Are Financial Advisors Useful? Evidence from Tax-Motivated Mutual Fund Flows

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1 Are Financial Advisors Useful? Evidence from Tax-Motivated Mutual Fund Flows Gjergji Cici, Alexander Kempf, and Christoph Sorhage * First Draft: November 2012 This Draft: May 2013 ABSTRACT This study is the first to show that financial advisors generate tangible benefits for their U.S. clients by documenting that financial advisors provide useful tax advice. Investors who purchase mutual funds through financial advisors exhibit a stronger tendency of avoiding taxable distributions than investors who buy shares directly. This difference gets stronger in situations that matter the most for investors and when investors who buy funds through financial advisors pay more for financial advice. Furthermore, this difference gets stronger in December but only when investors face large capital losses, consistent with financial advisors helping their clients engage in tax-loss selling. JEL classification: G11, G24, H24 Keywords: Mutual Funds, Taxable Fund Distributions, Financial Advisors, After-Tax Returns * Cici is from Mason School of Business, The College of William & Mary. Cici is also a Research Fellow at the Centre for Financial Research (CFR), University of Cologne. Kempf is from Department of Finance and Centre for Financial Research (CFR), University of Cologne. Sorhage is from Department of Finance and Centre for Financial Research (CFR), University of Cologne. The authors thank Peter Gore and Jason Zweig for their helpful comments. We also thank seminar participants at CFR, University of Cologne and Texas Tech University for comments on an earlier draft of this paper.

2 1 Introduction The value of advice provided by brokers and financial advisors to mutual fund investors has been part of an ongoing regulatory and academic debate. 1 Academic research has so far come up empty-handed in trying to assess the advertised benefits that investors are supposed to receive from professional financial advice. 2 In fact, extant empirical evidence suggests that financial advisors are unable to create value by helping their clients pick outperforming funds (e.g., Bergstresser, Chalmers, and Tufano (2009); Chalmers and Reuter (2012); Del Guercio and Reuter (2012); and Hackethal, Haliassos, and Jappelli (2012)). Yet, despite this and the fact that no evidence of other measurable benefits from financial advice exists, a sizable fraction of investors surprisingly continue to purchase mutual funds with the help of financial advisors. 3 Such behavior could reflect the fact that investors stand to gain from their association with financial advisors in ways that go beyond tangible performance-related benefits. For example, Bergstresser, Chalmers, and Tufano (2009) raise the possibility that financial advisors are indeed acting in their clients interests, but, as researchers, we have simply not been able to measure the many substantial tangible and intangible benefits that clients receive (pp. 4,153). The possibility that financial advisors provide such benefits has been 1 Regulatory efforts to ensure investor protection can be traced back to the suitability standard imposed on brokers by the Securities Exchange Act of 1934, which requires brokers to provide suitable advice to their clients and the fiduciary standard imposed on financial advisors by the Investment Advisers Act of 1940, which requires financial advisors to act in the best interest of their clients. 2 As with previous research, we lack the data to distinguish between brokers and financial advisors. Thus, in this study we will treat them as one group and for ease of exposition refer to them as financial advisors. 3 A 2012 survey by the Investment Company Institute (ICI) suggests that 53 percent of all U.S. households held mutual funds that were bought with the help of financial professionals such as brokers and financial advisors (Investment Company Institute (2013)). An older survey run by ICI suggests that investors reliance on financial advisors has not changed much through time. For example, results from this survey suggest that during the fraction of mutual fund assets brought with the help of financial professionals fluctuated slightly between 53 percent and 56 percent without a clear trend (Investment Company Institute (2008)). 1

3 advanced by other related studies. 4 However, to the best of our knowledge, supporting empirical evidence for this claim has so far not been documented. Our paper is the first to provide empirical evidence that financial advice creates value for investors. We do so by documenting that advice intended to help mutual fund investors with tax-management is valuable. As professionals with better training and access to resources that ordinary investors do not have, financial advisors are presumably more sophisticated than retail investors. Thus, they should be able to provide valuable advice to retail investors. To assess the value created by financial advisors, we compare the behavior of investors that operate under the guidance of financial advisors (hereafter, indirect channel investors) with that of investors that do not rely on financial advisors (hereafter, direct channel investors). Absent the influence of financial advice, indirect investors are likely to be less tax savvy than direct investors since, as shown by previous research, indirect investors are less sophisticated than direct channel investors. stronger tax avoidance patterns than the flows of direct investors, this difference can be attributed to the assistance provided by financial advisors. Our findings show evidence of tax avoidance in both channels. However, flows exhibit a much stronger tax avoidance pattern in the indirect channel. Specifically, the tax avoidance flow effect in the indirect channel is about 40% stronger than in the direct channel, 5 Thus, if indirect investors flows exhibit 4 A recent study by Del Guercio and Reuter (2012) fully espouses the view that unsophisticated investors from the intermediated channel seek advisory rather than portfolio management services. Other papers that make this point are: Bhattacharya, Hackethal, Kaesler, Loos, and Meyer (2012); Del Guercio, Reuter, and Tkac (2010); Hackethal, Haliassos, and Jappelli (2012); and Mullainathan, Noeth, and Schoar (2012). 5 This point was first raised by Gruber (1996) in his AFA presidential address and has been supported by more recent empirical evidence. For example, Malloy and Zhu (2004) show that investors from less affluent and less educated neighborhoods are more likely to invest through the intermediated channel. Chalmers and Reuter (2012) report that, among participants in the Oregon University defined contribution retirement plan, younger individuals with a lower level of education and income are more likely to choose an investment option with access to financial advice. In addition, survey evidence from ICI suggests that investors that seek financial advice are from households with lower income and financial assets (Investment Company Institute (2008)). 2

