Using deep learning and visual analytics to explore hotel reviews and responses

This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

PBL教学结合多媒体技术在物流专业课堂教学改革中的应用

文章分析了传统理论教学所存在的问题，结合《配送中心运营管理》课程的特点，将PBL教学模式与多媒体技术进行结合改革课堂教学模式，从而达到提高学生积极性和创造性的目的，对实施前准备工作、课堂教学中的组织和课堂后质量考核评价进行了设计和应用，从而达到培养学生职业能力的目的。     

Consequences of Debt Forgiveness: Strategic Default Contagion and Lender Learning

I use a unique data set of loans to small business owners to examine whether lenders face adverse consequences when they grant debt forgiveness to borrowers. I provide evidence consistent with borrowers communicating their debt forgiveness to other borrowers, who then more frequently strategically default on their own obligations. This strategic default contagion is economically large. When the lender doubles debt forgiveness, the default rate increases by 10.9% on average. Using an exogenous shock to the lender's forgiveness policy, my findings suggest that as the lender learns about the extent of borrower communication the lender tightens its debt forgiveness policy to mitigate default contagion.     

Factors affecting classroom participation and how participation leads to a better learning

The purpose of this paper is twofold. First, we analyze various course- and student-related factors that affect participation grades within several college accounting classes. The second purpose is to determine whether in-class participation grades have an effect on exam performance in these classes. We find that, unlike in liberal arts and sciences courses, in accounting courses, participation grades for female students do not differ from participation grades for their male counterparts, regardless of class level. Overall, course level and student major have no direct effect on participation grades; however, the accounting majors and students in the lower level courses have better attendance than do non-accounting majors and students in more advanced courses. We observe that class schedule, in particular class duration and semester, affects students’ participation performance. Lastly, we find that students who participate more in classroom discussions perform 25% better on exams than do those with lower participation grades. This relationship holds for the three elements of participation grading: frequency of participation, consistency of participation, and attendance.     

基于工作过程的数控编程与操作课程学习情境设计

现代制造企业对高职院校学生的职业技能要求越来越高,开展课程教学改革,实施理实一体化教学非常重要。数控编程与加工课程是数控技术专业核心课程,开展基于工作过程的数控编程与操作课程学习情境设计,实施理实一体化教学,有利于提高学生职业技能的培养。     

基于时间维度的组织学习理论——组织从过去经历中学习的模型

从经历中学习是组织学习的重要方面。从科学、严谨的研究范式出发,结合东方传统的"阴阳"哲学思想,基于时间维度提出了包含学习主体、经历样本、成功失败经历集、学习主体特征、"知"和"行"等要素的组织从过去经历中学习的模型,并对相关问题进行了探究。具体包括:学习主体从经历中学习的认知特性;不同类型经历对应的不同学习方法,成功经历和失败经历对学习效果的差异性影响,经历集影响"知"和"行"的7种自动的和有意的学习方式;"知"与"行"相互转化及其触发机制;客观世界从时间和空间两个维度客观展开的观点等。该系统模型为未来研究提供了理论框架和实证检验方向。     

Nowcasting South African gross domestic product using a suite of statistical models

Given lags in the release of data, a central bank must ‘nowcast’ current gross domestic product (GDP) using available quarterly or higher frequency data to understand the current state of economic activity. This paper uses various statistical modelling techniques to draw on a large number of series to nowcast South African GDP. We also show that GDP volatility has increased markedly over the last 5 years, making GDP forecasting more difficult. We show that all the models developed, as well as the Reserve Bank's official forecasts, have tended to overestimate GDP growth over this period. However, several of the statistical nowcasting models we present in this paper provide competitive nowcasts relative to the official Reserve Bank and market analysts' nowcasts.     

Structural breaks in the mean of dividend-price ratios: Implications of learning on stock return predictability

In their out-of-sample predictions of stock returns in the presence of structural breaks, Lettau and Van Nieuwerburgh (2008) implicitly assume that economic agents’ perception of the regime-specific mean for the dividend-price ratio is time-invariant within a regime. In this paper, we challenge this assumption and employ least squares learning with constant gain (or constant-gain learning) in estimating economic agents’ time-varying perception for the mean of dividend-price ratio. We obtain better out-of-sample predictions of stock returns than in Lettau and Van Nieuwerburgh (2008) for both the U.S. and Japanese stock markets. Our empirical results suggest that economic agents’ learning plays an important role in the dynamics of stock returns.