投资者对投资基金业绩反应的实证研究

我国关于投资基金收益对基金净流量的影响的实证研究几乎没有,笔者试图通过我国投资基金收益对基金净流量的影响的实证研究探讨我国投资者对于投资基金业绩的反应.     

开放式基金投资者的处置效应——基于中国面板数据的门限回归分析

在前景理论赎回模型分析的基础上,采用面板数据门限回归方法,重点探讨了开放式基金的业绩对投资者赎回的影响关系.实证结果表明,面板门限模型的解释力强于普通固定效应模型;基金收益对赎回影响始终为正,且收益越高越敏感,进一步证明了中国开放式基金的赎回是一种"处置效应";分红对赎回有促进作用,也存在门限转换特征,增加分红次数可以抑制赎回;市场收益越高,赎回越严重.     

基于周内多重分形波动率的基金投资风格漂移风险测度研究

基金发生投资风格漂移是把双刃剑，在获得短期超额收益时，也隐藏着巨大的风格漂移风险。本文首先以我国79只开放式股票型基金为样本，在量化投资风格漂移的基础上，分析发现其收益序列存在多重分形特征，据此构建周内多重分形波动率测度来刻画投资风格漂移收益的复杂波动特征，并与传统的GARCH族波动率计量模型的测度能力进行比较分析，实证结果发现本文构建的周内多重分形波动率测度更加精确，能更好刻画序列的复杂波动特征；然后，进一步构建MFVW VaR模型对基金投资风格漂移风险进行量化测度，发现该模型比传统的参数与非参数VaR模型能更好地对风格漂移风险进行有效测度，基金普遍存在较大的风格漂移风险；最后，对我国开放式股票型基金的产品创新策略与投资风格漂移监管策略进行了一些有益探讨。     

开放式基金业绩持续性的实证研究

一、引言基金业绩的持续性是指基金的业绩在一段时期内保持连贯性,即前期业绩表现较好的基金在下一期仍然较好,而前期业绩表现较差的基金在下一期仍然较差。利用基金业绩的这一特点,投资者可以根据基金的业绩表现,选择前期表现较好且具有持续性的基金进行投资,以获得超额收益。     

能力VS市场——中国基金业绩实证研究及剖析

文章在对我国基金经理人证券选择能力和时机把握能力进行实证的基础上,分析我国基金业绩的来源,并结合基金业绩的构成解释了在我国基金证券选择能力和时机把握能力都较差的情况下,基金战胜大盘的悖论。     

我国证券投资基金的评估与绩效研究

关于证券投资基金业绩的评估是投资领域中非常重要的一个问题,具有重要的理论与现实意义。但是我国的证券投资基金的业绩评估不是很完善,文章正是在这一背景下,借鉴西方的已有成果,提出并完善了我国证券投资基金业绩的收益、风险指标以及各种综合指标,并结合我国的实际情况进行了实证分析,得出我国的投资基金收益率低于市场、风险低于市场、基金对市场机会的把握能力并不理想的结论。     

基金的证券选择能力和市场时机选择能力

一、理论回顾 基金业绩评价的目的是衡量基金管理人或基金经理的管理能力.传统的特雷诺指数、夏普指数和詹森指数就是在资本资产定价模型(CAPM)下,将基金的收益和风险联系起来,经过风险调整后的投资基金业绩的评价指标.基金评价的另一个方面是对获得高于市场平均业绩的主要投资管理方法的评价,即基金的证券选择和市场时机选择能力.证券选择能力是基金管理人识别价格被低估的证券即构造最优证券组合的能力.时机选择能力是基金管理人正确地预测市场行情发展趋势,调节基金投资组合中股票、债券和现金的比重或调节各行业股票的比重,以获得最大收益的能力.詹森1968年提出了詹森模型对基金的业绩进行评价.其α指标法的模型表达式为:     

中国开放式基金绩效的实证研究

文章通过实证分析得到中国开放式基金的收益、风险调整收益、基金经理人的时机和股票选择能力等方面的指标,并运用无量纲数据标准化处理方法和因子分析法进行综合评价。实证结果表明:中国的大部分开放式基金可以战胜市场,但没有足够证据显示中国的开放式基金经理人具有显著市场选择能力和证券选择能力。     

