We develop a simple rational model of active portfolio management that provides a natural benchmark against which to evaluate observed relationship between returns and fund flows. Many effects widely regarded as anomalous are consistent with this simple explanation. In the model, investments with active managers do not outperform passive benchmarks because of the competitive market for capital provision, combined with decreasing returns to scale in active portfolio management. Consequently, past performance cannot be used to predict future returns, or to infer the average skill level of active managers. The lack of persistence in actively managed returns does not imply that differential ability across managers is nonexistent or unrewarded, that gathering information about performance is socially wasteful, or that chasing performance is pointless. A strong relationship between past performance and the flow of funds exists in our model: indeed, this is the market mechanism that ensures that no predictability in performance exists. Choosing parameters to match the flow-performance relationship and survivorship rates, we find these features of the data are consistent with the vast majority (80%) of active managers having at least

We document strong positive correlation between changes in institutional ownership and returns measured over the same period. The result suggests that either institutional investors positive feedback trade more than individual investors or institutional herding impacts prices more than herding by individual investors. We find evidence that both factors play a role in explaining the relation. We find no evidence, however, of return mean-reversion in the year following large changes in institutional ownership -- stocks institutional investors purchase subsequently outperform those they sell. Moreover, institutional herding is positively correlated with lag returns and appears to be related to stock return momentum. 1 "Herding" (a group of investors trading in the same direction over a period of time) and "feedback trading" (correlation between herding and lag returns) have the potential to explain a number of financial phenomena, e.g., excess volatility, momentum, and reversals in stoc...

This paper solves the dynamic investment problem of a risk averse manager compensated with a call option on the assets he controls. Under the manager’s optimal policy, the option ends up either deep in or deep out of the money. As the asset value goes to zero, volatility goes to infinity. However, the option compensation does not strictly lead to greater risk seeking. Sometimes, the manager’s optimal volatility is less with the option than it would be if he were trading his own account. Furthermore, giving the manager more options causes him to reduce volatility. MANAGERS WITH CONVEX COMPENSATION SCHEMES play an important role in financial markets. This paper solves for the optimal dynamic investment policy for a risk averse manager paid with a call option on the assets he controls. The paper focuses on how the option compensation impacts the manager’s appetite for risk when he cannot hedge the option position. On one hand, the convexity of the option makes the manager shun payoffs that are likely to be near the money. Under the optimal policy, the manager

This paper investigates the performance and capital inflows of private equity partnerships. Average fund returns (net of fees) approximately equal the S&P 500 although there is substantial heterogeneity across funds. Returns persist strongly across different funds raised by a partnership. Better performing partnerships are more likely to raise follow-on funds and larger funds. This relationship is concave so that top performing partnerships grow proportionally less than average performing partnerships. At the industry level, market entry and fund performance is cyclical; however, established funds are less sensitive to cycles than new entrants. Several of these results differ markedly from those for mutual funds.

We investigate the effect of scale on performance in the active money management industry. We first document that fund returns, both before and after fees and expenses, decline with lagged fund size, even after adjusting these returns by various performance benchmarks. We then explore a number of potential explanations for this relationship. We find that this relationship is most pronounced among funds that have to invest in small and illiquid stocks, which suggests that the adverse effects of scale are related to liquidity. Controlling for its size, a fund’s performance actually increases with the asset base of the other funds in the family that the fund belongs to. This suggests that scale need not be bad for fund returns depending on how the fund is organized. Finally, we explore the idea that scale erodes fund performance because of the interaction of liquidity and organizational diseconomies.

Mutual fund managers may decide to deviate from a well-diversified portfolio and con-centrate their holdings in industries where they have informational advantages. In this paper, we study the relation between the industry concentration and the perfor-mance of actively managed U.S. mutual funds from 1984 to 1999. Our results indicate that, on average, more concentrated funds perform better after controlling for risk and style differences using various performance measures. This finding suggests that investment ability is more evident among managers who hold portfolios concentrated in a few industries. ACTIVELY MANAGED MUTUAL FUNDS are an important constituent of the financial sector. Despite the well-documented evidence that, on average, actively man-aged funds underperform passive benchmarks, mutual fund managers might still differ substantially in their investment abilities.1 In this paper, we exam-ine whether some fund managers create value by concentrating their portfolios in industries where they have informational advantages.

