投资基金业绩评价的DEA方法

对近几年来我国的证券投资基金业绩评价理论进行了系统回顾和总结.在分析了传统评价方法不足的基础上,对比以往文献样本容量更大和样本区间长度更长的样本,选取适当的投入指标和产出指标,运用数据包络分析方法(即DEA评价方法)对2001年1月至2005年6月共222周的33只基金的业绩进行了实证分析,以期对我国的投资基金业绩做出正确和客观的评价.     

扫描统计量--检测基金业绩持续性的新方法

基金业绩持续性检测的一般方法是横截面回归与列联表分析，都是从基金行业层面整体检测持续性，不能有效的检测单只基金的业绩持续性。为此本文引入一种新的检测方法——扫描统计量，可以创新性地对单只基金进行有效分析。利用扫描统计量方法对我国基金业绩持续性进行了实证分析，发现部分基金存在持续性，给出了持续性强度的上限，为投资者买卖基金、基金业绩考核、风险监控提供了决策参考依据。     

对证券投资基金业绩评价指标间关系的分析

本文采用数理方法,对基金业绩评价中主要使用的特雷诺指数、夏普指数、詹森指数、M2指数进行了分析,从各个指标的基本原理出发,用数学方法推导了相互之间的异同关系,并且阐述了在进行基金业绩评价时的各个指标的使用条件.     

对我国投资基金业绩进行评价的实证研究

本采用单因素评价法与因子分析评价法相结合的综合评价法对我国基金的业绩表现进行了实证研究，研究结果表明：我国占大多数的基金能战胜市场。     

中国开放式基金择时能力及其业绩贡献评价研究

利用H—M模型检验了最新时期中国开放式基金的市场时机把握能力和证券选择能力。建立了投资收益Rp与择时系数γ值的函数关系Rρ=f（γ），得到不同择时能力系数7对应的投资收益率R，。建立了H—M模型中β1和β2参数与γ系数的函数关系γ=g（β1，β2），进而可以得到择时能力产生的投资收益Rp与单只基金的β1和β2参数之间的函数关系Rp=f（γ）=f[g（β1，β2）]。论文的创新与特色一是揭示了在中国市场上择时能力对提高基金业绩的作用。通过计算不同择时能力的投资组合的收益，并与无择时能力的被动管理组合的收益相比较，得到了择时能力在中国市场上能够有效地提高基金业绩的结论。二是揭示了择时能力的业绩贡献与H—M模型中β2，β2参数的对应关系：择时能力系数γ与β1/β2的比值有关，β1/β2的比值越大，择时能力越小，择时净收益Rcp也就越小。这一研究解决了H—M模型只能反映选股能力的业绩贡献大小、不能反映择时能力业绩贡献大小的问题，使基金投资者能够全面判断基金的投资业绩，有利于投资者的投资决策。     

我国封闭式投资基金业绩评价实证研究

随着我国基金行业超常发展,恰当的分析和评价基金业绩已越来越重要。本文根据CAPM的基本原理,利用国外先进的基金业绩评价方法对我国封闭式基金的综合业绩进行实证研究。研究结果表明:总体来看,基金获得的市场超额收益显著为负。基金经理不具有证券选择能力,但具有一定的市场择时能力,但这两种能力均不显著。同时,我们还发现不同投资风格的基金经理具有不同的证券选择能力和市场择时能力。     

基于业绩持续性的证券投资基金聚类与实证研究

目前基金的分类方法大多是从基金的性质特点出发的主观分类方法,没有反映出基金资产的实际运作效果。本文采用聚类的思想,以基金的业绩表现为基础,从业绩持续性的角度提出了一种新的分类方法:基于业绩持续性的基金聚类。通过对基金业绩持续性的研究,构造了一个业绩持续指数,并用该指数对样本进行聚类。实证研究结果表明,该分类方法是可行和有效的。     

