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

Identification of osteopontin-dependent signaling pathways in a mouse model of human breast cancer

<p>Abstract</p> <p>Background</p> <p>Osteopontin (OPN) is a secreted phosphoprotein which functions as a cell attachment protein and cytokine that signals through two cell adhesion molecules, α_vβ₃-integrin and CD44, to regulate cancer growth and metastasis. However, the signaling pathways associated with OPN have not been extensively characterized. In an in vivo xenograft model of MDA-MB-231 human breast cancer, we have previously demonstrated that ablation of circulating OPN with an RNA aptamer blocks interaction with its cell surface receptors to significantly inhibit adhesion, migration and invasion in vitro and local progression and distant metastases.</p> <p>Findings</p> <p>In this study, we performed microarray analysis to compare the transcriptomes of primary tumor in the presence and absence of aptamer ablation of OPN. The results were corroborated with RT-PCR and Western blot analysis. Our results demonstrate that ablation of OPN cell surface receptor binding is associated with significant alteration in gene and protein expression critical in apoptosis, vascular endothelial growth factor (VEGF), platelet derived growth factor (PDGF), interleukin-10 (IL-10), granulocyte-macrophage colony stimulating factor (GM-CSF) and proliferation signaling pathways. Many of these proteins have not been previously associated with OPN.</p> <p>Conclusion</p> <p>We conclude that secreted OPN regulates multiple signaling pathways critical for local tumor progression.</p

The comparison of optical variability of broad-line Seyfert 1 and narrow-line Seyfert 1 galaxies from the view of Pan-STARRS

By means of the data sets of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS), we investigate the relationship between the variability amplitude and luminosity at 5100 \AA, black hole mass, Eddington ratio, $R_{\rm Fe \, II}$ ( the ratio of the flux of Fe II line within 4435-4685 \AA ~to the broad proportion of $\rm H\beta$ line) as well as $R_{5007}$ (the ratio of the flux [O III] line to the total $\rm H\beta$ line) of the broad line Seyfert 1 (BLS1) and narrow line Seyfert 1 (NLS1) galaxies sample in g,r,i,z and y bands, respectively. We also analyze the similarities and differences of the variability characteristics between the BLS1 galaxies and NLS1 galaxies. The results are listed as follows. (1). The cumulative probability distribution of the variability amplitude shows that NLS1 galaxies are lower than that in BLS1 galaxies. (2). We analyze the dependence of the variability amplitude with the luminosity at 5100 \AA, black hole mass, Eddington ratio, $R_{\rm Fe \,II}$ and $R_{5007}$, respectively. We find significantly negative correlations between the variability amplitude and Eddington ratio, insignificant correlations with the luminosity at 5100 \AA. The results also show significantly positive correlations with the black hole mass and $R_{5007}$, significantly negative correlations with $R_{\rm Fe\, II}$ which are consistent with Rakshit and Stalin(2017) in low redshift bins (z<0.4) and Ai et al.(2010). (3). The relationship between the variability amplitude and the radio loudness is investigated for 155 BLS1 galaxies and 188 NLS1 galaxies. No significant correlations are found in our results.Comment: 10 pages, 5 figures, accepted by Astrophysics and Space Science, in Pres

Local strategies for China's carbon mitigation: An investigation of Chinese city-level CO2 emissions

This paper provides a systematic analysis that identifies the driving forces of carbon dioxide (CO2) emissions of 286 Chinese prefecture-level cities in 2012. The regression analysis confirms the economic scale and structure effects on cities' CO2 emissions in China. If China's annual economic growth continues at the rate of 7%, CO2 emissions will increase by about 6% annually. In addition, climate conditions, urbanization and public investment in R&D are identified as important driving forces to increase the CO2 emissions of Chinese cities. While an increment of the urbanization rate by 1% increases the CO2 emissions by about 0.9%; An increase in R&D investment by 1% can help reduce CO2 emissions by 0.21%. As cities in our study vary greatly based on their industry composition, development stage and geographical location, the patterns of their CO2 emissions are also variable. Our study improves the comprehensiveness and accuracy of previous carbon accounting method by distinguishing the scope 1 and scope 2 CO2 emissions and establishing a high spatial resolution dataset of CO2 emissions (CHRED). The analysis covers almost all Chinese prefectural cities and derives useful implications for China's low carbon development

