Revisiting the link between environmental performance and financial performance: who cares about private companies?

Purpose: The financial market change and the climate change in recent years have triggered the studies of the connection between corporate carbon performance and financial performance, although the link between the two remains elusive in private companies. This study examines the relationship between environmental and financial performance with a particular focus on private companies. Design/methodology/approach: This study compares public listed and private unlisted companies registered under the Australian NGER Act 2007 and investigates the link between carbon performance and financial performance in these two groups of companies during 2009 and 2010. Findings: The results show that carbon performance and financial performance are significantly negatively related in public listed companies, suggesting worse carbon performers tend to enjoy higher financial returns and stronger financial performers are more likely to pollute more and consume more energy. In private companies, no significant link between the two performances is found

The Economic Consequences of National Security Threats: The Case of the Korean Peninsula

This paper examines the impact of national security threats on a nation’s economic growth and fiscal policy based on a case study of the Korean peninsula. I construct four measures of provocations using a newly-assembled list of North Korean provocative events going back to 1960. The results show that the overall impact of North Korean provocations on South Korea’s short-run economic growth is negligible. Since inter-Korean relations have gone through four phases, this paper also estimates the impact of provocations over each subperiod. Provocations had a significant impact on South Korea’s economic growth during 1960-1970 and 1992-1997 when inter-Korean tensions were high, but the effects took on different signs. While provocations decreased South Korea’s economic growth during 1992-1997, it had a positive impact on South Korea’s macroeconomy before 1970. This paper provides evidence that the effect of national security threats may vary with the responses from the government and political factors such as the relation between the targeted country and the country that inflicts the threat.</p

Adaptive Algorithm for Multi-armed Bandit Problem with High-dimensional Covariates

This paper studies an important sequential decision making problem known as the multi-armed stochastic bandit problem with covariates. Under a linear bandit framework with high-dimensional covariates, we propose a general multi-stage arm allocation algorithm that integrates both arm elimination and randomized assignment strategies. By employing a class of high-dimensional regression methods for coefficient estimation, the proposed algorithm is shown to have near optimal finite-time regret performance under a new study scope that requires neither a margin condition nor a reward gap condition for competitive arms. Based on the synergistically verified benefit of the margin, our algorithm exhibits adaptive performance that automatically adapts to the margin and gap conditions, and attains optimal regret rates simultaneously for both study scopes, without or with the margin, up to a logarithmic factor. Besides the desirable regret performance, the proposed algorithm simultaneously generates useful coefficient estimation output for competitive arms and is shown to achieve both estimation consistency and variable selection consistency. Promising empirical performance is demonstrated through extensive simulation and two real data evaluation examples.</p

An Assessment of CSR Reporting Practice in China’s Mining and Minerals Industry

Purpose - This study assesses the current status of CSR reporting practice in China’s mining and minerals industry during 2007 – 2010. Design/methodology/approach - A sample of 176 mining and minerals companies listed on China’s domestic stock exchanges – Shanghai and Shenzhen stock exchanges is selected. Content analysis has been conducted to extract disclosure quantity, quality and contents from both corporate annual reports and CSR reports. The corporate reports are then assessed against the domestic CSR reporting framework – ‘Chinese CSR Report Preparation Guide (CASS-CSR 1.0)’. Findings - The study identifies that there is a dramatic increase in the number of reporting companies; disclosure quantity; and quality in China’s mining and minerals industry. The result is well coincident with the phase in historical development of CSR reporting in China – the rapid development of CSR reporting practice during the period of ‘building a harmonious society’ (mid 2000s – 2010). However, the disclosure quantity and quality still need considerable improvement

<i>ravA</i>, <i>ravR</i> and <i>ravS</i> differentially regulate bacterial virulence and swimming.

