A portrait of managed fund investors, P Brown, R da Silva Rosa, T McNaughton

Tags: disposition effect, proportion, Growth funds, managed funds, BT Financial Group, investments, risk categories, gender, managed fund, managed fund investors, Conservative Funds, unprecedented portrait, Income Funds, superannuation, individuals, systematic biases, investor, quartile, wealth, Australia, investors, independent investors, personal information, investment performance, conventional wisdom, fund risk, Balanced Funds, investment, fund management, older investors, Australian Taxation Office, Conservative fund, University of Western Australia, Philip Brown, Accounting and Finance, pro-choice argument, individual investors, PLR, sharemarket investors
Content: A portrait of managed fund investors By Philip Brown Ray da Silva Rosa Tracey McNaughton Abstract The advent of superannuation choice has focused attention on how it will be exercised. It is now well recognised that individuals are prone to systematic biases in evaluation of the relevant factors but the scope of the biases remains poorly understood, particularly in the market for managed funds. We use (hitherto unavailable) trade-by-trade information on individuals' investments in managed funds to provide an unprecedented portrait of managed fund investors in Australia, showing how gender, wealth and age influence the kind of funds investors select and their subsequent trading behaviour, in particular, their relative susceptibility to the "disposition effect" i.e., the tendency for individuals to sell their "winning" investments and hold on to their "losing investments". Our results reveal that investors behave differently in the managed fund and direct equity markets. For instance, across certain risk categories of funds, investors manifest a reverse disposition effect. The influence of gender is also strongly evident in our findings. Philip Brown is Professor of Accounting, The University of Western Australia and Professor of Accounting and Finance, The University of New South Wales. Ray da Silva Rosa is Associate Professor in Accounting and Finance, The University of Western Australia. Tracey McNaughton is Senior Economist, BT Financial Group. The views expressed in this paper are the authors and do not necessarily reflect those of the BT Financial Group. Where quoted or used, such views should be attributed to the authors.
1.0 Introduction The key issue in the advent of superannuation choice is how it will be exercised. An attractive general argument in favour of choice is that people are best placed to make decisions affecting their welfare. Evidence that the "wisdom of crowds" is usually more accurate than that of recognised experts ­ hence the superior performance of market economies over planned economies - lends impressive support to the pro-choice argument. Nevertheless, important considerations indicate superannuation choice is not an unqualified benefit. One is the presence of significant externalities; people's superannuation choices are likely to reflect the reality that the government will provide a "safety net" if their funds prove less successful than expected. Another is the substantial and persuasive evidence that people are typically overwhelmed by the decision to make a choice and that they are prone to systematic biases in their evaluation of the relevant factors. Crucially, the diversification of errors effect that allows the "wisdom of crowds" to dominate that of experts at the market level does not operate at the individual level so the biases must be addressed directly. The difficulties people experience in exercising superannuation choice effectively is not a sufficient argument against choice because well-structured interventions may largely ameliorate, if not eliminate, the problem. Understanding how investors' characteristics affect their trades in managed funds is a crucial step in designing appropriate interventions. We address this aim by providing an unprecedented portrait of managed fund investors in Australia, showing how gender, wealth and age influence the kind of funds investors select, and their subsequent trading behaviour, in particular, their relative susceptibility to the "disposition effect" i.e., the tendency for individuals to sell their "winning" investments and hold on to their "losing investments". Our results are drawn from analysis of the population of accounts, extending several decades, managed by one Australia's largest and oldest family of managed funds, BT Financial Group. We focus on gender, wealth and age because these factors have been shown to be associated with differences in investors' behavior and public policy is often channelled along these lines. For instance, some funds marketed to high net worth individuals are exempt from the provisions to provide a prospectus on the basis that such people are sophisticated investors. 2
