What Happens in the Field Stays in the Field: Exploring Whether Professionals Play Minimax in Laboratory Experiments

The minimax argument represents game theory in its most elegant form: simple but with stark predictions. Although some of these predictions have been met with reasonable success in the field, experimental data have generally not provided results close to the theoretical predictions. In a striking study, Palacios‐Huerta and Volij ( 2008 ) presented evidence that potentially resolves this puzzle: both amateur and professional soccer players play nearly exact minimax strategies in laboratory experiments. In this paper, we establish important bounds on these results by examining the behavior of four distinct subject pools: college students, bridge professionals, world‐class poker players, who have vast experience with high‐stakes randomization in card games, and American professional soccer players. In contrast to Palacios‐Huerta and Volij's results, we find little evidence that real‐world experience transfers to the lab in these games—indeed, similar to previous experimental results, all four subject pools provide choices that are generally not close to minimax predictions. We use two additional pieces of evidence to explore why professionals do not perform well in the lab: (i) complementary experimental treatments that pit professionals against preprogrammed computers and (ii) post‐experiment questionnaires. The most likely explanation is that these professionals are unable to transfer their skills at randomization from the familiar context of the field to the unfamiliar context of the lab.     

A heuristic framework for the bi-objective enhanced index tracking problem

The index tracking problem is the problem of determining a portfolio of assets whose performance replicates, as closely as possible, that of a financial market index chosen as benchmark. In the enhanced index tracking problem the portfolio is expected to outperform the benchmark with minimal additional risk. In this paper, we study the bi-objective enhanced index tracking problem where two competing objectives, i.e., the expected excess return of the portfolio over the benchmark and the tracking error, are taken into consideration. A bi-objective Mixed Integer Linear Programming formulation for the problem is proposed. Computational results on a set of benchmark instances are given, along with a detailed out-of-sample analysis of the performance of the optimal portfolios selected by the proposed model. Then, a heuristic procedure is designed to build an approximation of the set of Pareto optimal solutions. We test the proposed procedure on a reference set of Pareto optimal solutions. Computational results show that the procedure is significantly faster than the exact computation and provides an extremely accurate approximation.     

混合预期噪声交易模型及我国证券市场非理性交易的实证分析

 证券资产作为以获取未来收益为目的投资工具,它在价格形成及其市场运行模式上都取决于投资者的预期。基于有效市场假说（EMH）的资产定价理论虽然在理论体系及其形式化上都给出了完美的解决方案,但投资者理性和投资者具有完全一致的预期这两个基本假定在现实证券市场投资中却难以得到满足。本文更现实地依据投资者预期形成的差异,讨论了非一致有限理性预期下的证券市场价格的决定,提出了基于混合预期的噪声交易模型。同时本文还利用上海证券市场的相关实际数据,对该模型进行了实证检验,从而克服了行为金融学中噪声交易模型不能用于实证分析的障碍。     

微量元素添加剂的配合与利用

概述了微量矿物质添加剂预混料制作的原则、配方、配制技术及加工工艺。     

混杂试验方法(Ⅰ)

本文通过划分不完全区组,牺牲多因素试验中无关紧要的一些高级互作,增强局部控制作用,降低环境差异及试验条件的不均匀分配问题,提高试验的主效和一级互作研究精度     

混杂试验方法Ⅱ

本文通过混杂原理 ,应用多因素高水平试验处理组合的一部分进行试验 ,即保证试验研究的核心主效和一级互作得到正确估计 ,又能大量减少试验处理数 ,简化试验实施操作 ,提高试验研究效率     

A hybrid inventory management system responding to regular demand and surge demand

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.     

Restructuring in family firms: Balancing family objectives and economic prosperity

This study analyzes the extent to which family firms adopt distinct restructuring strategies (employee downsizing, management dismissals, asset retrenchment, dividend cuts) in response to declining performance. We hypothesize that family firms select different restructuring strategies than non-family firms because of the mixed gamble between economic benefits and socioemotional wealth (SEW), and that this difference diminishes as the severity of decline increases. The hypotheses were tested with a sample of 357 decline incidences in 283 German firms between 2005 and 2018 and are largely confirmed; at low decline severity, family firms downsize and retrench less, but this tendency is reversed as the severity increases. They tend to cut dividends more strongly and are less willing to dismiss managers, irrespective of their decline severity.     

Restricted likelihood ratio lack-of-fit tests using mixed spline models

Summary.  Penalized regression spline models afford a simple mixed model representation in which variance components control the degree of non-linearity in the smooth function estimates. This motivates the study of lack-of-fit tests based on the restricted maximum likelihood ratio statistic which tests whether variance components are 0 against the alternative of taking on positive values. For this one-sided testing problem a further complication is that the variance component belongs to the boundary of the parameter space under the null hypothesis. Conditions are obtained on the design of the regression spline models under which asymptotic distribution theory applies, and finite sample approximations to the asymptotic distribution are provided. Test statistics are studied for simple as well as multiple-regression models.     

Predicting Consumption Poverty using Non-Consumption Indicators: Experiments using Indonesian Data

Household consumption expenditure data is crucial for calculating important welfare measures such as poverty headcount rate. However, collecting such data is difficult and cumbersome. As an alternative, we experiment with three methods – consumption correlates model, poverty probability model, and wealth index principal components analysis (PCA) – to predict consumption expenditure and poverty using non-consumption indicators. The purpose is to use these alternatives for rapid monitoring and appraisal of social welfare as an early warning system. We test each method’s performance and find that the consumption correlates model is the best method to predict poverty quickly and relatively accurately. We find that education level, asset ownership, and consumption pattern are the best predictors of expenditure and poverty.