The Information Content of Quarterly Earnings in Syndicated Bank Loan Prices

We examine the information content of quarterly earnings announcements in the syndicated bank loan market, a hybrid public/private debt market that is exclusively comprised of informed institutional participants. In contrast to the literature on equity price reactions to earnings announcements, we find that bank loan returns experience no significant response on earnings announcement dates. However, we do find significant price movements in the secondary loan market four weeks prior to earnings announcement dates, around the time of the monthly covenant reports to members of the syndicate. Moreover, we find that the information content in syndicated bank loan prices is most pronounced for borrowers with predominantly intangible assets that experience declining earnings. Thus, we find evidence that when earnings announcements convey relevant information about the borrowing firm (i.e., for informationally opaque firms with declining creditworthiness), the syndicated bank loan market expeditiously incorporates that information into prices

Comparing Text-based, Blocks-based, and Hybrid Blocks/Text Programming Tools

ABSTRACT This dissertation investigates the comparative affordances and drawbacks of blocks-based, text-based, and hybrid blocks/text introductory programming tools. Blocks-based programming environments are growing in popularity and are increasingly being used in formal introductory programming contexts. To date, much of the work evaluating such tools has focused on their effectiveness in out-of-school contexts and emphasized engagement and attitudinal measures over content mastery. Given their growing presence in classrooms, it is important to understand the benefits and limitations of the use of the blocks-based programming approach in formal learning contexts relative to text-based or hybrid blocks/text alternatives. This dissertation will carry out a quasi-experimental study in high school computer science classrooms to answer questions related to the impact of blocks-based, text-based, and hybrid blocks/text introductory tools, assess the suitability of such tools for preparing students for future computer science learning opportunities, and explore the design space between blocks-based and text-based programming. The goal of this work is to better understand the tools we are using to introduce today's learners to computer science and lay the foundation for creating the tools of tomorrow

Student engagement is key to broadening participation in CS

© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. The Mobile CS Principles (Mobile CSP) course is one of the NSF-supported, College Board-endorsed curricula for the new Computer Science Principles AP course. Since 2013, the Mobile CSP project has trained more than 700 teachers, and the course has been offered to more than 20,000 students throughout the United States. The organizing philosophy behind the Mobile CSP course is that student engagement in the classroom is the key to getting students, especially those traditionally underrepresented in CS, interested in pursuing further study and careers in CS. The main strategies used to engage Mobile CSP students are: (1) a focus on mobile computing throughout the course, taking advantage of current student interest in smartphones; (2) an emphasis on getting students building mobile apps from day one, by utilizing the highly accessible App Inventor programming language; and (3) an emphasis on building creative,\u27socially useful\u27 apps to get students thinking about ways that computing can help their communities. In this paper we present and summarize two years of data of various types (i.e., student surveys, teacher surveys, objective assessments, and anecdotal reports from students and teachers) to support the hypothesis that engagement of the sort practiced in the Mobile CSP course not only helps broaden participation in CS among hard-to-reach demographics, but also provides them with a solid grounding in computer science principles and practices

The Information Content of Quarterly Earnings in Syndicated Bank Loan Prices

We examine the information content of quarterly earnings announcements in the syndicated bank loan market, a hybrid public/private debt market that is exclusively comprised of informed institutional participants. In contrast to the literature on equity price reactions to earnings announcements, we find that bank loan returns experience no significant response on earnings announcement dates. However, we do find significant price movements in the secondary loan market four weeks prior to earnings announcement dates, around the time of the monthly covenant reports to members of the syndicate. Moreover, we find that the information content in syndicated bank loan prices is most pronounced for borrowers with predominantly intangible assets that experience declining earnings. Thus, we find evidence that when earnings announcements convey relevant information about the borrowing firm (i.e., for informationally opaque firms with declining creditworthiness), the syndicated bank loan market expeditiously incorporates that information into prices

