Time-varying Managerial Overconfidence and Corporate Debt Maturity Structure

We examine the impact of managerial overconfidence on corporate debt maturity. We build upon the argument that managerial overconfidence is likely to mitigate the underinvestment problem, which is often the major concern for long-term debt investors. Within this context, we hypothesise that managerial overconfidence increases debt maturity. Our empirical evidence, based on time-varying measures of overconfidence derived from computational linguistic analysis and directors’ dealings in their own companies’ shares, supports this hypothesis. Specifically, we find that the changes in both first person singular pronouns and optimistic tone are positively related to the change in debt maturity. Moreover, we find that the insider trading-based overconfidence of CEO, who is most likely to influence investment decision and thus the underinvestment problem, has a stronger impact on debt maturity than the overconfidence of other directors (e.g. CFO). Overall, our study provides initial evidence for a positive overconfidence-debt maturity relation via overconfidence mitigating the agency cost of long-term debt

Happy Together? The Peer Effects of Dual Enrollment Students on Community College Student Outcomes

Nationally, 15% of first-time community college students were high school students taking college coursework through dual enrollment in the fall of 2010, and the percentage has risen since then. The growing numbers of dual enrollment students at community colleges raises concerns about how high school peers might influence traditionally aged college enrollees. Using administrative data from a large state community college system, this paper examines whether being exposed to a higher percentage of dual enrollment peers influences non-dual enrollment enrollees’ performance in college courses. Focusing on entry-level (or gateway) math and English courses and employing a two-way fixed effects model, the authors find that non-dual enrollment college enrollees exposed to a higher proportion of dual enrollment peers had lower pass rates and grades in gateway courses, higher course repetition, and lower subject persistence

Comparison of data-driven building energy use models for retrofit impact evaluation

A change-point (piecewise linear regression) model fitted to the pre-retrofit data as the counterfactual for the savings calculation, is considered to be the best approach to evaluating the energy savings of building retrofits ( ASHRAE Guideline 14). However, when applied to a large portfolio savings analysis with substantial multi-year data, the change-point model does not fit the data well in some cases. The study thus aims to improve the accuracy of the changepoint model by: 1) using more advanced non-linear models, 2) incorporating additional input features, and 3) increasing the time resolution of input variables. We found that random forest regression (RF) models with an array of climate (humidity, wind, solar radiation, etc.), time (day of the week, season, holiday), and energy consumption of the immediate past 1-4 hours (energy lag terms) outperformed the change-point model, shallow neural networks, and support vector machine regression (SVR). Our result implies that high resolution smart meter data should be used in place of monthly utility bills to more accurately evaluate retrofit savings. We further explored the relative contribution of the input variables to the random forest regression model using Shapley Value, a game theoretic variable importance metric. We found that the most important input feature is the energy consumption of the immediate past (or energy lag terms). We also found that solar radiation and weekend day indicators are more important than outdoor temperature. The improved model could provide better insights to portfolio managers in planning future energy retrofits. Policy makers could also use such models to evaluate the average energy saving potential for energy policy changes, such as the requirement of minimum insulation level, and lighting equipment efficiency

ESTABLISHING A COFFEE SHOP CHAIN IN CHINA

This essay presents a business plan for establishing a coffee shop chain in China. Currently, most of the coffee houses in China follow a differentiation strategy. This is to turn the regular fresh coffee experience into a high-priced luxurious experience. However, after almost twenty years of customer cultivation, the coffee consumption market in China is getting much more established, and customers want to drink coffee regularly. There is a big market demand for fresh coffee to consume as daily drinks with reasonable prices. This proposal is devoted to developing a coffee shop chain using a low cost strategy to offer affordable fresh coffee to consumers on a daily basis. The author assesses the competitive environment in the coffee house industry in China, identifies key success factors for the industry participants, and evaluates the viability of this new venture by analyzing the consistency of the strategy proposal and the company?s internal capabilities. The author concludes that this new venture can penetrate the market. The analysis also includes a five-year financial projection and profitability analysis. Assuming the company?s revenue growth rate is 20 percent per year, five years later, its annual net profit will be up to 350,316 RMB (CA$58,000)

Directed Evolution of Adeno-associated Virus Targeting Heart and Skeletal muscle

The goal of this project is to engineer gene vectors that target a single tissue type, especially striated muscles. A random library of adeno-associated virus (AAV) was constructed by shuffling the capsid genes of AAV serotypes 1 to 9, and the resulting chimeric genes were screened for muscle specificity by direct in vivo panning. Viruses expressed high in muscles were collected, and the process was repeated to construct chimeric AAV library. To further improve specificity, in vitro biopanning was done on three types of cultured cells that represent kidney, liver and muscles. For the desirable features, selection was based on infection rate, preferably high in muscle but low in liver. Using AAV2 as a standard, three reconstructed AAV were found to be expressed at higher frequencies in myotubes with comparable or lower frequencies in hepatocytes. These recombined AAV would be further selected in vivo through tail vein injection in mice to identify the virus that was the most specific to muscles.Bachelor of Scienc

