotted for Loss and

Gain. Error bars are corresponding 95% confidence intervals computed by the bootstrap (1,000 resamples).

(b) Difference in investor withdrawal between loss and gain for different age groups

The bars represent the difference between Loss and Gain investors in the withdrawal rate for different age

groups. Error bars are corresponding 95% confidence intervals computed by the bootstrap (1,000

resamples).

41

Figure 6. Matched Sample Results; (i) Helsinki vs. Non-Helsinki, and

(ii) Single-stock vs. Multiple-stock

(a) Investor withdrawal rate for matched sample: Helsinki vs. non-Helsinki

The bars represent the withdrawal rate for investors living in Helsinki vs. investors living outside Helsinki.

Two samples are matched based on age, gender, and investment duration. Error bars are corresponding 95%

confidence intervals computed by the bootstrap (1,000 resamples).

(b) Investor withdrawal rate for matched sample: single vs. multiple

The bars represent the withdrawal rate for investors trading a single stock vs. investors trading a portfolio of

multiple stocks. Two samples are matched based on age, gender, and investment duration. Error bars are

corresponding 95% confidence intervals computed by the bootstrap (1,000 resamples).

42

Table 1. Descriptive Statistics

This table reports the descriptive statistics of the sample used in the analysis. Panel A presents some characteristics

of investors such as age and gender. First-time investment profit is defined as loss if the realized return is negative,

gain if positive, and zero otherwise. Panel B shows the list of top 10 stock names that are most commonly invested

on by investors who initiate their first-time stock trading. Panel C list the years of exit and re-entry and summary

statistics on main variables are presented in Panel D. Withdrawal equal to one if an individual neither purchase nor

sell any stock within one year since unwinding her first investment in full, and otherwise zero. Loss is a dummy

variable equal to one if first investment position was unwound with a loss, and otherwise zero. MktRet and MktVol is

return and volatility (a standard deviation of daily return) respectively of OMX Helsinki Index, which are measured

for one month period after the investor’s exit. Duration is the length of time in days between entry and exit date.

Female is the dummy variable equal to one if women and zero if men. Age is the continuois variable for investor

age, Nokia is equal to 1 if investor’s first investment in on Nokia stock, zero otherwise, Helsinki is equal to 1 if

investor lives in Helsinki, zero otherwise. Duration(exit-rentry) is the length of time in days between exit and re-

entry date. First time return is the rate of return on investor’s first investment, First time return(loss) is the rate of

return for Loss group investors and First time return(gain) is the rate of return for Gain group investors. Exit-in-

Bear, being equal to one if an account fully unwound the first investment before 30th

of April in 2000, and otherwise

zero.

Panel A. Investor characteristics

No. of Accounts Proportion

Gender

Male 8,148 71%

Female 3,395 29%

Age

0-19 1,082 9%

20-39 5,411 47%

40-64 4,258 37%

65- 792 7%

First-time investment

Profit

Loss 2,451 21%

Zero 86 1%

Gain 9,006 78%

Total 11,543 100%

Panel B. Breakdown of the first-time investment (Top 10)

