Performance Chasing,Fund Flows and Fund Size in Real Estate Mutual Funds

Real estate mutual funds have grown dramatically in number, size, scope and assets under management over the last 15 years, but little assessment is evident. The present study addresses this limitation. Better prior period performance is associated with greater shares of fund inflows for a period. Returns, however, are negatively associated with increased fund flows and fund size. Investors chase past performance limiting fund managers’ ability to optimize investments. Under normal market conditions, but departing from typical mutual fund performance, real estate mutual fund returns generally exceed relevant benchmarks on a before expenses basis and match benchmark returns after expenses. The ability to meet and exceed benchmark returns, however, does not hold during the financial crisis period. Overall, more established funds are shown to have higher returns while fund turnover is not a determinant of returns.     

Why not use SDF rather than beta models in performance measurement?

This paper analyzes performance measurement based on stochastic discount factors, compared to beta models traditionally used in computing funds’ (Jensen) alphas. From a theoretical point of view, standard alphas suffer from several limitations. Our paper addresses this issue from an empirical point of view using a sample of Swiss mutual funds from 2000 to 2011. Our results suggest that the key for a “fair” comparison between stochastic discount function (SDF) and beta models is the specification of the set of primitive assets used to calibrate the SDF function. Once this is established, the size of (absolute) performance differences considerably decreases between the two model families. However, there are sizeable performance deviations in the cross-section of funds if conditioning information is incorporated in the tests, up to some 20 basis points per month, or about 2.3 % per year. In almost all cases, the SDF-alphas are lower than the standard (Jensen) alphas. In absolute terms, the average SDF-based underperformance of the funds is way larger than the average total expense ratio (TER) of the funds, both in a conditional and unconditional setting.     

Capacity effects and winner fund performance: the relevance and interactions of fund size and family characteristics

This study analyzes the existence of capacity effects and performance persistence for US equity mutual funds for the period from 1992 to 2007. We focus on winner funds and distinguish between capacity effects from both size and inflows and explore their interactions with two measures of family size, i.e. family total net assets under management (family TNA) and the number of funds at the family level (family breadth). The differentiation of family size allows us to analyze competing effects at the family level such as economies of scale as well as organizational complexity costs and conflicts of interest. Our empirical results confirm diseconomies of scale at the winner fund level and indicate that only small winner funds with low inflows significantly outperform the four-factor benchmark on a net return basis. There are no universal benefits from economies of scale at the family level, but our findings suggest the existence of conflicts of interest in families offering a relatively large number of funds. Small winner funds in families offering a small number of funds significantly outperform while economies of scale only materialize among extremely small winner funds. We provide detailed robustness checks for our empirical results. Overall, simply conditioning on fund size is not sufficient for selecting future outperforming funds. The results indicate that fund investors may earn positive abnormal returns when combining information on fund size with information on fund flows or fund family affiliations in their asset allocation decisions.     

European Mutual Fund Performance

This paper presents an overview of the European mutual fund industry and investigates mutual fund performance using a survivorship bias controlled sample of 506 funds from the five most important mutual fund countries. The latter is done using the Carhart (1997) 4-factor asset-pricing model. In addition we investigate whether European fund managers exhibit 'hot hands', persistence in performance. Finally the influence of fund characteristics on risk-adjusted performance is considered. Our overall results suggest that European mutual funds, and especially small cap funds are able to add value, as indicated by their positive after cost alphas. If we add back management fees, four out of five countries exhibit significant out-performance at an aggregate level. Finally, we detect strong persistence in mean returns for funds investing in the UK. Our results deviate from most US studies that argue mutual funds under-perform the market by the amount of expenses they charge.     

UK equity mutual fund alphas make a comeback

In this study, we re-visit the performance of 887 active UK equity mutual funds using a new approach proposed by Angelidis, Giamouridis, and Tessaromatis. The authors argue that mutual funds stock selection is driven by the benchmark index, so if the benchmark generates alpha, there will be a bias in interpretation of manager's stock-picking ability. In their model, the alpha of a fund is adjusted by the benchmark's alpha. By applying this method, we eliminate bias inflicted by the persistently negative alphas of FTSE 100 Index in the period 1992–2013. We find that adjusted Fama–French and Carhat alphas of UK equity mutual funds are higher than those implied by the standard three- and four-factor models and are overall positive, contrary to most of the existing literature on UK fund performance. This result is consistent across funds' investment styles and robust to the use of FTSE Small Cap as benchmark for a sub-sample of small cap funds.     