We investigate the role that non-portfolio fund differentiation and information/search frictions play in creating two salient features of the mutual fund industry: the large number of funds and the sizeable dispersion in fund fees. In a case study, we find that despite the financial homogeneity of S&P 500 index funds, this sector exhibits the fund proliferation and fee dispersion observed in the broader industry. We show how extra-portfolio mechanisms explain these features. These mechanisms also suggest an explanation for the puzzling late-1990s shift in sector assets to more expensive (and often newly entered) funds: an influx of high-information-cost novice investors. * Zvi Eckstein and Alan Sorensen provided thoughtful suggestions regarding earlier drafts. We also thank Judith

Abstract not found

This paper finds a positive, concave relation between the returns and share re-strictions of private investment funds, and shows that previously documented positive alphas can be interpreted as compensation for holding illiquid fund shares. The an-nual returns on funds with lockup provisions are approximately 4 % higher than those for non-lockup funds, and the alphas of funds with the most liquid shares are either negative or insignificant. This paper also finds a positive association between share restrictions and illiquidity in fund assets, suggesting that funds facing high redemption costs use restrictions to screen for investors with low-liquidity needs. The results are consistent with previous theories which posit that liquidity is priced, and that less liquid assets are held by investors with longer investment horizons. JEL classification: G11; G12

We thank Nicholas Barberis and Judith Chevalier for helpful comments. We thank Breno Schmidt for research assistance. ABSTRACT We use mutual fund flows as a measure for individual investor sentiment for different stocks, and find that high sentiment predicts low future returns. Fund flows are dumb money -by reallocating across different mutual funds, retail investors reduce their wealth in the long run. This dumb money effect is strongly positively related to the value effect. High sentiment also is associated high corporate issuance, interpretable as companies increasing the supply of shares in response to investor demand. Dumb money -Page 1 Individual retail investors actively reallocate their money across different mutual funds. Individuals tend to transfer money from low performing funds to high performing funds. In addition to looking at past returns of funds, individuals also may consider economic themes or investment styles in reallocating funds. Collectively, one can measure individual sentiment by looking at which funds receive inflows and which receive outflows, and can relate this sentiment to different stocks by examining the holdings of mutual funds. This paper tests whether sentiment affects stock prices, and specifically whether one can predict future stock returns using a flow-based measure of sentiment. If sentiment pushes stock prices above fundamental value, high sentiment stocks should have low future returns. For example, in 1999 investors sent $36 billion to Janus funds but only $20 billion to Fidelity funds, despite the fact that Fidelity had more than three times the assets under management at the beginning of the year. Thus in 1999 retail investors as a group made an active allocation decision to give greater weight to Janus funds, and in doing so they increased their portfolio weight in tech stocks held by Janus. By 2001, investors had changed their minds about their allocations, and pulled about $12 billion out of Janus while adding $10 billion to Fidelity. In this instance, the reallocation caused wealth destruction to mutual fund investors as Janus and tech stocks performed horribly after 1999. According to the "smart money" hypothesis of Gruber (1996) and Zheng (1999), some fund managers have skill and some individual investors can detect that skill, and send their money to skilled managers. Thus (in contrast to the Janus example) flows should be positively correlated with future returns. Gruber (1996) and Zheng (1999) show that the short term performance of funds that experience inflows is significantly better than those that experience outflows, suggesting that mutual fund investors have selection ability. 