最佳证券组合的选择

在讨论证券投资者的无差异曲线和所面对的证券组合有效集(有效界面)的基础上 ,提出一种选择最佳证券组合的方法。从而为证券投资者进行最优价值投资提供科学的依据。     

Portfolio selection in a data-rich environment

We model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables. This approach (hereafter diffusion index approach) offers the opportunity to exploit a much richer information base to improve portfolio selection. We use factor analysis to estimate the space spanned by the factors. This provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity. We consider an empirical application to illustrate the practical usefulness of our approach. The results indicate that the diffusion index approach helps to improve the portfolio performance.     

Location-scale portfolio selection with factor-recentered skew normal asset returns

This paper analyzes the single period portfolio selection problem on the location-scale return family. The skew normal distribution, after recentering and reparameterization, is shown to be in this family. The recentered and reparameterized distribution, called factor-recentered skew normal, can be expressed as a skew factor model which is characterized by a location parameter and two scale parameters. Risk preference on scale parameter is non-monotonic and risk averse investors prefer larger (smaller) scale when the scale is negative (positive). The three-parameter efficient set is a part of conical surface bounded by two lines. Positive-skewness portfolios and negative-skewness portfolios do not coexist in the efficient set. Numerical cases under constant absolute risk aversion are analyzed with its closed-form certainty equivalent. An asset pricing formula which nests the CAPM is obtained.     

Portfolio management with robustness in both prediction and decision: A mixture model based learning approach

We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying distribution can be always well approximated by utilizing a mixture distribution, if we are able to ensure that the component list of a mixture distribution includes all possible distributions corresponding to the scenario analysis of potential market modes. Adopting a mixture distribution enables us to (1) reduce the problem of distribution prediction to a parameter estimation problem in which the mixture weights of a mixture distribution are estimated under a Bayesian learning scheme and the corresponding credible regions of the mixture weights are obtained as well and (2) harmonize information from different channels, such as historical data, market implied information and investors׳ subjective views. We further formulate a robust mean-CVaR portfolio selection problem to deal with the inherent uncertainty in predicting the future return distributions. By employing the duality theory, we show that the robust portfolio selection problem via learning with a mixture model can be reformulated as a linear program or a second-order cone program, which can be effectively solved in polynomial time. We present the results of simulation analyses and primary empirical tests to illustrate a significance of the proposed approach and demonstrate its pros and cons.     

考虑投资组合收益率完全分布信息的投资组合优化研究

前四阶矩并不能完全决定收益率服从何种分布,因而对于偏好某种特定分布的投资者而言,以往的高阶投资组合优化方法并不适用,应当进行考虑投资组合收益率完全分布信息的投资组合优化。本文提出了一种考虑投资组合收益率完全分布信息的投资组合优化方法,通过Gram-Charlier渐进展开来近似投资组合收益率的概率密度函数,以KL散度来度量投资组合收益率的概率密度函数与目标概率密度函数的距离,从而构建了投资组合优化模型,并给出了具体算例。     

Portfolio selection methodology for a nuclear project

It is difficult to evaluate complex, publicly sensitive nuclear project proposals. Scientists, subject matter experts, politicians, and citizens often differ on scope, budget, time, and quality priorities. There are numerous qualitative factors and quantitative variables. Experts could widely disagree on criteria or neglect to discriminate between seemingly identical alternatives. An ineffective portfolio selection method could lead to safety problems, budget overruns, or outright project failures. This case study develops and examines a mixed‐method, integrated qualitative and quantitative portfolio selection model, applied to a “tritium extraction facility” project concept.     