ISIN Company Name Num. accounts Proportion Cumulative proportion

FI0009000681 NOKIA CORP 4,125 0.36 0.36

FI0009007371 SONERA OYJ 1,143 0.10 0.46

FI0009007264 BITTIUM CORP 516 0.04 0.50

FI0009002943 RAISIO PLC 454 0.04 0.54

FI0009000053 MERITA LTD 378 0.03 0.57

FI0009005987 UPM-KYMMENE CORP 362 0.03 0.60

FI0009801310 F-SECURE CORP 243 0.02 0.63

FI0009900070 HARTWALL OYJ 219 0.02 0.64

FI0009006738 ELCOTEQ SE 213 0.02 0.66

FI0009005961 FORTUM OYJ 197 0.02 0.68

Others 3,693 0.32 1.00

Total 11,543

43

Panel C. Exit and Re-entry by Year

Panel D. Summary Statistics of Variables

N Mean Std Dev Minimum Maximum

Withdrawal 11,543 0.5398 0.4984 0.0000 1.0000

Loss 11,543 0.2125 0.4091 0.0000 1.0000

MktRet 11,543 0.0101 0.1188 -0.3196 0.3940

MktVol 11,543 0.0257 0.0104 0.0055 0.0521

Duration 11,543 209.4173 288.0535 1.0000 2,652.0000

Female 11,543 0.2943 0.4557 0.0000 1.0000

Age 11,543 38.4988 16.2457 0.0000 105.0000

First-time return 11,543 0.2956 0.9554 -0.9849 29.0000

First-time return(loss) 2,451 -0.2664 0.2443 -0.9849 -0.0000

First-time return(gain) 9,006 0.4472 1.0172 0.0000 29.0000

Exit-in-bear 11,543 0.4984 0.5000 0.0000 1.0000

Nokia 11,543 0.3578 0.4794 0.0000 1.0000

Helsinki 11,543 0.2447 0.4300 0.0000 1.0000

Duration(exit-re-entry) 6,229 190.8208 353.4944 1.0000 3,146.0000

1995 1996 1997 1998 1999 2000a 2000b 2001 2002 2003

1995 30 8 - - - - - - - - 59 97

1996 85 35 - - - - - - - 375 495

1997 149 40 - - - - - - 394 583

1998 275 123 - - - - - 585 983

1999 621 356 89 - - - 957 2,023

2000a 708 243 15 - - 632 1,598

2000b 879 281 - - 1,053 2,213

2001 691 162 - 1,179 2,032

2002 430 94 995 1,519

30 93 184 315 744 1,064 1,211 987 592 94 6,229 11,543

No

re-entry

Sub total

Sub totalYear of the re-entry (within a year)

Year

of

the e

xit

44

Table 2. HSE: Baseline Regression

This table reports the estimated coefficients, standard errors, and p-values for the following logit regression:

Withdrawali,t+1 = β0 + β1Lossi,t + β2MktReti,t + β3MktVoli,t + β4Yeari,t + β5Durationi,t + 𝜖𝑖,𝑡

The dependent variable, Withdrawal is equal to one if an individual neither purchase nor sell any stock within one

year since unwinding her first investment in full, and otherwise zero. Loss is a dummy variable equal to one if first

investment position was unwound with a loss, and otherwise zero. MktRet and MktVol is return and volatility (a

standard deviation of daily return) respectively of OMX Helsinki Index, which are measured for one month period

after the investor’s exit. Duration is the length of time in days between entry and exit date. Year is a year dummy

variable with year 1995 a reference level, representing the year in which an investor exit the market. Due to the

dramatic changes in stock market conditions during the year of 2000, two time dummies, 2000a and 2000b are used

respectively for the year before and after the busrt of dot-com bubbles (that is, 30th

of April). ∗∗∗, ∗∗, and ∗ denote

statistical significance at 1%, 5%, and 10%.

Estimate

Standard

Error p-Value

Loss*** 0.9963 0.0537 <.0001

MktRet -0.1851 0.1985 0.3511

MktVol** -6.4130 2.7644 0.0203

Duration*** 0.0028 0.0001 <.0001

Year1996*** 0.6407 0.2395 0.0075

Year1997 0.0273 0.2334 0.9068

Year1998 -0.0457 0.2249 0.8388

Year1999** -0.5620 0.2216 0.0112

Year2000a*** -0.7266 0.2255 0.0013

Year2000b** -0.4938 0.2246 0.0279

Year2001 -0.3403 0.2231 0.1271

Year2002* -0.3855 0.2240 0.0852

Observation 11,543

45

Table 3. HSE and Magnitude of Loss


Withdrawali,t+1 = β0 + β1Lossi,t +β1Lossi,t×Magnitude(Loss) i,t

+ β3MktReti,t + β4MktVoli,t + β5Yeari,t + β6Durationi,t + 𝜖𝑖,𝑡



investment position was unwound with a loss, and otherwise zero. Magnitude(Loss) is a continuous variable

measuring the absolute value of negative realized returns on the first investment. MktRet and MktVol is return and

volatility (a standard deviation of daily return) respectively of OMX Helsinki Index, which are measured for one

month period after the investor’s exit. Duration is the length of time in days between entry and exit date. Year is a

year dummy variable with year 1995 a reference level, representing the year in which an investor exit the market.