1 Dumb money -Page 2 Our focus is on stocks, not on funds. We are interested in how investor sentiment affects stocks prices, and see fund flows as a convenient (and economically important) measure of sentiment. To test whether investor sentiment causes mispricing, one must test whether high sentiment today predicts low return in the future, and we focus on cross-sectional stock return predictability over periods of months and years. We ask the question of whether, over the longterm, investors are earning higher returns as a result of their reallocation across funds. For each stock, we calculate the mutual fund ownership of the stock that is due to reallocation decisions reflected in fund flows. For example, in December 1999, 17% of the shares outstanding of Cisco were owned by the mutual fund sector (using our sample of funds), of which 2.5% was attributable to disproportionately high inflows over the previous 3 years. That is, under certain assumptions, if flows had occurred proportionately to asset value (instead of disproportionately to funds like Janus), the level of mutual fund ownership would have been only 14.5%. This 2.5% difference is our measure of investor sentiment. We then test whether this measure predicts differential returns on stocks. Our main results are as follows. First, as suggested the example of Janus and Cisco in 1999, on average from 1980 to 2003, retail investors direct their money to funds which invest in stocks that have low future returns. To achieve high returns, it is best to do the opposite of these investors. We calculate that mutual fund investors experience total returns that are significantly lower due to their reallocations. Therefore, mutual fund investors are dumb money in the sense that their reallocations reduce their wealth on average. We call this predictability the "dumb money" effect. This dumb money effect poses a challenge to rational theories of fund flows. Second, the dumb money effect is highly related to the value effect. The returns on portfolios constructed using our flow-based measure of sentiment are quite positively correlated Dumb money -Page 3 with the returns on portfolios constructed using market-book ratio. Money flows into mutual funds that own growth stocks, and flows out of mutual funds that own value stocks. This pattern poses a challenge to risk-based theories of the value effect, which would need to explain why one class of investors (individuals) is engaged in a complex dynamic trading strategy of selling "high risk" value stocks and buying "low risk" growth stocks. Third, demand by individuals and supply from firms are highly related. When individuals indirectly buy more stock of a specific company (via mutual fund inflows), we also observe that company increasing the number of shares outstanding (for example, through seasoned equity offerings, stock-financed mergers, and other issuance mechanisms). This pattern is consistent with the interpretation that individual investors are dumb, and smart firms are opportunistically exploiting their demand for shares. These results give a different perspective on the issue of individuals vs. institutions. A large literature explores whether institutions have better average performance than individuals. In the case of mutual funds, for example, Daniel, Grinblatt, Titman, and Wermers (1997) show that stocks held by mutual funds have higher returns, and Chen, Unfortunately, since individuals ultimately control fund managers, it can be difficult to infer the views of fund managers by looking only at their holdings. For example, when the manager of tech fund experiences large inflows, his job is to buy more technology stocks, even if he thinks the tech sector is overvalued. So if we observe the mutual fund sector as a whole holding technology stocks, that does not imply that mutual managers as a whole believe tech stocks will outperform. It is hard for a fund manager to be smarter than his clients. Mutual fund Dumb money -Page 4 holdings are driven by both managerial choices in picking stocks and retail investor choices in picking managers. We provide some estimates of the relative importance of these two effects. This paper is organized as follows. Section I reviews the literature. Section II discusses the basic measure of sentiment and describes the data. Section III presents regression results on the determinants of sentiment and the relation between sentiment and future returns. Section IV uses calendar time portfolios to put the results in economic context, showing the magnitude of wealth destruction caused by flows, comparing the sentiment measure with other well-known strategies, and providing evidence on whether mutual fund managers have stock-picking skill. Section V presents conclusions. While this apparent contradiction between return-chasing and contrarianism is interesting, the hypothesis we wish to test does not depend on resolving this issue. We are interested in testing whether individual investor sentiment predicts future returns, so our hypothesis is not contingent on measuring whether investors are ultimately return-chasing or not. I. Background and literature review A. Determinants of fund flows For example, if individual investor sentiment causes prices to be wrong and prices eventually revert to fundamental value, then sentiment should negatively predict future returns no matter what -whether individuals over-react or under-react, whether they return-chase or not. As it turns out, in the data we study, mutual fund flows are indeed return-chasing, and flows tend to go to stocks that have gone up recently. B. Causal effects of flows on prices There is evidence that fund flows have positively contemporaneous correlations with