An examination of HMM‐based investment strategies for asset allocation

We develop and analyse investment strategies relying on hidden Markov model approaches. In particular, we use filtering techniques to aid an investor in his decision to allocate all of his investment fund to either growth or value stocks at a given time. As this allows the investor to switch between growth and value stocks, we call this first strategy a switching investment strategy. This switching strategy is compared with the strategies of purely investing in growth or value stocks by tracking the quarterly terminal wealth of a hypothetical portfolio for each strategy. Using the data sets on Russell 3000 growth index and Russell 3000 value index compiled by Russell Investment Services for the period 1995–2008, we find that the overall risk‐adjusted performance of the switching strategy is better than that of solely investing in either one of the indices. We also consider a second strategy referred to as a mixed investment strategy which enables the investor to allocate an optimal proportion of his investment between growth and value stocks given a level of risk aversion. Numerical demonstrations are provided using the same data sets on Russell 3000 growth and value indices. The switching investment strategy yields the best or second best Sharpe ratio as compared with those obtained from the pure index strategies and mixed strategy in 14 intervals. The performance of the mixed investment strategy under the HMM setting is also compared with that of the classical mean–variance approach. To make the comparison valid, we choose the same level of risk aversion for each set‐up. Our findings show that the mixed investment strategy within the HMM framework gives higher Sharpe ratios in 5 intervals of the time series than that given by the standard mean–variance approach. The calculated weights through time from the strategy incorporating the HMM set‐up are more stable. A simulation analysis further shows a higher performance stability of the HMM strategies compared with the pure strategies and the mean–variance strategy. Copyright © 2009 John Wiley & Sons, Ltd.     

Enhancement of equity portfolio performance using data envelopment analysis

This paper examines the applicability of data envelopment analysis (DEA) as a basis of selection criteria for equity portfolios. It is the first DEA application for constructing a combined equity investment strategy that aims to integrate the benefits of both value investing and momentum investing. The 3-quantile portfolios are composed of a comprehensive sample of Finnish non-financial stocks based on their DEA efficiency scores that are calculated using three variants of DEA models (the constant returns-to-scale, the super-efficiency, and the cross-efficiency models). The performance of portfolios is evaluated on the basis of the average return and several risk-adjusted performance metrics throughout the 1994–2010 sample period.     

基于文本信息考虑投资者情绪的均值回归策略设计——以东方财富股吧发帖文本和A股市场为例

在当前互联网时代下,越来越多的文本信息为人们所认识。借助机器学习等技术工具,目前已能较为便捷地从海量的文本数据中挖掘出与投资者行为、情绪有关的信息。基于此,本研究探讨了利用文本信息刻画投资者情绪,并对仅利用价格信息的均值回归策略进行改进。利用东方财富股吧中发帖内容等文本数据构建投资者情绪指标,结合非理性投资行为的特征,设计新的权重转移方程,得到新的均值回归策略。最后,利用部分沪深300成分股的价格数据和股吧文本数据进行实证检验,结果表明：相比于仅利用价格信息刻画均值回归特征的策略,本研究提出的考虑投资者情绪的策略有更好的收益表现。     

博彩型股票与投资者的博彩性偏好——基于中国股票市场数据的实证研究

在新兴资本市场中,股票价格容易受到投资者投资偏好的影响。其中,投资者的博彩性投资偏好是本文研究的重点。本文基于中国股票市场实际情况,借鉴并改进了过往文献对于博彩型股票的分类方法,将高个体偏度、高个体波动率、低价格和高超额换手率的股票定义为博彩型股票,并发现个人投资者过度配置了博彩型股票,存在明显的博彩性投资偏好。本文验证了在中国股票市场中博彩型股票未来收益表现较差的结论,说明投资者偏好博彩型股票的投资行为存在非理性偏误。同时,相对于主板市场而言,在投机性相对较强的中小板和创业板市场中的博彩型股票未来收益表现更差;无论市场在上升或下降环境中,博彩型股票的未来负超额收益一直稳定存在。因此,本文认为应在中国股票市场中加强投资者教育,以降低个人投资者的非理性行为偏差,维护股票市场的健康稳定发展。     