奇异方差矩阵的Markowitz's证券组合投资决策模型的最优化解法

基于Markowitz证券组合投资模型：min1/2W^tVW,s．t．W^te=1,W^tE（X）=μ0,分析方差矩阵V为一般对称矩阵时的情形,本文推广了证券组合投资模型的一个定理,并分类讨论了一般对称方差矩阵对应的证券组合投资模型的最优解,同时给出了求解最优证券组合的方法。     

A Portfolio Index GARCH model

This paper develops the structure of a parsimonious Portfolio Index (PI) GARCH model. Unlike the conventional approach to Portfolio Index returns, which employs the univariate ARCH class, the PI-GARCH approach incorporates the effects on individual assets, leading to a better understanding of portfolio risk management, and achieves greater accuracy in forecasting Value-at-Risk (VaR) thresholds. For various asymmetric GARCH models, a Portfolio Index Composite News Impact Surface (PI-CNIS) is developed to measure the effects of news on the conditional variances. The paper also investigates the finite sample properties of the PI-GARCH model. The empirical example shows that the asymmetric PI-GARCH-t model outperforms the GJR-t model and the filtered historical simulation with a t distribution in forecasting VaR thresholds.     

Uncovering the supplier selection knowledge structure: A systematic citation network analysis from 1991 to 2017

Because of the critical role supplier selection plays in organizational success, both practitioners and academics have expressed a strong interest in the subject area generating an extensive body of research. The volume and breadth of publications in this area makes it difficult to understand the knowledge structure of the field and the academic work that has influenced the evolution of the domain.This mixed-method study systematically reviews 246 papers that investigated supplier selection issues and were published between 1991 and 2017. We initially examined the statistics of papers by unveiling influential journals, affiliations and articles. Second, we utilized co-citation network analysis, an objective procedure to extract clusters of what has been researched in supplier selection literature. The study analytically retrieves six major knowledge clusters: 1) conceptual foundation of the field; 2) modeling of the procurement environment; 3) handling group-decision making and unprecise input data; 4) computational research; 5) green/sustainable research and 6) risk-based supplier selection. Finally, we conducted main path analysis to get an overview of the evolution of knowledge in the supplier selection field by uncovering its major paths and key articles.     

A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection

This paper develops a novel time-varying multivariate Copula-MIDAS-GARCH (TVM-Copula-MIDAS-GARCH) model with exogenous explanatory variables to model the joint distribution of returns. The model accounts for mixed frequency factors that affect the time-varying dependence structure of financial assets. Furthermore, we examine the effectiveness of the proposed model in VaR-based portfolio selection. We conduct an empirical analysis on estimating the 90%, 95%, 99% VaRs of the portfolio constituted of the Shanghai Composite Index, Shanghai SE Fund Index, and Shanghai SE Treasury Bond Index. The empirical results show that the proposed TVM-Copula-MIDAS-GARCH model is effective to investigate the nonlinear time-varying dependence among those three indices and performs better in portfolio selection.     

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

The performance of portfolio model can be improved by introducing stock prediction based on machine learning methods. However, the prediction error is inevitable, which may bring losses to investors. To limit the losses, a common strategy is diversification, which involves buying low-correlation stocks and spreading the funds across different assets. In this paper, a diversified portfolio selection method based on stock prediction is proposed, which includes two stages. To be specific, the purpose of the first stage is to select diversified stocks with high predicted returns, where the returns are predicted by machine learning methods, i.e. random forest (RF), support vector regression (SVR), long short-term memory networks (LSTM), extreme learning machine (ELM) and back propagation neural network (BPNN), and the diversification level is measured by Pearson correlation coefficient. In the second stage, the predictive results are incorporated into a modified mean–variance (MMV) model to determine the proportion of each asset. Using China Securities 100 Index component stocks as study sample, the empirical results demonstrate that the RF+MMV model achieves better results than similar counterparts and market index in terms of return and return–risk metrics.     

Subsampling the distribution of diverging statistics with applications to finance

In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hill's estimator in operationalizing Safety First portfolio selection.     

