基于随机因子模型的增强型指数基金

创新性的假设传统的Fama-French三因素模型中的三因素为服从正态分布的随机变量,进而获得了股票收益随机变量的分布信息.采取部分复制的原则建立增强型指数基金随机投资组合优化模型,通过引入投资组合风险概率约束给出增强型指数基金的绝对风险上限,针对增强型指数基金建立基于VaR的超额收益概率约束.引入最买入门槛限制降低增强型指数基金的管理费用,增强其流动性.最后,根据股票收益的概率分布特征,获得基于上述约束的指数基金和增强型指数基金的确定性优化模型,并同时基于上证A股进行了实证分析.     

基于协整的指数追踪及增强策略研究

选取沪深300指数和所有成分股2016年12月初至2017年5月底的所有日线级别数据,利用协整模型和追踪误差等检验指标,考察了不同追踪组合对沪深300指数及增加了10%年化收益的虚拟指数序列的追踪效果.结果发现:低PE组合、高价组合、低换手率组合在两个指数的追踪过程中均表现较好,高价组合和低换手率组合更是可以获得正的超额收益;在选择追踪组合时,单一标准优于组合标准.总的来说,协整模型能够较好实现对指数的追踪,也能够通过虚拟指数的设置,获得较为显著的alpha收益.     

新的指数跟踪方法及其应用

文章提出了一种新的非完全复制指数跟踪方法。该方法基于选择具有一定优良性质的股票组合的基础,构建跟踪组合来跟踪指数。新方法突破了文献已有方法事先指定股票来构造指数跟踪的传统框架,更有实际价值。实证分析表明,采用该方法构建的跟踪策略通过选择较少的股票同时得到的跟踪误差也较小,且优于目前比较流行的分层抽样策略。最后应用所给的方法对沪深300指数进行了股指期货的期现套利研究,结果表明该策略比文献已有的方法存在更大的套利空间。     

组合调整频率会影响投资者的回报率么?——基于分析师股票推荐的投资策略研究

利用巨灵数据库的分析师评级数据,从投资者角度建立动态组合,检验了按每日、每周、每两周、每月、每季度头寸调整频率后的超额收益,研究发现:"买入"评级组合在各种头寸调整频率策略下都获得了超越市场指数的超额回报率,其中按每周频率进行头寸调整策略获得的市场调整后平均超额回报率最高,按三个月频率进行头寸调整策略获得的市场调整后平均超额回报率最低。投资者在面对分析师一致认为"买入"评级股票时,可按照每周更新的频率构建组合,以获取较高的超额回报率。投资者如果按季度调整频率来卖出股票可能会错失投资机会,卖掉的股票很可能会跑赢市场,投资者可按每两周或每四周的频率调整投资组合中的股票,卖出负面推荐评级"减持/卖出"股票,降低投资亏损。     

媒体信息量异常或有害股票收益

本文借助一个独特的数据样本,运用媒体对股票的剩余关注度模型,实证研究异常媒体信息量与股票收益之间的关系,以期为投资者进行投资决策提供一定的参考和指导。研究发现:异常媒体信息量越大,该股票在下一个月的平均收益率越低,存在媒体效应;由此所构造的零投资组合经CAPM模型、FF三因素模型和Car-hart四因素模型调整后,均能获取显著的超额收益,结果具有稳健性。此外,实证结果还表明媒体效应所带来的超额收益源于媒体信息量异常大的股票组合的显著低收益,本文认为,这种不对称现象产生的原因可能更多的是由投资者情绪导致的股票价格对媒体报道的过度反应,并进而导致较低的期望收益。     

基于Sparse-Group Lasso的指数跟踪

在指数跟踪问题中,股票指数与行业板块的相关性往往是集中在某些特定的行业,且行业走向通常由几个有影响力的公司决定,因此如何选取具有代表性的行业和公司是提高跟踪精度的一个很好的切入点.在以往的研究方法中,Lasso等变量选择方法忽略了行业因素的影响,而分层抽样则忽略了不同行业和股票指数关联性大小的不同.文章引入Sparse-Group Lasso方法,实现了对行业及行业内部单一股票的筛选,同时对跟踪误差的定义进行扩展,综合考虑线性和非线性两种跟踪误差的优点对股票组合的权重进行优化.实证表明,基于Sparse-Group Lasso方法筛选的股票组合的稳健性一致优于依据市值筛选的股票组合,当股票组合规模较小时,基于Sparse-Group Lasso方法筛选的股票组合的跟踪误差也要优于依据市值进行筛选股票的方法.     

基于LARS-Lasso的指数跟踪及其在股指期货套利策略中的应用

本文将多元线性回归选择变量的Lasso方法引入到指数跟踪和股指期货套利策略研究,提出运用LARS算法实现非负限制下的Lasso选择现货组合问题,为业界给出了一种选择构造现货组合的股票的新方法。实证表明:采用本文提出的方法得到的现货组合,在组合含有较少数量股票的情况下,得到较文献中已有方法更小的跟踪误差。同时,利用本文的方法对沪深300仿真交易的期现套利进行研究,得到有重要市场价值的结果。     

债券投资组合业绩归因的多期实证研究

目前国内对投资组合的业绩归因研究主要从管理者层面着手,将超额收益的来源归结为择时能力和选股能力,但这并不适用于债券投资。本文基于Campisi模型,对债券定价公式进行分解,从债券自身的特性来研究组合的超额收益来源,并结合GRAP跨期处理方法,形成多期业绩归因模型,对长期债券投资组合进行归因分析。相对于单期的归因模型,多期归因模型可以对任意一段时间内投资组合的超额收益进行归因,而不是单期归因项的简单加总。本文以中证全债指数为基准组合,对32只债券构成的投资组合进行实证研究,结果表明模型符合市场情况和实际操作情况。因此本文提出的多期业绩归因研究具有实用性。     

金融市场下非负稀疏组LASSO在股指跟踪中的研究

作为一种流行的被动投资组合管理策略,指数跟踪主要侧重于复制或跟踪金融指数的表现.以股指为例,传统的投资策略通常考虑指数所有成分股的完全复制.然而,随着指数成分股数量的增加,完全复制通常会受到流动性差以及成本高的影响.因此,投资者倾向于购买部分成分股进行资产配置.此外,在股票市场中,股票之间还存在明显的"组群"效应.基于...     