Due to the dramatic changes in stock market conditions during the year of 2000, two time dummies, 2000a and

2000b are used respectively for the year before and after the busrt of dot-com bubbles (that is, 30th

of April). ∗∗∗, ∗∗,

and ∗ denote statistical significance at 1%, 5%, and 10%.

Estimate

Standard

Error p-Value

Loss*** 0.8785 0.0739 <.0001

Loss×Magnitude(Loss)** 0.5468 0.2408 0.0232

MktRet -0.1983 0.1987 0.3181

MktVol** -6.5837 2.7650 0.0173

Duration*** 0.0028 0.0001 <.0001

Year1996*** 0.6349 0.2392 0.0079

Year1997 0.0273 0.2330 0.9066

Year1998 -0.0487 0.2246 0.8283

Year1999** -0.5611 0.2212 0.0112

Year2000a*** -0.7294 0.2252 0.0012

Year2000b** -0.5019 0.2243 0.0252

Year2001 -0.3549 0.2228 0.1112

Year2002* -0.3964 0.2237 0.0764

Observation 11,543

46

Table 4. HSE and Time of Exit: Bull vs. Bear Markets


Withdrawali,t+1 = β0 + β1Lossi,t +β1Lossi,t×Exit-in-Bear i,t

+ β3MktReti,t + β4MktVoli,t + β5Exit-in-Beari,t + β6Durationi,t + 𝜖𝑖,𝑡






month period after the investor’s exit. Duration is the length of time in days between entry and exit date. Year is a

year dummy variable with year 1995 a reference level, representing the year in which an investor exit the market.

Due to the dramatic changes in stock market conditions during the year of 2000, two time dummies, 2000a and

2000b are used respectively for the year before and after the busrt of dot-com bubbles (that is, 30th

of April). Exit-in-

Bear, being equal to one if an account fully unwound the first investment before 30th

of April in 2000, and otherwise

zero. ∗∗∗, ∗∗, and ∗ denote statistical significance at 1%, 5%, and 10%.

Estimate

Standard

Error p-Value

Loss*** 0.6767 0.0857 <.0001

Loss×Exit-in-Bear*** 0.5282 0.1098 <.0001

Exit-in-Bear -0.0509 0.0515 0.3228

MktRet*** -0.7117 0.1910 0.0002

MktVol*** -23.1775 2.1690 <.0001

Duration*** 0.0028 0.0001 <.0001

Observation 11,543

47

Table 5. HSE and Investor Demographics


Withdrawali,t+1 = β0 + β1Lossi,t +β1Lossi,t×Female(or Age-bin) i,t

+ β3MktReti,t + β4MktVoli,t + β5Female(or Age-bin)i,t + β6Durationi,t + 𝜖𝑖,𝑡






month period after the investor’s exit. Duration is the length of time in days between entry and exit date. Female is

the dummy variable equal to one if women and zero if men. Age-bin is a categorical variable with 4 levels (0-19, 20-

39, 40-64, and 65+). The bin cosisting of accounts at age 0-19 is the reference level. Year is a year dummy variable

with year 1995 a reference level, representing the year in which an investor exit the market. Due to the dramatic

changes in stock market conditions during the year of 2000, two time dummies, 2000a and 2000b are used

respectively for the year before and after the busrt of dot-com bubbles (that is 30th