投资者情绪对科创板市场收益率的影响——基于文本数据挖掘视角

本文以科创板市场为主要研究对象,基于文本数据挖掘方法探究了新冠疫情发生前和疫情期间投资者情绪对市场收益率的影响及其作用机制。利用东方财富股吧2019年7月至2020年3月的日度科创板股票评论数据,基于Bi-LSTM深度学习技术对文本数据情感倾向进行分类,建立投资者情绪指数。通过构建双向固定效应的联立方程模型,采用2SLS方法估计投资者情绪对科创板市场收益率的作用,并检验在经济平稳运行和受新冠疫情冲击期间该作用的差异性。实证分析及稳健性检验的结果均表明,投资者情绪通过影响交易量进而影响科创板股票市场收益率,这种正向作用在1%的置信水平下显著。此外,投资者情绪对科创板收益率的影响在经济平稳运行和受新冠疫情冲击期间均保持稳健,且在新冠疫情期间作用更强。本研究成果对于新冠疫情期间我国证券市场监管层完善科创板交易机制,以及对中小投资者优化投资战略具有重要意义。     

影响收益的公司指标在最优投资组合选择中的应用

如何从数目巨大的市场股票集中选取一组特定的股票作为最优投资组合选择模型的输入,以确保最终的投资方案具有优异而稳定的表现一直是投资理论界和实务界关注的重点.为此,本文基于作者新近结合中国股市特性并采用新方法所确定影响中国股票收益的多个公司基本特性指标,设计了一个恰当的股票预选策略,并由此导出了新型而稳健的投资组合选择两阶段法.实证结果表明新方法能使投资者便捷地找到更稳健的投资策略.     

基于几种风险测度的多阶段组合优化研究

基于均值-方差(MV)、VaR(Value at Risk)、CVaR(Conditional VaR)、HMCR(p=1,2,3)(Higher Moment Coherent Risk)几种风险测度进行多阶段组合优化研究。首先从一致性公理和随机占优一致性角度分析几种风险测度的风险识别能力,认为HMCR(p=2,3)的风险识别能力最高,然后给出静态和动态下的风险规避型的规划函数及多阶段CVaR和HMCR模型,最后依据单阶段和多阶段优化模型,对上证50指数成份股进行实证分析。对比单阶段和多阶段下几种风险测度优化组合的累计收益率及几种风险测度之间的关系,结合上证50指数收益率发现,多阶段优化组合要整体优于单阶段优化组合,且HMCR(p=2,3)要优于指数收益率和其它几种风险测度。从投资者投资决策方面来分析,HMCR(p=2,3)型积极投资策略比较适用于股市平稳期、顶峰期和下降期,被动投资策略比较适用于股市上升期。     

Time-consistent reinsurance and investment strategies for mean–variance insurer under partial information

In this paper, based on equilibrium control law proposed by Björk and Murgoci (2010), we study an optimal investment and reinsurance problem under partial information for insurer with mean–variance utility, where insurer’s risk aversion varies over time. Instead of treating this time-inconsistent problem as pre-committed, we aim to find time-consistent equilibrium strategy within a game theoretic framework. In particular, proportional reinsurance, acquiring new business, investing in financial market are available in the market. The surplus process of insurer is depicted by classical Lundberg model, and the financial market consists of one risk free asset and one risky asset with unobservable Markov-modulated regime switching drift process. By using reduction technique and solving a generalized extended HJB equation, we derive closed-form time-consistent investment–reinsurance strategy and corresponding value function. Moreover, we compare results under partial information with optimal investment–reinsurance strategy when Markov chain is observable. Finally, some numerical illustrations and sensitivity analysis are provided.     

Diversification-consistent data envelopment analysis with general deviation measures

We propose new efficiency tests which are based on traditional DEA models and take into account portfolio diversification. The goal is to identify the investment opportunities that perform well without specifying our attitude to risk. We use general deviation measures as the inputs and return measures as the outputs. We discuss the choice of the set of investment opportunities including portfolios with limited number of assets. We compare the optimal values (efficiency scores) of all proposed tests leading to the relations between the sets of efficient opportunities. Strength of the tests is then discussed. We test the efficiency of 25 world financial indices using new DEA models with CVaR deviation measures.     

