Combined forecasts in portfolio optimization: A generalized approach

In this paper a general mathematical model for portfolio selection problem is proposed. By considering a forecasting performance according to the distributional properties of residuals, we formulate an extended mean-variance-skewness model with 11 objective functions. Returns and return errors for each asset obtained using different forecasting techniques, are combined in optimal proportions so as to minimize the mean absolute forecast error. These proportions are then used in constructing six criteria related to the mean, variance and skewness of return forecasts of assets in the future and forecasting errors of returns of assets in the past. The obtained multi-objective model is scalarized by using the conic scalarization method which guarantees to find all non-dominated solutions by considering investor preferences in non-convex multi-objective problems. The obtained scalar problem is solved by utilizing F-MSG algorithm. The performance of the proposed approach is tested on a real case problem generated on the data derived from Istanbul Stock Exchange. The comparison is conducted with respect to different levels of investor preferences over return, variance, and skewness and obtained results are summarized.     

Wavelet evolutionary network for complex-constrained portfolio rebalancing

Portfolio rebalancing problem deals with resetting the proportion of different assets in a portfolio with respect to changing market conditions. The constraints included in the portfolio rebalancing problem are basic, cardinality, bounding, class and proportional transaction cost. In this study, a new heuristic algorithm named wavelet evolutionary network (WEN) is proposed for the solution of complex-constrained portfolio rebalancing problem. Initially, the empirical covariance matrix, one of the key inputs to the problem, is estimated using the wavelet shrinkage denoising technique to obtain better optimal portfolios. Secondly, the complex cardinality constraint is eliminated using k-means cluster analysis. Finally, WEN strategy with logical procedures is employed to find the initial proportion of investment in portfolio of assets and also rebalance them after certain period. Experimental studies of WEN are undertaken on Bombay Stock Exchange, India (BSE200 index, period: July 2001–July 2006) and Tokyo Stock Exchange, Japan (Nikkei225 index, period: March 2002–March 2007) data sets. The result obtained using WEN is compared with the only existing counterpart named Hopfield evolutionary network (HEN) strategy and also verifies that WEN performs better than HEN. In addition, different performance metrics and data envelopment analysis are carried out to prove the robustness and efficiency of WEN over HEN strategy.     

主数据管理驱动的高校信息化SOA建设

异构信息系统在高校中的大量存在阻碍了数据资产整合与信息交互，面向服务的架构（SOA）的出现及在企业中的广泛采用为解决此问题提供了思路，但在高校中实施SOA存在难度大、难以形成以SOA为基础的信息化生态的问题。针对这些问题，提出主数据管理驱动的SOA建设方案。首先，在数据层面运用主数据管理平台对校级核心数据资产进行建模和整合；为实现数据同步和消费，并解决其中存在的协议转换及服务鉴权问题，提出了基于企业服务总线的解决方案；然后，针对遗留"信息孤岛"系统进行SOA改造，提出主数据驱动的建设方案。实验结果表明单用户、10用户、100用户及10000用户并发下的平均延迟分别为8、11、59及18 ms，表明在不同并发场景下所提方案性能均满足高校业务需求。实施结果表明，数据资产整合和信息交互问题得到了解决，因此方案具有可行性。     

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.     

A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs

A memetic approach that combines a genetic algorithm (GA) and quadratic programming is used to address the problem of optimal portfolio selection with cardinality constraints and piecewise linear transaction costs. The framework used is an extension of the standard Markowitz mean–variance model that incorporates realistic constraints, such as upper and lower bounds for investment in individual assets and/or groups of assets, and minimum trading restrictions. The inclusion of constraints that limit the number of assets in the final portfolio and piecewise linear transaction costs transforms the selection of optimal portfolios into a mixed-integer quadratic problem, which cannot be solved by standard optimization techniques. We propose to use a genetic algorithm in which the candidate portfolios are encoded using a set representation to handle the combinatorial aspect of the optimization problem. Besides specifying which assets are included in the portfolio, this representation includes attributes that encode the trading operation (sell/hold/buy) performed when the portfolio is rebalanced. The results of this hybrid method are benchmarked against a range of investment strategies (passive management, the equally weighted portfolio, the minimum variance portfolio, optimal portfolios without cardinality constraints, ignoring transaction costs or obtained with L₁ regularization) using publicly available data. The transaction costs and the cardinality constraints provide regularization mechanisms that generally improve the out-of-sample performance of the selected portfolios.     

