On Market Timing, Stock Picking, and Managerial Skills of Mutual Fund Managers with Manipulation-proof Performance Measure

On Market Timing, Stock Picking, and Managerial Skills of Mutual Fund Managers with Manipulation-proof Performance Measure Meifen Qian, Ping-Wen Sun, and Bin Yu International Institute for Financial Studies Jiangxi University of Finance and Economics Fan Chen Department of Finance Quinnipiac University Preliminary draft All comments are welcome This draft: January, 2014 The on-going debate over whether fund managers have skills and whether those skills are shortlived is still inconclusive. Using the performance measure that can t be manipulated with respect to the underlying distribution, time variation, nor estimation error, (the manipulation-proof performance measure (MPPM, Goetzmann et al. (2007)), we rank all U.S. domestic equity mutual funds from 1980 to 2012 on a quarterly basis and analyze their portfolio holding to contribute to the literature in two folds. First, managers ranked highest on MPPM in the current quarter earn largest fee-adjusted fund returns in the following quarter. Those managers hold younger, smaller, lower book-to-market, and momentum stocks. Second, taking long positions of the addition and short positions of the removal from their quarterly holdings from the highest ranked managers would outperform the lowest ranked managers by 12 basis points at the following quarter. Even though higher ranked managers have better stock picking skills, their fund returns are not large enough to offset their frequent transactions and higher expenses to insure positive alphas. JEL Classification: G11, G12, G14, G23 Key word: manipulation-proof performance measure (MPPM), equity mutual fund, portfolio risk, portfolio characteristics 1

1. Introduction Anecdotal evidence suggests mutual fund managers have stock picking and/or market timing skill to outperform their peers on a pre-expenses and risk-adjusted basis. Kacperczyk, Van Nieuwerburgh, and Veldkamp (2014) document an existence of value-creation mutual fund managers who have both market-timing (in recessions) and stock-picking (in booms) skills to generate persistence performance up to one year. Chung and Kim (2014) find that high consistency funds generate more than 2% additional risk-adjusted returns in the subsequent year after accounting for fund size, past performances, sample period, and expenses. Petajisto (2013), and Cremers and Petajisto (2009) find fund managers who hold different holdings than their benchmark index could outperform these benchmarks on fee-adjusted bases. Without using the holding data, Amihud and Goyenko (2013) regress fund returns on multifactor benchmark models to estimate the R 2 and document managerial stock selection skills would predict their future performance. On the other hand, a vast amount of literature find little evidence that fund managers generate positive abnormal returns over long horizons. French (2008), and Fama and French (2010) argue that actively-managed mutual funds cannot outperform passively-managed funds to conclude on average those fund managers do not have stock picking skills. Similarly, Bollen and Busse (2004) find superior performance from mutual fund managers are short-lived. 1 Given the literature controversial on the duration and the existence of managerial skills, perhaps a more important issue is whether we are using the appropriate performance measure that would be less likely for managerial manipulation. Recent mutual fund scandals such as late trading and market timing 2 has caused regulators emphasize on extensive disclosures and regulation reform. These issues are equally important if not more in hedge fund industry as many hedge funds in U.S. registered with the Securities and Exchange Commission (SEC) on a voluntary basis prior to the Dodd-Frank Act of 2010. If managers could generate high investment 1 As compared to Jensen (1969)for stock selection over periods of 10 20 years, and Treynor and Mazuy (1966) and Henriksson (1984) for market timing over periods of 6 10 years. Others such as Brown and Coetzmann (1995), Carhart (1997), and Porter and Trifts (1998) find the persistence performance is either time sensitive / sample specific or cannot provide additional risk-adjusted returns beyond common risk factors. 2 Since September 2003, the U.S. mutual fund industry has been mired in the worst scandal in its 65-year history. The scandal has produced in excess of 40 civil and criminal prosecutions, more than $2 billion in monetary sanctions, numerous Congressional hearings and bills, and a bevy of new regulations. For details, please refer to Bullard (2006). 2

returns with little risk or report overly consistent returns over a longer time frame, to what extent does the persistent performance on managerial skills could be another Bernie Madoff or Ponzi Schemes if we do not carefully select a manipulation-proof performance measure? Goetzmann, Ingersoll, Spiegel, and Welch (2007) argue that fund managers could use informationless manipulations such as writing at-the-money call options and/or increasing their leverages to alter / improve fund performance. 3 Since the performance measure in the current literature either assumes the underlying distribution (regardless of including the estimation error), or assumes stationary in the estimation of return distribution, 4 or induces estimation error (for example, induces positive biases), managers could manipulate their fund returns since the current compensation structure to mutual fund managers rewards mainly on fund size. Consequently, the (short-lived) persistence on performance could be achieved without the contribution from their informed managers and their intellectual buy side analysts to make the findings on managerial skills less robust. In this paper, we attempt to use the manipulation-proof performance measure (MPPM) 5, proposed by Goetzmann et al. (2007), to empirically rank fund managers and to examine managerial stock selection and market timing skills through analyzing their quarterly holdings. Using the MPPM to rank fund managers and to examine their quarterly holding have two major advantages. First, by using the MPPM to rank fund managers on their ex ante performance allow us to filter out those un-informed trades from fund managers who achieve superior fund performance through writing calls and puts options or simply altering leverages. Second, by looking into their quarterly holding and analyzing their changes from their quarterly holding through a time series trend allows us to establish linkages on their fund performance to stock selection and market timing skills. If managers achieve superior performance through luck or manipulation, the results from analyzing their quarterly holdings would serve this purpose to mitigate agency conflicts among managers and their fund holders. We claim we are not the first paper to use the MPPM evaluating fund managers since it has been used in recent fund performance studies. Titman and Tiu (2010) use MPPM to evaluate 3 Similarly, Weisman (2002), and Brown et al. (2004) also show that the traditional mutual fund performance measures can be gamed by fund managers. 4 For example, Carhart (1997) ranks by prior year return and by prior three-year abnormal return. 5 The MPPM could more correctly identify the percentage of funds that are beating the benchmark markets. 3

