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

Denoising Diffusion Medical Models

In this study, we introduce a generative model that can synthesize a large number of radiographical image/label pairs, and thus is asymptotically favorable to downstream activities such as segmentation in bio-medical image analysis. Denoising Diffusion Medical Model (DDMM), the proposed technique, can create realistic X-ray images and associated segmentations on a small number of annotated datasets as well as other massive unlabeled datasets with no supervision. Radiograph/segmentation pairs are generated jointly by the DDMM sampling process in probabilistic mode. As a result, a vanilla UNet that uses this data augmentation for segmentation task outperforms other similarly data-centric approaches.Comment: Accepted to IEEE ISBI 202

Bio-pharmacological screening on liver-protective and anti-hepatocarcinoma activities of Vietnam natural products

Le carcinome hépatocellulaire (HCC) est le cancer du foie le plus répandu et représente la seconde cause de décès par cancer dans le monde. Un mauvais pronostic et l'absence de traitement efficace en font un problème majeur de santé publique dans les pays en voie de développement, notamment en Asie du Sud-Est, justifiant pleinement la recherche de molécules ou d'approches thérapeutiques nouvelles contre l'HCC. Ce travail porte sur la recherche de molécules isolées de plantes vietnamiennes actives contre l'HCC. La première approche a consisté en un criblage pharmacologique de 33 substances naturelles qui a conduit à l'identification de 7 ent-kaurane diterpénoïdes isolés de Croton kongensis Gagnep. présentant des propriétés antiprolifératives originales. La seconde approche, par criblage in silico d'une banque de 354 substances naturelles, a permis d'identifier la solasonine comme inhibiteur de l'interaction mortalin - p53 induisant l'apoptose dans la lignée cellulaire humaine HepG2.Human hepatocellular carcinoma (HCC) is the most common type of liver cancer, the second most common cause of death from cancer worldwide. A very poor prognosis and a lack of effective treatments make liver cancer a major public health problem, notably in less developed regions, particularly in Eastern Asia. This fully justifies the search of new molecules and therapeutic strategies against HCC. The present work focused on finding bioactive compounds from Vietnamese plants against HCC. The first approach used classical screening of 33 natural compounds which resulted in the identification of 7 ent-kaurane diterpenoids isolated from Croton kongensis Gagnep. as potential agents. The second approach aimed at identifying molecules that could abrogate the interaction between Mortalin and p53 by in silico screening of a database of 354 natural compounds, which allowed the identification of Solasonine as a potent inhibitor of p53 - mortalin interactions

A Regularization of the Backward Problem for Nonlinear Parabolic Equation with Time-Dependent Coefficient

We study the backward problem with time-dependent coefficient which is a severely ill-posed problem. We regularize this problem by combining quasi-boundary value method and quasi-reversibility method and then obtain sharp error estimate between the exact solution and the regularized solution. A numerical experiment is given in order to illustrate our results

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

Influence of heterostructure on structure, electric and magnetic properties of Bi_0.5(Na_0.80,K_0.20)_0.5TiO₃/BaZrO₃ films prepared by the sol-gel method

This study reports on the structure, electric, and magnetic properties of Bi0.5(Na0.80,K0.20)0.5TiO3/BaZrO3 (BNKT/BZO) heterolayered films synthesized via chemical solution deposition on Pt/Ti/SiO2/Si substrates. The influence of different heterolayered configurations on the microstructure, electric and magnetic properties of the films was investigated thoroughly. The heterostructures are expected to generate strongly correlated electron systems in the BNKT and BZO layers that cause a magnetic interface effect in the BNKT/BZO conjunction layer. The BZO layer also prevents metal ion evaporation, resulting in a decline in oxygen vacancies and an enhancement in the electric and magnetic properties. The obtained results show that magnetic properties and leakage current density (J) of BNKT/BZO heterolayered films were greatly improved thanks to the heterolayered structure. Heterolayered 4BNKT/2BZO films (M42) yield the highest M s and M r values of 14.4 emu cm−3 and 1.7 emu cm−3, respectively, about three times higher than multilayered BNKT. Thanks to heterolayered structure, J decreases strongly from 16.0 × 10−4 A cm−2 for BNKT films to 1.4 × 10−4 A cm−2 for heterolayered M42 films. It has been verified that the leakage current in BNKT/BZO heterolayered films follows the Schottky barrier mechanism, with the barrier height fluctuating between 0.80 eV and 0.92 eV. The results of the study show that BNKT/BZO heterolayered films may be suitable for use in environmentally friendly multifunction devices.</p