Input/output selection in DEA under expert information,with application to financial markets

Data envelopment analysis (DEA), as generally used, assumes precise knowledge regarding which variables are inputs and outputs; however, in many applications, there exists only partial knowledge. This paper presents a new methodology for selecting input/output variables endogenously to the DEA model in the presence of partial (or expert’s) knowledge by employing a reward variable observed exogenous to the operation of the DMUs. The reward is an allocation of a limited resource by an external agency, e.g. capital allocation by a market, based on the perceived internal managerial efficiencies. We present an iterative two-stage optimization model which addresses the benefit of possibly violating the expert information to determine an optimal internal performance evaluation of the DMUs for maximizing its correlation with the reward metric. Theoretical properties of the model are analyzed and statistical significance tests are developed for the marginal value of expert violation. The methodology is applied in Fundamental Analysis of publicly-traded firms, using quarterly financial data, to determine an optimized DEA-based fundamental strength indicator. More than 800 firms covering all major sectors of the US stock market are used in the empirical evaluation of the model. The firms so-screened by the model are used within out-of-sample mean-variance long-portfolio allocation to demonstrate the superiority of the methodology as an investment decision tool.     

不完全市场中投资策略的实证研究

Klaus[1]采用随机动力系统理论分析了不完全市场中市场份额的长期演变行为,找到了可以获得最大收益的投资策略的显式表达式,并证明了这个投资策略是渐进稳定的.本文的工作是以股票作为基础资产,利用中国证券市场上的原始股票数据,检验Klaus的理论成果在不完善的中国股票市场上的运行情况,并对得到的结果进行分析.     

质量控制图原理在股票投资组合中的应用研究

质量控制图是用来控制生产过程质量的一种统计方法。其基本理论方法应用到股票投资组合中,可以降低投资风险,提高投资组合的收益率。通过实证研究表明:利用质量控制图原理来确定投资组合的选股、买点和卖点,可以使投资组合获得明显高于大盘指数的收益率,从而验证了质量控制图的基本理论方法完全可以应用于股票投资决策,并且具有显著的实际应用价值。     

基于因子模型的指数跟踪及实证分析

指数跟踪是指数基金和机构投资者广泛使用的被动投资管理策略. 通过建立股票收益的多因子模型, 提出了将组合的贝塔值控制在合适范围内, 并在期望超额收益非负的条件下, 最小化组合风险的指数跟踪模型. 同时,考虑到实际需要, 在模型中限制了组合中股票的数量和持有量.实证分析结果表明, 通过选取不同的控制参数,
该模型产生的跟踪组合既能实现较小的跟踪误差,也能实现一定的超额收益.     

随机波动率市场存在股票误价时的最优投资策略

本文研究了随机波动率市场中存在股票误价(mispricing)时的最优投资组合选择问题.假设投资者的目标是最大化终端财富的期望幂效用;其可投资于无风险资产、市场指数和两支相同权益或近似度极高的股票,其中至少有一支股票存在误价;市场收益的波动率和股票系统风险由Heston随机波动率模型刻画.运用动态规划方法和Lagrange乘子法,分别得到不存在/存在有限卖空约束时,投资者的最优投资策略及最优值函数的解析式,并通过理论分析和数值算例,阐述了投资时间水平和价格随机误差对最优投资策略的影响.     

Data envelopment analysis with nonlinear virtual inputs and outputs

An underlying assumption in DEA is that the weights coupled with the ratio scales of the inputs and outputs imply linear value functions. In this paper, we present a general modeling approach to deal with outputs and/or inputs that are characterized by nonlinear value functions. To this end, we represent the nonlinear virtual outputs and/or inputs in a piece-wise linear fashion. We give the CCR model that can assess the efficiency of the units in the presence of nonlinear virtual inputs and outputs. Further, we extend the models with the assurance region approach to deal with concave output and convex input value functions. Actually, our formulations indicate a transformation of the original data set to an augmented data set where standard DEA models can then be applied, remaining thus in the grounds of the standard DEA methodology. To underline the usefulness of such a new development, we revisit a previous work of one of the authors dealing with the assessment of the human development index on the light of DEA.     