hedge fund performance due to the nature of return smoothing from their illiquid assets. Huang et al. (2011) apply MPPM to evaluate actively-managed mutual funds for robustness check on their empirical results. Bhattachara et al. (2012) find using MPPM can distinguish sophisticated investors from retail investors while Sharpe ratio cannot. Even though mutual funds managers are less found to engage on return smoothing or investing on illiquid assets, mutual fund are still the target since they are widely documented using derivatives (Lynch-Koski and Potiff (1999), and Cao et al. (2010)). Our findings not only draw policy implication to the regulators but also shed lights to fund complexes that are in search for better compensation mechanisms to reward truly skillful fund managers rather than their manipulated counterparts. By combining the MPPM and quarterly holding from fund managers, we could alleviate labeling fraud-like outperformance or informationless trades from fund managers as managerial skills. Our results show that managers ranked highest on MPPM in the current quarter actually earn largest fee-adjusted fund returns in the following quarter. The outperforming managers from this ranking tend to hold younger, smaller, lower book-to-market, and momentum stocks in their quarterly reported holdings. Our results are also important to investors and fund holders. Investors could earn trading profits if they follow the quarterly holding from the highest ranked managers to engage long positions of the addition and short positions of the removal from managers quarterly holdings. However, it is important to note that even though the highest ranked managers have better stock picking skills, their fund returns are not large enough to offset their frequent transactions and related fund expenses to insure earning positive risk-adjusted performance (i.e. alphas). Our findings are consistent with Daniel, Grinblatt, Titman and Wermers (1997) who show that stocks that are picked by mutual funds outperform a characteristic-based benchmark with the gain being approximately equal in magnitude to the funds management fee. Our findings are also consistent with Fama and French (2010) who find mutual funds in aggregate realize net returns that underperform four-factor benchmark by about the costs in expense ratios and most mutual funds do not have the skill to produce benchmark adjusted expected returns that cover costs. On the other hand, our results do show that highest ranked MPPM managers trade more often (have higher quarterly churn rate) and hold small and growth firms to outperform 4

their counterparts, consistent with Yan and Zhang (2009) who argue those institutional traders are more informed. Our findings are contrast to Wermers (2000) who find fund managers could earn more from picking stocks to offset their trading costs. Our results indicate even though top quarter fund managers have stock picking sills, those skills are not warrant risk-adjusted performance considering the trading and managerial expenses. Overall, our findings show that MPPM is a more reliable performance measure for investors to select equity mutual funds. 6 We claim using the MPPM to be a more effective measure to rank managers and to predict fund performance. We also show that the common performance measures are less robust in measuring managerial skills and easier to manipulate. The remainder of this paper is structured as follows. Section 2 describes the research design, our hypothesis, and data construction. Section 3 reports our empirical findings. Section 4 concludes. 2. Research design, hypothesis, and data construction Our research questions are (1) whether MPPM can truly differentiate skillful and informed managers from the traditional performance measure models and (2) whether the highlyranked managers could outperform their lower-ranked counterparts both at their fund returns and their holding returns? Based on our research questions, we hypothesize funds with highest MPPM ranked will be more informed so they will demonstrate better market timing, stock picking (Kacperczyk et al. (2014)) and higher churn rate (Yan and Zhang (2009)). We will analyze their holdings to find whether they are significant from lower ranked managers. We also hypothesize sample funds with higher MPPM ranked managers can better predict future fund and holding-based performance. Our first step on the research design is to establish the MPPM is a more efficient performance measure than the common performance measures, such as the Sharpe ratio, Jensen's alpha, Treynor ratio, Sortino's downside-risk and Sortino, van der Meer and Plantinga's upsidepotential measures, Henriksson-Merton and Treynor-Mazuy timing measures, and others 6 For economy of presentation, we do not report the empirical results on the comparison of MPPM to 7 other performance measures. However, those results are available based on request. 5

(Lhabihant (20000, Ferson and Siegel (2001), Richard (2001), Bollen and Pool (2008)), which are not manipulation-proof due to superior performance can be achieved through engaging derivatives contracts and leverage. Goetzmann et al. (2007) argue that a manipulation-proof performance measure should meet the following four criteria: (1) the measure should produce a single score to rank each fund, (2) the score s value is irrelevant to fund size, (3) only informed investors are able to produce higher scoring portfolios by taking advantage of arbitrage opportunities, and (4) the measure should be consistent with standard financial market equilibrium conditions. In order to empirically compare different performance measures of our sample funds, we aim to compare all performance measures and to show the percentage of beating the market from MPPM is indeed lower than the rest of other common performance measures that are not manipulation-proof, an extension from Goetzmann et al. (2007) who use simulation framework to document. In addition, we need to test informed and uninformed market timers and be able to show that MPPM could be more correctly recognize both informed and uninformed mutual fund managers. We will specify those hypothesis and how we modify for our study in the content below. 2.1 MPPM We use The measure of MPPM is defined as shown in equation (1). T 1 1 1 MPPM n 1 r / 1 r 1 (1) t ft t T t1 t as a month and T as twelve months in our study. Hence, r t is a fund s monthly returns and rft is monthly risk-free rate from CRSP database. The parameter is defined as shown in equation (2) 1 b 1 f n r n r Var n1 rb We choose our benchmark portfolio r b as the monthly market value-weighted returns of common stocks (with share code 10 or 11) from CRSP database. We take average monthly r b and rf from January 1980 to December 2012 and substitute the average values into equation (2) to get our estimate to be around 2.9 in our MPPM calculation. 6 (2)