[Editorial] Special issue on computational thinking and coding in childhood

No description supplie

How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment

As Large Language Models (LLMs) gain in popularity, it is important to understand how novice programmers use them. We present a thematic analysis of 33 learners, aged 10-17, independently learning Python through 45 code-authoring tasks using Codex, an LLM-based code generator. We explore several questions related to how learners used these code generators and provide an analysis of the properties of the written prompts and the generated code. Specifically, we explore (A) the context in which learners use Codex, (B) what learners are asking from Codex, (C) properties of their prompts in terms of relation to task description, language, and clarity, and prompt crafting patterns, (D) the correctness, complexity, and accuracy of the AI-generated code, and (E) how learners utilize AI-generated code in terms of placement, verification, and manual modifications. Furthermore, our analysis reveals four distinct coding approaches when writing code with an AI code generator: AI Single Prompt, where learners prompted Codex once to generate the entire solution to a task; AI Step-by-Step, where learners divided the problem into parts and used Codex to generate each part; Hybrid, where learners wrote some of the code themselves and used Codex to generate others; and Manual coding, where learners wrote the code themselves. The AI Single Prompt approach resulted in the highest correctness scores on code-authoring tasks, but the lowest correctness scores on subsequent code-modification tasks during training. Our results provide initial insight into how novice learners use AI code generators and the challenges and opportunities associated with integrating them into self-paced learning environments. We conclude with various signs of over-reliance and self-regulation, as well as opportunities for curriculum and tool development.Comment: 12 pages, Peer-Reviewed, Accepted for publication in the proceedings of the 2023 ACM Koli Calling International Conference on Computing Education Researc

The Valuation of the Foreign Income of U.S. Multinational Firms: A Growth Opportunities Perspective

This paper demonstrates the value-relevance of foreign earnings for U.S. multinational firms by examining the associations between annual abnormal stock performance and changes in firms' domestic and foreign incomes disclosed through SEC Regulation 210.4-08(h). For 2570 firm-year observations between 1985 and 1993, both foreign and domestic earnings changes have significant positive associations with annual excess returns measures; however, the association coefficient on foreign income is significantly larger than the association coefficient on domestic income. This indicates that foreign earnings disclosures are value-relevant and suggests that firm value is more sensitive to foreign earnings than domestic earnings. We demonstrate that this larger foreign association coefficient is consistent with differences in growth opportunities between domestic and foreign operations. To further support the growth opportunity interpretation of the results, we demonstrate that larger foreign association coefficients are not due to the influence of exchange rate changes or the result of methodological problems such as differences in the timing of foreign versus domestic earnings recognition or misspecification in the earnings expectation process.

Both Sides of Corporate Diversification: The Value Impacts of Geographic and Industrial Diversification

This paper examines the effect of geographic and industrial diversification on firm value for a sample of over 20,000 firm-year observations of U.S. corporations from 1987-1993. Our" multivariate tests indicate the average value of a firm with international operations is 2.2% higher than comparable domestic single activity firms, while the average value of a firm with activities in multiple industrial segments is 5.4% lower than a portfolio of comparable focused domestic firms in similar activities. More importantly, we demonstrate that failure to control simultaneously for both dimensions of diversification results in over-estimation of the negative value impact of industrial diversification, but has little impact on estimates of the positive value impact of geographic diversification.

LMDA Canada Newsletter, May 2004

Contents include: Letter from LMDA Canada Chair, Creative Dramaturgy and New Play Development: A Preview of Canadian Theatre Review 119 Summer 2004, LMDA Canada Meeting Friday March 5 2004, LMDA Canada Membership Listhttps://soundideas.pugetsound.edu/lmdanewsletter/1031/thumbnail.jp

Block-based syntax from context-free grammars

Block-based programming systems employ a jigsaw metaphor to write programs. They are popular in the domain of programming education (e.g., Scratch), but also used as a programming interface for end-users in other disciplines, such as arts, robotics, and configuration management. In particular, block-based environments promise a convenient interface for Domain-Specific Languages (DSLs) for domain experts who might lack a traditional programming education. However, building a block-based environment for a DSL from scratch requires significant effort. This paper presents an approach to engineer block-based language interfaces by reusing existing language artifacts. We present Kogi, a tool for deriving block-based environments from context-free grammars. We identify and define the abstract structure for describing block-based environments. Kogi transforms a context-free grammar into this structure, which then generates a block-based environment based on Google Blockly. The approach is illustrated with four case studies, a DSL for state machines, Sonification Blocks (a DSL for sound synthesis), Pico (a simple programming language), and QL (a DSL for questionnaires). The results show that usable block-based environments can be derived from context-free grammars, and with an order of magnitude reduction in effort