Our analysis of the disposition effect will be of interest to investment advisers, fund managers and regulatory authorities, among others, who wish to know how investor psychology interacts with fund performance to affect trades in managed funds. The paper is organised as follows. Section II describes the data. Section III reviews and discusses the results, placing them in context of other related research findings. A summary and conclusion comprise section IV. II Data and reSearch Method Data BT Financial Group has operated in Australia (under various names) since 1969 and so the data provided by the Group for this study rank among the largest, most diverse and certainly longest time-series of transaction-by-transaction data on managed funds yet made available for investigation. These attributes are important because they expand the range of research questions that may be addressed and, importantly, increase confidence in the representativeness of the findings. The data provided include demographic details (excluding names and addresses) for over 850,000 BT retail investors, some of who first opened an account in 1974. The data include birth date, post-code, and, where available for individual investors, their gender, marital status, occupation, and whether they had an adviser. Other data provided consists of transaction-by-transaction records for the investors identified in the demographic data. The transactions data date back to the day each investor first opened an account with BT. For each transaction, we know the date the transaction took place, the type of transaction that took place (deposit, redemption, or switch), the name of the fund through which the transaction took place, the transaction amount, the number of units involved, the unit price, and the balance of the remaining holding. In total, we have information on over 6.8 million transactions that took place between December 1974 and August 2005. This paper is the first in a series of studies we plan to undertake of this impressive dataset. Research method 3
Odean (1998) is a landmark paper on the disposition effect. He shows that clients of a US discount stockbroker prefer to sell "winning" stocks and hold "losing" stocks even when trading costs are the same. We broadly follow Odean's research design, which entails examination of the frequency with which investors sell their winning and losing investments relative to their opportunities to sell each type. Note that we cannot simply compare the number of realised gains with realised losses. For instance, in a falling market an active trader may sell more losing investments rather than winning investments simply because he has more losers than winners. However, if we find that the trader sells 80% of all his winners and just 10% of all his losers, we can conclude that he is prone to the disposition effect because the proportion of his total gains realised is eight times that of the proportion of total losses realised. In our analysis, we define a "winning" ("losing") fund as one whose unit price is greater (less) than a given investor's volume-weighted unit Purchase Price. Unit prices are calculated net of fund management costs. For each investor, the performance status of each of their managed funds is calculated each day, i.e., our fundamental unit of analysis is each investor's holding in each managed fund each day. Across all investors and all managed funds over the period since December 1974, we have over 1.8 billion investor-days; 68% of the investordays are "winning" fund-days, 2% are days during which the unit price of the fund equalled the relevant investors' volume-weighted average purchase price and 30% of the days are "losing" fund-days. It is important to note that our study is not sensitive to the magnitude of these gains or loses; we will save this aspect of the investigation for further research. Our interest is in comparing the proportion of winning fund-days realised with the proportion of losing fund-days realised. If investors in managed funds behave the same as investors who trade directly in equities, a higher proportion of sales will occur within winning fund-days relative to the proportion of sales within losing fund-days. Note that, consistent with Odean's (1998) analysis, we assume that each day's decision is made independently of other investors and independently of decisions made in other days and for other investments. Notwithstanding this point, we investigate if differences in the 4
propensity to exhibit the disposition effect are associated with gender, wealth and risk of fund. III Findings Descriptives Table 1 shows the proportion of the over 1.8 billion investor-fund-days that we study that fall in each category of gender, wealth and age used in our analysis. Table 1 also shows the proportion of investor-fund-days that are in the winning zone, the break-even zone, and the loss zone. The salient points of interest in Table 1 are that the distribution of investor-trading-days is unevenly spread across the four fund risk categories. Growth funds, the riskiest category, accounts for a majority, around 57%, of investor trading days, with Balanced funds running a distant second with about 21%, and Conservative funds, the safest option, taking up around 17% of the investor-trading-days. The influence of gender on investment behaviour is among the most robust findings in behavioural finance. In general, men are overconfident and take more risks. Barber and Odean (2000) report that single men have share portfolios