Information Content of Credit Default Swaps: Price Discovery, Risk Transmission, and News Impact

This thesis comprises three empirical studies regarding information content of credit default swap (CDS). The first study provides further evidence of credit risk discovery between CDS and stock of the U.S. non-financial firms. Stock generally leads CDS in discovering credit risk information, with the exception of the stressful financial crisis period of 2008–2010. The CDS of investment-grade firms generally possesses higher informational efficiency than that of speculative-grade firms. High funding cost and central clearing counterparty hinder CDS from rapidly incorporating credit risk news. The second study investigates dynamics and determinates of credit risk transmission across the global systemically important financial institutions (G-SIFIs). The aggregate credit risk transmission across G-SIFIs dramatically increases from mid-2006 to mid-2008 and then fluctuates around 90% until 2014. Global systemically important banks (G-SIBs) and the U.S.–based G-SIFIs are major credit risk providers. More interbank loans, more non-banking income, higher extra loss absorbency requirement, and lower Tier 1 leverage ratio are positively related to a G-SIB’s role in credit risk transmission. Global systemically important insurers (G-SIIs) which have more non-traditional non-insurance activities, larger sizes, and more global sales tend to be credit risk senders. The final study examines the impact of sovereign credit rating and bailout events on sovereign CDS and equity index, especially their contemporaneous correlation, in the U.S., the U.K., and the Eurozone countries. The two assets are less negatively correlated at the arrivals of domestic rating events or surprises. Good and bad rating events present asymmetric effects on the asset correlation in Portugal, Netherlands, Ireland, Finland, and the U.S., while their symmetric effects are found in Spain, Italy, and Cyprus. Two assets become more negatively correlated on the announcement days of major bailouts. Bailout events have a stronger impact than domestic rating events. Greek rating news exerts spillover effect and generally has positive impact on the asset correlation in other economies

Multi-source adversarial transfer learning based on similar source domains with local features

Transfer learning leverages knowledge from other domains and has been successful in many applications. Transfer learning methods rely on the overall similarity of the source and target domains. However, in some cases, it is impossible to provide an overall similar source domain, and only some source domains with similar local features can be provided. Can transfer learning be achieved? In this regard, we propose a multi-source adversarial transfer learning method based on local feature similarity to the source domain to handle transfer scenarios where the source and target domains have only local similarities. This method extracts transferable local features between a single source domain and the target domain through a sub-network. Specifically, the feature extractor of the sub-network is induced by the domain discriminator to learn transferable knowledge between the source domain and the target domain. The extracted features are then weighted by an attention module to suppress non-transferable local features while enhancing transferable local features. In order to ensure that the data from the target domain in different sub-networks in the same batch is exactly the same, we designed a multi-source domain independent strategy to provide the possibility for later local feature fusion to complete the key features required. In order to verify the effectiveness of the method, we made the dataset "Local Carvana Image Masking Dataset". Applying the proposed method to the image segmentation task of the proposed dataset achieves better transfer performance than other multi-source transfer learning methods. It is shown that the designed transfer learning method is feasible for transfer scenarios where the source and target domains have only local similarities.Comment: Submitted to Information Fusio

A nonlinear dynamic approach to cash flow forecasting

We propose a novel grey-box model to capture the nonlinearity and the dynamics of cash flow model parameters. The grey-box model retains a simple white-box model structure, while their parameters are modelled as a black-box with a Padé approximant as a functional form. The growth rate of sales and firm age are used as exogenous variables because they are considered to have explanatory power for the parameter process. Panel data estimation methods are applied to investigate whether they outperform the pooled regression, which is widely used in the extant literature. We use the U.S. dataset to evaluate the performance of various models in predicting cash flow. Two performance measures are selected to compare the out-of-sample predictive power of the models. The results suggest that the proposed grey-box model can offer superior performance, especially in multi-period-ahead predictions

Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity

Lesion segmentation of ultrasound medical images based on deep learning techniques is a widely used method for diagnosing diseases. Although there is a large amount of ultrasound image data in medical centers and other places, labeled ultrasound datasets are a scarce resource, and it is likely that no datasets are available for new tissues/organs. Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain. As a source domain, redundant features that are not conducive to the task will be extracted. Migration between ultrasound images can avoid this problem, but there are few types of public datasets, and it is difficult to find sufficiently similar source domains. Compared with natural images, ultrasound images have less information, and there are fewer transferable features between different ultrasound images, which may cause negative transfer. To this end, a multi-source adversarial transfer learning network for ultrasound image segmentation is proposed. Specifically, to address the lack of annotations, the idea of adversarial transfer learning is used to adaptively extract common features between a certain pair of source and target domains, which provides the possibility to utilize unlabeled ultrasound data. To alleviate the lack of knowledge in a single source domain, multi-source transfer learning is adopted to fuse knowledge from multiple source domains. In order to ensure the effectiveness of the fusion and maximize the use of precious data, a multi-source domain independent strategy is also proposed to improve the estimation of the target domain data distribution, which further increases the learning ability of the multi-source adversarial migration learning network in multiple domains.Comment: Submitted to Applied Soft Computing Journa

Profiling Heterogeneous Circulating Tumor Cells (CTC) Populations in Pancreatic Cancer Using a Serial Microfluidic CTC Carpet Chip

Although isolation of circulating tumor cells (CTCs) from pancreatic adenocarcinoma patients is feasible, investigating their clinical utility has proven less successful than other cancers due to the limitations of epithelial cellular adhesion molecule (EpCAM)‐only based CTC assays. An integrated technology‐ and biology‐based approach using a microfluidic “Carpet Chip” is presented to study the biological relevance of heterogeneous CTC populations. Both epithelial CTCs (EpCs) and epithelial‐to‐mesenchymal transition (EMT)‐like CTCs (EMTCs) are isolated simultaneously from the whole blood of pancreatic cancer (PaCa) patients (n = 35) by separately targeting two surface markers: EpCAM and CD133. Recovery of cancer cell lines spiked into whole blood is ≥97% with >76% purity. Thirty‐four patients had ≥5 EpCs mL−1 and 35 patients had ≥15 EMTCs mL−1. Overall, significantly higher numbers of EMTCs than EpCs are recovered, reflecting the aggressive nature of PaCa. Furthermore, higher numbers of EMTCs are observed in patients with lymph node involvement compared to patients without. Gene expression profiling of CTCs from 17 patients reveals that CXCR1 is significantly upregulated in EpCs, while known stem cell markers POU5F1/Oct‐4 and MYC are upregulated in EMTCs. In conclusion, successful isolation and genomic profiling of heterogeneous CTC populations are demonstrated, revealing genetic signatures relevant to patient outcomes.“Carpet Chip” uses sequential immunoaffinity‐based microfluidics to study the biological relevance of heterogeneous circulating tumor cell (CTCs). Both epithelial (EpCs) and epithelial‐to‐mesenchymal transition (EMT)‐like CTCs (EMTCs) are detectable from the blood of pancreatic cancer patients. Based on our observations of EMTCs and patient lymph node involvement, individualizing therapies targeting genes involved in EMT could reduce metastasis, thereby improving patient survival.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147173/1/adbi201800228-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147173/2/adbi201800228.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147173/3/adbi201800228_am.pd

Bitcoin futures: trade it or ban it?

This paper examines the impact of South Korea’s ban on Bitcoin futures on intraday spot volatility, liquidity and volatility–volume relationship. The results show that while reducing the permanent component of intraday spot volatility, the imposition of a ban on Bitcoin futures trading increases the transitory component. For intraday spot liquidity, different liquidity proxies indicate heterogeneous results. Moreover, we identify a positive and unidirectional effect of intraday spot volume on volatility. This effect appears to be stronger in the post-ban period. Overall, over the past few months, South Korea’s Bitcoin futures ban generally has had a significant impact on the intraday dynamics of the Bitcoin spot market

Bitcoin futures: trade it or ban it?