2.2 Market timing and stock picking We follow Kacperczyk, Van Nieuwerburgh, and Veldkamp (2014) to construct market timing Timing and stock picking Picking as shown in equation (3) and (4). j t Timing j N j j m m t i t i t i t t i1,,, R 1 (3) j t j For fund j at the end of quarter t, we calculate each stock i s value weight wit, in the fund and m each stock i s value weight w it, in the market portfolio of common stocks. it, is each stock i s m factor-loading on market return from past 12 months from the market model. Rt 1 is the quarterly returns of the market portfolio of common stocks in the following quarter t 1. When skillful fund managers expect the market return is positive in the next quarter, they will put more weights on stocks with higher market betas in their funds than those stocks weights in the market portfolio. Similarly, when skillful managers expect the market return is negative in the next quarter, they will put less weights on stocks with higher market betas in their funds than those stocks weights in the market portfolio. From the above portfolio management, skillful fund managers will receive higher scores in market timing, j N j j m i m t i t i t t i t t i1,, 1, 1 Picking R R (4) For the measure of stock picking j Timing t. j i Picking t of fund j at the end of quarter t, t 1 R is the quarterly return of stock i in the following quarter t 1. When fund managers are better at picking stocks i m whose future idiosyncratic component Rt 1 i, trt 1 will outperform its systematic component R m i, t t 1, they will put more weights on those stocks in their funds than those stocks weights in the market portfolio. Those fund managers will have higher scores in stock picking, j Picking t. 2.3 Average quarterly churn rate CRKT Even though we only have end of the quarter holding data, we follow Yan and Zhang (2009) to construct a fund s turnover estimate as shown in equation (5), (6), (7), and (8). 7

N k CR _ buy S S S P S P S P (5) k, t k, i, t k, i, t1 k, i, t i, t k, i, t 1 i, t 1 k, i, t 1 i, t i1 N k CR _ sell S S S P S P S P (6) k, t k, i, t k, i, t1 k, i, t i, t k, i, t 1 i, t 1 k, i, t 1 i, t i1 We first summarize each fund k 's stock i holding cash inflow/outflow in quarter t and aggregate those stocks' cash inflow/outflow to be fund k's aggregate purchase and sale at the end of quarter t as shown in equation (5) and (6). S k,i,t-1 and S k,i,t are number of shares held by fund k at the end of quarter t-1 and t. P i,t-1 and P i,t are listed share price of stock i at the end of quarter t- 1 and t. We use CRSP s price adjustment factor to adjust stock splits and stock dividends to calculate stock i's price change P i,t at the end of quarter t. CR kt, min CR _ buy, CR _ sell Nk S P S P i1 k, t k, t k, i, t i, t k, i, t 1 i, t 1 2 We then calculate each fund k's churn rate as shown in equation (7). We use the minimum of aggregate purchase and sale divided by the fund k's average portfolio holding value during quarter t to be its churn rate at the end of quarter t. (7) 3 1 AVG _ CR CR (8) k, t k, t j 4 j0 Finally, we calculate each fund's average churn rate over past four quarters as shown in equation (8). 2.4 Other stock characteristic variables In order to find out the information content for the stock holdings among different portfolio managers, we have chosen several stock characteristics variable in our analysis. Specifically, we include four risk related characteristics. They are size, book to market, past performance, and gross profits to total assets. We also include three variables to measure the liquidity characteristics of their holdings. They are monthly turnover, illiquidity ratio (Amihud (2002), the monthly trading volume divided by its share outstanding in the most recent 3 months), and the daily high-low spread (Corwin and Schultz (2012) closed-form solution of high-low 8

estimator for the most recent quarter). We have also include age, dividend yield, and return volatility in our analysis. 2.5 Data construction For our empirical analysis, we use CRSP Survivorship Bias Free Mutual Fund Database for mutual fund return and fund characteristics. We use quarterly holding for all mutual funds from Morningstar Direct Database. We merge the two databases only if the observations of the monthly return data on both databases exist and to merge the CRSP dataset and Morningstar dataset by share class, year, and month. We also use CRSP U.S. Stock Database to extract stock price to compute portfolio holding returns. Our sample period is from January 1980 to December 2012. We restrict our data on actively-managed domestic equity mutual funds for which the holdings data are most complete and reliable. Consequently, we eliminate bond, balanced, money market, international, and index funds. 7 We further exclude funds which in the previous month manage less than $5 million and funds that did not disclose their holdings in the previous 36 months. We also exclude funds without total net assets, return data. In order to calculate the annualized MPPM score, funds in the final sample should have at least 12 months of consecutive return data. For funds with multiple share classes, we compute fund-level variables by aggregating across the multiple share classes. Since the holding data is generally available on quarterly except few funds voluntarily report their holdings on monthly frequency, we restrict our analysis on quarterly basis. The final sample includes 5,243 distinct funds and a total of 229,315 fund-quarter observations. 3. Empirical results 3.1 Fund characteristics We report summary statistics of our sample funds in Table 1. In Panel A, we split the sample across different time period to see how sensitive the data of each sub-sample to the whole 7 For a complete data process on how to select the active domestic equity funds can refer to Huang, Sialm, and Zhang (2011). 9

sample period. On average, our sample funds are 12.55 years from their inceptions and carrying expense ratios of 1.3% annually. The quarterly raw returns are on average of 2.22% net of fees, or about 8.88% annually. The average fund size has grown rapidly across sub-sample, from 237 million dollar in total net assets in the 1980s to grow to more than 1 billion dollar in the 2000s. In order to examine the different skillsets of fund managers, at the end of each quarter from our entire sample period from January 1980 to December 2012, we rank each fund by its MPPM from its past 12 months returns to sort them into quintile. In Panel B, the average total net assets for rank 0 (lowest MPPM) group is 627 million dollars while for other ranks are all above 1 billion dollars. Fund age, expense ratios, and portfolio turnover show u-curve patterns. Highest ranked fund managers trade more often and charge higher expenses when they are compared to the middle quintile managers. However, the lowest ranked managers charge the highest expenses and have the highest portfolio turnover among the 5 groups. From the sample statistics, it does not seem to suggest fund size matters, a finding consistent with Elton, Gruber, and Blake (2012). We do not find major differences on common stock holding proportion among these 5 groups. For the quarterly value-weighted fund returns, the lowest ranked funds carry -0.88% while the highest ranked funds carry 5.66%. After using the past 12 months fund returns to calculate the quarterly value-weighted MPPM, the average quarterly MPPMs across the five groups from the lowest to the highest are -16.80%, -4.32%, 0.53%, 5.27%, and 14.31% respectively. [Insert Table 1 here] 3.2 Can MPPM predict future fund and holding returns? Since there are some variation among fund characteristics among different ranked fund managers, we are interested to see whether different MPPM ranked managers can predict future fund and holding returns. Based on the data frequency and its availability, we carry the weights of stock holdings in the current quarter to calculate their value-weighted holding returns and we use their fund size to calculate value-weighted fund returns for the subsequent quarter to provide insights on whether higher ranked managers produce better fund and holing returns. 10