with the highest level of risk followed by, in descending order, married men, married women and single women. Strikingly, our data present a picture substantially at odds with what prior research leads us to expect (see Panel B). Gender is known for about 70% of all investor-fund-days and women make up half of these. However, around 55% of female investor-days are in Growth funds while just over 45% of male investor-days are in the same category. The apparent greater appetite for risk shows up in the least risky fund category as well; female investors have around 18% of their total investor days in the Conservative category of funds while males have just under 20%. Investigating this result further we discover that, consistent with prior research, among younger investors (those aged less than 30), men are more risk loving than females, but for older investors (those aged 50 and above), women are more risk loving than 5
men. Similarly, for middle aged investors (those between the ages of 30 and 50) women are more risk loving than men. This result is demonstrated in Chart 1. Our findings have significant policy implications. A difference in managed fund risk profile across genders may be expected not only because of supposedly innate differences in risk preferences but also because of systematic differences in wealth across genders. Women generally are less wealthy than men and attitudes to risk are shaped by total wealth held. Shefrin and Statman (2000) propose that investors think of their portfolios as pyramids of assets, with each layer of the pyramid intended to meet a particular goal and getting progressively riskier as we progress from the bottom layer. Investors' dominant goal is financial security and so assets in the bottom (i.e., largest) layer of the pyramid are devoted to this goal. Once security is assured, further investments are progressively more risky (viewed in isolation) as investors aim to improve on their wealth status. One implication of this view is that women, simply by virtue of having fewer financial assets than men, will tend to have a greater proportion of their assets in less risky funds. Our results are not consistent with this proposition. Our results show female investors tend to be more aggressive than male investors when they are older, presumably at a time when wealth differences between the genders are at there greatest, and less aggressive when they are younger, presumably when wealth difference are not as apparent. Interestingly, our results also suggest that women are right to tilt more heavily towards riskier funds. The last three rows of Table 1 (Panel E) show the proportion of investor-days in each risk category of funds that are in the gain zone, the breakeven zone and the loss zone. The two riskiest categories funds have a much higher proportion of investor days in the gain zone, over 70%, than the two ostensibly lower risk categories, Conservative and Income, which have just around 45% and 52%, respectively, of their investor days in the gain zone. However, our results do not indicate the magnitude of gains and losses. It may be that losses incurred in the riskier category of funds are much higher. We leave this issue for future research. Note that we do not have information on gender for about 30% of investor-fund-days. There is evidence that these investor-fund-days are different; around 74% are in the riskiest 6
category of funds, compared with around 50% for investor-fund-days where gender is known. We return to discuss the implications of this observation further on. Table 1 (Panel C) also classifies investor-fund-days by the tax quartile of the investors' suburb. We use this as our proxy for investor wealth. Allocation to quartiles is determined after ranking Australian suburbs on the basis of their average taxable income in 2003 (obtained from the Australian Taxation Office). As might be expected, investors from the suburbs in the highest tax quartile account for a majority of investor-fund-days, just under 52%. In contrast, investors from suburbs in the lowest tax quartile comprise just 8% of all investor-fund-days. Reviewing the distribution of investor-days across risk categories after holding tax quartile constant confirms Shefrin and Statman's (2000) behavioural finance prediction that wealthier investors have a higher proportion of their wealth in riskier assets. Table 1 shows that investors in the top tax quartile suburbs have over 61% of all their investor-fund-days in the Growth funds and just 16% in Conservative funds, whilst investors from the bottom tax quartile suburbs have about 50% of their investor-fund-days in Growth funds and 18% in Conservative funds. People are often advised to shift from riskier to safer investments as they age. Jagganathan and Kocherlakota (1996) provide a very good analysis of the merits of this counsel, contending that it is often right but for the wrong reasons. In any event, the findings reported in Panel D of Table 1 show that the advice appears to be widely followed, at least in Australia. People aged sixty and over when they first invested in a BT Financial fund have the largest share of total investor-fund-days, at 19.2%. However, just 46.2% of their investor-fund-days are in Growth funds whilst 24.8% are in Conservative funds. In contrast, investors in their twenties have 60.5% of their investor-fund-days in Growth funds and just 12.7% in Conservative. In summary, review of the distribution of investor holdings across risk categories and by gender, wealth and age reveals a picture only partially consistent with expectations. In line with the literature, we find an association between wealth, age and the distribution of 7