4 suggesting that the tax awareness of indirect channel investors is at a level that surpasses that of their more sophisticated direct channel peers. Applied to the whole universe of U.S.-based equity funds, our estimates suggest that financial advisors helped indirect channel investors avoid roughly half a billion dollars in taxes over our sample period. This evidence is consistent with financial advisors helping indirect channel investors avoid taxable distributions more than their direct channel peers. Extending our investigation, we argue that if financial advisors do indeed provide tax management services for their clients, then their advice ought to lead to stronger tax avoidance behavior in critical situations that affect investors in the most adverse ways. One such critical situation arises in the face of distributions that can cause large tax liabilities. Another critical situation is when investors are facing distributions that are hard to predict and consequently make financial planning more challenging. Our results support this view. First, we show that the tax avoidance difference between direct and indirect investors is more pronounced for distributions that lead to larger tax liabilities. Second, we find that the tax avoidance difference gets stronger for distributions made by funds with a history of highly volatile taxable distributions. Taken together, these findings suggest that the effect of taxrelated financial advice does not only affect the decisions of investor, but also appears to have a targeted effect in helping investors even more when it comes to avoiding the least desirable tax events. We extend our analysis in another direction. If advisors are indeed providing taxmanagement services and their clients are getting what they are paying for based on their needs, then we ought to observe a positive relation between tax-avoidance behavior and the size of the loads that investors are paying. We find that indirect investors that invest in funds 3

5 with higher loads, display stronger tax avoidance than indirect investors that invest in funds that charge lower loads. This result proves consistent with the view that some investors need more financial advice and that advisors respond to their needs by exerting more effort in return for a higher compensation. We next explore whether the tax avoidance advice from financial advisors interacts with other tax-related considerations. Ivković and Weisbenner (2009) show that, consistent with tax-loss selling, investors propensity to sell fund shares that have declined in value is more pronounced in December. We hypothesize that tax-loss selling interacts with the tax avoidance effect. Consider an investor who is subject to large unrealized capital losses in the shares he holds in a fund, which is about to make a taxable distribution. The optimal strategy for him is to redeem his shares right before the distribution date, allowing him to harvest capital losses and avoid a taxable distribution at the same time. Our results confirm that the tax avoidance difference between direct and indirect investors gets stronger in December but only for funds where investors are most likely to be subject to capital losses. This finding is consistent with indirect channel investors being advised by their financial advisors to not only delay additional investments until after the distribution date but to also redeem shares that have declined in value prior to the distribution date in order to harvest losses for tax-loss selling purposes. Our paper is related to a growing number of studies that examine whether financial advice generates measurable benefits for its recipients. Bergstresser, Chalmers, and Tufano (2009); Chalmers and Reuter (2012); Del Guercio and Reuter (2012) and Hackethal, Haliassos, and Jappelli (2012) show that financial advisors are unable to help investors pick outperforming funds. Mullainathan, Noeth, and Schoar (2012) document that financial 4

6 advisors fail to moderate their clients behavioral biases. Bhattacharya, Hackethal, Kaesler, Loos, and Meyer (2012) show that investors inattention to financial advice even when the advice is unbiased is a major impediment to financial advice achieving its goals. We contribute to this literature with findings suggesting that financial advisors are providing useful tax management advice to fund investors in the U.S. and that fund investors indeed act on this advice. To the best of our knowledge, ours is the first study to provide evidence of tangible benefits delivered by financial advisors to their clients in the U.S. As such, our evidence provides concrete support for the view espoused by Del Guercio and Reuter (2012) and Del Guercio, Reuter, and Tkac (2010) that indirect channel investors demand and receive financial advisory services rather than purely portfolio management services. Our study is also related to a second group of studies that examine how tax considerations shape decisions of individual fund investors (e.g., Barclay, Pearson, and Weisbach (1998); Bergstresser and Poterba (2002); Ivković and Weisbenner (2009) and Johnson and Poterba (2010)). We contribute to this literature by documenting that mutual fund investors are not homogeneous when responding to taxes. Instead, investors reaction to taxes is related to the distribution channel through which they transact, whereby indirect channel investors display stronger tax awareness shaped in large part by financial advice. The remainder of this paper is organized as follows. In Section 2, we discuss our data set and sample summary statistics. Section 3 presents our findings on mutual fund investors avoidance of taxable distribution across the direct and indirect distribution channels. In Section 4 we investigate whether financial advice leads to stronger tax avoidance behavior in critical situations that affect investors in the most adverse ways. We investigate whether advisors help to their clients is positively related to their compensation in Section 5 and 5

