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。     

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.     

单形深度中位数在封闭式基金排名中的应用

本文介绍了单形深度函数理论,通过改进夏普指数公式,利用单形深度函数中位数稳健性的特点,将单形深度函数中位数应用于改进的夏普指数公式,对国内的封闭式基金进行了排名与评级,并将其与中信星级和样本星级进行了对比.最后,通过定义基金业绩偏离度提出将其用于评价基金捕捉市场机遇的能力的观点.     

上市公司经营业绩的分类与评价

首先运用R型聚类分析,从反映经营状况的9项指标中,筛选出5项典型指标作为分类指标,然后根据分类指标,运用Q型聚类中的系统聚类分析法将机械工业45家上市公司分成5类,并进一步按盈利性、成长性和财务安全性对每一类公司的业绩状况进行总体归纳评价.在此基础上,运用理想点法并考虑指标的重要性,对每一类各个公司的业绩进行评价和排序,通过分类与评价得出了一些有价值的结论.     

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。     

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。     

封闭式基金的业绩评估

本论文以资产组合理论和资本资产定价模型(CAPM)为理论基础,采用单指数模型来对收集的数据做回归分析。然后应用业绩评估的传统理论和M~2测度理论来对封闭基金的业绩进行评估。论文收集20只封闭式基金最近的月业绩和周业绩数据来做为样本,论文包含两部分。一是对相关理论进行介绍,以及它们在实际处理中的应用(比如基准投资组合的选取,无风险利率的构造)。二是结合样本数据进行的实证研究,计算出各评估指标,并进行排名。然后从经济学和实际意义上对结果加以解释和分析。     

基于最小凸输入需求集方法的基金业绩评价

本文首先回顾了传统基金业绩评价方法，然后运用Chang（1999）的最小凸输入需求集（MCIRS）方法对我国2000年前上市的20只封闭式证券投资基金在2000年的业绩进行综合评价。实证研究表明：同益、景博、汉盛、金鑫、兴和、泰和、天元、普丰共8只基金相对有效，而其余12只基金则相对无效。并将结果与数据包络分析（DEA）的结果进行了比较。     

大数据挖掘用于电力企业评价体系构建

随着大数据时代的来临,电力企业需要探索新的管理模式.从数据出发,对大量历史监测指标数据进行统计分析,从中筛选出影响企业发展的关键指标,提取关键指标的历史特征.采用层次聚类和Kmeans聚类两种聚类方法对关键指标进行聚类分析,并且结合管理经验,与专家讨论对指标类簇进行合理的命名,最终将关键指标分为了服务电网、运营指数、风险指数三个能够反映公司管理程度的一级指数,并且在一级指数下又分为了若干个子指数,建立了适合新源控股公司管理发展的评价体系.与传统的公司评价体系构建的方法相比,基于大数据挖掘方法构建的评价体系更能够体现出各个指标之间的内在联系,从而能够更好的为决策者提供管理建议.     

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。     

Optimal construction and rebalancing of index-tracking portfolios

Index funds aim to track the performance of a financial index, such as, e.g., the Standard?&?Poor’s?500 index. Index funds have become popular because they offer attractive risk-return profiles at low costs. The index-tracking problem considered in this paper consists of rebalancing the composition of the index fund’s tracking portfolio in response to new market information and cash deposits and withdrawals from investors such that the index fund’s tracking accuracy is maximized. In a frictionless market, maximum tracking accuracy is achieved by investing the index fund’s entire capital in a tracking portfolio that has the same normalized value development as the index. In the presence of transaction costs, which reduce the fund’s capital, one has to manage the trade-off between transaction costs and similarity in terms of normalized value developments. Existing mathematical programing formulations for the index-tracking problem do not optimize this trade-off explicitly, which may result in substantial transaction costs or tracking portfolios that differ considerably from the index in terms of normalized value development. In this paper, we present a mixed-integer linear programing formulation with a novel optimization criterion that directly considers the trade-off between transaction costs and similarity in terms of normalized value development. In an experiment based on a set of real-world problem instances, the proposed formulation achieves a considerably higher tracking accuracy than state-of-the-art formulations.     

Index tracking with fixed and variable transaction costs

Index tracking is a form of passive portfolio (fund) management that attempts to mirror the performance of a specific index and generate returns that are equal to those of the index, but without purchasing all of the stocks that make up the index. We present two mixed-integer linear programming formulations of this problem. In particular we explicitly consider both fixed and variable transaction costs. Computational results are presented for data sets drawn from major world markets.     

Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios

Index tracking is a passive investment strategy in which a fund (e.g., an ETF: exchange traded fund) manager purchases a set of assets to mimic a market index. The tracking error, i.e., the difference between the performances of the index and the portfolio, may be minimized by buying all the assets contained in the index. However, this strategy results in a considerable transaction cost and, accordingly, decreases the return of the constructed portfolio. On the other hand, a portfolio with a small cardinality may result in poor out-of-sample performance. Of interest is, thus, constructing a portfolio with good out-of-sample performance, while keeping the number of assets invested in small (i.e., sparse). In this paper, we develop a tracking portfolio model that addresses the above conflicting requirements by using a combination of L0- and L2-norms. The L2-norm regularizes the overdetermined system to impose smoothness (and hence has better out-of-sample performance), and it shrinks the solution to an equally-weighted dense portfolio. On the other hand, the L0-norm imposes a cardinality constraint that achieves sparsity (and hence a lower transaction cost). We propose a heuristic method for estimating portfolio weights, which combines a greedy search with an analytical formula embedded in it. We demonstrate that the resulting sparse portfolio has good tracking and generalization performance on historic data of weekly and monthly returns on the Nikkei 225 index and its constituent companies.     

A note on statistical models for individual hedge fund returns

In recent years, a large number of research papers and monographs on the analysis of hedge fund returns have been published. Typically, the authors of these studies implicitly or explicitly treat monthly returns of hedge funds as independent and identically distributed observations. The Hedge Fund Index might be able to serve that role. But the returns of an individual hedge fund are not like that. They behave autoregressively depending on the time periods. This stochastic behavior should be modeled as a combined/regime switching stochastic process of two processes: i.i.d. process and autoregressive process. This paper first depicts the autoregressiveness of hedge fund returns. Then we introduce our statistical model for returns of an individual hedge fund and then, with our retrospective view, we perform several data analyses for individual hedge funds’ return data.     

Three fuzzy goal programming models for index portfolios

Studies show that most actively managed mutual funds struggle to beat the market, driving an increase in the popularity of index investing. Index investing instruments, including index funds and Exchange-traded Funds, aim to track market performance. This study pursues both tracking error minimization and excess return maximization, two conflicting objectives, to construct an index portfolio. In the real-world financial environment, the desires and expectations of decision makers are generally imprecise. This study applies fuzzy theory to deal with imprecise objectives. This study represents minimizing tracking error and maximizing excess return as ‘fuzzy goals’ to improve traditional goal programming, which is suitable for handling multiple conflicting objectives, but subject to establishing crisp goals. Three fuzzy goal programming (FGP) models that track indexes are compared and discussed, and the results show that through certain membership functions and tracking models, an index tracking portfolio with a tracking error lower than the 0050 index fund, and a similar excess return to 0050 index fund can be constructed using additive type FGP. max-min type FGP underperforms the additive type FGP in index fund construction.     

Seasonality and idiosyncratic risk in mutual fund performance

This paper examines the relationship between seasonality, idiosyncratic risk and mutual fund returns using multifactor models. We use a large sample containing the return histories of 728 UK mutual funds over a 23-year period to measure fund performance. We present evidence that idiosyncratic risk cannot be eliminated, we also find evidence of seasonality in all fund categories. Specifically, we find a close relation between the seasonality and the end of the tax-year. We document that the idiosyncratic risk puzzle cannot explain seasonality in fund performance in the UK. Although, we do find that idiosyncratic risk can account for the seasonality in the month of April. Thus, the results show a link between the tax-loss selling hypothesis in April and idiosyncratic risk in that month. Finally, we report evidence that idiosyncratic risk is negatively related to expected returns for most fund classes.     

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.     

基金风险改变了投资者的业绩敏感程度吗？——基于基金业绩波动与资金净流量关系的实证研究

本文利用我国2005年至2011年期间开放式基金的面板数据,研究了基金业绩波动对投资者业绩敏感程度的影响。在验证了基金资金净流量与基金业绩的正相关关系后,实证研究发现:(1)基金业绩波动降低了投资者对基金业绩的敏感程度:基金业绩波动越大,相同业绩提升带来的资金净流量越少;(2)对于不同业绩类型的基金,业绩波动对基金“业绩—资金净流量”关系的反向影响程度也有所不同:这一影响主要体现在绩劣基金中,中等业绩基金次之,在明星基金中反而体现为正向影响。