Informed trading around earnings and mutual fund alphas

We examine whether informed trading around earnings announcements drives mutual fund performance. The measure is motivated by prior studies arguing that a mutual fund is skilled if it buys stocks with subsequent high earnings announcement returns. We find that this measure predicts future mutual fund returns. On average, after adjusting for Carhart’s four risk factors, the top decile of mutual funds outperforms the bottom decile by 44 basis points per quarter. By decomposing fund alphas into two components in their relations to earnings, we find that this measure is only associated with earnings-related fund alphas. This measure can also be used to predict stock returns at future earnings announcements.     

Model uncertainty,performance persistence and flows

Model uncertainty makes it difficult to draw clear inference about mutual fund performance persistence. I propose a new performance measure, Bayesian model averaged (BMA) alpha, which explicitly accounts for model uncertainty. Using BMA alphas, I find evidence of performance persistence in a large sample of US funds. There is a positive and asymmetric relation between flows and past BMA alphas, suggesting that fund investors respond to the information in BMA alphas. My findings are robust to various sensitivity analyses, including alternative measures of post-ranking performance, flows and total net assets, and alternative econometric model specifications.     

Investor learning and mutual fund family

In this paper we revisit the cross-fund learning method suggested by Jones and Shanken (2005) and construct a linear hierarchical model to consider the learning across funds within the fund family during the performance evaluation. We provide a full Bayesian treatment on all the factors of the pricing model and allow both the fund family and the individual manager to have dependent prior information regarding funds' alphas. The simulation results suggest that returns from peer funds within the family significantly affect investors' updating on fund alphas since the posterior distribution on fund alphas experiences a faster shrinkage than those reported in the previous literature. The model can also be simulated with specific prior belief on different factors of the pricing model, i.e. fund alphas, betas and factor loadings of each pricing benchmark, to better address the learning issue.     

Bayesian Alphas and Mutual Fund Persistence

We use daily returns to compare the performance predictability of Bayesian estimates of mutual fund performance with standard frequentist measures. When the returns on passive nonbenchmark assets are correlated with fund holdings, incorporating histories of these returns produces a performance measure that predicts future performance better than standard measures do. Bayesian alphas based on the Capital Asset Pricing Model (CAPM) are particularly useful for predicting future standard CAPM alphas. Over our sample period, priors consistent with moderate to diffuse beliefs in managerial skill dominate more skeptical prior beliefs, a result that is consistent with investor cash flows.     

Measuring performance in a dynamic world: Conditional mean–variance fundamentals

We develop conditional alpha performance measures that are consistent with conditional mean–variance analysis and the magnitude and sign of the implied true conditional time-varying alphas. The sequence of conditional alphas and betas is estimable from surprisingly simple unconditional regressions. Other common performance measures are derivable from the conditional investment opportunity set based on its conditional asset return moments. Our bootstrap analysis of Morningstar mutual fund returns data demonstrates that the differences between existing conditional alpha measures and our proposed alpha are substantive for typical parameterizations.     

Performance aspects of Greek bond mutual funds

This paper examines the performance characteristics of Greek bond funds and the impact of fund flows on portfolio returns. The evidence shows that on average bond funds do not offer risk-adjusted profits exceeding the returns of the benchmark index, which is in consistence with the US and international evidence. Returns before fees are slightly superior to the returns of the benchmark index, but when fees are considered they lag considerably. The security selection and market timing skills of fund managers are also tested using both an unconditional and a conditional model to test for the impact of public information variables. We also find that fund flows impact negatively on market timing.     

Who incentivizes the mutual fund manager,new or old shareholders?

This study tests whether mutual fund shareholders continue to trade in response to fund returns after they make their initial investment in fund shares. It decomposes the relationship between fund returns and shareholder flow in a large, proprietary panel of all shareholder transactions in one midsize no-load mutual fund family. Results show that both new and old shareholders buy shares during periods of good returns; however, shareholder outflow is essentially unrelated to fund returns. This lack of a return-sell relationship is not driven by locked-in pension assets, shareholders’ ignorance of ongoing fund returns, or embedded capital gains. However, there is evidence that exchanges between equity funds in the family are related more strongly to returns of the destination fund than to returns of the origination fund. This may indicate that flow between equity mutual funds is driven by shareholders buying new funds rather than selling old funds. Supermarket shareholders are smart insofar as they exchange into funds that subsequently outperform their prior funds during their individual holding periods.     

Mutual fund performance with learning across funds

The average level and cross-sectional variability of fund alphas are estimated from a large sample of mutual funds. This information is incorporated, along with the usual regression estimate of alpha, in a (roughly) precision-weighted average measure of individual fund performance. Substantial “learning across funds” is documented, with significant effects on investment decisions. In a Bayesian framework, this form of learning is inconsistent with the assumption, made in the past literature, of prior independence across funds. Independence can be viewed as an extreme scenario in which the true cross-sectional distribution of alphas is presumed to be known a priori.     