7 whether the tax avoidance effect interacts with tax-loss selling in Section 6. Section 7 concludes. 2 Data 2.1 Data Sources and Sample Construction We obtain mutual fund data from four databases Thomson Reuters Lipper Flows, Thomson Reuters Mutual Fund Holdings, Center for Research in Security Prices (CRSP) Stock Files, and CRSP Survivor-Bias Free U.S. Mutual Fund databases. Data on the primary distribution channels of U.S. equity fund shares as well as weekly data on net flows and assets under management are from Thomson Reuters Lipper Flows (Lipper). Lipper assigns each fund share class to one of its three distribution channel categories. 6 Share classes sold primarily through brokers and financial advisors are placed in the brokered channel (hereafter indirect channel) category while share classes sold directly to investors are placed in the direct channel category. 7 The remaining distribution channel comprises share classes sold primarily to institutional investors. Holdings data for U.S. equity funds are from Thomson Reuters Mutual Fund Holdings Database. The database reports the name, identifier, and number of shares for each security 6 Previous studies (e.g., Del Guercio and Reuter (2012); Del Guercio, Reuter, and Tkac (2010); and Bergstresser, Chalmers, and Tufano (2009)) have relied on the distribution channel classifications from Financial Research Corporation (FRC). However, since FRC s classification is based on Lipper s, differences between the two classification schemes are very small as documented by Bergstresser, Chalmers, and Tufano (2009). 7 Given the recent growth in the activity of fee-based financial advisors which sell no-load funds, but charge a fee as a percentage of the client s asset they manage, we expect there to be some funds classified as direct channel funds, part of which of sold by fee-based financial advisors. However, this effect would work against us finding a difference in the behavior of direct and indirect channels (see Bergstresser, Chalmers, and Tufano (2009); Chalmers and Reuter (2012); and Del Guercio and Reuter (2012) for a similar argument). 6

8 held by each mutual fund on each reporting date. Holdings data were supplemented with individual stock prices and other information from the CRSP Monthly and Daily Stock Files. Information on share class characteristics, such as funds returns, expense ratios, turnover ratios, and investment objectives was obtained from the CRSP Mutual Fund database. We estimate weekly returns for each share class by compounding daily returns. Since CRSP offers investment objectives from several data providers we combine them into a single investment style classification similar to Pástor and Stambaugh (2002). For the share classes we also obtain information on distribution dates, amounts, and reinvestment prices (NAV) from CRSP. We eliminate all distributions from our sample that are tax-exempt. Similar to Sialm and Starks (2012) we normalize distribution amounts by the NAV of the respective fund share at the distribution date. The resulting distribution yields that we use to assess the size of distributions correspond to the number of shares an investor could have purchased with the distributed amount. We analyze flows at the share class level instead of at the fund level. Two considerations make analysis at the share class level more attractive. First, most share classes are distributed primarily only through one distribution channel, and accordingly the Lipper classification of primary distribution channels is done at the share class level. Second, mutual funds allocate received dividends and realized capital gains on a pro-rata basis when making distributions and these distributions are paid net of expenses, causing distributions to differ across share classes. 8 8 The fact that distributions are paid net of expenses is explicitly stated on the websites of many mutual fund families (e.g., the websites of Waddel&Reed and Nicholas Company Inc. fund families, respectively, at and Likewise, differences in distribution amounts across share classes are directly observable in asset managements distribution reports (e.g., the websites of Pioneer Investments and Thornburg Investment Management fund 7

9 To arrive at our final sample, we start by excluding all share classes with missing MFLINKS code. We next proceed by excluding shares sold through the institutional channel. We do so based on the following reasons: First, we want to examine the investment behavior of retail investors. Second, sale of institutional shares may be driven by a set of dynamics different from the retail fund market (Bergstresser, Chalmers, and Tufano (2009), pp. 4134). Finally, we want to ensure comparability to previous studies, most of which exclude the institutional distribution channel from the analysis. Since our focus is on taxable and actively managed U.S. equity funds, we take additional steps to exclude index, international, sector, balanced, fixed-income, and taxexempt funds. We further require that each fund share has at least 52 weeks of flow and return data. Our final sample includes 722,280 share class-week observations. It covers 2,430 U.S. domestic equity fund shares over the period September 1999 the first point of time for which all data is available to June dataset. 2.2 Sample Characteristics Table I presents summary statistics for each year and distribution channel in our - Insert Table I approximately here - The number of share classes increases from 363 in 1999 to 2,412 in 2011, consistent with an increase in investment choices for retail investors. 9 The indirect channel share classes families, respectively, at us.pioneerinvestments.com/funds/distributions and 9 The Investment Company Institute (2012) provides a comprehensive overview on the evolution of the mutual fund industry. 8

