基于PID控制器的动态投资组合模型

在投资过程中, 投资者都是在发生亏损或预期发生亏损时才对投资头寸进行调整, 即投资者调整投资组合的目的是保证组合能获得正收益. 基于该思想, 以组合的预期收益率与通过组合要实现的收益率之差作为控制量, 通过PID 控制器动态调整组合中各证券的投资权重, 以实现组合下一期的预期收益率与人们在投资之初确定的收益率相等的目标. 仿真结果表明, 按该模型配置的投资组合的预期收益率能够达到目标收益率.     

基于非正态分布收益率的限制卖空投资组合模型及其优化仿真

针对收益率服从非正态分布的风险资产建立限制卖空的均值-VaR投资组合模型,与马克维兹的均值-方差投资组合模型及收益率服从正态分布的均值-VaR投资组合模型进行比较分析。应用实例显示均值-VaR投资组合模型的投资效果优于均值-方差投资组合模型,基于非正态分布收益率的均值-VaR模型的投资效果略优于基于正态分布收益率的均值-VaR模型。     

Intradaily dynamic portfolio selection

A portfolio selection model which allocates a portfolio of currencies by maximizing the expected return subject to Value-at-Risk (VaR) constraint is designed and implemented. Based on an econometric implementation using intradaily data, the optimal portfolio allocation is forecasted at regular time intervals. For the estimation of the conditional variance from which the VaR is computed, univariate and multivariate GARCH models are used. Model evaluation is done using two economic criteria and two statistical tests. The result for each model is given by the best forecasted intradaily investment recommendations in terms of the optimal weights of the currencies in the risky portfolio. The results show that estimating the VaR from multivariate GARCH models improves the results of the forecasted optimal portfolio allocation, compared to using a univariate model.     

Models and Simulations for Portfolio Rebalancing

In 1950 Markowitz first formalized the portfolio optimization problem in terms of mean return and variance. Since then, the mean-variance model has played a crucial role in single-period portfolio optimization theory and practice. In this paper we study the optimal portfolio selection problem in a multi-period framework, by considering fixed and proportional transaction costs and evaluating how much they affect a re-investment strategy. Specifically, we modify the single-period portfolio optimization model, based on the Conditional Value at Risk (CVaR) as measure of risk, to introduce portfolio rebalancing. The aim is to provide investors and financial institutions with an effective tool to better exploit new information made available by the market. We then suggest a procedure to use the proposed optimization model in a multi-period framework. Extensive computational results based on different historical data sets from German Stock Exchange Market (XETRA) are presented.     

Sparse portfolio rebalancing model based on inverse optimization

This paper considers a sparse portfolio rebalancing problem in which rebalancing portfolios with minimum number of assets are sought. This problem is motivated by the need to understand whether the initial portfolio is worthwhile to adjust or not, inducing sparsity on the selected rebalancing portfolio to reduce transaction costs (TCs), out-of-sample performance and small changes in portfolio. We propose a sparse portfolio rebalancing model by adding an l₁ penalty item into the objective function of a general portfolio rebalancing model. In this way, the model is sparse with low TCs and can decide whether and which assets to adjust based on inverse optimization. Numerical tests on four typical data sets show that the optimal adjustment given by the proposed sparse portfolio rebalancing model has the advantage of sparsity and better out-of-sample performance than the general portfolio rebalancing model.     

基于MIC架构稳健投资组合并行算法及实现

投资组合选择是数量化投资管理领域中的一项关键技术,目前其在应用中亟需高性能算法与实现研究。本论文针对现实投资场景下的稳健投资组合选择最优化模型,设计出高效的并行算法,利用并行计算技术多层级优化性能,实现对稳健投资组合计算的快速响应。该稳健投资组合将模糊集理论与投资组合理论相结合,建立基于可能性理论和机会测度的投资组合模型,用BP神经网络算法和遗传算法对模型进行求解,并在最新的高性能计算集成众核(Many Integrated Core,MIC)架构上实现并行。文章选取上证50指数成份股近两年的交易数据,对并行算法及其性能进行分析。结果显示,该算法计算得到的投资组合收益率优于经典模型收益率和上证50指数同期收益率,基于MIC架构的并行求解性能优于传统的CPU架构,平均并行效率达到80%。     