Reverse Survivorship Bias

Mutual funds often disappear following poor performance. When this poor performance is partly attributable to negative idiosyncratic shocks, funds' estimated alphas understate their true alphas. This paper estimates a structural model to correct for this bias. Although most funds still have negative alphas, they are not nearly as low as those suggested by the fund‐by‐fund regressions. Approximately 12% of funds have net four‐factor model alphas greater than 2% per year. All studies that run fund‐by‐fund regressions to draw inferences about the prevalence of skill among mutual fund managers are subject to reverse survivorship bias.     

Portfolio performance measurement using APM-free kernel models

A general asset-pricing framework is used to derive a conditional asset-pricing kernel that accounts efficiently for time variation in expected returns and risk, and is suitable to perform (un)conditional evaluations of passive and dynamic investment strategies. The positive abnormal unconditional performance of Canadian equity mutual funds over the period 1989–1999 becomes negative with conditioning, and is robust to the removal of ex post index mimickers. The reversal in the size-based performance results with limited information conditioning is alleviated somewhat with an expansion of the conditioning set. The performance statistics are weakly sensitive to changes in the level of relative risk aversion of the uninformed investor. Unconditional positive performances based on averages of individual fund performances lose their significance when cross-correlations are accounted for using the block-bootstrap method. Estimates of survivorship bias due to the elimination of funds with shorter lives, which range from 36 to 58 basis points per year, are stable across performance models but differ across groupings by fund objective.     

Measuring bond mutual fund performance with portfolio characteristics

This paper studies the performance of U.S. bond mutual funds using measures constructed from a novel data set of portfolio weights. Active fund managers exhibit outperformance before costs and fees generating, on average, gross returns of 1% per annum over the benchmark portfolio constructed using past holdings (approximately the same magnitude as expenses and transaction costs combined). This suggests that fund managers are able to earn back their fees and costs. There is evidence of neutral ability to time different portfolio allocations (sector, credit quality, and portfolio maturity allocations) and only a subgroup of bond funds exhibit successful timing ability. One performance measure based on portfolio holdings predicts future fund performance and provides information not contained in the standard measures. These results provide the first evidence of the value of active management in bond mutual funds.     

Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

This paper analyzes the contribution of hedge funds to optimal asset allocations between 1993 and 2010. The preferences of specific institutional investors are captured by implementing a Bayesian asset allocation framework that incorporates heterogeneous expectations regarding hedge fund alpha. Mean-variance spanning tests are used to infer the ability of hedge funds to significantly enhance the mean-variance efficient frontier. Further, a novel democratic variance decomposition procedure sheds light on the dynamics in the co-movement of hedge fund returns with a set of common benchmark assets. The empirical findings indicate that portfolio benefits of hedge funds are time-varying and strongly depend on investor optimism regarding hedge funds’ ability to generate alpha. In general, allocations to hedge funds improve the global minimum variance portfolio even after controlling for short-selling restrictions and minimum diversification constraints. However, due to dynamics underlying the composition of the aggregate hedge fund universe, the factor structure of hedge fund returns has become more similar to the benchmark assets over time.     

Evaluating managed fund performance using conditional measures: Australian evidence

Most studies of managed fund performance use measures that are susceptible to bias caused by common time variation in risks and risk premia. We evaluate the performance of Australian managed funds 1983–1995 using lagged public information variables that have been shown to predict stock returns, such as interest rates and dividend yields, to control for the variation. The results indicate an improvement in performance relative to traditional measures and confirm the importance of using conditioning information, especially dividend yield, in performance evaluation. Jensen alphas are higher when estimated with the conditional model and the number of significant timing coefficients is greatly reduced.     

Performance and risk exposure of international mutual funds

This study examined whether internationally diversified mutual funds increase a U.S. investor 's risk-adjusted return above that on a domestic benchmark mutualfund. Average returns on about one-half of the international funds exceeded the domestic benchmark fund's return. The risk-adjusted returns on the international mutual funds were not significantly different from that on the domestic benchmark fund. These results differ from earlier studies which generally found superior returns on international mutual funds. The benefits for the U.S. investor of holding an internationally diversified mutual fund appear to be limited for the period studied.     

Efficiency with costly information: a reinterpretation of evidence from managed portfolios

We investigate the informational efficiency of mutual fund performancefor the period 1965-84. Results are shown to be sensitive tothe measurement of performance chosen. We find that returnson S&P stocks, returns on non-S&P stocks, and returnson bonds are significant factors in performance assessment.Once we correct for the impact of non-S&P assets on mutualfund returns, we find that mutual funds do not earn returnsthat justify their information acquisition costs. This is consistentwith results for prior periods.