We report our findings in Table 2. Sample funds with higher ranked MPPM this quarter generate both higher future fund returns and holding returns in the subsequent quarter. The lowest ranked MPPM funds generate value-weighted fund returns of 2.41% in the next quarter. Their holding returns are 2.85% per quarter. On the other hand, the highest ranked MPPM funds achieve value-weighted fund returns of 3.25% in the subsequent quarter. Their holding returns are 3.65% per quarter. In addition, the top ranked managers exhibit the best market timing score of 0.0294 and the best stock picking score of 0.0194, both indicate they have better market timing and stock picking skills, consistent with Kacperczyk, Van Nieuwerburgh, and Veldkamp (2014). Furthermore, Equity mutual funds with highest MPPM rank also exhibit higher average quarterly churn rate of 4.92%, a pattern of a U-curve that is similar to the lowest ranked managers (with the churn rate of 4.93%). However, it is fair to say the higher churn rate for the lowest ranked group are suffering redemptions from their existing fund holders due to subpar performance. On the other hand, highest ranked managers are more likely to have rapid fund inflows to drive the higher churn rate. [Insert Table 2 here] 3.3 Stock holding characteristics From the first two Tables, it is reasonable to conclude highest ranked fund managers have better timing and stock picking skills to generate higher fund and holding returns. We need to address whether the holdings contain certain characteristics that contain valuable and informed information to suggest their managerial skills or risk-taking behaviors? We report stock holding characteristics of sample funds from those five different ranked MPPM groups in Table 3. Our results show that fund managers with highest ranked MPPM tend to hold smaller stocks (with average size of 19.324 billion). They hold stocks with the lowest book-to-market ratio (0.371). They appear to use momentum strategies as they tend to hold the stocks with the highest past year performance of 45.83%. There is no significant difference on the operating profits (6.49%) when it is compared to the other ranked managers. From the findings, it appears that highest ranked managers prefer small, growth, and momentum stocks that have better past performance. 11

We further look into stock liquidity characteristics to see whether different ranked managers have any preference on liquidity from their holdings. Our results show managers with highest ranked hold stocks with average monthly turnover rate of 14.51% (second highest from the five groups), with Amihud (2002) illiquidity ratio of 0.077 (medium among five groups), and with high-low spread of 0.72% (medium among five groups). The findings suggest highest ranked managers prefer stocks with higher turnover rate, above average price impact and bid-ask spread, indication of stocks with relative higher information asymmetry. Even though managers in the lowest ranked display similar liquidity preferences on stocks, they apparently do not earn liquidity premiums to cover their risks. Highest ranked managers, on the other hand, are better informed to profit from the illiquidity. Further we examine three additional stock characteristics from their holdings for robustness check. Among five groups of ranked managers, highest ranked managers hold stocks with relative young stocks (second youngest across quintile (average age of 298 months)), lower dividend yield (second lowest annual dividend yield (1.87%)), and higher return volatility stocks (second highest monthly return volatility (9.88%) among the five groups). Not surprisingly, lowest ranked managers have similar holding characteristics with highest ranked managers. However, lowest ranked managers take additional idiosyncratic risks that do not pay off. Overall, our results show that even though lower ranked managers take additional risk on liquidity and stocks with higher information asymmetry, they end up with lowest fund and holding returns for the subsequent quarter. Highest ranked managers have informed information to earn additional returns based on the risk they take. [Insert Table 3 here] 3.4 Common risk factors analysis (Carhart (1997) four-factor) for stock holdings Up to this point, we are convinced that lowest and highest ranked managers prefer taking additional risk but no necessarily generate better fund and holding returns on the subsequent quarter. We further apply common risk factors to examine the stock holdings to see whether the stock picking, timing, and other managerial skills can be explained by the common risk factors. 12

In Table 4, we use Carhart (1997) four-factor model to examine the quarterly holdings. Our results show that Carhart (1997) four-factor model (risk factors) explain the holding return almost completely. None of the five groups has significant alphas. Among the five groups, highest ranked funds have the highest factor-loading on market risk premium (1.11), highest factor-loading on SMB (0.26), negative factor-loading on HML (-0.02), and highest factorloading on UMD (0.27). The lowest ranked funds exhibit the same signs on factor-loadings in market risk premium, SMB, and HML but has the lowest factor-loading of on UMD (-0.29). Analysis from the four-factor loading concludes managers of highest ranked prefer momentum strategies but lowest ranked managers place weights on the contrarian stocks. Even though managers have better skills in stock selection and market timing, the risk-adjusted performance on the holding base is insignificant. [Insert Table 4 here] 3.5 Are funds with highest MPPM more informed? Are higher ranked managers more informed? To get to the multivariate regression analysis, we perform the Fama and MacBeth (1973) regression to regress the subsequent quarterly stock returns on total equity mutual fund ownership, stocks owned by the highest MPPM ranked funds, and stocks owned by the lowest MPPM ranked funds for the current quarter, after controlling for other stock characteristics in Table 5. The results in Model 1 show that aggregate holding by all sample funds does not have explanation power on future returns. For common stock characteristics, stock size, book-tomarket ratio, past year performance, and gross profits to total asset do have some explanation power on future stock returns. As expected, larger size stocks tend to have lower future return. Higher book-to-market ratio stocks tend to have higher future returns. Stocks have better past year performance tend to have higher future returns. Stocks with higher gross profits to total asset ratio tend to earn higher returns in the future. Model 2 and Model 3 indicate lowest MPPM ranked funds and highest MPPM ranked funds have prediction power to the future stock returns. We find highest MPPM ranked funds positively predicts the stock return of the subsequent 13