Efficient relaxation scheme for the SIR and related compartmental models

In this paper, we introduce a novel numerical approach for approximating the SIR model in epidemiology. Our method enhances the existing linearization procedure by incorporating a suitable relaxation term to tackle the transcendental equation of nonlinear type. Developed within the continuous framework, our relaxation method is explicit and easy to implement, relying on a sequence of linear differential equations. This approach yields accurate approximations in both discrete and analytical forms. Through rigorous analysis, we prove that, with an appropriate choice of the relaxation parameter, our numerical scheme is non-negativity-preserving and globally strongly convergent towards the true solution. These theoretical findings have not received sufficient attention in various existing SIR solvers. We also extend the applicability of our relaxation method to handle some variations of the traditional SIR model. Finally, we present numerical examples using simulated data to demonstrate the effectiveness of our proposed method.Comment: 17 pages, 21 figures, 2 table

TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars

Semantic segmentation is a common task in autonomous driving to understand the surrounding environment. Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the road. However, original semantic segmentation models are computationally expensive and require high-end hardware, which is not feasible for embedded systems in autonomous vehicles. This paper proposes a lightweight model for the driveable area and lane line segmentation. TwinLiteNet is designed cheaply but achieves accurate and efficient segmentation results. We evaluate TwinLiteNet on the BDD100K dataset and compare it with modern models. Experimental results show that our TwinLiteNet performs similarly to existing approaches, requiring significantly fewer computational resources. Specifically, TwinLiteNet achieves a mIoU score of 91.3% for the Drivable Area task and 31.08% IoU for the Lane Detection task with only 0.4 million parameters and achieves 415 FPS on GPU RTX A5000. Furthermore, TwinLiteNet can run in real-time on embedded devices with limited computing power, especially since it achieves 60FPS on Jetson Xavier NX, making it an ideal solution for self-driving vehicles. Code is available: url{https://github.com/chequanghuy/TwinLiteNet}.Comment: Accepted by MAPR 202

Invited review. Bond dissociation enthalpies in benzene derivatives and effect of substituents: an overview of density functional theory (B3LYP) based computational approach

In this review, we have mainly focused on the recent computational studies on the bond dissociation enthalpies (BDE) of the X‒H bonds of the para and meta substituted benzene derivatives (3Y-C6H4X‒H and 4Y-C6H4X‒H with X = O, S, Se, NH, PH, CH2, SiH2 and Y = H, F, Cl, CH3, OCH3, NH2, CF3, CN, NO2). In addition, the remote substituent effects on the BDE(X‒H), the radical stability and parent one have also been discussed in terms of the calculated ground state effect, radical effect and total effect. Model chemistry of ROB3LYP/6-311++G(d,p)//B3LYP/6-311G(d,p) can reproduce the BDE values with the accuracy of 1.0‒2.0 kcal/mol. The good linear correlations between Hammett constants and BDE values were discovered for both para and meta substitutions in phenols, thiophenols, benzeneselenols, anilines and phenylposphines with the R-squared lager than 0.94. In contrast, it does not occur in case of toluenes and phenylsilanes.Keywords. Benzene derivatives, density functional theory, bond dissociation enthalpies, substituent effects, radical effect, ground state effect, total effect, Hammett constants

Alkaloids and Their Pharmacology Effects from <em>Zanthoxylum</em> Genus

Zanthoxylum genus (Rutaceae) comprises about 212 species distributed in warm temperature and subtropical areas in the worldwide. Zanthoxylum species have been used in traditional for the treatment of tooth decay, snakebites, blood circulation problems, stomach problems, inflammation, rheumatic, and parasitic diseases. The chemical investigations of Zanthoxylum have been studied by many scientists over the world. Several classes of compounds have been isolated from this genus such as alkaloids, coumarins, and monoterpenes. Of these, alkaloids are the main components and play an important role in Zanthoxylum species. Alkaloids have been shown the potential promise about biological activities: cytotoxic, antimalarial, leishmanicidal, anti-inflammatory, analgesic, antiviral, and antibacterial activities. This chapter will focus on the structure elucidation and pharmacological activities of alkaloids from Zanthoxylum species. In addition, the absolute configuration of some alkaloids from Zanthoxylum genus will be also discussed