开放式基金流动性赎回风险实证分析与评价

本基于Vikram Nanda，M．P．Narayanan(2000)的基金管理能力与流动性需求、流动性赎回风险关系模型的修正，研究结果表明，基金管理所预期的利润与其管理能力正相关，而与流动性成本和流动性需求的风险呈负相关；负担基金管理的边际能力相对于低流动性需求投资的数量和高流动性需求投资的风险而言是递减的；最低赎回费用与高流动性需求投资的风险和低流动性需求投资的相对稀缺性正相关。在此基础上对我国开放式基金的流动性赎回风险进行实证分析评价，并提出了几点政策建议。     

基于行为金融视角的基金申赎行为实证研究

以开放式证券投资基金申购、赎回行为背后的行为金融机理为研究对象,通过分析相关变量作用于基金投资者心理的方式和路径,探讨其对基金申购、赎回所产生的影响。在理论分析的基础上,提出基金业绩、规模、存续时间、价位、分红等变量能够各自基于信号传递、心理账户、预期框定偏差、预期惯性、处置效应等多元化路径影响投资者心理,并作用于基金申购、赎回和基金流量,并据此提出了相关假设。进一步地,通过建立结构方程模型,以我国327只股票型开放式证券投资基金为样本,对2011至2013年三组年度截面数据进行了检验。实证结果表明信号传递、预期框定偏差路径始终稳定存在,预期惯性路径仅在2011、2013年实证检验中存在,处置效应路径只在2012年检验中存在、且呈现非对称性特征,而心理账户路径则无法得到证实,据此得出结论认为基金业绩对基金申赎影响最为显著但不具稳定性。最后,从投资者行为模式和基金市场发展环境等角度对实证结果进行了分析。     

基于非对称幂分布的中国开放式基金业绩评价和检验

证券投资基金是现代金融业的重要组成部分。随着基金业的迅速发展,证券投资基金已成为我国资本市场最大、最有影响力的机构投资者。目前,开放式基金的数量和规模已远远超过封闭式基金,因此,本文主要探讨开放式基金的业绩评价和排名。已有大量实证研究发现基金收益具有尖峰厚尾性、非对称性和强正相关性,基于此,本文使用非对称幂分布(Asymmetric Power Distribution,APD)来拟合基金收益率分布。不同于其它文献的是,我们主要着眼于Sharpe比率估计量SR,研究36只开放式基金实际日收益率下SR和基于APD标准差和VaR的修正SR,并使用双样本统计量对SR进行假设检验,结论证明了假设检验是显著的,且在基金排名和评价的应用中是非常可行的。     

Hedge fund performance appraisal using data envelopment analysis

In this paper we apply data envelopment analysis (DEA) to evaluate the performance of hedge fund classifications. The purpose of alternative investment strategies such as hedge funds is to offer absolute returns, so using passive benchmarks to measure their performance could be ineffective. With the increasing number of hedge funds available, institutional investors, pension funds, and high net worth individuals urgently need a trustworthy efficiency appraisal method. DEA can achieve this. An important benefit of the DEA measure is that benchmarks are not required, thereby alleviating the problem of using traditional benchmarks to examine non-normal distribution of hedge fund returns. We suggest that DEA be used as a complimentary technique (or method) for the selection of efficient hedge funds and funds of hedge funds for investors. Using DEA can shed light and further validate hedge fund manager selection with other methodologies.     

Mutual funds performance appraisal using stochastic multicriteria acceptability analysis

Mutual fund investors are concerned with the selection of the best fund in terms of performance among the set of alternative funds. This paper proposes an innovative mutual funds performance evaluation measure in the context of multicriteria decision making. We implement a multicriteria methodology using stochastic multicriteria acceptability analysis, on Greek domestic equity funds for the period 2000-2009. Combining a unique dataset of risk-adjusted returns such as Carhart’s alpha with funds’ cost variables, we obtain a multicriteria performance evaluation and ranking of the mutual funds, by means of an additive value function model. The main conclusion is that among employed variables, the sophisticated Carhart’s alpha plays the most important role in determining fund rankings. On the other hand, funds’ rankings are affected only marginally by operational attributes. We believe that our results could have serious implications either in terms of a fund rating system or for constructing optimal combinations of portfolios.     

