Pricing of Contingent Convertibles

This paper discusses the pricing of Contingent Convertible bonds (CoCos) withstock price triggers. CoCos are a new kind of hybrid securities that aim to provide a capital bu er for banks in times of nancial distress. They are debt securities during periods of economic stability, but automatically convert into equity when a predetermined trigger is breached. Therefore, CoCos are attractive from a regulatory perspective, and several regulators have already shown an interest in using them to manage nancial crisis. The fair values of CoCos are driven by their structures, and the goal of this paper is to price CoCos with stock price triggers that have varying structures in terms of the trigger level, conversion ratios, and their maturity. This paper presents rst the general form of the price and credit spread of CoCos without modeling stock price dynamics. Then, assuming the Black-Scholes model, we provide two explicit pricing formulas for CoCos. Because CoCos combine debt-like and equity-like features, they are priced using the credit derivatives (reduced form) and equity derivatives approaches. In addition to the analytical formulas presented herein, pricing by Monte Carlo simulation is also shown. In order to examine the suitability of the Black-Scholes assumptions, the formulas used in this study are applied to the CoCos issued by Credit Suisse. Because the market trigger, implied by the formulas, is associated with a constant accounting trigger, it is expected to be constant over time. The comparative statics of the formulas show that the mathematical structuresof the formulas explain the economic structure of CoCos. However, we nd that the formula in the equity derivatives approach is more accurate than that in the credit derivatives approach because of its more realistic treatment of cash ow. Its accuracy is con rmed by Monte Carlo simulation, as the estimated con dence interval includes the price evaluated using the equity derivatives approach. If the interest rate is equal to the dividend yield, we nd that the two analytical formulas provide the same price. The empirical analysis of the CoCos of Credit Suisse demonstrates that the Black-Scholes assumptions are empirically unreasonable for pricing CoCos, because the implied market trigger is volatile over time. Given that the constant volatility assumption of the Black-Scholes model is empirically unreasonable, this paper suggests the stochastic volatility model (Heston model) to be a suitable alternative for modeling stock price dynamics, because it produces a more realistic fat-tail distribution of stock returns. Thus, the pricing under the Heston model is expected to show a constant implied trigger over time

The Influence of Self-Construal on Consumer Responses to Sizing Discrepancy

Purpose—This study examines how consumers’ self-construal moderates their buying behavior in situations requiring consumers to buy larger-than-expected clothing sizes. We explore the potential effectiveness of two distinct communication strategies - emotional versus informational ad appeals - to mitigate the negative effects of sizing discrepancies. Design/methodology/approach—A total of three experiments were conducted to examine the proposed framework. Studies 1 and 2 investigate whether self-construal moderates the relationship between sizing discrepancy and purchasing intentions. Study 3 examines the effectiveness of communication strategies in reducing the detrimental effects of sizing discrepancy. Findings—When encountering sizing discrepancies, we find that consumers with an interdependent self-construal have lower purchase intentions than those with an independent self-construal. We demonstrate that an emotional communication strategy is more effective for consumers with an interdependent self-construal, whereas an informational communication strategy is more effective for consumers with an independent self-construal. Originality—With the lack of a universal sizing system, consumers often struggle to find clothes that fit as expected. However, extant research has not explored cross-cultural differences in how consumers respond to sizing discrepancies and how managers can reduce any potential negative effects

The proteomic landscape shows oncologic relevance in cystitis glandularis

Background The relationship between cystitis glandularis (CG) and bladder malignancy remains unclear. Methods We identified the oncologic significance of CG at the molecular level using liquid chromatography-tandem mass spectrometry-based proteomic analysis of 10 CG, 12 urothelial carcinoma (UC), and nine normal urothelium (NU) specimens. Differentially expressed proteins (DEPs) were identified based on an analysis of variance false discovery rate < 0.05, and their functional enrichment was analyzed using a network model, Gene Set Enrichment Analysis, and Gene Ontology annotation. Results We identified 9,890 proteins across all samples and 1,139 DEPs among the three entities. A substantial number of DEPs overlapped in CG/NU, distinct from UC. Interestingly, we found that a subset of DEP clusters (n = 53, 5%) was differentially expressed in NU but similarly between CG and UC. This “UC-like signature” was enriched for reactive oxygen species (ROS) and energy metabolism, growth and DNA repair, transport, motility, epithelial-mesenchymal transition, and cell survival. Using the top 10 shortlisted DEPs, including SOD2, PRKCD, CYCS, and HCLS1, we identified functional elements related to ROS metabolism, development, and transport using network analysis. The abundance of these four molecules in UC/CG than in NU was consistent with the oncologic functions in CG. Conclusions Using a proteomic approach, we identified a predominantly non-neoplastic landscape of CG, which was closer to NU than to UC. We also confirmed a small subset of common DEPs in UC and CG, suggesting that altered ROS metabolism might imply potential cancerous risks in CG