How Extreme Events in China Would Be Affected by Global Warming-Insights From a Bias-Corrected CMIP6 Ensemble

In recent years, concurrent climate extreme conditions (i.e., hot-dry, cold-dry, hot-wet, and cold-wet) have led to various unprecedented natural disasters (e.g., floods, landslide, wildfire, droughts, etc.), causing significant damages to human societies and ecosystems. This is especially true for China where many unprecedented natural disasters have been reported due to the recent warming in local climate. In this paper, we focus on the issue of ultra-extreme events (1‰ threshold) and address how future global warming would affect the climate extreme conditions in China. Specifically, to reduce the uncertainties from models, we use a downscaled and bias-corrected CMIP6 ensemble under two continuously-warming scenarios to evaluate the impact of global warming on ultra-extreme events over China. The results show that, under both SSP245 and SSP585 scenarios, extreme hot conditions would become dominant in most regions of China and some regions are likely to experience over 50 extreme hot days at future warming levels. The frequency of extreme cold events is projected to be small. More frequent extreme hot-wet events with concurrence in the same month and year would be expected for China under the continuously-warming scenarios. This is particularly obvious for the west where more than 6 hot-wet months are likely to take place under future warming scenarios. This may imply that more extreme heat waves and flooding events would coincide in the same month or year for China in the future. For univariate ultra-extreme events, both extreme hot events and extreme wet events would drop by above 25% from 2.0°C to 1.5°C global warming level, particularly under the SSP245 scenario. When the global mean temperature is limited to 1.5°C rather than 2°C, the avoided impacts of hot-wet and cold-wet extremes concurring in the same month will be larger than those of dry-related compound extremes. Overall, the results suggest that slowing down global warming can reduce the frequency of concurrent climate extreme conditions in China, highlighting the importance of immediate action toward carbon emission reduction

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

MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network

Picking the first arrival times of prestack gathers is called First Arrival Time (FAT) picking, which is an indispensable step in seismic data processing, and is mainly solved manually in the past. With the current increasing density of seismic data collection, the efficiency of manual picking has been unable to meet the actual needs. Therefore, automatic picking methods have been greatly developed in recent decades, especially those based on deep learning. However, few of the current supervised deep learning-based method can avoid the dependence on labeled samples. Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR). In this paper, for hard rock seismic gather data, we propose a Multi-Stage Segmentation Pickup Network (MSSPN), which solves the generalization problem across worksites and the picking problem in the case of low SNR. In MSSPN, there are four sub-models to simulate the manually picking processing, which is assumed to four stages from coarse to fine. Experiments on seven field datasets with different qualities show that our MSSPN outperforms benchmarks by a large margin.Particularly, our method can achieve more than 90\% accurate picking across worksites in the case of medium and high SNRs, and even fine-tuned model can achieve 88\% accurate picking of the dataset with low SNR

Considering the Impacts of Metal Depletion on the European Electricity System

The transformation of the European electricity system could generate unintended environment-related trade-offs, e.g., between greenhouse gas emissions and metal depletion. The question thus emerges, how to shape policy packages considering climate change, but without neglecting other environmental and resource-related impacts. In this context, this study analyzes the impacts of different settings of potential policy targets using a multi-criteria analysis in the frame of a coupled energy system and life cycle assessment model. The focus is on the interrelationship between climate change and metal depletion in the future European decarbonized electricity system in 2050, also taking into account total system expenditures of transforming the energy system. The study shows, firstly, that highly ambitious climate policy targets will not allow for any specific resource policy targets. Secondly, smoothing the trade-off is only possible to the extent of one of the policy targets, whereas, thirdly, the potential of recycling as a techno-economic option is limited.</p