investor holdings across risk categories. Wealth and youth are positively associated with holdings of riskier funds. However, contrary to other behavioural finance research, women investors are, if anything, more rather than less likely to invest in riskier funds. One implication of this finding is that investor behaviour in the share trading market does not necessarily predict behaviour in the managed fund market. Findings discussed next support this observation. The disposition effect across gender, wealth and age Table 2 shows the ratio of the proportion of gains realised (PGR) to the proportion of losses realised (PLR) across various risk categories of funds and by gender, wealth and age. A ratio greater than one is consistent with the disposition effect; it reveals that investors realise a higher proportion of their gains relative to their losses. previous research has shown the disposition effect to be negatively associated with fund performance. We leave this issue for future research. The first row after the column headings in Table 2 reveals results very different to those that Odean (1998) might lead us to expect. Across all funds, the ratio of PGR to PLR is just 0.62. Investors are more likely to sell their losing rather than their winning funds. However, the result is conditional on fund risk. The proportion of winning Conservative funds sold is 2.5 times the proportion of losing Conservative funds sold. In contrast, the proportion of winning Growth funds sold is just 0.29 times that of losing Growth funds sold. As Table 1 shows, there are many more investor-fund-days in Growth funds than in Conservative funds so the Growth results drive those for the whole population of funds. As noted, our results are surprising and we defer attempting to explain them until we have had opportunity to progress beyond ad hoc speculation. In the interim, we investigate if the relative propensity to sell winning or losing stocks is related to gender, wealth and age, in addition to risk of fund. Even if the reasons for a relationship remain unclear, finding an association contributes to a more detailed mapping of investor behaviour in the market for managed funds. 8
Panel B of Table 2 shows the association between gender and the disposition effect. Across all funds, the ratio of PGR to PLR for women is 0.52 while for men it is 0.88. In short, both men and women realise a higher proportion of their losing fund days than of their winning fund days, contrary to the disposition effect. However, women exhibit the reverse disposition effect to a greater extent than men. Panel B shows this tendency is evident across all risk categories of funds, although both men and women exhibit the classic disposition effect in the Conservative fund category. A curious aspect of Panel B is that the ratio of PGR to PLR when gender is unknown falls outside the range of values spanned by the ratios for men and women at the two riskiest fund categories. Further, around 75% of investor-days where gender is unknown are in the Growth category of portfolio, compared to 55% for women investor-days and 45% of male investor-days. The reasons are not obvious. In other contexts, non-conformance with the disposition effect has been linked to greater investor sophistication (Shapira and Venezia 2000; Locke and Mann 2000; Brown, Chapel, Da Silva Rosa, & Walter 2006). One sign of a lack of investor sophistication is excessive trading behaviour. Barber and Odean (2001) show that when trading directly in the share market, single men trade the most, at an average rate of 85% of annual turnover, with negative consequences for their total return. In Panel E of Table 1, we investigate whether mens' greater propensity to exhibit the disposition effect is associated with greater trading activity. At the aggregate level, we do find male investors have a higher proportion of managed fund realisations than female investors. Panels C and D, of Table 2, report the ratio of PGR to PLR across funds of different risk but within ranges of wealth and age, respectively. By and large, ranking investors on wealth based on their suburb's income tax quartile does reveal some differences in the ratio of PGR to PLR. Outside of those invested in Growth funds, wealthier investors tend to be more prone to the disposition effect than less wealthy investors. Excluding Growth funds, the ratio of PGR to PLR is 1.64 for investors in the top tax quartile and 1.14 for investors in the bottom tax quartile. 9