一种新的数据预处理算法--NLCA

针对数据预处理的方法进行了研究,提出了基于非线性相关性分析与量化（Non-Linear Correlation Analysis,NLCA）算法。NLCA算法是一种基于在多重图中通过对多重边聚合从而达到约简的工具,它包括边聚合与点聚合。这种算法能够很好地表示实时数据全局的相关性,改进了现有使用联合概率的单一计算方法。对该算法进行了大量实际数据的验证,显示出它是一种优于现有的数据预处理方法。     

Dynamic Network Model of Managing Investment Portfolio under Random Stepwise Changes in Volatilities of Financial Assets

The problem of managing a portfolio of risk (ordinary shares) and no-risk (bank account, reliable bonds) investments was considered. The portfolio model was described in the state space by a system of stochastic differential (or difference) equations with random stepwise parameters. Management of the investment portfolio was formulated as a dynamic problem of tracking some reference portfolio with an investor-defined yield. An approach to determining the optimal management strategy with quadratic criterion-based feedback was proposed. Results of numerical modeling were presented.     

A multiple objective stochastic portfolio selection problem with random Beta

When selecting a portfolio, we need to consider, in general, the portfolio return and portfolio risk. Many risk measures have been used in portfolio selection problems as the Beta risk measure, introduced by the capital asset pricing model. Most of the existing research papers suppose that security's Beta has a deterministic value. Recently, many researchers argued that in selecting the optimal portfolio, securities’ Beta should be considered as an uncertain parameter. In this paper, we set up fundamentals to model the portfolio's Beta as a random variable and propose a multiple objective stochastic portfolio selection model with random Beta. To solve the proposed model, we apply a stochastic goal programming approach. A numerical example from the US stock exchange market is reported.     

Portfolio model for analyzing human resources: An approach based on neuro-fuzzy modeling and the simulated annealing algorithm

This paper presents a new model for developing a human resources portfolio based on a neuro-fuzzy approach. The adaptive neural network is constructed based on the Boston Consulting Group (BCG) portfolio matrix. The adaptive neural network was established by applying the simulated annealing algorithm. The model enables decision makers to evaluate and assess human resources potential in accordance with the environment and its circumstances. The purpose of creating this model is to enable insight into the existing potential and plan assets to improve and promote the employees’ potential in a company. The model allows the priorities of the suggested strategies to be defined, which eliminates one of the flaws of the classic BCG portfolio matrix. In this neuro-fuzzy model the input variables are described using fuzzy sets that are represented by Gaussian functions. Using expert reasoning a unique knowledge base is formed which enables employees to be scheduled by strategies. The portfolio model is tested in a realistic industrial environment.     

Mean-variance-skewness model for portfolio selection with transaction costs

A mean-variance-skewness model is proposed for portfolio selection with transaction costs. It is assumed that the transaction cost is a V-shaped function of the difference between the existing portfolio and a new one. The mean-variance-skewness model is a non-smooth programming problem. To overcome the difficulty arising from non-smoothness, the problem was transformed into a linear programming problem. Therefore, this technique can be used to solve large-scale portfolio selection problems. A numerical example is used to illustrate that the method can be efficiently used in practice.     

A portfolio approach to multi-product newsboy problem with budget constraint

This paper investigates a portfolio approach to multi-product newsboy problem with budget constraint, in which the procurement strategy for each newsboy product is designed as portfolio contract. A portfolio contract consists of a fixed-price contract and an option contract. We model the problem as a profit-maximization model, and propose an efficient solution procedure after investigating the structural properties of the model. We conduct numerical studies to show the efficiency of the proposed solution procedure, and to compare three models with different procurement contracts, i.e., fixed-price contract, option contract, and portfolio contract. Numerical results are shown to demonstrate the advantage of the portfolio model, and sensitivity analysis is provided for obtaining some managerial insights.     

An approach to portfolio selection using an ARX predictor for securities’ risk and return

It is well known that every investment carries a risk associated, and depending on the type of investment, it can be very risky; for instance, securities. However, Markowitz proposed a methodology to minimize the risk of a portfolio through securities diversification. The selection of the securities is a choice of the investor, who counts with several technical analyzes to estimate investment’s returns and risks. This paper presents an autoregressive exogenous (ARX) predictor model to provide the risk and return of some Brazilian securities – negotiated at the Brazilian stock market, BOVESPA – to select the best portfolio, herein understood as the one with minimum expected risk. The ARX predictor succeeded in predicting expected returns and risks of the securities, which resulted in an effective portfolio. Additionally the Markowitz theory was confirmed, showing that diversification reduces the risk of a portfolio.     