quarter and lowest MPPM ranked funds negatively predicts the stock return of the subsequent quarter. This findings confirm that managers with the highest ranking have better stock picking skills and may be more informed. Managerial skills could predict future stock returns. [Insert Table 5 here] 3.6 Quarterly buying and selling portfolio returns of MPPM ranked funds As investors, what can they learn from the MPPM ranked managers as the quarterly holding are public disclosed? We turn to analyze the holding (change) from managers reported quarterly holding. We would compare stocks bought and sold by different MPPM ranked fund managers. Our results in Table 6 indicate in general stocks bought by managers will outgain stocks sold by the managers in the subsequent quarter. However, Stocks bought by highest ranked managers outperform stocks sold by those managers at 0.63% in the following quarter. This finding shows that highest ranked managers are more informed. If investors take long position on the stocks purchased by the highest ranked managers and short on the stocks sold by those managers could earn trading profits prior to their transaction costs. For robustness, we will use Carhart (1997) four-factor models to re-examine the copycat portfolios. If we follow the highest ranked managers to buy from their reported quarterly stocks they purchased and sell those stocks that managers sold, could we earn risk-adjusted returns for the subsequent quarter? Our result shows that the copycat portfolios would earn additional 22 basis point per quarter if we follow the disclosed portfolio holding on a quarterly basis. [Insert Table 6 and Table 7 here] 4. Conclusion With the controversial findings on managerial skills in the asset management industry and the battle between active management and passive (index) strategies, we call to revisit the issues with a better and efficient performance measure that can t be manipulated to re-examine 14

the portfolio managers on their stock picking and market timing skills. After comparing seven other common performance measures and finalizing the performance measure that can t be manipulated with respect to the underlying distribution, time variation, nor estimation error, (the manipulation-proof performance measure (MPPM, Goetzmann et al. (2007)), we generate new insights to the literature. By analyzing both fund returns and their quarterly holdings, we find a subgroup of fund managers consistently generate higher fee-adjusted fund returns. They demonstrate better stock picking and market timing skills when they are compared with the rest of the fund managers. Their holdings contain valuable information. Those top ranked managers hold stocks with higher information asymmetry and positively predict the subsequent quarterly holding returns. They earn risk-adjusted returns on their holding when they are compared to their lower ranked counterparts. The findings are useful for the regulators and investors. With regulators focus on higher disclosure, investors can avoid selecting fund managers who achieve superior performance through engaging derivative contracts and leverage with the MPPM ranked funds and their quarterly disclosed portfolios. Even though it is encouraging to know a subgroup of fund managers are better informed, it is also important to know those managers charge higher expenses and trade more often so the returns are not large enough to offset their expenses. This paper also calls for new compensation mechanisms to reward managers who have informed information. The current managerial compensation on mutual funds reward fund size thus induce managers to manipulate their fund returns to attract additional fund flows. Through our analysis on the MPPM, one can distinguish skillful managers from their manipulated counterparts thus would reduce the agency conflicts in the asset management industry. References Amihud, Y., 2002, Illiquidity and stock returns: cross-section and time-series effects, Journal of Financial Markets 5, 31-56. 15

Bali, T. G., S. J. Brown, and M. O. Caglayan, 2011, Do hedge funds exposures to risk factors predict their future returns? Journal of Financial Economics 101, 36-68. Bhattacharya, U., A. Hackethal, S. Kaesler, B. Loos, and S. Meyer, 2012, Is unbiased financial advice to retail investors sufficient? Answers from a large field study. Review of Financial Studies. 25, 975-1032. Bollen, N. P., and Busse, J. A., 2004, Short-term persistence in mutual fund performance, The Review of Financial Studies. 18, 569-597. Bollen, N. P., and Pool, V. K., 2008, Conditional return smoothing in the hedge, Journal of Financial and Quantitative Analysis. 43, 267-298. Brown, S., Gallagher, D., Steenbeek, O., and Swan, P., 2005, Double or Nothing Patterns of Equity Fund Holdings and Transactions, Working Paper, New York University. Brown, S., and W. Goetzmann, 1995, Performance persistence. Journal of Finance 50, 679-698. Bullard,. M., 2006, The mutual fund as a firm: Frequent trading, fund arbitrage, and the SEC's response to the mutual fund scandal, Houston Law Review 42, 1271-1330. Cao. C, 2010, Derivatives do affect mutual fund returns: Evidence from the financial crisis of 1998, Journal of Futures Market 31, 629 658. Carhart, M., 1997, On persistence in mutual fund performance. Journal of Finance 52, 57-82. Chung Y., and T., Kim, 2014, Law of large numbers in mutual funds: A simple but effective way to identify persistent performances among actively-managed mutual funds, Working paper, University of California, Riverside. Corwin, S. and P. Schultz, 2012, A simple way to estimate bid-ask spreads from daily high and low prices, Journal of Finance 67, 719-760. Cremers, M. and A. Petajisto, 2009, How active is your fund manager? A new measure that predicts performance, Review of Financial Studies 22, 3329-3365. Daniel, K., M., Grinblatt, S. Titman, and R.Wermers, 1997, Measuring mutual fund performance with characteristic-based benchmarks, Journal of Finance 52, 1035-1058. Elton, E., M. Gruber and C. Blake, 2012, Does mutual fund size matter? The relationship between size and performance, Review of Asset Pricing Studies 2, 31-55. Falkenstein, E., 1996, Preferences for stock characteristics as revealed by mutual fund portfolio holdings, Journal of Finance 51, 111-135. 16