Performances of socially responsible investment and environmentally friendly funds

The socially responsible investment (SRI) funds performances remain inconclusive. Hence, more studies need to be conducted to determine if SRI funds systematically underperform or outperform conventional funds. This paper has employed dynamic mean-variance model using shortage function approach to evaluate the performance of SRI and Environmentally friendly funds (EF). Unlike the traditional methods, this approach estimates fund performance considering both the return and risk at the same time. The empirical results show that SRI funds outperformed conventional funds in EU and US. In addition, the results of EU are among the top-performing categories. EF do not perform as well as SRI, but perform in manners equal or superior to conventional funds. These results show statistically significant in some cases.     

Analysis of hedge fund strategies using slack-based DEA models

Hedge funds have made a significant impact on the performance of world financial markets in recent times. Our objective in this paper is to develop a robust framework for the evaluation of hedge funds by incorporating a maximum number of performance measures through public data sources. We analyse the hedge fund strategies (styles) using a variety of classical risk-return measures with the help of slack-based Data Envelopment Analysis (DEA) models to determine a unique performance indicator. The main thrust is to investigate the risk return profile of 4730 hedge funds classified under 18 different strategies using multiple inputs and outputs. The originality of the work lies in applying Slack-Based DEA to decipher the risk-return profile of these strategies using advanced risk-return measures such as Value at Risk, drawdown, lower and higher partial moments and skewness. We find that the correlation between the ranking of hedge fund strategies based on Sharpe ratio and the DEA models is very low; at the same time, there is a significant correlation between rankings obtained by the application of DEA using different sets of input/output measures. We have also compared the DEA rankings with other traditional financial ratios such as modified Sharpe ratio, Sortino ratio and Calmar ratio. The paper also studies the impact of events such as the Asian financial crisis on the performance of hedge funds. The study around the event shows that only a relatively small number of strategies performed better during times of turmoil.     

Fuzzy risk adjusted performance measures: Application to hedge funds

In this paper, following the notion of probabilistic risk adjusted performance measures, we introduce that of fuzzy risk adjusted performance measures (FRAPM). In order to deal efficiently with the closing-based returns bias induced by market microstructure noise, as well as to handle their uncertain variability, we combine fuzzy set theory and probability theory. The returns are first represented as fuzzy random variables and then used in defining fuzzy versions of some adjusted performance measures. Using a recent ordering method for fuzzy numbers, we propose a ranking of funds based on these fuzzy performance measures. Finally, empirical studies carried out on fifty French hedge funds confirm the effectiveness and give the benefits of our approach over the classical performance ratios.     

The impact of mismeasurement in performance benchmarking: A Monte Carlo comparison of SFA and DEA with different multi-period budgeting strategies

Performance-based budgeting has received increasing attention from public and for-profit organizations in an effort to achieve a fair and balanced allocation of funds among their individual producers or operating units for overall system optimization. Although existing frontier estimation models can be used to measure and rank the performance of each producer, few studies have addressed how the mismeasurement by frontier estimation models affects the budget allocation and system performance. There is therefore a need for analysis of the accuracy of performance assessments in performance-based budgeting. This paper reports the results of a Monte Carlo analysis in which measurement errors are introduced and the system throughput in various experimental scenarios is compared. Each scenario assumes a different multi-period budgeting strategy and production frontier estimation model; the frontier estimation models considered are stochastic frontier analysis (SFA) and data envelopment analysis (DEA). The main results are as follows: (1) the selection of a proper budgeting strategy and benchmark model can lead to substantial improvement in the system throughput; (2) a “peanut butter” strategy outperforms a discriminative strategy in the presence of relatively high measurement errors, but a discriminative strategy is preferred for small measurement errors; (3) frontier estimation models outperform models with randomly-generated ranks even in cases with relatively high measurement errors; (4) SFA outperforms DEA for small measurement errors, but DEA becomes increasingly favorable relative to SFA as the measurement errors increase.     

基金投资行为与投资绩效实证研究

本文主要研究2000年末到2004年6月国内基金投资行为与投资绩效。我们将基金交易行为分为:新进入、完全退出和对仓位进行调整3类,并分别研究其投资行为。研究结果显示,我国基金交易频率很高,近90%的基金采用动量投资策略,基金新进入股票时动量效应最强。价值型基金更易采取动量投资策略,高动量组收益高于低动量组收益。