Similarly, with the exception of Growth funds, Panel D reveals older investors tend to be more prone to the disposition effect than younger investors. The ratio of PGR to PLR is 65% higher for investors over the age of 50 than for investors under the age of 30. Summary and Implications We use (hitherto unavailable) trade-by-trade information on individuals' investments in managed funds to provide an unprecedented portrait of managed fund investors in Australia, showing how gender, wealth and age influence the kind of funds investors select and their subsequent trading behaviour, in particular, their relative susceptibility to the "disposition effect". Our results both confirm and deny conventional wisdom. Confirming conventional wisdom, our results show that men do tend to trade more frequently than women and wealthier investors have a greater proportion of their portfolio in riskier assets. Moreover, consistent with the lifecycle theory of investing, we find older investors are more risk averse than younger investors. On the other hand, in contrast to prior research into the behaviour of sharemarket investors, our results show that female investors have a greater appetite for risk than their male counter-parts. This result is most apparent among older investors. In addition, we find no evidence of the disposition effect at the aggregate level. Moreover, we find wealthier, older investors, are more inclined to exhibit higher, not lower, win-loss realisation ratios. For fund managers, investment advisers, and regulators, a number of implications fall out of this research. First, and most importantly, a significant behavioural bias that previous research has shown to be detrimental to investment performance is not only reduced, but is eliminated, by investing in managed funds rather than individual securities. Second, the shape and design of investor education may need to be refined to take into account different attitudes toward risk between genders, age-groups, and wealth levels. Our research implies that high net worth individuals are not necessarily more sophisticated when it comes to making investment sell decisions, for example. A third implication centres on superannuation plan construction and design. Many of the behavioural biases exhibited in our results can be addressed by the way investment decisions are framed and the type of 10
default options available. For example, given their different attitudes toward risk, should investment menu design become as much gender-specific as they are becoming age-specific? References Barber, B. and T. Odean. 2000. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors." Journal of Finance 55: 773­806. Brown, P., Chapel, N., Da Silva Rosa, R., & Walter, T., "The Reach of the Disposition Effect: Large Sample Evidence Across Investor Classes" 2006 Locke, P. and S. Mann, 2000. "Do Professional Traders Exhibit Loss Realization Aversion?" Odean, T., 1998, "Are Investors Reluctant to Realize Their Losses?" Journal of Finance v53: 1775-1798. Shapira Z., Venezia I. (2000). "Patterns of behavior in professionally managed and independent investors". Journal of Banking & Finance, 25, 1573-1588. Shefrin, H. and M. Statman. 2000. "Behavioral Portfolio Theory." Journal of Financial and quantitative analysis 35, 2 (June): 127­151.Jagganathan and Kocherlakota (1996)
An important note on the use of BT Financial Group client information
BT Financial Group (BT) is dedicated to protecting the privacy and security of the personal
information of its customers. BT is bound by the Commonwealth Privacy Act and the National
Principles for the handling of personal information, which are set out in that Act and cover the
collection, storage, use and disclosure of personal information. A copy of the BT Privacy Policy
can obtained free of charge from BT by calling 132 135. BT's use of client data for statistical
analysis complies with the Privacy Act, which limits the use and disclosure of data to ensure that
individuals cannot be identified. Access to BT client data containing personal information is
limited
to
authorised
BT
employees.
11
Table 1 Percentage of investor-trading days by gender, wealth, age, and fund performance Our principal unit of analysis is each investor's holding in a BT Financial Group managed fund each trading day over the period December 1975 to August 2005 (i.e., over 1.8 billion investor-fund-days). Funds are sorted into risk categories: Growth funds are those funds comprised predominantly of equities. Balanced funds are those funds with a mix of equities, fixed-interest, property, and cash. The Income funds are predominately comprised of property and fixed-interest, whilst the Conservative funds are made up, almost entirely of cash and fixed-interest. Tax quartile refers to the quartile in which an investor's suburb belongs (identified by postal code). Allocation to quartiles is determined after ranking Australian suburbs on the basis of their average taxable income in 2003 (obtained from the Australian Taxation Office). This is our proxy for investor wealth. Investors are sorted into age-groups based on the date they first joined a BT Financial Group fund. Gain-Days, Breakeven-Days and Loss-Days are the proportion of investor-fund-day holdings which are, respectively, in the winning, break-even, or loss zones. Sell-Days is the number of investor-fund-days during which a sale took place disaggregated across gender and fund type.