stock returns (see, for example, While individuals were sending mutual fund money to tech funds in 1999, and thus indirectly purchasing tech stocks, they may have also been buying tech stocks directly in their brokerage accounts, or investing in hedge funds that bought tech stocks. Dumb money -Page 6 Thus the hypothesis we wish to test is that stocks owned by funds with big inflows are overpriced. These stocks could be overpriced because inflows force mutual funds to buy more shares and thus push stock prices higher, or they could be overpriced because overall demand (not just from mutual fund inflows) pushes stock prices higher. In either case, inflows reflect the types of stocks with high investor demand. C. Styles A paper closely related to ours is Teo and Woo (2001), who also find evidence for a dumb money effect. Following While Teo and Woo (2001) provide valuable and convincing evidence, our approach is more general. The benefit is that we do not have to define specific styles or categories, such as value/growth. While categorical thinking and style classification are undoubtedly important in determining fund flows, from a practical point of view it is difficult for the researcher to identify all relevant categories used by investors over time. For example, the growth/value category was not widely used in 1980. Instead, we impose no categorical structure on the data and just follow the flows. Most strikingly, we are able to document that the fund flow effect is highly related to the value effect, a finding that could not have been discovered using the method of Teo and Woo (2001). II. Constructing the flow variable Previous research has focused on different ownership levels, such as mutual fund Dumb money -Page 7 ownership as a fraction of shares outstanding (for example, Our central variable is FLOW, the percent of the shares of a given stock owned by mutual funds that are attributable to fund flows. This variable is defined as the actual ownership by mutual funds minus the ownership that would have occurred if every fund had received identical proportional inflows (instead of experiencing different inflows and outflows), every fund manager chose the same portfolio weights in different stocks as he actually did, and stock prices were the same as they actually were. We define the precise formula later, but the following example shows the basic idea. Suppose at quarter 0, the entire mutual fund sector consists of two funds: a technology fund with $20 B in assets and a value fund with $80 B. Suppose at quarter 1, the technology fund has an inflow of $11 B and has capital gains of $9 B (bringing its total assets to $40 B), while the value fund has an outflow of $1 B and capital gains of $1 B (so that its assets remain constant). Suppose that in quarter 1 we observe the technology fund has 10% of its assets in Cisco, while the value fund has no shares of Cisco. Thus in quarter 1, the mutual fund sector as a whole owns $4 B in Cisco. If Cisco has $16 B in market capitalization in quarter 1, the entire mutual fund sector owns 25% of Cisco. We now construct a world where investors simply allocate flows in proportion to initial fund asset value. Since in quarter 0 the total mutual fund sector has $100 B in assets and the total inflow is $10 B, the counterfactual assumption is that all funds get an inflow equal to 10% Dumb money -Page 8 of their initial asset value. To simplify, we assume that the flows all occur at the end of the quarter (thus the capital gains earned by the funds are not affected by these inflows). Thus in the counterfactual world the technology fund would receive (.20)*(10) = $2 B (giving it total assets of $31 B), while the value fund would receive (.80)*(10) = $8 B (giving it total assets of $89). In the counterfactual world the total investment in CISCO is given by (.1)*(31) = $3.1, which is 19.4% of its market capitalization. Hence, the FLOW for CISCO, the percent ownership of Cisco due to the non-proportional allocation of flows to mutual funds, is 25 -19.4 = 5.6%. FLOW is an indicator of what types of stocks are owned by funds experiencing big inflows. It is a number that can be positive, as in this example, or negative (if the stock is owned by funds experiencing outflows or lower-than-average inflows). It reflects the active reallocation decisions by investors. What FLOW does not measure is the amount of stock that is purchased with inflows; one cannot infer from this example that the technology fund necessarily used its inflows to buy Cisco. To the contrary, our assumption in constructing the counterfactual is that mutual fund managers choose their percent allocation to different stocks in a way that is independent of inflows and outflows. Is it reasonable to assume that managers choose their portfolio weights across stocks without regard to inflows? Obviously, there are many frictions (for example, taxes and transaction costs) that would cause mutual funds to change their stock portfolio weights in different stocks in response to different