Fama, E. F. and K. R. French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, 3-56. Fama, E. F. and K. R. French, 2010, Luck versus skill in the cross-section of mutual fund returns, Journal of Finance 65, 1915-1947. Fama, E. and J. Macbeth, 1973, Risk, return, and equilibrium: empirical tests, Journal of Political Economy 81, 607-636. Ferson, W., and A. Siegel. 2001, The Efficient Use of Conditioning Information in Portfolios, Journal of Finance 3, 967 82. French, K., 2008, The cost of active investing, Journal of Finance 63, 1537 1573. Goetzmann, W., J. Ingersoll, M. Spiegel, and I. Welch, 2007, Portfolio performance manipulation and manipulation-proof performance measures, Review of Financial Studies 20, 1503-1546. Gompers, P and A. Metrick, 2001, Institutional investors and equity prices, Quarterly Journal of Economics 116, 229-259. Henriksson., R., 1984, Market timing and mutual fund performance: An empirical investigation, The Journal of Business 57, 73-96. Huang, J., C. Sialm, and H. Zhang, 2011, Risk shifting and mutual fund performance, Review of Financial Studies 24, 2575-2616. Jensen, M. 1969, Risk, the pricing of capital assets, and the evaluation of investment portfolios. Journal of Business 42, 167 247. Kacperczyk, M., S. Van Nieuwerburgh, and L. Veldkamp, 2014, Time-varying fund manager skill, Journal of Finance forthcoming. Lynch-Koski, J. and Pontiff, J., 1999, How are derivatives used? Evidence from the mutual fund industry, Journal of finance 54, 791 816. Lhabitant, Francois-Serge. 2000, Derivatives in Portfolio Management: Why Beating the Market is Easy, Derivatives Quarterly 6, 39 45. Newey, W. and K. West, 1987, A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix, Econometrica 55, 703-708. 17

Novy-Marx, R., 2013, The other side of value: The gross profitability premium, Journal of Financial Economics 108, 1-28. Petajisto, A., 2013, Active share and mutual fund performance, Yale University, working paper. Porter, G., and J., Trifts, 1998, Performance persistence of experienced mutual fund managers, Financial Service Review 7, 57-68. Richard, B., 2001, How to Game our Sharpe Ratio, Journal of Alternative Investments. 4, 38-46. Titman, S., Tiu, C.. 2011, Do the Best Hedge Funds Hedge? Review of Financial Studies. 24, 123-168. Treynor, J., and K. Mazuy. 1966, Can mutual funds outguess the market? Harvard Business Review 44, 131 6. Wermers, R., 2000, Mutual fund performance: an empirical decomposition into stock-picking talent, style, transaction costs, and expenses, Journal of Finance 55, 1655-1703. Weisman, A.,2002, Informationless investing and hedge fund performance measurement bias, Journal of Portfolio Management 28, 80-91. Yan, X. and Z. Zhang, 2009, Institutional investors and equity returns: are short-term institutions better informed? Review of Financial Studies 22, 893-924. 18

Table 1 Summary Statistics for Domestic Equity Mutual Funds This table reports mean, median and standard deviation on fund characteristics of all domestic mutual funds in our sample. Data is from CRSP Survivorship Bias Free Mutual Fund Database. The sample period is from January 1980 to December 2012. Fund characteristic variables include total net assets (TNA) which measures total net assets under management (in million dollars). Age (in years) is measured since fund s inception. Expenses (in percentage) is the expense ratio. Common stock proportion (in percentage) is the holding that is composed in common stocks. Portfolio turnover (in percentage) is calculated by taking either the total amount of securities purchased or the total amount of securities sold over a year divided by the total net assets of the fund. Fund returns (in percentage) are quarterly returns calculated from CRSP monthly fund returns. MPPM (in percentage) is calculated from the past 12-month fund returns. Panel A reports fund characteristics for all sample funds and split the sample to sub-period from 1980-1989, 1990-1999, and 2000-2012. In Panel B, we rank all domestic equity mutual funds by manipulation-proof performance measure (MPPM (Goetzmann et al. (2007)). We rank each fund by its MPPM from its past 12 months returns to sort them into quintile at the end of each quarter. Rank 4 indicates for best quintile and Rank 0 for the bottom quintile. Panel A Fund characteristics in sample period and sub-periods Overall sample 1980-1989 1990-1999 2000-2012 Variables Mean Std. Dev. Median Mean Std. Dev. Median Mean Std. Dev. Median Mean Std. Dev. Median TNA (Millions) 1066.23 4825.01 159.00 236.77 555.81 71.91 835.62 3216.92 141.38 1243.90 5505.39 184.90 Age (Years) 12.55 12.92 8.00 17.84 15.36 15.00 12.20 14.15 7.00 12.04 11.99 9.00 Expense Ratio (%) 1.26 1.27 1.20 1.23 0.67 1.10 1.32 0.95 1.22 1.25 1.41 1.20 Common Stock Proportion (%) 87.72 22.66 95.30 58.59 40.85 80.00 82.21 26.38 92.00 93.65 10.91 96.62 Fund Returns (%) 2.22 10.81 2.99 3.94 10.00 4.26 4.56 10.25 4.14 1.22 10.94 2.31 MPPM (%) -0.20 25.15 4.67 1.87 21.17 5.07 7.08 16.72 8.34-2.94 27.37 3.04 Number of Funds 5,243 723 2,603 4,582 19

