Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

Heterogeneity Study of the Visual Features Based on Geographically Weighted Principal Components Analysis Applied to an Urban Community

Communities in urban space are the most basic living units. Community visual features directly reflect the local living quality and influence the perception of residents and visitors. The evaluation of the community visual features is of great significance to the space design under the guidance of urban landscape recognition and urban space perception. Based on the street view image data, this paper analyzes the composition of visual features in the community space scale by using the geographically weighted principal components analysis. GWPCA can not only reflect the global characteristics, but also analyze the local components, thus describing the visual features of the community in a comprehensive manner. The results show that: (1) community visual features have significant spatial heterogeneity at different statistical scales, and the spatial heterogeneity of community visual features can provide a basis for urban landscape planning and design; (2) the combination mode of dominant visual elements can reflect different community landscapes. The analysis of this paper further illustrates the effectiveness and application prospect of street view images in identifying the landscape composition mode of urban space from the medium-micro perspective. This conclusion is helpful for planners to learn the dominant visual features of the community through street view images, and, further, use the classification of elements of street view images to guide the planning and design of cityscape.

Exploring Tertiary ESL learners’ Affective Attributes Toward Online Reading

The Covid-19 pandemic has significantly accelerated digitization of higher education worldwide and reading online has become vital for students to grasping new knowledge and understanding course contents. As educators, it is important to understand our students’ affective attributes towards the new normal of reading online. Within this scope, the study aims to develop a self-report instrument on affective attributes towards online reading among tertiary ESL learners, and to examine the specific affective attributes students display towards online reading. The Survey of Online Reading Affective Attributes was adapted and developed to measure tertiary ESL learners’ affective attributes towards online reading for academic and study-related purposes. This questionnaire was administered to 130 tertiary ESL learners. Principal components analysis was performed and the results confirmed the three affective factors were self-efficacy, motivation, and anxiety. In addition, the analysis of the survey data revealed that the majority of students, in general, were relatively self-efficacious, motivated, and less anxious towards online reading. However, it was discovered that nearly one-third of students were relatively anxious towards online reading and one-fifth of them were less self-efficacious. The limitations of this study and pedagogical recommendations are further discussed in this paper.