Invasive weed optimization for solving index tracking problems

Index tracking belongs to one of the most important types of problems in portfolio management. In contrast to classical (active) portfolio management and optimization, passive portfolio management usually seeks to replicate a given index for a financial market. Due to transaction costs or legal or other practical limitation on the tradability of the respective assets, such indexes are often not fully replicated by a respective portfolio. Instead, one seeks to use a subset of the index assets (or other types of assets) to obtain a portfolio most similar to the index. This index tracking problem can be formulated as an optimization problem with respect to the minimization of the tracking error. In this article, we explore possibilities to solve the index tracking problem with invasive weed optimization (IWO), a rather new population-based metaheuristics algorithm. The complexity of this real-life problem and especially its solution space and restrictions require substantial adaptation of the original IWO algorithm. We explore different possibilities to adapt IWO to the considered type of problem. The adapted IWO method is tested using MSCI USA Value data, and systematic studies to find suitable parameter values are conducted. Although the method basically works well, the obtained results do not fully reach our intended benchmark. Reasons for that and possibilities for further improvement of the methodology are discussed.     

The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms

This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms (MOEAs) together with a steady state evolutionary algorithm on the mean–variance cardinality constrained portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2), and e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using formal metrics proposed by the evolutionary multiobjective optimization community on publicly available data sets which contain up to 2196 assets.     

基于综合赋权法的高校资产管理评价研究

为了解决高校资产管理绩效难以准确地评估的问题,对评价体系指标赋权的方法进行了回顾,提出高校资产管理的绩效评价问题要考虑的主客观因素;利用主客观权重组合方法,将主客观权值组合成为指标的组合权,评判专家再对评价指标评分,通过对指标的权值和评分向量的计算得出高校资产管理的绩效值。实验验证表明：设计的高校资产管理绩效计算方法有效地解决了高校资产管理绩效的评价问题,从而为高校资产管理绩效的评价提供了新的思路和方法。     

Localization of RFID-equipped assets during the operation phase of facilities

Indoor localization has gained importance as it has the potential to improve various processes related to the lifecycle management of facilities and to deliver personalized and location-based services (LBSs). Radio Frequency Identification (RFID) based systems, on the other hand, have been widely used in different applications in construction and maintenance. This paper investigates the usage of RFID technology for indoor localization of RFID equipped assets during the operation phase of facilities. The location-related data on RFID tags attached to assets are extracted from a Building Information Model (BIM) and can provide context-aware information inside the building which can improve Facilities Management (FM) processes. First, using the current location of the assets saved on the tags attached to fixed assets, an FM personnel is able to read tags from a distance and locate them on a floor plan. Fixed tags with known positions act as reference tags for RFID reader localization techniques. In this scenario, the user can also estimate his/her location by scanning the surrounding reference tags. Furthermore, the paper investigates an approach to locate moveable assets using received signals from available reference tags in the building based on pattern matching and clustering algorithms. As a result, a user equipped with an RFID reader is able to estimate his/her location, as well as the location of target assets, without having access to any Real-Time Location System (RTLS) infrastructure. Several case studies are used to demonstrate the feasibility of the proposed methods.     

区块链技术在数字资产安全交易中的应用

针对传统数据资产交易平台依靠中心化的管理机构完成交易过程,不能保证数据资产交易过程中的安全性的弊端,利用区块链技术去中心化、去信任、难以篡改等技术特征.提出了一种基于区块链技术的新型数字资产安全交易方法.首先阐明传统数字资产交易平台的弊端,剖析了区块链技术在数据资产安全交易中的关键技术;其次,分别从数据存储、交易信息加密、验证节点间的共识算法方面提出具体的实施方案;最后,针对数据资产交易过程中的验证节点共识算法进行验证分析,实验结果表明,本文所提出的方法能很好的适用于数字资产安全交易.     

资产重组中金融风险的H∞控制理论应用研究

在随机资产模型的基础上，建立了资产重组问题的离散时间系统模型，运用降阶方法推导了在资产重组过程中使风险最小的奇异控制策略．最后，根据复变函数理论求解范数的方法求得了问题的解析解．     

Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment

Catchment managers face considerable challenges in managing ecological assets. This task is made difficult by the variable and complex nature of ecological assets, and the considerable uncertainty involved in quantifying how various threats and hazards impact upon them. Bayesian approaches have the potential to address the modelling needs of environmental management. However, to date many Bayesian networks (Bn) developed for environmental management have been parameterised using knowledge elicitation only. Not only are these models highly qualitative, but the time and effort involved in elicitation of a complex Bn can often be overwhelming. Unfortunately in environmental applications, data alone are often too limited for parameterising a Bn. Consequently, there is growing interest in how to parameterise Bns using both data and elicited information. At present, there is little formal guidance on how to combine what can be learned from the data with what can be elicited. In a previous publication we proposed a detailed methodology for this process, focussing on parameterising and evaluating a Bn. In this paper, we further develop this methodology using a risk assessment case study, with the focus being on native fish communities in the Goulburn Catchment (Victoria, Australia).     

