Fast Computation of the Fitness Function for Protein Folding Prediction in a 2D Hydrophobic-Hydrophilic Model

Protein Folding Prediction (PFP) is essentially an energy minimization problem formalised by the definition of a fitness function. Several PFP models have been proposed including the Hydrophobic-Hydrophilic (HP) model, which is widely used as a test-bed for evaluating new algorithms. The calculation of the fitness is the major computational task in determining the native conformation of a protein in the HP model and this paper presents a new efficient search algorithm (ESA) for deriving the fitness value requiring only O(n) complexity in contrast to the full search approach, which takes O(n2). The improved efficiency of ESA is achieved by exploiting some intrinsic properties of the HP model, with a resulting reduction of more than 50% in the overall time complexity when compared with the previously reported Caching Approach, with the added benefit that the additional space complexity is linear instead of quadratic

Executive cash compensation and tax aggressiveness of Chinese firms

We examine the influence of corporate compensation policies on firms’ tax aggressiveness in an emerging market where executive compensation is primarily in cash form. Based on a hand-collected dataset of 958 firm-year observations of Chinese listed firms for the 2006-2012 period, we find that firms paying higher executive cash compensation are associated with lower tax aggressiveness. This relationship also holds for the excess cash compensation measures which control for executive shareholding, firm profitability, size, growth opportunity, and board independence. We further document that mutual funds ownership pressure firms paying higher compensation to reduce their tax aggressiveness, suggesting adverse selection by mutual funds on firms exhibiting risky tax avoidance activities. High leverage offsets the negative link between cash compensation and tax aggressiveness, indicating a complementary effect between debt and tax avoidance, and, hence, suggesting that creditor monitoring is weak. These results are robust to the system-GMM estimation, which simultaneously account for the endogeneity of executive compensation, tax aggressiveness, ownership and control, leverage, and corporate governance. Our findings on Chinese firms have important policy implications for developing countries around the world with concentrated ownership structure, weak institutional environment, widespread corruption, ineffective rule of law, and ongoing significant social and political transformation

A new guided genetic algorithm for 2D hydrophobic-hydrophilic model to predict protein folding

This paper presents a novel guided genetic algorithm (GGA) for protein folding prediction (PFP) in 2D hydrophobic-hydrophilic (HP) by exploring the protein core formation concept. A proof of the shape for an optimal core is provided and a set of highly probable sub-conformations are defined which help to establish the guidelines to form the core boundary. A series of new operators including diagonal move and tilt move are defined to assist in implementing the guidelines. The underlying reasons for the failure in the folding prediction of relatively long sequences using Unger's genetic algorithm (GA) in 2D HP model are analysed and the new GGA is shown to overcome these limitations. The overall strategy incorporates a swing function that provides a mechanism to enable the GGA to test more potential solutions and also prevent it from developing a schema that may cause it to become trapped in local minima. While the guidelines do not force particular conformations, the result is a number of conformations for particular putative ground energy and superior prediction accuracy, endorsing the improved performance compared with other well established nondeterministic search approaches

A Guided Genetic Algorithm for Protein Folding Prediction using a 3D Hydrophobic-Hydrophilic Model

In this paper, a Guided Genetic Algorithm (GGA) has been presented for protein folding prediction (PFP) using 3D Hydrophobic-Hydrophilic (HP) model. Effective strategies have been formulated utilizing the core formation of the globular protein, which provides the guideline for the Genetic Algorithm (GA) while predicting protein folding. Building blocks containing Hydrophobic (H) -Hydrophilic (P or Polar) covalent bond are utilized such a way that it helps form a core that maximizes the fitness. A series of operators are developed including Diagonal Move and Tilt Move to assist in implementing the building blocks in three-dimensional space. The GGA outperformed Unger's GA in 3D HP model. The overall strategy incorporates a swing function that provides a mechanism to enable the GGA to test more potential solutions and also prevent it from developing a schema that may cause it to become trapped in local minima. Further, it helps the guidelines remain non-rigid. GGA provides improved and robust performance for PFP

Earnings Management and Underperformance after Seasoned Equity Offerings: A Cross-Country Study

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Secrecy and the impact of mandatory IFRS adoption on earnings quality in Europe

This study examines how differences in national culture, as indicated by financial secrecy, affect the impact of mandatory adoption of IFRS on earnings quality across the countries of Europe. Using 24,034 firm-year observations from 16 European countries over the period 1998-2014, we find that the higher the level of secrecy in a country the lower the level of earnings quality of firms, as measured by signed abnormal accruals. We find that mandatory adoption of IFRS improves earnings quality in all countries. However, our study indicates that the impact of mandatory adoption of IFRS on earnings quality is stronger the higher the level of secrecy in a country. Our evidence thus helps to explain the different impacts of IFRS adoption on earnings quality across different jurisdictions

Discriminatory related party transactions: A new measure

Purpose: The purpose of this study is to develop a new measure for discriminatory related party transactions (DRPTs). There are currently measures for such discriminatory transactions but the new measure has a strong theoretical basis and is less susceptible to measurement error. Design/methodology/approach: This paper develops and tests a new measure for these discriminatory transactions. Type I and Type II error rates and the power of the new measure are compared with an existing measure using computer-simulated and real data. Findings: The capital market sensitivity of the new measure is also tested and compared with the existing measure. The new measure is found to be superior. Practical implications: The new measure of DRPTs has the potential to contribute to both further research on the impact of related party transactions and policy-making in relation to DRPTs. Originality/value: This paper has developed and tested a new measure for DRPTs