Tropical cyclone intensity estimation through convolutional neural network transfer learning using two geostationary satellite datasets

Accurate prediction and monitoring of tropical cyclone (TC) intensity are crucial for saving lives, mitigating damages, and improving disaster response measures. In this study, we used a convolutional neural network (CNN) model to estimate TC intensity in the western North Pacific using Geo-KOMPSAT-2A (GK2A) satellite data. Given that the GK2A data cover only the period since 2019, we applied transfer learning to the model using information learned from previous Communication, Ocean, and Meteorological Satellite (COMS) data, which cover a considerably longer period (2011–2019). Transfer learning is a powerful technique that can improve the performance of a model even if the target task is based on a small amount of data. Experiments with various transfer learning methods using the GK2A and COMS data showed that the frozen–fine-tuning method had the best performance due to the high similarity between the two datasets. The test results for 2021 showed that employing transfer learning led to a 20% reduction in the root mean square error (RMSE) compared to models using only GK2A data. For the operational model, which additionally used TC images and intensities from 6 h earlier, transfer learning reduced the RMSE by 5.5%. These results suggest that transfer learning may represent a new breakthrough in geostationary satellite image–based TC intensity estimation, for which continuous long-term data are not always available

Acting on anger: Cultural value moderators of the effects of consumer animosity

The recent rise in protectionism and demonization of foreign countries has increased the risk of brands falling victim to the negative effects of consumer animosity, or strong negative affect directed at a foreign country. We investigate the role of cultural values as moderating the relationship between consumer animosity and willingness to buy. The combined results of a meta-analysis and six experiments in the US and China offer strong evidence that collectivism and long-term orientation mitigate the negative effects of consumer animosity and support the contention that animosity’s effect on willingness to buy is much stronger than on product judgments

Becoming a doctor: using social constructivism and situated learning to understand the clinical clerkship experiences of undergraduate medical students

Abstract Background Despite the emphasis on the uniqueness and educational importance of clinical clerkships in medical education, there is a lack of deep understanding of their educational process and outcomes. Especially due to an inherent trait of clinical clerkships which requires participation in the workplace outside the classroom, it is difficult to fully comprehend their educational potential using traditional learning perspectives such as imbibing outside knowledge. Accordingly, this study aims to explore the experiences of a rotation-based clerkship of medical school students from the perspective of social constructivism of learning, which can empirically examine what and how medical students learn during clinical clerkship in South Korea. By providing an insight into the workings of the clerkship process, this study contributes to a better understanding of how a learning-friendly environment can be cultivated at clinical clerkships. Methods The study utilized a basic qualitative study to understand what and how medical students learn during their clinical clerkships. Semi-structured, in-depth individual interviews were conducted with eight sixth-graders who had experienced a two-year clerkship at Ajou University Medical School. Data were analyzed based on Lave and Wenger’s situated learning theory and Wenger’s social theory in learning. Results We found that the medical students had developed different aspects of their professional identities such as values, functionality, career decisions, sociality, and situating during their clinical clerkships. Further, professional identity was formed through a combination of participation and reification—the processes involved in the negotiation of meaning. This combination was facilitated by the students’ first experience and relationships with professors, classmates, and patients. Finally, non-learning occurred in the context of over-participation (learning anxiety and alienation) or over-reification (evaluation and e-portfolio). Conclusions This study revealed five sub-professional identities and their formation process from the learners’ perspective, thereby uncovering the unique learning characteristics and advantages of rotated-based clerkship and contributing to a further understanding of how gradual improvements can be made to the traditional clerkship education of medical students