inflows. Thus, we view FLOW as an imperfect measure of demand for stocks due to retail sentiment. In equilibrium, of course, a world with different flows would also be a world with different stock prices, so once cannot interpret the counterfactual world as an implementable alternative for the aggregate mutual fund sector. Later, when we discuss the effects of flows on Dumb money -Page 9 investor wealth, we consider an individual investor (who is too small to affect prices by himself) who behaves like the aggregate investor. We test whether this individual representative investor benefits from the active reallocation decision implicit in fund flows. For individual investors, refraining from active reallocation is an implementable strategy. A. Flows We calculate mutual fund flows using the CRSP US Mutual Fund Database. The universe of mutual funds we study includes all domestic equity funds that exists at any date between 1980 and 2003 for which quarterly net asset values (NAV) are available and for which we can match CRSP data with the common stock holdings data from Thomson Financial (described in the next subsection). Since we do not observe flows directly, we infer flows from fund return and net asset value (NAV) as reported by CRSP. Let where MGN is the increase in total net assets due to mergers during quarter t . Note that Counterfactual flows are computed under the assumption that each fund receives a pro rata share of the total dollar flows to the mutual fund sector between date k t − and date t , with the proportion depending on NAV as of quarter t-k. More precisely, in order to compute the Dumb money -Page 10 FLOW ownership at date t , we start by looking at the net asset value of the fund at date k t − . Then, for every date s we track the evolution of the fund's counterfactual NAV using: Let it x be the net asset value of fund i in month t as a percentage of total asset of the mutual fund sector: The counterfactual under proportional flows is: The difference between it x and it x reflects the active decisions of investors to reallocate money from one manager to another over the past k quarters in a way that is not proportional to the NAV of the funds. This difference reflects any deviation from value weighting by the NAV of Dumb money -Page 12 the fund in marking new contributions. In theory, this difference could reflect rebalancing away from high performing funds and into poorly performing funds, in order to maintain some fixed weights (instead of market weights). In practice, investors tend to unbalance (not rebalance), sending money from poorly performing funds to high performing funds. B. Holdings Thomson We use a series of filters to eliminate data errors and to handle missing reports (see appendix). In matching the holdings data to the CRSP mutual fund database, we utilized fund tickers, fund names and total net asset values. Our matching system works better in the latter part of the sample: coverage of the dollar assets of the total CRSP universe of funds rises from about In this paper, we focus on a three year horizon in calculating FLOW. Since our goal is to understand the long-term effects on investor wealth, the longer the horizon, the better. Since our sample is less that 25 years long, three years is approaching the longest horizon that is appropriate given data limitations. Three years is also the approximate frequency of the value effect or reversal effect in stock returns. Dumb money -Page 14 We first describe the data for funds. For example, if the company has 100 shares and has a seasoned equity issue of an additional 50 shares, the composite issuance measure is 33%, meaning that 33% of the existing shares today were issued recently. We define the variable in this way to make it comparable to the flow measure (both are expressed as a fraction of market value of the company, and are variables bounded above by 100% and unbounded below). The measure can be negative (reflecting for example repurchases) or positive (reflecting for example options given to executives, seasoned equity offerings, stock-financed mergers). Issuance and value are strongly related: growth firms tend to issue stock, value firms tend to repurchase stock. Past research, such as Fama and French (1993) and Daniel and Titman (2004), shows that when either issuance or market-book is high today, returns are low over the next year. Alcoa coming in to favor around 1995 while Cisco falls out of favor at the same time, then the two reverse in the later 1990's. Looking at the figures, there is some sense that the three different variables (market-book, issuance and flows) are positively correlated, but clearly the three variables also contain some information independent of each other. III. Regression results: Flows and returns A. Univariate relation between returns and flows We show the predictive power of flows, and for comparison we show several other variables that are related to flows, and which may have their own previously documented predictive power for returns. The dependent