不完全市场下基于对数效用的动态投资组合

应用鞅方法研究不完全市场下的动态投资组合优化问题。首先,通过降低布朗运动的维数将不完全金融市场转化为完全金融市场,并在转化后的完全金融市场里应用鞅方法研究对数效用函数下的动态投资组合问题,得到了最优投资策略的显示表达式。然后,根据转化后的完全金融市场与原不完全金融市场之间的参数关系,得到原不完全金融市场下的最优投资策略。算例分析比较了不完全金融市场与转化后的完全金融市场下最优投资策略的变化趋势,并与幂效用、指数效用下最优投资策略的变化趋势做了比较。     

What is the impact of stock market contagion on an investor’s portfolio choice?

Stocks are exposed to the risk of sudden downward jumps. Additionally, a crash in one stock (or index) can increase the risk of crashes in other stocks (or indices). Our paper explicitly takes this contagion risk into account and studies its impact on the portfolio decision of a CRRA investor both in complete and in incomplete market settings. We find that the investor significantly adjusts his portfolio when contagion is more likely to occur. Capturing the time dimension of contagion, i.e. the time span between jumps in two stocks or stock indices, is thus of first-order importance when analyzing portfolio decisions. Investors ignoring contagion completely or accounting for contagion while ignoring its time dimension suffer large and economically significant utility losses. These losses are larger in complete than in incomplete markets, and the investor might be better off if he does not trade derivatives. Furthermore, we emphasize that the risk of contagion has a crucial impact on investors’ security demands, since it reduces their ability to diversify their portfolios.     

Stochastic DEA with ordinal data applied to a multi-attribute pricing problem

Multiple attribute pricing problems are highly challenging due to the dynamic and uncertain features in the associated market. In this paper, we address the condominium multiple attribute pricing problem using data envelopment analysis (DEA). In this study, we simultaneously consider stochastic variables, non-discretionary variables, and ordinal data, and present a new type of DEA model. Based on our proposed DEA, an effective performance measurement tool is developed to provide a basis for understanding the condominium pricing problem, to direct and monitor the implementation of pricing strategy, and to provide information regarding the results of pricing efforts for units sold as well as insights for future building design. A case study is executed on a leading Canadian condominium developer.     

Portfolio optimization with a neural network implementation of the coherent market hypothesis

Capital market research seems to be widely governed by traditional static linear models like arbitrage pricing theory and capital asset pricing model, though there is some evidence that better results can be achieved using nonlinear approaches. In this study we described a portfolio optimization model based on artificial neural networks embedded in the framework of a nonlinear dynamic capital market model, the coherent market hypothesis. The main advantage of this theory is that it drops the premise of rational investors and therefore relaxes the precondition of approximately normally distributed stock returns. Neural networks are used to estimate the return distributions in order to forecast the fundamental situation and the level of group behavior of the specific stocks. On the basis of these forecasts the relative stock performance is predicted and used to manage stock portfolios, In a simulation with out-of-sample data from 1991–1994 a portfolio constructed from the eight best ranked stocks achieved an annual return rate about 25% higher than that of the market portfolio and one built from the eight worst ranked stocks attained a return about 25% lower than the market portfolio's return rate. A hedging strategy based on the two aforementioned portfolios leads to a consistently positive annual return of about 25% regardless of the movements of the market portfolio with only 41% of the risk of a buy and hold strategy in the market portfolio.     

基于财富管理视角的IPO市值配售网上申购策略

网上申购新股是一种风险较低而收益可观的投资策略,若操作得当可带来持续稳定的正收益.A股IPO发行方式自20世纪90年代以来反复变化,2012年5月起基本稳定为限定发行市盈率、市值配售的方式.基于财富管理视角,首先分析影响网上申购收益的几个因子并推导出其计算公式,然后对2016年市场情况进行实证研究,接着制定出未来1~2年可行的操作策略,最后建议后续研究针对新股发行政策变化和证券市场走势变化适时调整相关策略.