Growth and Geometry Split in Light of the DES-Y3 Survey

We test the smooth dark energy paradigm using Dark Energy Survey (DES) Year 1 and Year 3 weak lensing and galaxy clustering data. Within the $\Lambda$CDM and $w$CDM model we separate the expansion and structure growth history by splitting $\Omega_\mathrm{m}$ (and $w$) into two meta-parameters that allow for different evolution of growth and geometry in the Universe. We consider three different combinations of priors on geometry from CMB, SNIa, BAO, BBN that differ in constraining power but have been designed such that the growth information comes solely from the DES weak lensing and galaxy clustering. For the DES-Y1 data we find no detectable tension between growth and geometry meta-parameters in both the $\Lambda$CDM and $w$CDM parameter space. This statement also holds for DES-Y3 cosmic shear and 3x2pt analyses. For the combination of DES-Y3 galaxy-galaxy lensing and galaxy clustering (2x2pt) we measure a tension between our growth and geometry meta-parameters of 2.6$\sigma$ in the $\Lambda$CDM and 4.48$\sigma$ in the $w$CDM model space, respectively. We attribute this tension to residual systematics in the DES-Y3 RedMagic galaxy sample rather than to new physics. We plan to investigate our findings further using alternative lens samples in DES-Y3 and future weak lensing and galaxy clustering datasets.Comment: 19 pages, 14 figures, to be submitte

Desarrollo y validación de una escala PLEs desde la perspectiva del alumno y el aprendizaje en la educación terciaria

The study's goal is to create and validate a Personal Learning Environment Scale (PLEs) from the learner and learning perspective (named PLEsS-LL) to ensure effective learning in Chinese tertiary education. 657 undergraduates participated in the study after completing scale development steps. Six factors were extracted from the PLEsS-LL using Exploratory Factor Analysis (EFA). Confirmatory Factor Analysis (CFA) supported the six-factor scale with 22 items. Furthermore, the PLEsS-LL was redesigned as a questionnaire to assess learners' readiness for PLE learning. The findings indicated that participants were comfortable learning in PLEs in general. They were mostly positive in terms of learning motivation and problem-solving abilities. They did, however, report less confidence in self-directed learning. Meanwhile, male participants outperformed female participants in all categories except learning motivation. The reasons were explained, and suggestions for future PLE design were made. The PLEsS-LL could be used as a resource or guide for learner preparation in the PLE context in higher education around the world.El objetivo del estudio es crear y validar una Escala de Entornos Personales de Aprendizaje (PLEsS) desde la perspectiva del alumno y el aprendizaje (llamada PLEsS-LL) para garantizar un aprendizaje efectivo en la educación terciaria china. 657 estudiantes universitarios participaron en el estudio después de completar los pasos de desarrollo de escala. Se extrajeron seis factores del PLEsS-LL mediante Análisis Factorial Exploratorio (EFA). El Análisis Factorial Confirmatorio (AFC) apoyó la escala de seis factores con 22 ítems.  Además, el PLEsS-LL fue rediseñado como un cuestionario para evaluar la preparación de los alumnos para el aprendizaje PLE. Los hallazgos indicaron que los participantes se sentían cómodos al aprender en PLE en general. En su mayoría fueron positivos en términos de motivación de aprendizaje y habilidades para resolver problemas. Sin embargo, informaron menos confianza en el aprendizaje autodirigido. Mientras tanto, los participantes masculinos superaron a las participantes femeninas en todas las categorías, excepto en la motivación de aprendizaje. Se explicaron las razones y se hicieron sugerencias para el diseño futuro de PLE. El PLEsS-LL podría utilizarse como un recurso o guía para la preparación del alumno en el contexto de los PLEs en la educación superior de todo el mundo

Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation

Transformer and its variants are a powerful class of architectures for sequential recommendation, owing to their ability of capturing a user's dynamic interests from their past interactions. Despite their success, Transformer-based models often require the optimization of a large number of parameters, making them difficult to train from sparse data in sequential recommendation. To address the problem of data sparsity, previous studies have utilized self-supervised learning to enhance Transformers, such as pre-training embeddings from item attributes or contrastive data augmentations. However, these approaches encounter several training issues, including initialization sensitivity, manual data augmentations, and large batch-size memory bottlenecks. In this work, we investigate Transformers from the perspective of loss geometry, aiming to enhance the models' data efficiency and generalization in sequential recommendation. We observe that Transformers (e.g., SASRec) can converge to extremely sharp local minima if not adequately regularized. Inspired by the recent Sharpness-Aware Minimization (SAM), we propose SAMRec, which significantly improves the accuracy and robustness of sequential recommendation. SAMRec performs comparably to state-of-the-art self-supervised Transformers, such as S$^3$Rec and CL4SRec, without the need for pre-training or strong data augmentations

The Impact of Dual Enrollment on College Application Choice and Admission Success

Dual enrollment is one of the fastest growing programs that support the high school-to-college transition. Yet, there is limited empirical evidence about its impact on either students’ college application choices or admission outcomes. Using a fuzzy regression discontinuity approach and data from two cohorts of ninth-grade students in one anonymous state, the authors of this paper found that taking dual enrollment credits increased the total number of colleges students applied to and the likelihood of applying to any moderately or highly selective in-state four-year institution. Attempting dual enrollment credits also increased the total number of in-state four-year colleges a student got admitted to and the probability of being admitted to a highly selective in-state four-year college. Heterogeneous analysis further indicates that the gains were primarily driven by Black students