variable is monthly returns in percentage points, while the independent variables are the latest available percentilized independent variables, variously updated at the annual (market-book), monthly (for momentum, reversals, and issuance), or semiannual/quarterly (flows) frequency. We first discuss the results for flows. The table shows flows over horizons stretching from three months (one quarter, the shortest interval we have for calculating flows) to three years. Looking at the first column, it is striking that for every horizon but three months, high flows today predict low future stock returns. This relation is statistically significant at the one year and three year horizon. If one is interested in the long-term effects of investor reallocation (whether over time investors benefit from reallocating money across different funds), longer horizons are the appropriate measure. This dumb money effect is the central result of this paper. Perhaps surprisingly, we find no solid evidence for the smart money effect in raw returns, even at the horizons of six to twelve months where one might expect price momentum to dominate. This difference from previous results may be due to two factors. First, by focusing on stock returns instead of fund returns, we avoid many complications involving expense ratios, trading costs, and cash holdings by funds. Second, our measure of flows is quite different than standard because we focus on net flows into individual stocks, not net flows into individual funds. Gruber (1996) and Zheng (1999) focus on funds that have disproportionately high inflows, while we focus on stocks that are disproportionately owned by fund with inflows (as measured by dollar flows compared to market capitalization of the stock). For example, if Cisco is owned by 100 large funds, all of which have slightly higher than average inflows, our measure would classify Cisco as a high sentiment stock. In contrast, the papers cited above would look at individual funds, perhaps small funds that had very high inflows in the past. The second column shows regressions where returns have been adjusted to control for value, size, and momentum. Following Daniel, Grinblatt, Titman, and Wermers (1997), it subtracts from each stock return the return on a portfolio of firms matched on market equity, market-book, and prior one-year return quintiles (a total of 125 matching portfolios). 4 Here the dumb money effect is substantially reduced, with the coefficient falling from -0.90 to -0.34 for three year flows, still significantly negative but less than half as large. As we shall see, this partially reflects the fact that high sentiment stocks tend to be stocks with high market-book. Thus using a three year horizon, the dumb money effect is statistically distinct from the value effect, but obviously highly related. Dumb money -Page 20 One might ask whether the dumb money effect is an implementable strategy for outside investors using information available in real time. Our methodology involves substantial built-in staleness of flows largely reflecting the way that Thomson Financial has structured the data. 5 So the variable in To address this issue, Indeed, lagging the six month flow variable causes a substantial improvement in predictive power. This improvement probably reflects that by skipping the most recent six months, we avoid the positive correlation of short-term momentum and short-term flows. For comparison, In summary, three year mutual fund flows strongly negatively predict future stock returns, and there is no horizon at which flows reliable positively predict returns. The dumb money effect is present controlling for value and momentum, present in both large and small cap stocks, and present in different time periods. In terms of statistical significance, sign, and absolute magnitude, it is similar to the value, reversal, and the issuance effects. Dumb money -Page 22 IV. Calendar time portfolios, economic significance, and manager skill In this section, we move from cross-sectional regression evidence to examining monthly returns on calendar time portfolios. We start by forming standard long/short portfolio returns consisting of the top quintile and bottom quintile of various variables. Panel A of Panel B of In summary, using calendar time portfolio returns shows that the dumb money effect is a statistically strong effect. The evidence on whether the dumb money effect is fully explained by value is mixed at best. The dumb money effect is certainly highly correlated with the value effect. A. The magnitude of wealth destruction So far we have shown that stocks owned by funds with large inflows have poor subsequent returns. What is the economic significance of this fact? In this section, we measure the wealth consequences of active reallocation across funds, for the average investor. We abstract from the important issues of fund expenses and trading costs, and look only at the effect on mean returns earned by investors. These expenses and trading costs are another real source of