风险效用投资组合模型的遗传算法求解

给出一个折衷考虑风险最小化和收益最大化的单目标决策方法,以单位风险收益最大化为决策目标建立了投资组合的非线性分式规划模型,考虑到分式规划问题的求解难度,利用遗传算法求解模型,并给出算法步骤。最后,给出了数值算例,结果表明该算法是简单有效的。     

A milp bi-objective model for static portfolio selection of R&;D projects with synergies

This paper presents a multi-objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as well as about synergies between projects at the activity and project level. The latter aspects are predominant in the context of portfolio selection of R&D projects in public organizations. Previous works on portfolio selection of R&D projects considered interdependencies mainly at the project level. In a few works considering activity level information the models and solution techniques were restricted to problems with a few projects. We study a generalization of our previous model and show that incorporating interdependencies and activity funding information is useful for obtaining portfolios with better quality. Numerical results are presented to demonstrate the efficiency of the proposed approach for large models.     

基于战略对应的IT项目组合选择模型

把信息技术项目当作组合来管理可以通过平衡风险和收益来促进企业目标和IT应用的结合,但由于决策信息的不确定性和IT项目目标与企业战略的难以对应,企业面临IT项目组合选择的挑战。构建基于战略对应的IT项目组合选择模型,其中模糊集和模糊层次分析法用来刻画不确定信息和评估IT项目风险、成本及收益,关键成功因素法用来提高IT项目与企业战略的对应,并建立模糊0－1整数规划。利用定性可能性理论把模糊组合选择模型转化为一般可求解的整数规划形式,最后用一个案例说明模型的用法。     

Modeling and managing portfolios including listed private equity

Listed private equity (LPE) provides investors with a liquid means of considering private equity in their portfolios. This paper presents a first-order autoregressive Markov-switching model (ARMS) which is able to capture the characteristics of the asset classes bonds, stocks, and LPE, such as heavy tails and autocorrelation. Optimizing a portfolio between bonds, stocks, and LPE shows that an investor benefits from including LPE due to the high diversification effects, which also holds for a very risk-averse investor. Allocating a portfolio with the presented Markov-switching optimization can help to significantly outperform a portfolio which is optimized assuming an underlying geometric Brownian motion (GBM) - even during the financial crisis: The terminal value of a portfolio of a model investor with medium risk aversion was on average 8.7% higher over the three years 2007-2009 than the GBM portfolio.     

An optimization model of the portfolio adjusting problem with fuzzy return and a SMO algorithm

Based on possibilistic mean and variance theory, this paper deals with the portfolio adjusting problem for an existing portfolio under the assumption that the returns of risky assets are fuzzy numbers and there exist transaction costs in portfolio adjusting precess. We propose a portfolio optimization model with V-shaped transaction cost which is associated with a shift from the current portfolio to an adjusted one. A sequential minimal optimization (SMO) algorithm is developed for calculating the optimal portfolio adjusting strategy. The algorithm is based on deriving the shortened optimality conditions for the formulation and solving 2-asset sub-problems. Numerical experiments are given to illustrate the application of the proposed model and the efficiency of algorithm. The results also show clearly the influence of the transaction costs in portfolio selection.     

不确定环境下基于VaR和CVaR的投资组合优化模型

对不确定环境下的投资组合问题进行研究,使用不确定测度来定义不确定环境下的VaR和CVaR,并用VaR和CVaR度量风险,建立基于VaR和CVaR风险控制的投资组合优化模型,并设计了集成遗传算法、99-方法的混合智能算法来求解此模型,最后通过实例验证了模型和算法的有效性。     

基于改进粒子群算法的投资组合选择模型

研究了在实际投资决策中存在交易成本(税收和交易费用)和投资数量约束下的投资组合选择问题,并进一步设计了一种求解该问题的改进粒子群算法.最后,给出了一个数值例子,说明该模型和方法的有效性.