特质波动率与股票收益——基于Fama-French五因子模型的研究北大核心CSCD

股票收益与风险之间的关系一直以来都是金融学理论研究的核心问题.近年来,针对股票收益与特质风险之间的关系的研究受到了越来越多的学者的关注.在有效市场的理论下,通过构建多样化投资组合,投资者可以分散公司的特质风险,使组合收益更多的与市场风险相关.然而由于投资者之间信息不对称、市场政策差异化、资金成本不均衡等因素的影响,往往无法构建其目标组合,于是无法分散的特质风险就会在一定程度上影响股票收益.该文章以沪深A股自2000年1月4日至2015年12月31日共计3871个交易日的日收益率数据作为研究样本,通过滚动窗口分析的方法,以最新的五因子模型为切入点,研究发现了在中国股票市场中特质波动率与股票收益之间的负相关关系,并通过构建高特质风险与低特质风险股票组合进一步发现特质波动率与收益之间的负相关关系在经济意义上显著.     

中国股票市场收益率分布曲线的实证

股票价格行为的随机理论认为市场收益服从正态分布 ,但在现实中这一假设不一定成立 ,市场收益率更多地呈现出偏离正态分布的形式。本文检验中国市场的收益率分布形态。     

Concepts for safety stock determination under stochastic demand and different types of random production yield

We consider a manufacturer’s stochastic production/inventory problem under periodic review and present methods for safety stock determination to cope with uncertainties that are caused by stochastic demand and different types of yield randomness. Following well-proven inventory control concepts for this problem type, we focus on a critical stock policy with a linear order release rule. A central parameter of this type of policy is given by the safety stock value. When non-zero manufacturing lead times are taken into account in the random yield context, it turns out that safety stocks have to be determined that vary from period to period. We present a simple approach for calculating these dynamic safety stocks for different yield models. Additionally, we suggest approaches for determining appropriate static safety stocks that are easier to apply in practice. In a simulation study we investigate the performance of the proposed safety stock variants.     

A hierarchical approach for one-dimensional cutting stock problems in the steel industry that maximizes yield and minimizes overgrading

In the steel industry, as hot steel products exit the producing facility, they are cut at primary saws (hotsaws) into shorter pieces. After these pieces cool, they are inspected for defects and either applied directly to customer orders or are further cut to ordered lengths at secondary saws (cold saws). In this case study, we will describe a hierarchical algorithm, DYNACUT_CS, that efficiently and effectively generates cutting patterns for material that is to be cut at cold saws. DYNACUT_CS strives to maximize yield over all the material cut and simultaneously tries to minimize overgrading (applying higher quality material than specified by the customer). An example will be given to illustrate how the algorithm works. This approach has been implemented for a variety of products at several different Bethlehem Steel Corporation facilities.     

Optimizing routes and stock

This work is motivated by a problem proposed to the authors by a bakery company in Northern Spain. The objective is to design the daily routes over the week in order to minimize the total traveled distance. For reducing this total distance, some flexibility in the dates of delivery is introduced, which will cause a stock. Therefore, we study the problem under the bi-objective perspective, “minimizing” simultaneously the total traveled distance and the stock. A bi-objective mixed-integer linear model for the problem is formulated and two methodologies of solution are presented. The first one is based on a series of linked variable neighborhood searches and the second one is based on NSGA-II provided of specific operators. Numerical results showing the obtained estimated Pareto front in both cases are presented.     

艾滋疫情控制中的资金预警机制与分配策略研究

在艾滋病疫情控制的过程中,存在着诸多可能影响艾滋病疫情蔓延进而导致疫情控制目标无法实现的不确定因素,因而疫情控制的决策机构必须能够及时针对疫情变化调整方案.为此,首先,本文提出了一种艾滋病疫情控制中的资金预警机制.在疫情蔓延趋势即将偏离理想目标时发出预警,并给出需追加资金的需求估值.其次,对于控制区域下包括多个分区域的情况,利用数据包络分析方法给出了一种综合考虑疫情危险程度与经济状况的资金分配策略.从而在项目实施的不同阶段出现实际可用资金无法满足疫情控制的需求时,实现了项目可用资金的合理分配,提高了资金在疫情控制上的利用效率.最后,以一个数值例子说明了所提资金预警机制及分配策略的必要性及其有效性.     

基于DEA的基金绩效评价研究

用数据包络分析(DEA)方法对天天基金网中最新银河评级一年期表现优秀的三大类共39只基金绩效进行了进一步的评价研究,选取能充分体现基金风险与收益的统计指标,均值与方差,运用DEA有效性原理比较并分析了39只基金的技术有效性与规模有效性,给出了无效基金仿效的标杆.     

Heuristic for the rectangular two-dimensional single stock size cutting stock problem with two-staged patterns

Two-staged patterns are often used in manufacturing industries to divide stock plates into rectangular items. A heuristic algorithm is presented to solve the rectangular two-dimensional single stock size cutting stock problem with two-staged patterns. It uses the column-generation method to solve the residual problems repeatedly, until the demands of all items are satisfied. Each pattern is generated using a procedure for the constrained single large object placement problem to guarantee the convergence of the algorithm. The computational results of benchmark and practical instances indicate the following: (1) the algorithm can solve most instances to optimality, with the gap to optimality being at most one plate for those solutions whose optimality is not proven and (2) for the instances tested, the algorithm is more efficient (on average) in reducing the number of plates used than a published algorithm and a commercial stock cutting software package.     

Modeling multistage cutting stock problems

In multistage cutting stock problems (CSP) the cutting process is distributed over several successive stages. Every stage except the last one produces intermediate products. The list of intermediate products may be given or arbitrary. The goal is to minimize the total amount of material taken out of stock to cut finished products sufficient to meet customer demands. If the intermediate sizes are given, the column generation technique can be applied to multistage cutting problems. If the intermediate sizes are not given then another dimension is added to the problem complexity. We propose a special procedure for this case that dynamically generates both rows (intermediate sizes) and columns (patterns). We refer to this method as row-and-column generation. The method uses an auxiliary problem embedded into the frame of the revised simplex algorithm. It is a non-linear knapsack problem that can be solved efficiently. In contrast to the column generation method the developed technique cannot guarantee the optimal solution. However, the results of computational experiments are very promising and prove that the method is a valuable addition to the set of tools for modeling and solving multistage CSPs.     

A behavioral stock market model

Stock exchanges are modeled as nonlinear closed-loop systems where the plant dynamics is defined by known stock market regulations and the actions of agents are based on their beliefs and behavior. The decision of the agents may contain a random element, thus we get a nonlinear stochastic feedback system. The market is in equilibrium when the actions of the agents reinforce their beliefs on the price dynamics. Assuming that linear predictors are used for prediction of the price process, a stochastic approximation procedure for finding market equilibrium is described. The proposed procedure is analyzed using the theory of Benveniste et al. (Adaptive algorithms and stochastic approximations. Springer, Berlin, 1990). A simulation result is also presented.