Mass Spectrometry-Based Proteomic Discovery of Prognostic Biomarkers in Adrenal Cortical Carcinoma

Adrenal cortical carcinoma (ACC) is an extremely rare disease with a variable prognosis. Current prognostic markers have limitations in identifying patients with a poor prognosis. Herein, we aimed to investigate the prognostic protein biomarkers of ACC using mass-spectrometry-based proteomics. We performed the liquid chromatography–tandem mass spectrometry (LC–MS/MS) using formalin-fixed paraffin-embedded (FFPE) tissues of 45 adrenal tumors. Then, we selected 117 differentially expressed proteins (DEPs) among tumors with different stages using the machine learning algorithm. Next, we conducted a survival analysis to assess whether the levels of DEPs were related to survival. Among 117 DEPs, HNRNPA1, C8A, CHMP6, LTBP4, SPR, NCEH1, MRPS23, POLDIP2, and WBSCR16 were significantly correlated with the survival of ACC. In age- and stage-adjusted Cox proportional hazard regression models, only HNRNPA1, LTBP4, MRPS23, POLDIP2, and WBSCR16 expression remained significant. These five proteins were also validated in TCGA data as the prognostic biomarkers. In this study, we found that HNRNPA1, LTBP4, MRPS23, POLDIP2, and WBSCR16 were protein biomarkers for predicting the prognosis of ACC

Proteomic-Based Machine Learning Analysis Reveals PYGB as a Novel Immunohistochemical Biomarker to Distinguish Inverted Urothelial Papilloma From Low-Grade Papillary Urothelial Carcinoma With Inverted Growth

BackgroundThe molecular biology of inverted urothelial papilloma (IUP) as a precursor disease of urothelial carcinoma is poorly understood. Furthermore, the overlapping histology between IUP and papillary urothelial carcinoma (PUC) with inverted growth is a diagnostic pitfall leading to frequent misdiagnoses. MethodsTo identify the oncologic significance of IUP and discover a novel biomarker for its diagnosis, we employed mass spectrometry-based proteomic analysis of IUP, PUC, and normal urothelium (NU). Machine learning analysis shortlisted candidate proteins, while subsequent immunohistochemical validation was performed in an independent sample cohort. ResultsFrom the overall proteomic landscape, we found divergent &apos;NU-like&apos; (low-risk) and &apos;PUC-like&apos; (high-risk) signatures in IUP. The latter were characterized by altered metabolism, biosynthesis, and cell-cell interaction functions, indicating oncologic significance. Further machine learning-based analysis revealed SERPINH1, PKP2, and PYGB as potential diagnostic biomarkers discriminating IUP from PUC. The immunohistochemical validation confirmed PYGB as a specific biomarker to distinguish between IUP and PUC with inverted growth. ConclusionIn conclusion, we suggest PYGB as a promising immunohistochemical marker for IUP diagnosis in routine practice.N

In-depth proteome analysis of brain tissue from Ewsr1 knockout mouse by multiplexed isobaric tandem mass tag labeling

Abstract EWS RNA binding protein 1 (EWSR1) is a multifunctional protein whose epigenetic signatures contribute to the pathogenesis of various human diseases, such as neurodegenerative disorders, skin development, and tumorigenic processes. However, the specific cellular functions and physiological characteristics of EWSR1 remain unclear. In this study, we used quantitative mass spectrometry-based proteomics with tandem mass tag labeling to investigate the global proteome changes in brain tissue in Ewsr1 knockout and wild-type mice. From 9115 identified proteins, we selected 118 differentially expressed proteins, which is common to three quantitative data processing strategies including only protein level normalizations and spectrum-protein level normalization. Bioinformatics analysis of these common differentially expressed proteins revealed that proteins up-regulated in Ewsr1 knockout mouse are mostly related to the positive regulation of bone remodeling and inflammatory response. The down-regulated proteins were associated with the regulation of neurotransmitter levels or amino acid metabolic processes. Collectively, these findings provide insight into the physiological function and pathogenesis of EWSR1 on protein level. Better understanding of EWSR1 and its protein interactions will advance the field of clinical research into neuronal disorders. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026994