On the index tracking and the statistical arbitrage choosing the stocks by means of cointegration: the role of stock picking

In this paper, we propose a new methology for Index Tracking (IT) by means of cointegration which provides some significant improvements on that field. As the quality of the tracking portfolio (TP) depends highly on the stock selection procedure, we propose picking the stocks using a model selection technique based on optimizing the cointegration level of the TP and the benchmark index instead of selecting, as in previous papers the assets by ad hoc decisions. To illustrate an empirical application of these techniques we use daily closing prices in the Dow Jones Industrial Average (DJIA) index over two different periods; one period which goes from 1 January 1990 to 31 December 2001 previously used by other authors, and the bear and a turmoil period, which goes from January 2007 to May 2012, inside the current financial crisis. Using only five assets we are able to successfully track the DJIA index and our results improve the IT technique based on cointegration that chooses stocks with maximum capitalization level. We also have compared our results with a more traditional procedure based on correlation and again our results reveal superiority. The empirical illustration not only has been focused on the TP itself, but has also been extended to tracking the index with an added profitability of 5, 10, 15 or 20% and to long-short strategies, producing profitable results.     

A Novel Method for Solving Unbounded Knapsack Problem

Knapsack problem is one kind of NP-Complete problem. Unbounded knapsack problems are more complex and harder than general knapsack problem. In this paper, we apply QGAs （ Quantum Genetic Algorithms） to solve unbounded knapsack problem and then follow other procedures. First, present the problem into the mode of QGAs and figure out the corresponding genes types and their fitness functions. Then, find the perfect combination of limitation and largest benefit. Finally, the best solution will be found. Primary experiment indicates that our method has well results.     

New results in retrial queueing systems with breakdown of the servers

We consider retrial queues with servers that are subject to active breakdowns. First, we concentrate on Markovian queueing models. For the multiserver case we obtain sufficient conditions for ergodicity. For the singleserver case we introduce new performance measures: the "orbit" idle period and the "orbit" busy period. Further, we investigate the asymptotic behaviour under high intensity of retrials. We also give an algorithm to compute the steady-state probabilities for the M/G/l retrial queue with breakdowns. The algorithm is based on a regenerative approach.     

Stochastic Optimization: a Review

We review three leading stochastic optimization methods—simulated annealing, genetic algorithms, and tabu search. In each case we analyze the method, give the exact algorithm, detail advantages and disadvantages, and summarize the literature on optimal values of the inputs. As a motivating example we describe the solution—using Bayesian decision theory, via maximization of expected utility—of a variable selection problem in generalized linear models, which arises in the cost-effective construction of a patient sickness-at-admission scale as part of an effort to measure quality of hospital care.     

遗传算法在阵列天线方向图综合中的应用

遗传算法是一种高效的搜索算法。本文采用遗传算法对18元天线阵列方向图进行综全优化设计，并与用Woodward法得到的结果进行了比较。     

组合证券投资的期望值模型的研究

本文是在Markowitz现代组合投资理论的基础上 ,建立了组合证券投资的期望值模型 ,并讨论了基于随机模拟技术的遗传算法对此模型的求解。     

Min–Max exact and heuristic policies for a two-echelon supply chain with inventory and transportation procurement decisions

We study the problem in which one supplier delivers a product to a set of retailers over time by using an outsourced fleet of vehicles. Since the probability distribution of the demand is not known, we provide a Min–Max approach to find robust policies. We show that the optimal Min-Expected Value policy can be very poor in the worst case. We provide a Min–Max Dynamic Programming formulation that allows us to exactly solve the problem in small instances. Finally, we implement a Min–Max Matheuristic to solve benchmark instances and show that it is very effective.     

基于遗传算法的动力吸振器参数的优化设计

以附有动力吸振器的两自由度振动系统作为研究对象,利用达朗伯原理建立了数学模型,并进行无量纲化运算。针对动力吸振器的3个参数γ,μ和κ,运用AMESim软件中的Design Exploration模块进行基于遗传算法的优化设计。计算结果表明,3个参数的收敛情况各有不同,γ趋于定义域上限,而μ和κ则趋于一个定值。该结论对于在实际应用中合理设计动力吸振器的参数,并最大限度的抑制相关振动具有重要意义。     

一种动态交通导航的寻优模型研究与仿真

针对目前的车辆路径算法不能满足动态导航的要求,提出了一种用于动态交通导航的优中手优模型。该模型定义了出行时间、路径风险性和路径适宜度等三大指标,并以三大指标的目标函数作为适应度因子,通过遗传算法寻找最优路径。仿真实验表明,诙模型能够根据不同的指标提供多种最优出行方案,并动态计算最优路径,满足车辆动态导航的要求。     

非线性关系下绿色施工管理多目标权衡研究

绿色施工管理除去工期、费用、质量三大目标外,还考虑环境影响、社会影响,形成了更全面、复杂的目标管理体系。分析了绿色施工管理各单项工作综合费用、质量、环保水平、安全指数与持续时间之间的非线性关系,利用多属性效用函数理论及最优化理论,构建了非线性关系下的绿色施工管理多目标权衡模型。采用遗传算法求解,求得的最满意解为绿色施工管理的各目标控制要求。最后应用实例验证了模型的合理性,并讨论了实现绿色施工管理的一个困境。