Cross-Sectional Learning and Short-Run Persistence in Mutual Fund Performance

Using monthly return data of more than 6,400 US equity mutual funds we investigate short-run performance persistence over the period 1984-2003. We sort funds into rank portfolios based on past performance, and evaluate the portfolios' out-of-sample performance. To cope with short ranking periods, we employ an empirical Bayes approach to measure past performance more efficiently. Our main finding is that when funds are sorted into decile portfolios based on 12-month ranking periods, the top decile of funds earns a statistically significant, abnormal return of 0.26 percent per month. This effect persists beyond load fees, and is mainly concentrated in relatively young, small cap/growth funds

On the Use of Multifactor Models to Evaluate Mutual Fund Performance

We show that multifactor performance estimates for mutual funds suffer from systematic biases, and argue that these biases are a result of miscalculating the factor premiums. Because the factor proxies are based on hypothetical stock portfolios and do not incorporate transaction costs, trade impact, and trading restrictions, the factor premiums are either over- or underestimated. We argue that factor proxies based on mutual fund returns rather than stock returns provide better benchmarks to evaluate professional money managers

Spillover Effects of Marketing in Mutual Fund Families

This paper investigates the presence of spillover effects of marketing in mutual fund families. We find that funds with high marketing expenses generate spillovers, and enhance cash inflows to family members with low marketing expenses. In particular, low-marketing funds that are operated by a family with high marketing expenses have substantially larger inflows after positive returns than otherwise similar funds that are operated by a family with low marketing expenses, while they have smaller outflows after negative returns. One way to interpret the spillovers is that they are a by-product of individual fund marketing whereby the entire family is made more visible to investors. An alternative explanation of this observation is that funds with low marketing expenses are directly subsidized by family members with high marketing expenses. We develop and perform a set of tests to evaluate these two alternative hypotheses. The results of all tests support the subsidization hypothesis, and suggest that at least part of the spillovers can be attributed to favoritism. These results suggest that conflicts of interest between investors and fund families have been exacerbated by competition in the mutual fund industry

Variational approximation for mixtures of linear mixed models

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the EM algorithm. The conventional approach to determining a suitable number of components is to compare different mixture models using penalized log-likelihood criteria such as BIC.We propose fitting MLMMs with variational methods which can perform parameter estimation and model selection simultaneously. A variational approximation is described where the variational lower bound and parameter updates are in closed form, allowing fast evaluation. A new variational greedy algorithm is developed for model selection and learning of the mixture components. This approach allows an automatic initialization of the algorithm and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparametrize the model and show empirically that there is a gain in efficiency by variational algorithms similar to that in MCMC algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler which suggests that reparametrizations can lead to improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG

Genetic and physiological dissection of transpiration efficiency in wheat

OBJECTIVE: To determine the incidence and prevalence of facioscapulohumeral muscular dystrophy (FSHD) in the Netherlands. METHODS: Using 3-source capture-recapture methodology, we estimated the total yearly number of newly found symptomatic individuals with FSHD, including those not registered in any of the 3 sources. To this end, symptomatic individuals with FSHD were available from 3 large population-based registries in the Netherlands if diagnosed within a 10-year period (January 1, 2001 to December 31, 2010). Multiplication of the incidence and disease duration delivered the prevalence estimate. RESULTS: On average, 52 people are newly diagnosed with FSHD every year. This results in an incidence rate of 0.3/100,000 person-years in the Netherlands. The prevalence rate was 12/100,000, equivalent to 2,000 affected individuals. CONCLUSIONS: We present population-based incidence and prevalence estimates regarding symptomatic individuals with FSHD, including an estimation of the number of symptomatic individuals not present in any of the 3 used registries. This study shows that the total number of symptomatic persons with FSHD in the population may well be underestimated and a considerable number of affected individuals remain undiagnosed. This suggests that FSHD is one of the most prevalent neuromuscular disorders

Neural networks for increased accuracy of allergenic pollen monitoring

Computer Systems, Imagery and Medi

Pancreatitis of biliary origin, optimal timing of cholecystectomy (PONCHO trial):Study protocol for a randomized controlled trial

Background: After an initial attack of biliary pancreatitis, cholecystectomy minimizes the risk of recurrent biliary pancreatitis and other gallstone-related complications. Guidelines advocate performing cholecystectomy within 2 to 4 weeks after discharge for mild biliary pancreatitis. During this waiting period, the patient is at risk of recurrent biliary events. In current clinical practice, surgeons usually postpone cholecystectomy for 6 weeks due to a perceived risk of a more difficult dissection in the early days following pancreatitis and for logistical reasons. We hypothesize that early laparoscopic cholecystectomy minimizes the risk of recurrent biliary pancreatitis or other complications of gallstone disease in patients with mild biliary pancreatitis without increasing the difficulty of dissection and the surgical complication rate compared with interval laparoscopic cholecystectomy.Methods/Design: PONCHO is a randomized controlled, parallel-group, assessor-blinded, superiority multicenter trial. Patients are randomly allocated to undergo early laparoscopic cholecystectomy, within 72 hours after randomization, or interval laparoscopic cholecystectomy, 25 to 30 days after randomization. During a 30-month period, 266 patients will be enrolled from 18 hospitals of the Dutch Pancreatitis Study Group. The primary endpoint is a composite endpoint of mortality and acute re-admissions for biliary events (that is, recurrent biliary pancreatitis, acute cholecystitis, symptomatic/obstructive choledocholithiasis requiring endoscopic retrograde cholangiopancreaticography including cholangitis (with/without endoscopic sphincterotomy), and uncomplicated biliary colics) occurring within 6 months following randomization. Secondary endpoints include the individual endpoints of the composite endpoint, surgical and other complications, technical difficulty of cholecystectomy and costs.Discussion: The PONCHO trial is designed to show that early laparoscopic cholecystectomy (within 72 hours) reduces the combined endpoint of mortality and re-admissions for biliary events as compared with interval laparoscopic cholecystectomy (between 25 and 30 days) after recovery of a first episode of mild biliary pancreatitis.</p

Local linear regression with adaptive orthogonal fitting for the wind power application

Short-term forecasting of wind generation requires a model of the function for the conversion of me-teorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric ap-proach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. This may lead to an inaccurate estimate of the true power curve, owing to the assumption that a noise component is present on the response variable axis only. Therefore, this assump-tion is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive es-timation method is introduced in order to accommodate nonstationarity. This has the additional benefit of lowering the computational costs of updating local coefficients every time new observations become available. The estimation method is based on tracking the left-most eigenvector of the augmented covari-ance matrix. A robustification of the estimation method is also proposed. Simulations on semi-artificial datasets (for which the true power curve is available) underline the properties of the proposed regression and related estimation methods. An important result is the significantly higher ability of local polynomia