statistical significance at 1%, 5%, and 10%. Panel A. Gender

Estimate

Standard

Error p-Value

Loss*** 1.0171 0.0618 <.0001

Loss×Female -0.0552 0.1208 0.6478

Female*** 0.1410 0.0490 0.004

MktRet -0.1915 0.1987 0.3351

MktVol** -6.4038 2.7686 0.0207

Duration*** 0.0028 0.0001 <.0001

Year Fixed Effect Yes

Observation 11,543

Panel B. Age

Estimate

Standard

Error p-Value

Loss*** 0.7868 0.1682 <.0001

Loss×Age(20-39) 0.1424 0.1830 0.4363

Loss×Age(39-64) 0.2821 0.1917 0.1412

Loss×Age(65-)* 0.5390 0.2782 0.0526

Age(20-39)*** 0.2250 0.0813 0.0057

Age(39-64) 0.0263 0.0827 0.7501

Age(65-) -0.0479 0.1135 0.6727

MktRet -0.1821 0.1992 0.3607

MktVol** -6.3349 2.7747 0.0224

Duration*** 0.0029 0.0001 <.0001

Year Fixed Effect Yes

Observation 11,543

48

Table 6. HSE and Brand Stocks


Withdrawali,t+1 = β0 + β1Lossi,t +β1Lossi,t×Nokia i,t

+ β3MktReti,t + β4MktVoli,t + β5Nokiai,t + β6Durationi,t + 𝜖𝑖,𝑡



investment position was unwound with a loss, and otherwise zero. Nokia is equal to 1 if investor’s first investment in

on Nokia stock, zero otherwise. MktRet and MktVol is return and volatility (a standard deviation of daily return)

respectively of OMX Helsinki Index, which are measured for one month period after the investor’s exit. Duration is

the length of time in days between entry and exit date. Female is the dummy variable equal to one if women and zero

if men. Age-bin is a categorical variable with 4 levels (0-19, 20-39, 40-64, and 65+). The bin cosisting of accounts at

age 0-19 is the reference level. Year is a year dummy variable with year 1995 a reference level, representing the year

in which an investor exit the market. Due to the dramatic changes in stock market conditions during the year of 2000,

two time dummies, 2000a and 2000b are used respectively for the year before and after the busrt of dot-com bubbles

(that is, 30th

of April). ∗∗∗, ∗∗, and ∗ denote statistical significance at 1%, 5%, and 10%.

Estimate

Standard

Error p-Value

Loss*** 0.9836 0.0626 <.0001

Loss×Nokia -0.1270 0.1209 0.2935

Nokia*** -0.2304 0.0468 <.0001

MktRet -0.1137 0.1993 0.5685

MktVol** -6.4877 2.7676 0.0191

Duration*** 0.0028 0.0001 <.0001

Year1996*** 0.6944 0.2401 0.0038

Year1997 0.0083 0.2337 0.9716

Year1998 -0.0284 0.2255 0.8997

Year1999*** -0.5719 0.2221 0.0100

Year2000a*** -0.7674 0.2261 0.0007

Year2000b** -0.4792 0.2251 0.0332

Year2001 -0.3322 0.2235 0.1373

Year2002 -0.3404 0.2246 0.1296

Observation 11,543

49

Table 7. HSE and Investor Location


Withdrawali,t+1 = β0 + β1Lossi,t +β1Lossi,t×Helsinki i,t

+ β3MktReti,t + β4MktVoli,t + β5Helsinkii,t + β6Durationi,t + 𝜖𝑖,𝑡

The dependaet variable, Withdrawal is equal to one if an individual neither purchase nor sell any stock within one


investment position was unwound with a loss, and otherwise zero. Helsinki is equal to 1 if investor lives in Helsinki,

zero otherwise. MktRet and MktVol is return and volatility (a standard deviation of daily return) respectively of OMX

Helsinki Index, which are measured for one month period after the investor’s exit. Duration is the length of time in

days between entry and exit date. Female is the dummy variable equal to one if women and zero if men. Age-bin is a

categorical variable with 4 levels (0-19, 20-39, 40-64, and 65+). The bin cosisting of accounts at age 0-19 is the

reference level. Year is a year dummy variable with year 1995 a reference level, representing the year in which an

investor exit the market. Due to the dramatic changes in stock market conditions during the year of 2000, two time

dummies, 2000a and 2000b are used respectively for the year before and after the busrt of dot-com bubbles (that is,

30th

of April). ∗∗∗, ∗∗, and ∗ denote statistical significance at 1%, 5%, and 10%.