10 represent the dominant form of distribution in the retail investment sector. About 75% of the share classes in our sample are sold through the indirect channel, which is consistent with Bergstresser, Chalmers, and Tufano (2009). In terms of assets, directly sold share classes are significantly larger in all periods of our sample and they grow at a higher rate on average. An interesting observation is that although they are more numerous, indirect share classes control a smaller amount of total assets. This is consistent with Del Guercio and Reuter (2012) who show that the total assets of indirect share classes are only about two thirds of the total assets of direct share classes. Also consistent with previous studies that examined mutual fund distribution channels (e.g. Bergstresser, Chalmers, and Tufano (2009) and Del Guercio and Reuter (2012)), indirect channel share classes have higher expense ratios during all periods. For the direct and indirect channels we report the number and size of distributions in Table II. Summary statistics are presented by year in Panel A and by calendar month in Panel B. - Insert Table II approximately here - There are a total of 18,866 taxable distributions in our sample that are categorized either as capital gain or dividend distributions. Comparing distributions across channels shows that the number of distributions is much larger for the indirect channel than for the direct channel. This holds for the entire sample period and almost every single year and is attributable to the fact that there are more shares in the indirect than in the direct channel. On the contrary, the size of distributions is significantly larger in the direct channel than in the indirect channel in most years. 9

11 It is noteworthy that the number and size of distributions varies considerably over time. In particular, the number and size of distributions increases in both channels towards the beginning of the financial crisis, which is consistent with large outflows during this period forcing mutual funds to sell securities and realize capital gains. Panel B shows considerable intra-year effects of distributions irrespective of the distribution channel. In particular, we observe a larger number of distributions and distributions that are bigger occurring towards the end of the year. 3 Tax Avoidance Differences in Direct and Indirect Channels This section explores our Tax Advisory Hypothesis, which postulates that flows of indirect investors exhibit stronger tax avoidance patterns than flows of direct investors. Our measurement of the tax avoidance flow effect is based on a two-step procedure. First, for each share class i around each taxable distribution event, we compute the flow change from the week before to the week after the distribution week t as follows, (1) F i,t = F i,t+1 F i,t 1, where F i,t is the net flow of fund share class i in week t normalized by the share class s assets under management (AUM) lagged by one week. Looking at the share class s flow change is attractive because it directly captures investors net reaction around distribution weeks and minimizes the influence of share class- and fund-level characteristics on flows. Second, flow changes around distribution weeks are compared with flow changes around non-distribution weeks. To avoid flow changes of non-distribution weeks being affected by surrounding distribution events, we eliminate all non-distribution weeks that are 10

12 preceded or followed by a distribution in the two weeks before or after. The intuition behind our approach for measuring tax avoidance behavior is that if investors are delaying their investments in a particular share class in the week prior to the distribution week to avoid that distribution, then flows in the week before should be lower than in the week after, resulting in a higher flow change around distribution weeks compared to non-distribution weeks, all else equal. The implicit assumption here is that the redemption activity of existing investors is similar before and after the distribution week. However, if redemption activity is higher before rather than after the distribution week because existing investors want to avoid distributions, this would work in the same direction as the main effect we are trying to capture and help document an even stronger tax avoidance effect. If the opposite is true, then that would work against us finding a tax avoidance effect. To test the Tax Advisory Hypothesis, we employ several regression specifications where the dependent variable is our flow change measure, F i,t. 10 Our base model specification is as follows: (2) F i,t = α 0 + α 1 D Distr. i,t + β 0 D Ind i + β 1 D Distr. i,t D Ind i + γ R i,t 1 + ε i,t Our main independent variables are the Distribution dummy (D Distr. i,t ) which equals one if share class i is subject to a taxable distribution in week t and the Indirect dummy (D i Ind ) which equals one if fund share i is sold indirectly. Our key test for the Tax Advisory Hypothesis is based on the interaction of these two variables, which is, in effect, a difference 10 We acknowledge that F i,t+1 is affected by net inflows in week t since inflows in t determine the assets under management in t. As a robustness check we employ F i,t net inflows i,t+1 net inflows i,t 1 AUM i,t 2 AUM i,t 2 specification and repeat our analyses. Our results (not reported) remain qualitatively the same. in an alternative 11

13 in difference test measuring how the effect of distributions on the flow change variable differs between indirect and direct channels. To control for flows reacting to past performance, we include Delta Return ( R i,t 1 ), the differential weekly return of share class i between week t and t In further regressions we extend our baseline specification by sequentially including time (month and year) fixed effects, investment objective fixed effects as well as other fund and share class-level controls. Those controls include the one-year return, expense ratio, logarithm of assets under managements, and turnover ratio. The first three control variables are at the share class level, while the last one, turnover ratio, is at the fund portfolio level since multiple share classes are backed by the same portfolio and thus share the same turnover. To account for possible correlations both within time periods and funds share classes, we cluster standard errors on both fund and week as in Petersen (2009). - Insert Table III approximately here - Results reported in Table III confirm a general pattern of tax avoidance in fund flows around taxable distributions. In all models, the incremental effect of a distribution on the flow change in the direct channel is about 0.35 percentage points, i.e. the flow in the week after a taxable distribution is about 0.35 percentage points larger than the flow in the week before. This suggests that for the average fund with $250 million in assets, direct channel investors delay investing roughly as much as $875 thousand in the week following the distribution. As expected, the intercept suggests that this effect is non-existent for the non-distribution weeks. 11 Flow reactions to past fund performance was first documented as an empirical regularity by Ippolito (1992); Chevalier and Ellison (1997); and Sirri and Tufano (1998) and has been confirmed by a large number of subsequent studies. 12

