Dynamic credit contagion and aggregate loss in networks

We develop a dynamic model to illustrate the credit risk contagion mechanism caused by interaction between firms. Specifically, we formulate the sources of risk into idiosyncratic risk and contagion risk, and introduce recovery ability to model the scenario of a firm changing from default into normal status. Our result shows that there always exists a steady state in a network under some trivial conditions. For quasi-regular networks and bipartite networks, the expected aggregate loss remains unchanged as long as the product of the contagion probability and the partner number is fixed.     

The use of ICTs and income distribution in Brazil: A machine learning explanation using SHAP values

This study explores the complex relationship between information and communication technologies (ICTs) and socioeconomic characteristics. We employ a cutting-edge explainable machine learning approach, known as SHAP values, to interpret an XGBoost and neural network model, as well as benchmark traditional econometric methods. The application of machine learning algorithms combined with the SHAP methodology reveals complex nonlinear relationships in the data and important insights to guide tailored policy-making. Our results suggest that there is an interaction between education and ICTs that contributes to income prediction. Furthermore, level of education and age are found to be positively associated with income, while gender presents a negative relationship; that is, women earn less than men on average. This study highlights the need for more efficient public policies to fight gender inequality in Brazil. It is also important to introduce policies that promote quality education and the teaching of skills related to technology and digitalization to prepare individuals for changes in the job market and avoid the digital divide and increasing social inequality.     

Growth vs value investing: Persistence and time trend before and after COVID-19

Value investing and growth investing allow economic experts to adopt different investment strategies depending on their chosen specialty; the two investment types have been conditioned by the pandemic, changing the trend of investments and their results. This research aims to analyze the behavior and trends of the different investment strategies before and after the health crisis. We use methodologies based on fractional integration and cointegration to analyze the persistence and trend of the series and their relationship in the long run. We find that the shock is long-lived and causes a change in trend; however, we find no evidence of mean reversion. In addition, we use multivariate wavelet analysis to analyze the correlation between both time series, concluding that a growth-based investment strategy is more successful than a value-based investment strategy. We use neural networks to corroborate our results.     

Crossing the bridge from network training to development: A guide to move trainees from classroom insights to effective networks

Professional networks help employees accomplish work tasks, progress in their careers, and thrive personally. Decades of research suggest that achieving these outcomes requires more than simply amassing 'more' network contacts. Instead, networks with certain characteristics (e.g., networks that are open, diverse, and deep) enhance effectiveness. Network training teaches trainees about effective networks and helps trainees identify their network development needs by providing feedback on their current network. Once back on the job, trainees are assumed to take appropriate actions to develop their networks. However, our research and experience training MBA students, executives, and employees at all levels suggest that trainees often struggle to develop their networks after training. We studied 119 trainees engaged in network training and development to understand why. Our investigation revealed that many trainees fail to set network development goals that match their personalized feedback, identify strategies that match their goals, and take actions to develop networks that match their strategies. These mismatches create gaps in the bridge that trainees build to take themselves from network training to development. Further, even after building strong bridges by aligning their feedback, goals, strategies, and actions, many trainees encounter on-the-job barriers that prevent them from improving the effectiveness of their network. We offer a guide to help address translational gaps and mitigate on-the-job barriers, thereby enhancing the translation of network training insights into network development.     

基于城市轨道交通网络的广告营销站点选择研究

城市轨道交通网络背景下的广告营销已成为一种流行的产品推广方式，快速、准确筛选出传播范围广、影响力高的站点，运营方和投资商对其投入大量广告可以产生较高的宣传力度和回报收益。影响力最大化的提出，为解决站点筛选提供了一种新的思路，首先提出一种融合节点结构信息和网络拓扑结构信息的网络表示学习模型，并将其用于学习网络节点的特征向量；进一步将网络节点的特征向量输入聚类算法中有效筛选候选种子集，同时结合贪婪策略从候选种子集中筛选出种子节点集合，进而有效提升网络节点影响力最大化问题求解效果。以武汉地铁网络为例进行验证，结果表明所提方法在传播范围上优于现有的影响力最大化方法，筛选高影响力站点具备合理性。