In the column All Funds the percentages sum to 100% over each kind of categorisation (i.e.,
vertically). In the columns classifying investor-trading-days by risk category of funds, each
row sums to 100%.
All Funds
Conservative Funds
Income Funds
Balanced Funds
Growth Funds
% total investor-days
16.7%
Panel A 5.3%
20.8%
57.2%
Female Male Gender not known
34.5% 35.9% 29.6%
17.8% 19.9% 11.5%
Panel B 6.7% 4.1% 5.3%
20.5% 30.9% 8.8%
55.1% 45.2% 74.4%
Panel C
Bottom tax quartile Second tax quartile Third tax quartile Top tax quartile Tax quartile not known
8.0% 17.5% 22.7% 51.8% 0.02%
18.02% 17.40% 16.55% 16.32% 22.98%
6.22% 6.00% 5.56% 4.82% 5.25% Panel D
25.98% 24.34% 23.41% 17.56% 6.21%
49.79% 52.26% 54.47% 61.29% 65.56%
=<20 years >20=<30 years >30=<40 years >40=<50 years >50=<60 years >60 years Age not known
0.9% 7.4% 13.8% 16.2% 16.9% 19.2% 25.7%
10.4% 12.7% 13.6% 15.1% 20.3% 24.8% 12.3%
2.6% 2.7% 2.9% 3.6% 5.5% 11.2% 4.1% Panel E
16.6% 24.2% 30.4% 29.6% 25.9% 17.8% 8.0%
70.4% 60.5% 53.2% 51.6% 48.3% 46.2% 75.7%
Sell Days ­ Female share Sell Days ­ Male share
47.6% 52.4%
63.9% 58.8%
3.5% 2.8%
10.3% 17.9%
22.3% 20.5%
Gain-Days Breakeven-Days Loss-Days
67.7% 2.0% 30.3%
45.5% 11.4% 43.1%
52.4% 0.2% 47.5%
75.3% 0.1% 24.6%
72.8% 0.2% 27.0%
12
Chart 1. Percentage of investor-trading days disaggregated by gender, age, and risk level
Female 57.4 42.6 56.5 43.5 49.2 50.8
Male 63.3 36.7 42.6 57.4 37.3 62.7
% in Growth
% not in Growth
Female
55.1
44.9
Male
45.2
54.8
<30 years old 30-40 years old >50 years old
13
Table 2 PGR/PLR across funds by various gender, wealth and age Ratio of proportion of gains realised (PGR) to proportion of losses realised (PLR) across various risk categories of funds and by gender, wealth and age.
All Investors Female Male Gender not known Bottom tax quartile Second tax quartile Third tax quartile Top tax quartile Tax quartile not known =<20 years >20=<30 years >30=<40 years >40=<50 years >50=<60 years >60 years Age not known
All Conservative Funds Funds
0.62
2.50
Income Funds Panel A 1.07
Balanced Funds 0.91
Panel B
0.52
1.62
0.83
0.63
0.88
3.37
1.30
0.92
0.46
2.55
1.17
1.87
Panel C
0.65
2.08
0.94
0.76
0.66
2.24
0.87
0.81
0.66
2.71
0.96
0.85
0.63
2.89
1.21
1.08
0.38
0.81
1.05
1.11
Panel D
0.41
0.99
0.79
0.46
0.46
2.20
0.98
0.50
0.61
2.62
1.15
0.72
0.66
2.67
1.17
0.81
0.87
3.54
1.12
1.04
0.62
1.67
0.80
0.80
0.49
2.80
1.36
2.04
Growth Funds 0.29 0.29 0.36 0.25 0.30 0.32 0.30 0.30 0.24 0.35 0.33 0.36 0.32 0.30 0.26 0.26
14

P Brown, R da Silva Rosa, T McNaughton

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