We investigate the impact of corporate irresponsibility on future stock price crash by employing a unique dataset of 1,529 penalties imposed on 411 United States (U.S.) firms, from 2003 to 2015. We provide robust evidence that the total amount of penalties (in U.S. dollars) imposed on firms are negatively associated with firm-specific future stock price crash risk. Our findings are consistent with the following view that imposition of penalties remove uncertainty about a particular firm's future, investors please that the case is closed, the firm successfully manages the aftermath of misconduct and the firm's financial gains are often larger compared to the total cost of the penalty imposed. Moreover, we find corporate social responsibility (CSR) to be a channel through which penalties impact stock price crash risk. Our findings demonstrate that the negative association between monetary penalties and stock price crash risk is more pronounced in the postfinancial crisis and in environmentally sensitive firms.
Maturity transformation risk is highlighted as one of the major causes of recent global financial crisis. Basel III has proposed new liquidity regulations for transformation function of banks and hence to monitor this risk. Specifically, net stable funding ratio (NSFR) is introduced to enhance medium-and long-term resilience against liquidity shocks. Islamic banking is widely accepted in many parts of the world and contributes to a significant portion of the financial sector in many countries. Using a data-set of 68 fully fledged Islamic banks from 11 different countries, over a period from 2005-2014, this study attempts to analyze various factors that may significantly affect the maturity transformation risk in these banks. We utilize a 2-step system GMM estimation technique on unbalanced panel and find bank capital, credit risk, financing, size and market power as significant bank specific factors in determining maturity transformation risk. Furthermore, gross domestic product and inflation are found to be the significant macroeconomic factors that influence this risk. However, we find no evidence for the effect of bank profitability, cost efficiency and income diversity on maturity transformation risk in Islamic banking system.
This work addresses the aging of the memory sub-system due to NBTI (Negative Bias Temperature Instability) in systems that have to provide a guaranteed level of service, and specifically, a guaranteed lifetime. Our approach leverages a novel cache architecture in which a smart joint use of redundancy and power management allows us to obtain caches that meet a desired lifetime target with minimal energy consumption. This is made possible by exploiting the possibility of putting the cache sub-block used for redundancy into a deep low-power state, thus allowing more energy saving than a regular architecture. Sacrificing a portion of the cache for aging mitigation only marginally affects performance thanks to the non-linear dependency of miss rate versus cache size, which allows to find the best cache size that maximizes the objective. Simulation results show that it is possible to meet the target lifetime by achieving energy reductions (measured over the lifetime of the system) ranging from 3X to 10X (2X to 8X) for a lifetime target of 15 (25) years, with marginal miss rate overhead.
Aging of transistors can substantially shorten the lifetime of devices in sub-nanometric technologies. Without any countermeasure, the first component which becomes unreliable will determine the life span of an entire device. This problem is even more relevant for memory arrays, where failure of a single SRAM cell would cause the failure of the whole system. Traditional implementation of power management by turning idle cache lines into a low-energy state can also mitigate the aging effects caused by Negative Bias Temperature Instability (NBTI) provided that idleness is correctly exploited.In this work, we propose a cache structure which deals with cell failures by gracefully degrading its performance. By this partitioning-based strategy, various sub-blocks will become un reliable at different times, and the cache will keep functioning with reduced efficiency. Coupling such aging mitigation with the resulting energy reduction techniques we can obtain up to 2.5x lifetime extension and 40% energy savings with respect to a power managed cache.
Accurate elucidation of genome wide protein-protein interactions is crucial for understanding the regulatory processes of the cell. High-throughput techniques, such as the yeast-2-hybrid (Y2H) assay, co-immunoprecipitation (co-IP), mass spectrometric (MS) protein complex identification, affinity purification (AP) etc., are generally relied upon to determine protein interactions. Unfortunately, each type of method is inherently subject to different types of noise and results in false positive interactions. On the other hand, precise understanding of proteins, especially knowledge of their functional associations is necessary for understanding how complex molecular machines function. To solve this problem, computational techniques are generally relied upon to precisely predict protein interactions. In this work, we present a novel method that combines structural and non-structural biological data to precisely predict protein interactions. The conceptual novelty of our approach lies in identifying and precisely associating biological information that provides substantial interaction clues. Our model combines structural and non-structural information using Bayesian statistics to calculate the likelihood of each interaction. The proposed model is tested on Saccharomyces cerevisiae's interactions extracted from the DIP and IntAct databases and provides substantial improvements in terms of accuracy, precision, recall and F1 score, as compared with the most widely used related state-of-the-art techniques.
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