投资绩效评估指标及应用

文章深入分析了几种投资业绩评估指标的特点,并在中国市场上选取了若干基金,针对不同的投资业绩评估指标进行实证检验,得出相关结论。     

基金业绩分析——基于有向学习网络的研究

本文使用LASSO算法构建了基于基金持股数据的基金间动态学习网络,将基金研究中传统的无向网络扩展为有向网络,并检验了正向学习与反向学习两种不同的学习模式(信息利用方式) 对基金业绩的影响,进而探讨了其背后的经济含义。实证结果表明:当基金作为被学习者(信息被观测方)时,被正向学习会显著提高其业绩,被反向学习会显著降低其业绩;当基金作为主动学习者(信息观测方)时,无论是正向学习还是反向学习均不会对其业绩造成显著影响;对基金学习动机的分析表明,基金参与学习是为了提升相对自己上期的业绩、防止业绩倒退,且反向学习相对更加有效。本文分析了信 息传递方向、信息利用方式对基金业绩的影响,为如何将统计学习方法应用于金融问题的分析提供了一个新的视角。     

A Tobit Ridge Regression Estimator

This article analyzes the effects of multicollienarity on the maximum likelihood (ML) estimator for the Tobit regression model. Furthermore, a ridge regression (RR) estimator is proposed since the mean squared error (MSE) of ML becomes inflated when the regressors are collinear. To investigate the performance of the traditional ML and the RR approaches we use Monte Carlo simulations where the MSE is used as performance criteria. The simulated results indicate that the RR approach should always be preferred to the ML estimation method.     

Joint coverage probability in a simulation study on continuous-time Markov chain parameter estimation

Parameter dependency within data sets in simulation studies is common, especially in models such as continuous-time Markov chains (CTMCs). Additionally, the literature lacks a comprehensive examination of estimation performance for the likelihood-based general multi-state CTMC. Among studies attempting to assess the estimation, none have accounted for dependency among parameter estimates. The purpose of this research is twofold: (1) to develop a multivariate approach for assessing accuracy and precision for simulation studies (2) to add to the literature a comprehensive examination of the estimation of a general 3-state CTMC model. Simulation studies are conducted to analyze longitudinal data with a trinomial outcome using a CTMC with and without covariates. Measures of performance including bias, component-wise coverage probabilities, and joint coverage probabilities are calculated. An application is presented using Alzheimer's disease caregiver stress levels. Comparisons of joint and component-wise parameter estimates yield conflicting inferential results in simulations from models with and without covariates. In conclusion, caution should be taken when conducting simulation studies aiming to assess performance and choice of inference should properly reflect the purpose of the simulation.     

On the robustness properties for maximum likelihood estimators of parameters in exponential power and generalized T distributions*

Abstract

Examining the robustness properties of maximum likelihood (ML) estimators of parameters in exponential power and generalized t distributions has been considered together. The well-known asymptotic properties of ML estimators of location, scale and added skewness parameters in these distributions are studied. The ML estimators for location, scale and scale variant (skewness) parameters are represented as an iterative reweighting algorithm (IRA) to compute the estimates of these parameters simultaneously. The artificial data are generated to examine performance of IRA for ML estimators of parameters simultaneously. We make a comparison between these two distributions to test the fitting performance on real data sets. The goodness of fit test and information criteria approve that robustness and fitting performance should be considered together as a key for modeling issue to have the best information from real data sets.     

A note on the distribution of mean square error of forecasting

Simulation has been a very important and widely used method in the study of misspecification or order determination in time series analysis. Mean square error of forecasting (MSEF) has been a major criterion for comparing the performance of different models. In simulation studies, standard deviations of MSEF's are calculated from the computed values of the MSEF's, In this note, the distribution of MSEF from simulation studies is established. Exact variance of the MSEF can be obtained from the prespecified values of the model selected for simulation. This variance should be a more appropriate criterion for evaluating the performance between models.     

Bi-level variable selection via adaptive sparse group Lasso

Penalization has been extensively adopted for variable selection in regression. In some applications, covariates have natural grouping structures, where those in the same group have correlated measurements or related functions. Under such settings, variable selection should be conducted at both the group-level and within-group-level, that is, a bi-level selection. In this study, we propose the adaptive sparse group Lasso (adSGL) method, which combines the adaptive Lasso and adaptive group Lasso (GL) to achieve bi-level selection. It can be viewed as an improved version of sparse group Lasso (SGL) and uses data-dependent weights to improve selection performance. For computation, a block coordinate descent algorithm is adopted. Simulation shows that adSGL has satisfactory performance in identifying both individual variables and groups and lower false discovery rate and mean square error than SGL and GL. We apply the proposed method to the analysis of a household healthcare expenditure data set.     