This paper examines the impact of South Korea’s ban on Bitcoin futures on intraday spot volatility, liquidity and volatility–volume relationship. The results show that while reducing the permanent component of intraday spot volatility, the imposition of a ban on Bitcoin futures trading increases the transitory component. For intraday spot liquidity, different liquidity proxies indicate heterogeneous results. Moreover, we identify a positive and unidirectional effect of intraday spot volume on volatility. This effect appears to be stronger in the post-ban period. Overall, over the past few months, South Korea’s Bitcoin futures ban generally has had a significant impact on the intraday dynamics of the Bitcoin spot market

Data-Driven Sustainable Supply Chain Decision Making in the Presence of Low Carbon Awareness

Low-carbon production is a vital solution for many environmental problems, as is consumers’ consciousness about carbon playing a more important role and ultimately passing upstream. Supply chains are eager to seek sustainable development via appropriate decision making with data-driven methods. Consistent with this aim, we investigated decisions toward lower carbon efforts and prices in a two-echelon supply chain via a game theoretical approach. The decision-making scenarios of decentralized, centralized, and cost-sharing contracts were investigated and compared. The results show that the level of improvement in environmental performance is positively correlated with the degree of cooperation between partners. Cooperation between partners would be even more significant with an increase in consumers’ low carbon awareness. Furthermore, cost-sharing contracts improve the performance of the entire supply chain compared with decentralized cases. Finally, we implemented numerical experiments to verify the modeling results. Therefore, this study provides theoretical support toward sustainable operations for supply chains concerning low carbon awareness

Endoscopic submucosal dissection for gastric ectopic pancreas: a single-center experience

Abstract Background and objective Endoscopic submucosal dissection (ESD) is a minimal invasive technology and could allow “en bloc” resection for superficial gastric tumors. The aim of this study is to evaluate the safety and feasibility of ESD for gastric ectopic pancreas (EP). Methods A total of 93 patients diagnosed with ectopic pancreas who underwent ESD between January 2011 and June 2017 were enrolled. The demographic, clinical, and endoscopic data were collected and analyzed. Results The average maximal diameter of lesions was 1.01 (range 0.4–3.0) cm with mean age of patients which was 39.75 (range 15–66) years. Overall, all of procedures en bloc was successful. The median operative time was 76.87 (range 30–160) min. A total of 12 patients experienced complications. In seven patients, bleeding occurred during the operation and was treated using hot biopsy forceps or metal clip. Five cases suffered from pneumoperitoneum which was managed well. The mean length of postoperative hospital stay was 5.7 (range 2–17) days. There was no relapse in any cases during the follow-up. Conclusion ESD appears to be a safe and feasible approach for curative treatment in gastric ectopic pancreas. Larger studies are needed to identify the role and the outcomes of ESD in another center

Mutational Chemotaxis Motion Driven Moth-Flame Optimizer for Engineering Applications

Moth-flame optimization is a typical meta-heuristic algorithm, but it has the shortcomings of low-optimization accuracy and a high risk of falling into local optima. Therefore, this paper proposes an enhanced moth-flame optimization algorithm named HMCMMFO, which combines the mechanisms of hybrid mutation and chemotaxis motion, where the hybrid-mutation mechanism can enhance population diversity and reduce the risk of stagnation. In contrast, chemotaxis-motion strategy can better utilize the local-search space to explore more potential solutions further; thus, it improves the optimization accuracy of the algorithm. In this paper, the effectiveness of the above strategies is verified from various perspectives based on IEEE CEC2017 functions, such as analyzing the balance and diversity of the improved algorithm, and testing the optimization differences between advanced algorithms. The experimental results show that the improved moth-flame optimization algorithm can jump out of the local-optimal space and improve optimization accuracy. Moreover, the algorithm achieves good results in solving five engineering-design problems and proves its ability to deal with constrained problems effectively