Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

The futility of decision making research

We have each spent more than 50 years doing research that has had little impact. Even more lamentable is that our field, judgment and decision making (JDM), has on the whole had little impact during that span. We attribute that failure to the use of methodologies that emphasize testing models rather than looking for differences in behavior. The “cognitive revolution” led the field astray, toward the goal of studying model fit rather than comparing observable results. With modeling as the goal, experimentation was stultified. Simple tasks became dominant. Although a poor metaphor for real decision making, the gambling paradigm has lasted forever because the inputs to the decision are known to the researcher and thus easily modeled.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

带搬运时间的批量生产制造系统生产作业计划方法

为给考虑搬运时间的批量生产制造系统中的同类型工件制定生产作业计划,对批量工件的加工与搬运时间、搬运车辆调度和工件移动方式决策等问题进行深入研究.构建同类型批量工件在不同移动方式中的加工与搬运时间模型并设计相应的计算流程图.建立基于生产周期和搬运车辆总投入数量两个决策目标的工件移动方式决策模型.研究结果表明:本研究可有效选择工件移动方式,可为考虑搬运时间的同类型批量工件制定精确的生产作业计划,并可对搬运车辆进行合理调度.     

大数据科学的认知和数学基础引论

 大数据不仅在科学、工程与计算智能中有着广泛的应用，而且在人类感知、估计、量化、记忆和推理的认知机制中发挥着基础性作用。通过对大数据科学理论的基础研究，提出一组大数据系统的一般原理和分析方法。为了从形式上解释大数据的起源和本质，探讨大数据的认知基础及其数学模型，严格地引出了根植于科学、工程和社会各个领域的大数据的一般模式。研究发现大数据不再是传统实域上的纯数，而是一个前所未有的新型数学结构，称为递归类型化超结构（RTHS）。这一大数据系统的基本拓扑特性揭示了大数据工程的复杂性及其操作与处理的全新认知、理论挑战，以及可选解决方案。     

基于正态分布概率模型的渗滤液回灌影响分析

渗滤液回灌能有效加速填埋场的稳定并处理多余的渗滤液, 而预测回灌过程中渗滤液的运移规律对于合理设置回灌井的间距和数量具有重要的意义. 考虑了垃圾体的非均质性, 建立了垃圾体的渗透系数随空间正态分布变化的概率模型. 利用 COMSOL Multiphysics^® 软件, 首先对单孔隙度的横纵向均质模型和正态分布模型的 渗透系数以及压力水头进行了比较, 其次研究了在横纵向渗透系数比值、回灌速率和回灌时间等不同 影响因素下, 均质模型和正态分布模型的含水率以及横向影响范围的变化规律. 结果表明, 随着横纵向渗透系数比值的增加, 最终在 100 d 时: 均质模型的含水率由 0.609 增加到 0.68, 横向影响范围从4.842 m 增加到 6.79 m; 正态分布模型的含水率由 0.573 增加到 0.610, 横向影响范围从4.097 m 变为 4.04 m. 这说明横纵向渗透系数比值对正态分布模型的含水 率和渗滤液横向影响范围的影响明显高于均质模型. 而当回灌速率和回灌时间相同时, 正态分布模型渗滤液的渗流速度更快, 易于优先达到饱和含水率, 并在短暂的峰值后迅速下降, 且随着回灌速率和回灌时间的增加, 达到饱和含水率和峰值的时间也逐渐增加.     

Existence of macroscopic spatial superpositions in collapse theories

It is shown that the collapse dynamics in the CSL model will entangle two independent systems under certain conditions, and their state after collapse may be an entangled superposition of spatially separated states. However, since the conditions can hardly be satisfied in reality, the occurrence of such superpositions is very improbable, and thus collapse theories still provide a promising solution to the measurement problem.     

异方差半参数变系数部分线性EV模型的经验似然

利用经验似然方法考虑异方差半参数变系数部分线性EV模型中兴趣参数置信域的构造.分别在误差方差已知和未知情形下,构造模型参数部分的经验对数似然比统计量,并验证非参数情形下的Wilks定理成立.模拟研究表明经验似然估计方法具有优良的性质.     

线性代数向量组正交化的教学改革

线性代数课程中的向量组正交化的传统方法,即施密特正交化过程。多年来,很多教材都是沿用施密特正交化过程方法,但其计算量比较大。论述了使用齐次线性方程组求非零解的方法,将向量组正交化,产生一种新的构思。