Essays on credit ratings : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, Albany, New Zealand

Credit ratings play an important role as a gatekeeper of capital markets. Firms with higher credit ratings are likely to access the capital markets at a lower cost. Hence, understanding credit rating properties is essential, and this topic is of great importance for academics, regulators, and practitioners. This thesis includes three essays on credit ratings. Traditional issuer-paid credit rating agencies (CRAs hereafter) such as Standard & Poor’s (S&P hereafter), Moody’s and Fitch Ratings (Fitch hereafter) have faced criticisms about the lack of timeliness and accuracy in negative signals due to the conflict of interest in their business model. However, this is not the case for the positive signals. In contrast, investor-paid CRAs, without conflict of interest in their business model, issue more timely and accurate negative signal. The first essay investigates how institutional investors who have advanced trading skills and knowledge respond to credit rating changes issued by two types of CRAs: issuer- and investor-paid CRAs. I find that investors react asymmetrically: they abnormally sell stocks surrounding rating downgrades by investor-paid CRAs, while abnormally buying stocks around rating upgrades by issuer-paid CRAs. In contrast, they have no significant reaction to positive signals from the investor-paid CRA and negative signals from the issuer-paid CRAs. The first essay suggests that, through their trades, institutional investors do capitalize on value-relevant rating information: negative and positive signals provided by investor- and issuer-paid CRAs respectively. More importantly, I further find that a dynamic trading strategy specifically based on rating downgrades by investor-paid CRA and rating upgrades by issuer-paid CRAs generates significant abnormal returns. The second essay focuses on the relationship between politics and credit ratings. Specifically, I investigate whether political similarities between CRAs and bond issuers impact credit ratings. I find that a higher degree of similarity of political affiliation leads to a decrease in timeliness and accuracy of rating downgrades prior to default events. The findings support the notion that CRAs tend to maintain/assign relative rating advantages to politically similar firms via favourable rating activities. I further show that these politically similar firms tend to increase the proportion of political donations to their favoured party following favourable credit ratings. Interestingly, this result is confined to Republican-leaning firms. The results indicate that CRAs successfully use biased credit ratings as an indirect channel of political party support. The second essay thus contributes to the body of knowledge on the importance of political connections in corporate finance as well as CRAs’ rating behaviours. The third essay examines the effect of natural disasters on credit ratings. Natural disasters are exogenous shocks to CRAs’ rating behaviours. I find that firms located in the disaster states (i.e., affected firms) are downgraded by CRAs. I also find the same patterns in changes in stock returns of affected firms. The findings support hypothesis that credit rating changes are driven by firm’s fundamental changes caused by natural disasters. By using instrumental variable (IV) analysis to extract affected firms’ rating changes caused by natural disasters, I further investigate the spill-over effects of natural disasters on rating changes of non-affected firms (i.e., firms are not located in the disaster states). I find that the affected firms’ rating changes positively spill-over to connected firms’ rating changes which are not directly impacted by natural disasters. Connected firms are selected from the same industry, the adjoining states, or supplier-customer relationships with the affected firms. I also find the negative spill-over effects from the affected firms’ rating changes to their competitors’ rating changes. Finally, I replicate the spill-over channels for stock returns, a proxy for market reactions to natural disasters, and find delays in the stock return spill-over. This is significant evidence on CRAs’ sensitivity to natural extreme events

A new stability results for the backward heat equation

In this paper, we regularize the nonlinear inverse time heat problem in the unbounded region by Fourier method. Some new convergence rates are obtained. Meanwhile, some quite sharp error estimates between the approximate solution and exact solution are provided. Especially, the optimal convergence of the approximate solution at t = 0 is also proved. This work extends to many earlier results in (f2,f3, hao1,Quan,tau1, tau2, Trong3,x1).Comment: 13 page

CONCEPT MAPPING INFLUENCING STUDENTS’ ABILITY TO SUMMARIZE READING PASSAGES

Concept mapping has been advocated as a facilitative tool for enhancing meaningful learning of reading comprehension of students in several ways. In particular, this strategy positively influences how students comprehend texts and summarize important ideas or information given in a particular text passage. However, research into the effects of concept mapping on students’ ability to summarize passages has not been explored in the context of teaching and learning English as a foreign language inVietnam. This paper therefore examines the effects of concept mapping on students’ ability to summarize reading passages within a community college context in the Mekong Delta. Using an experimental study, pretest, posttest, and questionnaire were undertaken with twenty six sophomores over the second semester of a reading course. The findings show that concept mapping had positive effects on students’ ability to summarize reading passages and that students perceived the use of this reading strategy as being a facilitative tool for meaningful learning. The paper concludes by discussing the pedagogical implications and insights into the relationship between concept mapping and summarizing skills in reading comprehension in wider contexts.  Article visualizations

An improved stability result for a heat equation backward in time with nonlinear source

We consider a nonlinear backward heat conduction problem in a strip. The problem is ill-posed in the sense that the solution (if it exists) does not depend continuously on the data. We shall use a modified integral equation method to regularize the nonlinear problem. The error estimates of Hölder type of the regularized solutions are obtained

An improved stability result for a heat equation backward in time with nonlinear source

We consider a nonlinear backward heat conduction problem in a strip. The problem is ill-posed in the sense that the solution (if it exists) does not depend continuously on the data. We shall use a modified integral equation method to regularize the nonlinear problem. The error estimates of Hölder type of the regularized solutions are obtained

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in IJCVR on September 201