14 Results from our main test based on the interaction term suggest that the tax avoidance effect is significantly stronger in the indirect channel than in the direct channel. The coefficient on the interaction term of about 0.16 percentage points is significant in all models. It suggests that the incremental effect of a distribution on the flow change in the indirect channel is about 0.51 percentage points, thus 40 percent larger than in the direct channel. For a fund with $250 million in assets, this suggests that indirect channel investors delay investing as much as $400 thousand more than direct channel investor when facing an average distribution. Applying our coefficient estimate to the universe of U.S.-based equity funds suggests that financial advisors helped indirect channel investors avoid roughly half a billion dollars in taxes over our sample period. 12 These findings support our Tax Advisory Hypothesis. Regarding the control variables, Delta Return has a significantly positive impact on the flow change variable, which is consistent with flows following returns. All our results are virtually identical in the various models suggesting that all other controls besides Delta Return have no notable impact. In summary, our results suggest that mutual fund investors exhibit behavior that is consistent with a tax avoidance motivation in both channels. However, the effect of tax avoidance on flows is much stronger among indirect channel investors. This is consistent 12 We arrived at this estimate by employing the following inputs. Following Investment Company Institute (2012), we assumed that the universe of U.S.-based equity funds had total assets under management of $5.2 trillion. Using a lower bound estimate from Investment Company Institute (2008) we assumed that 53% of these fund assets were in the indirect channel. We used an approximate annual distribution yield of 3.5 percent for the average indirect fund that we calculated based on our sample and we employed an average tax rate of 25%. The annual tax saving estimate was then multiplied by 13 years, the length of our sample period. The resulting calculation was *$5.2 trillion*0.53*0.035*0.25*13. 13

15 with financial advisors informing their clients about impending distributions and advising them accordingly to delay investments until after taxable distributions take place. 4 Tax Avoidance Differential and Magnitude of Tax Consequences In this section we test an additional hypothesis that extends the Tax Advisory Hypothesis. It postulates that financial advice should lead to stronger tax avoidance behavior in critical situations that affect investors in the most adverse ways. One such critical situation arises in the face of distributions that can cause large tax liabilities. Another critical situation is when investors are facing distributions that are hard to predict and consequently make financial planning more challenging. 4.1 Tax Avoidance Differential and Size of Tax Liabilities We investigate whether the value of financial advice increases with the tax liability of underlying distributions, i.e., whether the tax avoidance differential between direct and indirect channel investors increases with the associated tax liability. We argue that financial advisors are more experienced, have access to superior information and technologies, and thus can come up with more precise estimates of the size of distributions than the estimates that direct channel investors can come up with on their own. To calculate tax liabilities we multiply each distribution with the tax rate that the distribution is subject to. Since tax rates depend on investors income, we use tax rates that apply to the median income of U.S. households as a proxy for a representative investor. 13 More specifically, we employ the median income of an U.S. household using U.S. Census 13 As a robustness check, we repeat this analysis using the highest income tax rates that could apply to an investor. Results (not reported) remain qualitatively the same. 14

16 Bureau data for each year. Then we use historical information on federal tax rates of individual income and calculate for each point in time the marginal tax rates for long-term gain distributions, short-term gain distributions, and dividends that apply to the respective median-income household. 14 We split fund distributions into three equally sized groups every year based on the size of their associated tax liability. We then compare investors reactions to distributions that fall in the large, medium, and low tax liability groups across the direct and indirect channel. - Insert Table IV approximately here - Results from Table IV suggest that the tax avoidance differential between indirect and direct channel investor increases with the size of the associated tax liability. In particular, the tax avoidance differential effect among distributions with large tax liabilities amounts to 0.53 percentage points (p-value<0.01). Moving from large-tax-liability to medium-tax-liability distributions, the tax avoidance differential, although statistically significant, declines almost by a factor of three. Moving from medium-tax-liability to small-tax-liability distributions, the tax avoidance differential drops even further and remains only marginally significant. Interestingly, direct channel investors also exhibit tax avoidance behavior but only for the largest-tax-liability. In summary, our combined results from this section suggest that financial advice becomes more valuable for distributions with larger tax burdens. 14 Information on federal individual income tax rates was taken from the Tax Foundation s website, 15