基于下半矩风险度量与t分布的单向金融指数跟踪模型

本文探讨沿如下新的思路构造投资组合来最有效地单向"跟踪"某个金融指数;为灵活刻画不同投资者对跟踪风险的感知,我们采用跟踪偏差相应阶数的下半矩作为风险的度量;为较好描述证券收益分布的厚尾现象,我们假设市场上风险资产的收益服从多元t分布.在分析基于上述框架所建立跟踪优化模型基本性质的同时,我们还设计出相应的求解算法.实证检验结果不仅表明新模型与算法的有效性和实用价值,而且较好地克服了基于MAD等准则的传统跟踪方法的不足.     

Dynamic Index Tracking and Risk Exposure Control Using Derivatives

We develop a methodology for index tracking and risk exposure control using financial derivatives. Under a continuous-time diffusion framework for price evolution, we present a pathwise approach to construct dynamic portfolios of derivatives in order to gain exposure to an index and/or market factors that may be not directly tradable. Among our results, we establish a general tracking condition that relates the portfolio drift to the desired exposure coefficients under any given model. We also derive a slippage process that reveals how the portfolio return deviates from the targeted return. In our multi-factor setting, the portfolio’s realized slippage depends not only on the realized variance of the index but also the realized covariance among the index and factors. We implement our trading strategies under a number of models, and compare the tracking strategies and performances when using different derivatives, such as futures and options.     

基于下半概率风险度量并兼顾收益分布厚尾性的新型金融指数跟踪模型

考虑到投资者通常采取安全第一的准则,采用跟踪偏差的下半概率作为跟踪风险的度量;而为在恰当描述证券收益分布的厚尾特性的同时克服机会约束对模型求解所造成的困难,假设风险资产的收益服从多元t分布,由此建立了新型金融指数跟踪模型.在分析所建立模型结构特性的基础上,文中还导出了该模型的解析最优解.实证结果表明了新模型的有效性和实用价值.     

投资者情绪影响下资本资产定价的区制性研究——基于股票论坛发帖的情感分析

本文首先建立一个考虑投资者情绪的资本资产定价模型,研究发现,投资者情绪是资产定价的系统性因子且对其影响具有区制性(存在三个区制)。在此基础上通过仿真揭示投资者情绪对资产定价影响存在区制性的原因在于当投资者情绪增加时,最优组合超额收益受组合效应与情绪效应的综合影响。最后基于股票论坛发帖的情感分析构建投资者情绪指标,实证检验了本文的理论模型,并发现基于普通的线性回归模型得到的投资者情绪对股指超额收益影响,一方面会在投资者情绪处于第二区制内时将其对股指超额收益影响方向弄反,另一方面会在投资者情绪处于第三区制内时低估其增加导致的股指超额收益平均增加程度。     

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.     

A mixed 0–1 LP for index tracking problem with CVaR risk constraints

Index tracking problems are concerned in this paper. A CVaR risk constraint is introduced into general index tracking model to control the downside risk of tracking portfolios that consist of a subset of component stocks in given index. Resulting problem is a mixed 0?C1 and non-differentiable linear programming problem, and can be converted into a mixed 0?C1 linear program so that some existing optimization software such as CPLEX can be used to solve the problem. It is shown that adding the CVaR constraint will have no impact on the optimal tracking portfolio when the index has good (return increasing) performance, but can limit the downside risk of the optimal tracking portfolio when index has bad (return decreasing) performance. Numerical tests on Hang Seng index tracking and FTSE 100 index tracking show that the proposed index tracking model is effective in controlling the downside risk of the optimal tracking portfolio.     

On a Local-Search Heuristic for a Class of Tracking Error Minimization Problems in Portfolio Management

In this paper we describe a 2-phase simulated annealing heuristic approach for a special class of portfolio management problems: the problem of optimizing a stock fund with respect to tracking error and transaction costs over time subject to a set of complex constraints with a linear factor return model “feeding” the objective function with data. Our results on managing two real-world funds of a major German capital investment company have shown that this meta-heuristic provides proposals for the fund manager which are feasible with respect to the investment guidelines and excellent in quality in acceptable time. Thus the approach is ideally suited to be used routinely and interactively within a decision support system to assist the fund manager in his complex task of portfolio control and optimization.     

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.     

二阶随机占优约束下考虑偏度的投资组合模型

在DentchevaRuszczynski(2006)模型的基础上,考虑偏度对构建投资组合的影响,建立了二阶随机占优约束下最大化组合收益率偏度的投资组合优化模型,并应用分段线性近似方法将模型转化为一个非线性混合整数规划问题.利用中国股票市场的历史数据对所建模型进行了实证分析,结果表明,所建新模型比均值-方差-偏度模型和市场指数具有更稳健的表现.     

中证开放式基金指数和GARCH-M模型

基于开放式基金指数周收益率时间序列的非正态性和厚尾特性,以中证开放式基金指数为例,运用GARCH-M模型进行研究,系统地分析我国不同类型的开放式基金的投资风险.实证分析表明:GARCH-M模型对中证开放式基金指数周收益率的拟合效果较好,并为预测我国开放式基金的投资风险提供了科学依据.