Estimate

Standard

Error p-Value

Loss*** 1.0352 0.0627 <.0001

Loss×Helsinki -0.1829 0.1180 0.1210

Helsinki*** 0.2637 0.0529 <.0001

MktRet -0.1938 0.1987 0.3295

MktVol** -6.9074 2.7696 0.0126

Duration*** 0.0028 0.0001 <.0001

Year1996*** 0.6578 0.2397 0.0061

Year1997 0.0574 0.2337 0.8058

Year1998 -0.0202 0.2252 0.9284

Year1999** -0.5279 0.2219 0.0173

Year2000a*** -0.6947 0.2258 0.0021

Year2000b** -0.4566 0.2249 0.0423

Year2001 -0.3056 0.2234 0.1713

Year2002 -0.3561 0.2242 0.1122

Observation 11,543

50

Table 8. HSE for Non-NOKIA Stocks


Withdrawali,t+1 = β0 + β1Lossi,t + β2MktReti,t + β3MktVoli,t + β4Yeari,t + β5Durationi,t + 𝜖𝑖,𝑡



investment position was unwound with a loss, and otherwise zero. MktRet and MktVol is return and volatility (a

standard deviation of daily return) respectively of OMX Helsinki Index, which are measured for one month period

after the investor’s exit. Duration is the length of time in days between entry and exit date. Year is a year dummy

variable with year 1995 a reference level, representing the year in which an investor exit the market. Due to the

dramatic changes in stock market conditions during the year of 2000, two time dummies, 2000a and 2000b are used

respectively for the year before and after the busrt of dot-com bubbles (that is, 30th


statistical significance at 1%, 5%, and 10%.

Estimate

Standard

Error p-Value

Loss*** 0.9820 0.0634 <.0001

MktRet -0.0491 0.2500 0.8444

MktVol** -7.0944 3.3218 0.0327

Duration*** 0.0025 0.0001 <.0001

Year1996 0.5307 0.3268 0.1043

Year1997 -0.2535 0.2953 0.3907

Year1998 -0.2251 0.2910 0.4392

Year1999*** -0.7611 0.2839 0.0073

Year2000a*** -1.0404 0.2874 0.0003

Year2000b*** -0.7594 0.2885 0.0085

Year2001 -0.4350 0.2862 0.1286

Year2002 -0.3869 0.2909 0.1835

Observation 7,455

51

Table 9. HSE for Multiple Stock Entries


Withdrawali,t+1 = β0 + β1Lossi,t +β1Lossi,t×Multiple i,t

+ β3MktReti,t + β4MktVoli,t + β5Multiplei,t + β6Durationi,t + 𝜖𝑖,𝑡



investment position was unwound with a loss, and otherwise zero. Helsinki is equal to 1 if investor lives in Helsinki,

zero otherwise. MktRet and MktVol is return and volatility (a standard deviation of daily return) respectively of OMX

Helsinki Index, which are measured for one month period after the investor’s exit. Duration is the length of time in

days between entry and exit date. Female is the dummy variable equal to one if women and zero if men. Age-bin is a

categorical variable with 4 levels (0-19, 20-39, 40-64, and 65+). The bin cosisting of accounts at age 0-19 is the

reference level. Year is a year dummy variable with year 1995 a reference level, representing the year in which an

investor exit the market. Due to the dramatic changes in stock market conditions during the year of 2000, two time

dummies, 2000a and 2000b are used respectively for the year before and after the busrt of dot-com bubbles (that is,

30th

of April). Multiple is equal to 1 if investor initate trading with a portfolio of multiple stocks and zero if a single

stock. ∗∗∗, ∗∗, and ∗ denote statistical significance at 1%, 5%, and 10%.

Estimate

Standard

Error p-Value

Loss*** 0.9970 0.0537 <.0001

Loss×Multiple 0.2241 0.1604 0.1624

Multiple -0.0084 0.0802 0.9162

MktRet -0.2189 0.1895 0.2480

MktVol** -6.3948 2.6343 0.0152

Duration*** 0.0029 0.0001 <.0001

Year1996*** 0.6251 0.2382 0.0087

Year1997 0.0188 0.2315 0.9353

Year1998 -0.0339 0.2240 0.8796

Year1999*** -0.5706 0.2206 0.0097

Year2000a*** -0.7417 0.2240 0.0009

Year2000b** -0.5031 0.2233 0.0243

Year2001 -0.3640 0.2220 0.1010

Year2002* -0.3773 0.2230 0.0907

Observation 12,718

the first cut is the deepest: stock market re-entry ...€¦ · 2 the first cut is the deepest:...

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