17 4.2 Tax Avoidance Differential and Hard-To-Predict Distributions We next examine whether the value of financial advice is greater for distributions that lead to hard-to-predict tax liabilities. Such distributions are undesirable from investors point of view because they make financial planning more challenging. Our hypothesis hinges on the argument that financial advisors are in a better position to assess which distributions are associated with liabilities that are harder to predict because their experience working repeatedly with select mutual funds gives them greater familiarity with the distribution patterns of these funds. To identify distributions with tax liabilities that are harder to predict, we split fund distributions into three equally sized groups every year based on the volatility of historical tax liabilities of distributions made by the corresponding share class during the previous three years. We argue that the tax liabilities of distributions from share classes that made distributions with very volatile tax liabilities in the past are harder to predict because in such situations it would be harder to extrapolate from past distribution patterns. Using a similar approach as in the previous section, we then compare investors reactions to large, medium, and low volatility distributions across the direct and indirect channels. Since the previous section shows that the size of the tax liability is related to the tax avoidance behavior, we add the size of the tax liability as an additional control. - Insert Table V approximately here - As hypothesized, Table V results suggest that the tax avoidance differential between indirect and direct channel investors increases with the historical volatility of the corresponding share class distributions. In particular, the tax avoidance differential effect 16

18 among distributions coming from share classes with highly volatile historical distributions amounts to roughly 0.40 percentage points (p-value<0.05), suggesting that the tax avoidance behavior of indirect investors in this distribution group is much stronger than that of direct channel investors. Moving from high to medium volatility distributions, the tax avoidance differential, although statistically significant at the 10%-level, declines by more than half. Moving from medium to low volatility distributions, the tax avoidance differential drops even further, becoming statistically insignificant. Interestingly, this pattern seems to come from direct investors being unable to avoid high volatility distributions. Their flow reaction to distributions in the high volatility group is much smaller than in the medium or low volatility groups. In contrast, flow reaction to distributions is about the same in all volatility groups for the indirect investors. For example, based on Model 1 the effects of a distribution on the flow change in the indirect channel for the high, medium, and low volatility groups are, respectively, 10, 12, and 11 percentage points. This suggests that financial advisors are capable to avoid highly volatile distributions, but direct investors are not. Taken together, these findings suggest that the effect of tax-related financial advice appears to have a targeted effect in helping investors avoid the least desirable tax events. 5 Tax Avoidance Differential and Loads Del Guercio and Reuter (2012) provide support for the view that unsophisticated investors who are more likely to seek the advice of financial advisors care less about active portfolio management and more about financial advisory services. Because of this clientele effect mutual fund families spend fewer resources on the active management of funds that are offered through the indirect channel. A related study by Christoffersen, Evans, and Musto 17

19 (2013) shows that performance of funds sold in the indirect channel is negatively related to the size of brokers compensation. A related hypothesis that would be consistent with both studies is that the least sophisticated investors from the indirect channel that need more financial advice are willing to pay and receive more financial advisory services. The resulting prediction is that there should be a positive relation between loads and the tax avoidance behavior of investors in the indirect channel. We split fund distributions among indirectly sold funds into three groups every week based on the size of the load of the corresponding share class. Using a similar approach as in the previous sections, we then compare the indirect investors reactions to large, medium, and low load distributions with all investors from the direct channel, who are not paying for financial advice. Since the previous sections show that the size of the tax liability and their historic volatility is related to tax avoidance behavior, we control for both of these effects as additional controls. - Insert Table VI approximately here - Results from Table VI suggest that the tax avoidance differential between indirect and direct channel investors is the highest when indirect investors that pay the highest loads are compared with all other direct investors. In particular, the tax avoidance differential effect between indirect investors that pay high loads and all direct investors amounts to roughly 0.29 percentage points (p-value<0.01). Furthermore, the tax avoidance behavior of indirect channel investors monotonically increases with loads. These findings suggest that investors 18

20 that pay more for financial advice appear to receive more advisory services in the form of tax management than investors that pay less for such advice. 6 Interaction with Tax-Loss Selling In this section we examine whether the tax avoidance differential effect interacts with tax-loss selling, another widely-studied tax strategy. Ivković and Weisbenner (2009) document a greater propensity for investors to sell fund shares that have depreciated in value in December. This suggests that investors engage in tax-loss selling at the end of the year in order to reduce their tax obligations. We hypothesize that the tax avoidance differential effect will get stronger in the presence of tax-loss selling considerations. This is perhaps best illustrated by the following example. Consider an investor who is subject to large unrealized capital losses in the shares he holds in a fund, which is about to make a taxable distribution. The optimal strategy for him is to redeem his shares right before the distribution date because this would allow him to harvest capital losses and avoid a taxable distribution at the same time. Such redemptions prior to a distribution would add to the tax avoidance effect of other (both existing and new) investors who simply choose to delay their fund investments until after the distribution date. 15 To test for the hypothesized interaction between tax-loss selling and the tax avoidance differential effect, we first identify funds whose investors are most likely to engage in taxloss selling. Not having cost basis information for the shares held by each individual investor, we argue that funds which performed worst during the previous year while having low levels 15 Edward Jones, for instance, a leading financial advisor, points out that one key step in their approach is the strategic realization of losses to offset gains to manage tax outcomes. Similar strategies are discussed in the financial press (Diliberto (2008)). 19