Model selection in stochastic frontier analysis with an application to maize production in Kenya

This paper shows how to compute the standard errors for partial effects of exogenous firm characteristics influencing firm inefficiency under a range of popular stochastic frontier model specifications. We also develop an R²-type measure to summarize the overall explanatory power of the exogenous factors on firm inefficiency. The paper also applies a recently developed model selection procedure to choose among alternative stochastic frontier specifications using data from household maize production in Kenya. The magnitude of estimated partial effects of exogenous household characteristics on inefficiency turns out to be very sensitive to model specification, and the model selection procedure leads to an unambiguous choice of best model. We propose a bootstrapping procedure to evaluate the size and power of the model selection procedure. The empirical application also provides further evidence on how household characteristics influence technical inefficiency in maize production in developing countries.

A Huff model with firm heterogeneity and selection. Application to the Italian retail sector

ABSTRACT

In this paper, firm heterogeneity (in terms of productivity, i.e., marginal costs) is incorporated into a Huff model of competition in the Italian retail sector. A higher market potential in the trade area is associated with higher average productivity and lower productivity dispersion through selection of the best stores. The analysis, based on a unique data set encompassing 14,212 Italian retailers, finds support for this relationship in Southern Italy, but not in Northern and Central Italy (where opposite results are obtained in some cases), suggesting the selection dynamics are affected by context factors (other than provincial/regional accessibility) related to an upper geographical scale. The results are robust to controlling for local context factors such as financial risk and floor size restrictions. Floor size restrictions are found to enhance selection.     

私立大专院校会计师选任模式与审计质量之探讨

私立大专院校委托会计师查核签证财务报表已行之多年，会计师选任模式也一再变动，会计师事务所是否提供更好的审计质量不得而知，而且五大会计师事务所在私校审计市场是否维持高审计质量，亦为值得关切的议题。本文以台湾私立大专院校为研究对象，针对“教育部”委托会计师项目查核私立大专院校，并依“教育部”是否补助审计公费将会计师选任模式分为自选（“教育部”补助半数审计公费）、公开招标指派（全数补助）及自选（不补助审计公费）三种选任模式下追行探讨，寻找会计师选任模式与审计质量间的关联性。本研究实证结果发现：（1）五大会计师事务所查核学校的盈余管理程度比非五大查核学校低，意味着高审计质量查核人员的专业能力较强并具有超然独立性，可阻止或降低盈余管理；（2）在2004年度自行遴选（“教育部”不补助审计公费）会计师事务所模式下，依私立大学体系、私立技职体系或私立大专院校三种分类其整体盈余管理程度皆较低，整体审计质量较高。本研究结果为非营利组织及“教育部”在选任会计师或制定相关政策时提供参考。     

Organizational design with a budget constraint

In this paper we point out that the presence of the budget constraint substantially affects the relative performance of organizational decision rules. For instance, if there are only two types of projects to decide upon, the hierarchy always performs better than the polyarchy. The same result holds for any distribution of the quality of projects. We also illustrate that optimal mechanisms for variable evaluation costs involve stopping rules which look hierarchical, but avoid costly duplication as in hierarchies or polyarchies. Received: 5 November 1995 / Accepted: 18 September 1997     

Bayesian portfolio selection using a multifactor model

This article develops a new portfolio selection method using Bayesian theory. The proposed method accounts for the uncertainties in estimation parameters and the model specification itself, both of which are ignored by the standard mean-variance method. The critical issue in constructing an appropriate predictive distribution for asset returns is evaluating the goodness of individual factors and models. This problem is investigated from a statistical point of view; we propose using the Bayesian predictive information criterion. Two Bayesian methods and the standard mean-variance method are compared through Monte Carlo simulations and in a real financial data set. The Bayesian methods perform very well compared to the standard mean-variance method.     

Non-linear exchange rate relationships: An automated model selection approach with indicator saturation

This paper examines whether the explanatory power of exchange rate models can be improved by allowing for cross-country asymmetries and non-linear effects of fundamentals. Both appear to be crucial. The samples include the USD versus pound and yen from 1982:10 to 2013:10, and automated model selection is conducted with indicator saturation. Several non-linear effects are significant at 1%. Further, many of the indicators present in the linear models are eliminated once allowing for non-linearities; suggesting some of the structural breaks found in previous work were an artifact of the misspecified linear functional form. These conclusions are robust to estimation using principal components.