Distributed media indexing based on MPI and MapReduce

Web-scale digital assets comprise millions or billions of documents. Due to such increase, sequential algorithms cannot cope with this data, and parallel and distributed computing become the solution of choice. MapReduce is a programming model proposed by Google for scalable data processing. MapReduce is mainly applicable for data intensive algorithms. In contrast, the message passing interface (MPI) is suitable for high performance algorithms. This paper proposes an adapted structure of the MapReduce programming model using MPI for multimedia indexing. Experimental results are done on various multimedia applications to validate our model. The experiments indicate that our proposed model achieves good speedup compared to the original sequential versions, Hadoop and the earlier versions of MapReduce using MPI.     

Mean-variance model for portfolio optimization with background risk based on uncertainty theory

The aim of this paper is to develop a mean-variance model for portfolio optimization considering the background risk, liquidity and transaction cost based on uncertainty theory. In portfolio selection problem, returns of securities and assets liquidity are assumed as uncertain variables because of incidents or lacking of historical data, which are common in economic and social environment. We provide crisp forms of the model and a hybrid intelligent algorithm to solve it. Under a mean-variance framework, we analyze the portfolio frontier characteristic considering independently additive background risk. In addition, we discuss some effects of background risk and liquidity constraint on the portfolio selection. Finally, we demonstrate the proposed models by numerical simulations.     

Single liner shipping service design

The design of container shipping networks is an important logistics problem, involving assets and operational costs measured in billions of dollars. To guide the optimal deployment of the ships, a single vessel round trip is considered by minimizing operational costs and flowing the best paying demand under commercially driven constraints. This paper introduces the Single Liner Shipping Service Design Problem. Arc-flow and path-flow models are presented using state-of-the-art elements from the wide literature on pickup and delivery problems. A Branch-and-Cut-and-Price algorithm is proposed, and implementation details are discussed. The algorithm can solve instances with up to 25 ports to optimality, a very promising result as real-world vessel roundtrips seldom involve more than 20 ports.     

风险资本约束下保险公司的最优比例再保险-投资策略

本文研究保险公司的再保险-投资问题.假定保险公司的整体风险由风险资本(Capital-at-Risk,CaR)来度量;盈余过程由扩散模型近似表示;在任意时刻保险公司可购买比例再保险(或获取新业务)和投资无风险资产与多种风险资产;风险资产的价格由几何布朗运动驱动.保险公司的目标是在整体风险CaR受约束的条件下最大化终端财...     

Robust hedging strategies

While investing in foreign assets may bring additional benefits in terms of risk diversification, it may also expose the portfolio to a further source of risk derived from changes in the value of the foreign currencies. Hedging strategies for international portfolios have usually focused on the use of forward contracts to mitigate the currency risk. We propose an alternative formulation aimed at the reduction of the overall portfolio risk by assuming the returns are uncertain and maximizing the portfolio return for the worst possible outcome of the returns. This technique known as robust optimization provides a first guarantee on the portfolio value thanks to the non-inferiority property. We further complement our approach with forward contracts on the foreign exchange rates and options on the assets. Because the total return on any asset will be the product of its local return and currency return, the models proposed are bilinear and non convex. A reformulation of both the uncertainty set and the objective function as a semidefinite problem will yield an approximate tractable model. We compare the hedging alternatives proposed with simulated and historical market data and conclude on their relative benefits.     

Prediction of bankruptcy Iranian corporations through artificial neural network and Probit-based analyses

In this study, multilayer perceptron (MLP) of artificial neural networks is utilized to build a new model for bankruptcy prediction. A precise MLP-based relationship is obtained to classify samples of 136 bankrupt and non-bankrupt Iranian corporations using their financial ratios. A Probit analysis is performed to benchmark the MLP model. Ratios of sales to current assets ratio, operational income to sales, quick assets to total assets, and total liability to total assets are used as the effective predictive financial ratios. A comparative study is further conducted on the classification accuracy of the MLP, Probit, and other existing models. The proposed MLP model has a significantly better performance than the Probit and other models found in the bankruptcy prediction literature.