Table 1 (Cont.) Summary Statistics for Domestic Equity Mutual Funds This table reports mean, median and standard deviation on fund characteristics of all domestic mutual funds in our sample. Data is from CRSP Survivorship Bias Free Mutual Fund Database. The sample period is from January 1980 to December 2012. Fund characteristic variables include total net assets (TNA) which measures total net assets under management (in millions of dollars). Age (in years) is measured since fund s inception. Expenses (in percentage) is the expense ratio. Common stock proportion (in percentage) is the holding that is composed in common stocks. Portfolio turnover (in percentage) is calculated by taking either the total amount of securities purchased or the total amount of securities sold over a year divided by the total net assets of the fund. Fund returns (in percentage) are quarterly returns calculated from CRSP monthly fund returns. MPPM (in percentage) is calculated from the past 12-month fund returns. Panel A reports fund characteristics for all sample funds and split the sample to sub-period from 1980-1989, 1990-1999, and 2000-2012. In Panel B, we rank all domestic equity mutual funds by manipulationproof performance measure (MPPM (Goetzmann et al. (2007)). We rank each fund by its MPPM from its past 12 months returns to sort them into quintile at the end of each quarter. Rank 4 indicates for best quintile and Rank 0 for the bottom quintile. Panel B Fund characteristics in different MPPM rank groups Rank 0 (Lowest MPPM) Rank 1 Rank 2 Rank 3 Rank 4 (Highest MPPM) Variables Mean Std. Dev. Median Mean Std. Dev. Median Mean Std. Dev. Median Mean Std. Dev. Median Mean Std. Dev. Median TNA (Millions) 627.02 2608.59 99.10 1044.47 4551.19 161.31 1360.65 6070.20 190.50 1247.14 5532.61 192.70 1050.97 4591.61 171.30 Age (Years) 11.80 11.92 8.00 13.06 13.35 9.00 13.30 13.79 9.00 13.09 13.53 9.00 11.49 11.76 8.00 Expense Ratio (%) 1.55 2.54 1.34 1.22 0.65 1.18 1.13 0.59 1.10 1.16 0.59 1.13 1.26 0.60 1.23 Common Stock 86.12 25.88 95.58 88.07 22.39 95.55 88.41 21.39 95.38 88.62 20.94 95.30 87.38 22.27 94.80 Proportion (%) Turnover (%) 113.43 126.75 76.00 84.81 88.63 62.00 75.57 82.39 55.00 79.80 87.24 58.00 92.90 104.48 63.00 Fund Returns (%) -0.88 12.86 0.65 1.10 10.14 2.04 2.05 9.40 2.85 3.20 9.38 3.88 5.66 10.77 5.67 MPPM (%) -16.80 32.40-6.86-4.32 21.35 1.11 0.53 19.50 4.66 5.27 18.66 8.11 14.31 19.89 15.28 20

Table 2 Subsequent quarterly fund performance and portfolio holding returns based on prior MPPM rankings of the domestic equity mutual funds This table reports subsequent quarterly fund performance and portfolio holding returns of the sample funds. We rank each fund by its MPPM from its past 12 months returns to sort them into quintile at the end of each quarter. Rank 4 indicates for best quintile and Rank 0 for the bottom quintile. Mutual fund data is from CRSP Survivorship Bias Free Mutual Fund Database and stocks returns are from CRSP U.S. Stock Database. The sample period is from January 1980 to September 2012. Quarterly fund returns are calculated from CRSP monthly fund returns. Quarterly holding returns are on the basis of most recent each fund's quarter end holdings and the quarter end value-weight of each stock in a fund is applied for the next quarter. Equally-weighted returns for each quintile MPPM group is the average fund returns in each group. Value-weighted return for each quintile MPPM group is based on the funds' holding value in most recent quarter end. Market timing and stock picking variables are from Kacperczyk, Van Nieuwerburgh, and Veldkamp (2014). Average churn rate (per annual) is from Yan and Zhang (2009). Average number of funds in each group is the average quarterly number of funds during our sample period. T-statistics are reported under each measuring variable. Prior fund ranking Subsequent performance Rank 0 (lowest MPPM) Rank 1 Rank 2 Rank 3 Rank 4 (Highest MPPM) Equally-weighted fund returns 2.42% 2.75% 2.91% 3.18% 3.59% t-stat 2.73 3.53 3.85 4.13 4.17 Equally-weighted portfolio holding returns 3.11% 3.18% 3.33% 3.61% 3.99% t-stat 3.15 3.76 4.08 4.30 4.17 Value-weighted fund returns 2.41% 2.74% 2.85% 3.07% 3.25% t-stat 2.76 3.56 3.85 4.02 3.87 Value-weighted holding returns 2.85% 3.11% 3.20% 3.45% 3.65% t-stat 3.06 3.80 4.01 4.23 3.97 Value-weighted timing 0.0281 0.0269 0.0247 0.0265 0.0294 t-stat 3.27 3.92 4.16 4.59 4.45 Value-weighted stock picking -0.0242-0.0107-0.0029 0.0052 0.0194 t-stat -8.94-7.08-2.92 4.30 8.07 Value-weighted average churn rate 4.93% 4.60% 4.21% 4.47% 4.92% t-stat 52.14 47.26 42.32 43.02 38.03 Average number of funds per quarter 295 303 303 304 291 t-stat 15.58 16.11 16.19 16.30 16.07 21