21 of capital gain overhang in their portfolios are most likely to be good tax-loss selling candidates in December. This is so because they are subject to both short-term and long-term portfolio paper losses, which would suggest that the shares of the average investor in these funds are subject to capital losses. Each sample week we sort share classes into terciles based on their fund s capital gains overhang at the end of the previous quarter. 16 Within each overhang tercile, we further sort share classes into terciles based on their compounded one-year NAV-return. We use NAV-returns rather than total returns because NAV-returns best reflect appreciation or depreciation of the underlying shares, which in turn drives the tax-loss-selling decisions of investors as shown in Ivković and Weisbenner (2009). Based on this sorting, we construct a Tax-Loss Group which consists of all share classes that belong to the low overhang low return group. We estimate a regression model based only on weekly observations that correspond to distribution weeks as follows: 17 (3) F i,t = α 0 + α 1 TLG i,t + α 2 Dec i + α 3 TLG i,t Dec i + β 0 D i Ind + β 1 TLG i,t D i Ind + β 2 Dec i D i Ind + β 3 TLG i,t Dec i D i Ind + γ R i,t 1 + ε i,t, where TLG i,t represents the Tax-Loss Group, a binary variable that equals one if share class i belongs to the group that we consider as most likely to be subject to tax-loss selling in week t. Dec i is a December dummy, that equals one if the observation occurs in the 16 The capital gain overhang of each mutual fund is computed by aggregating the capital gain overhangs of all positions. We use historical quarterly trades and prices at which stocks were purchased to estimate the cost basis of each position. 17 The choice to restrict the regression observations to only distribution weeks is made primarily to keep the model tractable by reducing the number of interaction terms. However, when we repeat the analysis for all observations, i.e. with the entire set of required interaction terms, our results (not reported) remain qualitatively the same. 20

22 month of December. Our key test is based on the triple interaction, TLG i,t Dec i D i Ind, which measures whether the tax avoidance differential is stronger in December for funds that are candidates for tax-loss selling. - Insert Table VII approximately here - Table VII results show that there is a general December effect across all investors. Thus, investors seem to take a closer look at their investments and react more to distributions in December. However, the most interesting insight comes from the huge positive coefficient on the triple interaction term. This suggests that the tax avoidance differential between indirect and direct channel investors gets significantly stronger in December for funds that are most likely candidates for tax-loss selling. Thus, there is an interaction effect between the tax avoidance differential effect and tax-loss selling, suggesting that financial advisors alert their clients to not only avoid distributions but to also engage in tax-loss selling in December if they currently hold fund shares that have depreciated in value. 7 Conclusion With more than 200 thousand personal financial advisors, the market for financial advice in the U.S. is characterized by tremendous size and activity. 18 What happens in this market affects the investment decisions of millions of investors and shapes portfolio decisions that collectively cover billions of dollars. Despite this level of activity in this important market and the number of individuals that are affected by it, our understanding of the economic forces that shape the interactions among its different players is limited at best. 18 Bureau of Labor Statistics: 21

23 Recent studies have begun to address the gap between its importance and our rather limited knowledge of the market for financial advice. Using the mutual fund industry as a testing ground, most of these studies have analyzed the performance of investment choices made by mutual fund investors that were shaped by financial advice. A common finding is that outcomes from investment decisions shaped by financial advice were inferior to those that do not rely on such advice. This evidence lends itself to a natural question: If investors do not get any performance benefits from the financial advice they receive, what explains the presence of financial advisors and why are investors willing to pay for such advice? Our paper provides an answer to this question and thus contributes to the academic literature that seeks to understand the role of financial advisors in their clients decision making. It does so by being the first to provide evidence of tangible benefits delivered by financial advisors. The tangible benefits we document appear in the form of useful tax management advisory services to mutual fund investors, which help them to engage in tax avoidance strategies. Financial advice puts its beneficiaries, indirect channel investors, at a clear advantage over their peers who do not receive financial advice. A detailed exploration of this dimension through which investors receive assistance from financial professionals suggests that financial advice appears to target situations when investors need this advice the most. In other words, we document financial advice to be even more valuable when investors are facing situations that significantly increase the size or the unpredictability of their tax liabilities. This, taken together with our evidence that investors tax avoidance behavior shaped by financial advisors is intensified by what appear to be taxloss selling considerations, could suggest that financial advice comprehensively addresses not one but several facets of tax-management. 22