Percentile-based Empirical Distribution Function Estimates for Performance Evaluation of Healthcare Providers

Hierarchical models are widely-used to characterize the performance of individual healthcare providers. However, little attention has been devoted to system-wide performance evaluations, the goals of which include identifying extreme (e.g., top 10%) provider performance and developing statistical benchmarks to define high-quality care. Obtaining optimal estimates of these quantities requires estimating the empirical distribution function (EDF) of provider-specific parameters that generate the dataset under consideration. However, the difficulty of obtaining uncertainty bounds for a square-error loss minimizing EDF estimate has hindered its use in system-wide performance evaluations. We therefore develop and study a percentile-based EDF estimate for univariate provider-specific parameters. We compute order statistics of samples drawn from the posterior distribution of provider-specific parameters to obtain relevant uncertainty assessments of an EDF estimate and its features, such as thresholds and percentiles. We apply our method to data from the Medicare End Stage Renal Disease (ESRD) Program, a health insurance program for people with irreversible kidney failure. We highlight the risk of misclassifying providers as exceptionally good or poor performers when uncertainty in statistical benchmark estimates is ignored. Given the high stakes of performance evaluations, statistical benchmarks should be accompanied by precision estimates.     

A Simulation Study of Some Biasing Parameters for the Ridge Type Estimation of Poisson Regression

This article proposes several estimators for estimating the ridge parameter k based on Poisson ridge regression (RR) model. These estimators have been evaluated by means of Monte Carlo simulations. As performance criteria, we have calculated the mean squared error (MSE), the mean value, and the standard deviation of k. The first criterion is commonly used, while the other two have never been used when analyzing Poisson RR. However, these performance criteria are very informative because, if several estimators have an equal estimated MSE, then those with low average value and standard deviation of k should be preferred. Based on the simulated results, we may recommend some biasing parameters that may be useful for the practitioners in the field of health, social, and physical sciences.     

Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout

This paper compares the performance of weighted generalized estimating equations (WGEEs), multiple imputation based on generalized estimating equations (MI-GEEs) and generalized linear mixed models (GLMMs) for analyzing incomplete longitudinal binary data when the underlying study is subject to dropout. The paper aims to explore the performance of the above methods in terms of handling dropouts that are missing at random (MAR). The methods are compared on simulated data. The longitudinal binary data are generated from a logistic regression model, under different sample sizes. The incomplete data are created for three different dropout rates. The methods are evaluated in terms of bias, precision and mean square error in case where data are subject to MAR dropout. In conclusion, across the simulations performed, the MI-GEE method performed better in both small and large sample sizes. Evidently, this should not be seen as formal and definitive proof, but adds to the body of knowledge about the methods’ relative performance. In addition, the methods are compared using data from a randomized clinical trial.     

INFERENCE ABOUT THE MEAN OF A POISSON DISTRIBUTION IN THE PRESENCE OF A NUISANCE PARAMETER

In this note we examine the problem of estimating the mean of a Poisson distribution when a nuisance parameter is present. Using a condition of Cox (1958) about ancillarity in the presence of a nuisance parameter, we justify that inference about the parameter should be carried out using the conditional distribution given the appropriate ancillary statistics. A small simulation study has been done to compare the performance of the conditional likelihood approach and the standard likelihood approach.     

A Jackknifed estimators for the negative binomial regression model

Shrinkage estimator is a commonly applied solution to the general problem caused by multicollinearity. Recently, the ridge regression (RR) estimators for estimating the ridge parameter k in the negative binomial (NB) regression have been proposed. The Jackknifed estimators are obtained to remedy the multicollinearity and reduce the bias. A simulation study is provided to evaluate the performance of estimators. Both mean squared error (MSE) and the percentage relative error (PRE) are considered as the performance criteria. The simulated result indicated that some of proposed Jackknifed estimators should be preferred to the ML method and ridge estimators to reduce MSE and bias.