Table 3 Stock holding characteristics based on MPPM rankings of the domestic equity mutual funds This table reports current value-weighted stock holding characteristics for the sample funds. We rank each fund by its MPPM from its past 12 months returns to sort them into quintile at the end of each quarter. Rank 4 indicates for best quintile and Rank 0 for the bottom quintile. Mutual fund data is from CRSP Survivorship Bias Free Mutual Fund Database and stock characteristics are from CRSP U.S. Stock Database. The sample period is from January 1980 to December 2012. Size (in millions) is the quarterly-end market capitalization of the stock. Book to market ratio is the most recent fiscal quarterly-end book equity value to divide by fiscal quarterly-end market equity value. 8 Past year performance is stock's cumulative past 12-month returns. Operating profits to total asset is taking operating profits (revenue minus cost of goods sold) to divide by total assets. Monthly turnover is taking average trading volume to divide by shares outstanding in most recent three months. Amihud (2002) illiquidity ratio of stock is taking the average daily absolute return to divide by dollar trading volume in millions in the current quarter. Daily (high-low) spread is the closed form solution defined in Corwin and Schultz (2012). Age is number of months since recorded in CRSP Database. Dividend yield is the cumulative cash dividends of the recent 12 month to divide by stock price in the quarter end. Monthly volatility is the monthly standard deviation of recent 24 months stock returns. Rank 0 (lowest MPPM) Rank 1 Rank 2 Rank 3 Rank 4 (Highest MPPM) Size (in millions) 20,555 26,855 26,876 25,022 19,324 Book to market ratio 0.387 0.388 0.385 0.383 0.371 Past year performance (month t-11 to t) 18.19% 23.01% 26.31% 31.37% 45.83% Operating profits to total asset 6.47% 6.60% 6.50% 6.51% 6.49% Monthly turnover (month t-2 to t) 14.73% 12.87% 12.69% 12.91% 14.51% Amihud (2012) illiquidity ratio 0.132 0.051 0.043 0.080 0.077 Daily spread 0.77% 0.68% 0.66% 0.67% 0.72% Age (in months) 296 344 351 344 298 Dividend yield (month t-11 to t) 1.81% 2.09% 2.15% 2.11% 1.87% Monthly volatility (month t-23 to t) 10.28% 9.28% 9.16% 9.26% 9.88% 8 Book equity is equal to book value of shareholders equity, plus balance sheet deferred taxes and investment tax credits, minus book value of preferred stock. 22

Table 4 Monthly four-factor model analysis of portfolio holdings of sample funds based on their MPPM rankings This table reports subsequent monthly value-weighted alphas and betas from the portfolio holdings of sample funds by using Carhart (1997) fourfactor model. We rank each fund by its MPPM from its past 12 months returns to sort them into quintile at the end of each quarter. Rank 4 indicates for best quintile and Rank 0 for the bottom quintile. Value weights of each stock of a fund in the subsequent quarter is based on the value weights of each stock of a fund at the end of current quarter. Mutual fund data is from CRSP Survivorship Bias Free Mutual Fund Database and stock characteristics are from CRSP U.S. Stock Database. The sample period is from April 1980 to December 2012. Alpha is the intercept of the four-factor model. Mktrf_beta is the factor loading on the market risk premium. SMB_beta is the factor loading on the size factor. HML_beta is the factor loading on the value factor. UMD_beta is the factor loading on the momentum factor. T-statistics are reported under each number. Rank 0 (lowest MPPM) Rank 1 Rank 2 Rank 3 Rank 4 (Highest MPPM) Alpha 0.0004 0.0003-0.0000-0.0002-0.0011 t-stat 0.47 0.61-0.04-0.48-1.56 Mktrf_beta 1.0745 1.0521 1.0566 1.0797 1.1088 t-stat 53.54 89.6 126.34 111.18 70.07 SMB_beta 0.0553-0.0155-0.0251 0.0637 0.2606 t-stat 1.94-0.93-2.12 4.62 11.59 HML_beta -0.0832 0.0114 0.0209 0.0324-0.0243 t-stat -2.74 0.64 1.65 2.21-1.01 UMD_beta -0.2924-0.1157-0.0172 0.0952 0.2719 t-stat -15.79-10.68-2.23 10.62 18.62 23

Table 5 Regressions analysis of subsequent quarterly stock returns among different MPPM ranked sample funds This table reports Fama and Macbeth (1973) regression results for subsequent quarterly stock returns on being held among highest and lowest MPPM (Goetzmann et al. (2007)) ranked funds. Mutual fund data is from CRSP Survivorship Bias Free Mutual Fund Database and stock characteristics are from CRSP U.S. Stock Database. The sample period is from April 1980 to September 2012. Total fund holding of a stock is shares held by the domestic equity mutual funds in our sample divided by shares outstanding of the stock. Stocks held by highest (lowest) MPPM ranked funds is shares held by the funds in the highest (lowest) MPPM ranked group divided by shares outstanding of the stock. Ln (Size) is the logarithm of a stock's market capitalization in millions. Ln (B/M) is the logarithm of a stock's book to market ratio. Past year performance is the cumulative stock returns of a stock from past 12 months. Ln (Operating profits to total asset) is the logarithm of a stock's operating profits (revenue minus costs of goods sold) divided by total assets. Ln (Monthly turnover) is the logarithm of a stock's recent three months' monthly turnover. Ln (Age) is the logarithm of a stock's number of months since recorded in the CRSP Database. Ln (Dividend yield + 1) is the logarithm of one plus a stock's past 12-month cash dividends divided by quarter end stock price. Ln (Monthly volatility) is the logarithm of the monthly standard deviation of recent 24 months of stock returns. Newey and West (1987) corrected t-statistics are reported under each number. Model 1 Model 2 Model 3 Intercept 0.0762 0.0757 0.0772 t-stat 2.53 2.51 2.56 Total fund holding -0.0101-0.0262 0.0163 t-stat -0.64-1.59 0.85 Stocks held by highest MPPM ranked funds 0.0831 t-stat 2.51 Stocks held by lowest MPPM ranked funds -0.0675 t-stat -1.95 Ln (Size) -0.0033-0.0034-0.0035 t-stat -2.66-2.67-2.68 Ln (B/M) 0.0102 0.0103 0.0102 t-stat 4.09 4.24 4.17 Past year performance 0.0137 0.0136 0.0135 t-stat 2.22 2.23 2.19 Ln (Operating Profits to Total Asset) 0.0109 0.0109 0.0108 t-stat 9.73 9.78 9.69 Ln (Monthly Turnover) 0.0001 0.0000 0.0001 t-stat 0.03 0.00 0.02 Table 5 continued Ln (Age) 0.0008 0.0009 0.0007 t-stat 0.84 0.91 0.78 Ln (Dividend Yield + 1) 0.0578 0.0605 0.0611 t-stat 1.45 1.52 1.54 Ln (Monthly Volatility) -0.0038-0.0039-0.0036 t-stat -0.55-0.58-0.53 24