24 REFERENCES Barclay, Michael J., Neil D. Pearson, and Michael S. Weisbach, 1998, Open-end mutual funds and capital-gains taxes, Journal of Financial Economics 49, Bergstresser, Daniel, John M. R. Chalmers, and Peter Tufano, 2009, Assessing the Costs and Benefits of Brokers in the Mutual Fund Industry, The Review of Financial Studies 22, Bergstresser, Daniel, and James Poterba, 2002, Do after-tax returns affect mutual fund inflows?, Journal of Financial Economics 63, Bhattacharya, Utpal, Andreas Hackethal, Simon Kaesler, Benjamin Loos, and Steffen Meyer, 2012, Is Unbiased Financial Advice to Retail Investors Sufficient? Answers from a Large Field Study, The Review of Financial Studies 25, Chalmers, John, and Jonathan Reuter, 2012, What is the Impact of Financial Advisors on Retirement Portfolio Choices and Outcomes?, Working Paper Series. Chevalier, Judith, and Glenn Ellison, 1997, Risk Taking by Mutual Funds as a Response to Incentives, The Journal of Political Economy 105, Christoffersen, Susan E., Richard B. Evans, and David K. Musto, 2013, What do Consumers Fund Flows Maximize? Evidence from Their Brokers Incentives, The Journal of Finance 58, Del Guercio, Diane, and Jonathan Reuter, 2012, Mutual Fund Performance and the Incentive to Generate Alpha, The Journal of Finance (forthcoming). Del Guercio, Diane, Jonathan Reuter, and Paula A. Tkac, 2010, Demand for Financial Advice, Broker Incentives, and Mutual Fund Market Segmentation, Working Paper Series. Diliberto, Roy, 2008, Some Taxes May Not Be Inevitable, Financial Advisor Magazine August 2008 Issue. Gruber, Martin J., 1996, Another Puzzle: The Growth in Actively Managed Mutual Funds, The Journal of Finance 51, Hackethal, Andreas, Michael Haliassos, and Tullio Jappelli, 2012, Financial advisors: A case of babysitters?, Journal of Banking & Finance 36, Investment Company Institute, 2008, Ownership of Mutual Funds Through Professional Financial Advisers 2007, Investment Company Institute Research Series 17, Investment Company Institute, 2012, Investment Company Fact Book 2012, Investment Company Institute Research Series Investment Company Institute, 2013, Ownership of Mutual Funds Through Investment Professionals 2012, Investment Company Institute Research Perspective 19, Ippolito, Richard A., 1992, Consumer Reaction to Measures of Poor Quality: Evidence from the Mutual Fund Industry, Journal of Law and Economics 35, Ivković, Zoran, and Scott Weisbenner, 2009, Individual investor mutual fund flows, Journal of Financial Economics 92, Johnson, Woodrow T., and James M. Poterba, 2010, The Effect of Taxes on Shareholder Inflows around Mutual Fund Distribution Dates, Working Paper Series. Malloy, Christopher J., and Ning Zhu, 2004, Mutual Fund Choices and Investor Demographics, Working Paper Series. 23

25 Mullainathan, Sendhil, Markus Noeth, and Antoinette Schoar, 2012, The Market for Financial Advice: An Audit Study, Working Paper Series. Pástor, Ľuboš, and Robert F. Stambaugh, 2002, Mutual fund performance and seemingly unrelated assets, Journal of Financial Economics 63, Petersen, Mitchell A., 2009, Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches, The Review of Financial Studies 22, Sialm, Clemens, and Laura T. Starks, 2012, Mutual Fund Tax Clienteles, The Journal of Finance 67, Sirri, Erik R., and Peter Tufano, 1998, Costly Search and Mutual Fund Flows, The Journal of Finance 53, Smith, Elizabeth R., 2001, Fund Strategies to Soothe Tax Pain, The New York Times, 24

26 Table I Share Class Characteristics by Distribution Channel This table reports descriptive statistics for our sample from U.S. equity fund share classes are categorized by their primary channel of distribution. We classify a share class as belonging to the Direct (Indirect) distribution channel based on classification provided by Lipper. Assets represents the average assets under management per share class in million USD. Net Flow is the average share class net flow, which is defined as the weekly net flow per share class normalized by its assets lagged by one week; Expense Ratio is the average expense ratio of the share classes during the respective year. Net Flow and Expense Ratio are in percentage points. ***, **, * denote statistical significance at the 1%, 5%, and 10% significance level, respectively. Share Class Characteristics Number of Share Classes Assets Net Flow Expense Ratio Year Direct Indirect Direct Indirect Difference Direct Indirect Difference Direct Indirect Difference , ,283.8 *** -7.4% 9.7% -17.0% *** 1.17% 1.58% -0.42% *** , ,149.7 *** 7.2% 14.9% -7.7% 1.21% 1.68% -0.47% *** , *** 21.7% 19.4% 2.3% 1.22% 1.71% -0.49% *** *** 12.3% 11.9% 0.3% 1.24% 1.77% -0.53% *** *** 14.3% 19.1% -4.9% 1.26% 1.83% -0.57% *** , *** 13.0% 12.4% 0.6% 1.26% 1.85% -0.59% *** ,059 1, *** 11.4% 6.6% 4.8% * 1.21% 1.80% -0.58% *** ,206 1, *** 6.8% 3.2% 3.6% 1.21% 1.77% -0.55% *** ,307 1, *** 2.6% -2.6% 5.1% ** 1.22% 1.74% -0.52% *** , *** -1.0% -11.9% 10.9% *** 1.21% 1.72% -0.51% *** , *** 6.6% -10.5% 17.1% *** 1.21% 1.76% -0.55% *** , *** 2.1% -9.9% 12.0% *** 1.22% 1.80% -0.57% *** ,807 1, *** 4.0% -6.0% 10.0% *** 1.20% 1.77% -0.58% *** All Years 609 1,821 1, *** 7.5% -1.1% 8.6% *** 1.22% 1.78% -0.56% *** 25

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