(a) Genomic organisation of ravARS genes and putative secondary structures of their protein products. Protein secondary structures were predicted by the SMART program. TrM: transmembrane domain; PAS: Per-ARNT-Sim domain; DHp: dimerisation and histidine phosphotransfer domain; CA: catalytic and ATP binding domain; REC: receiver domain. GGDEF and EAL domains are also shown. (b) Subcellular localisation of RavA, RavR and RavS. Western blotting was used to detect the proteins in different subcellular fractions. The known membrane-bound RpfC and cytoplasmic HPPK were detected as controls. (c) The ratio of RavA, RavR and RavS proteins in bacterial cells. Semi-quantitative western blotting was used to estimate the levels of these proteins (n = 3). (b) and (c), each experiment was repeated three times. (d–e) Mutation of ravA and ravR rather than ravS attenuated bacterial virulence significantly. (d) Bacterial virulence against host plant Brassica oleracea cv Zhonggan 11. Strains were inoculated onto plant leaves by scissor cutting. The lesion length was recorded 10 d after inoculation. The negative control was an inoculation of 10 mM MgCl2. EV: empty vector. (e) Quantification of the lesion length in (d). Average lengths and standard deviations are shown. Asterisk: significant difference, as tested by Student’s t-test (P ≤ 0.05, n = 30 inoculation sites). (f) Extracellular polysaccharide (EPS) production of bacterial strains. EPS production was measured as the dry weight of EPS vs. the dry weight of bacterial cells. Asterisk: significant difference, as tested by Student’s t-test (P ≤ 0.05, n = 3). (g–h) Swimming motility of bacterial strains. (g) Bacterial strains were inoculated in NYG plates containing 0.15% agar and grown at 28°C for 28 h. (h) Average diameters of the migration zones of (g). Asterisk: significant difference, as tested by Student’s t-test (P ≤ 0.05, n = 10).</p

Tweedie’s Compound Poisson Model With Grouped Elastic Net

<p>Tweedie’s compound Poisson model is a popular method to model data with probability mass at zero and nonnegative, highly right-skewed distribution. Motivated by wide applications of the Tweedie model in various fields such as actuarial science, we investigate the grouped elastic net method for the Tweedie model in the context of the generalized linear model. To efficiently compute the estimation coefficients, we devise a two-layer algorithm that embeds the blockwise majorization descent method into an iteratively reweighted least square strategy. Integrated with the strong rule, the proposed algorithm is implemented in an easy-to-use R package HDtweedie, and is shown to compute the whole solution path very efficiently. Simulations are conducted to study the variable selection and model fitting performance of various lasso methods for the Tweedie model. The modeling applications in risk segmentation of insurance business are illustrated by analysis of an auto insurance claim dataset. Supplementary materials for this article are available online.</p

Supplementary_Figure_1_revised – Supplemental material for Urinary markers in treatment monitoring of lung cancer patients with bone metastasis

Supplemental material, Supplementary_Figure_1_revised for Urinary markers in treatment monitoring of lung cancer patients with bone metastasis by Pei Cheng Jin, Bo Gou and Wei Qian in The International Journal of Biological Markers</p

Arg⁶⁵⁶ in the CA domain of RavS is a key residue for c-di-GMP binding.

(a, b) c-di-GMP binds to the DHp-CA domain of RavS. PAS-A, PAS-B and DHp-CA in (a), and DHp and CA domains of RavS in (b), were purified and the interaction with c-di-GMP was measured by MST. Each assay was repeated three times. Standard deviations are shown. (c) Molecular docking model of the interaction between c-di-GMP and DHp-CA of RavS. c-di-GMP is shown as a ball-and-stick model and DHp-CA is shown in ribbon representation. (d) Schematic view of the predicted docking site between c-di-GMP and DHp-CA. Potential hydrogen bonds are indicated as blue dashed lines. Autodock software was used to predict the interaction. (e) Effect of amino acid substitutions on RavS–c-di-GMP binding. Ten recombinant proteins of the DHp-CA region, each containing a substitution of a putative essential residue involved in c-di-GMP binding, were used in the MST assay to quantify the Kd value of RavS–c-di-GMP binding. fl-c-di-GMP was used in the MST assay. Each experiment was repeated three times. (f) Substitution of Arg656 of RavS resulted in the loss of RavS-RavR phosphotransfer upon stimulation by c-di-GMP. A total of 5 μM RavSΔN or RavSΔN(R656A) was phosphorylated by 10 μCi [γ-32P]ATP for 15 min in the absence or presence of 15 or 100 μM c-di-GMP, and then 15 μM RavRΔEAL was added and incubated for 30 min at 25°C. The reactions were terminated with 5× SDS loading buffer and the products were separated by 12% SDS-PAGE, exposed to a phosphor screen and analysed by Typhoon FLA7000. The experiment was repeated three times.</p

Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension

<p>Sufficient dimension reduction (SDR) is known to be a powerful tool for achieving data reduction and data visualization in regression and classification problems. In this work, we study ultrahigh-dimensional SDR problems and propose solutions under a unified minimum discrepancy approach with regularization. When <i>p</i> grows exponentially with <i>n</i>, consistency results in both central subspace estimation and variable selection are established simultaneously for important SDR methods, including sliced inverse regression (SIR), principal fitted component (PFC), and sliced average variance estimation (SAVE). Special sparse structures of large predictor or error covariance are also considered for potentially better performance. In addition, the proposed approach is equipped with a new algorithm to efficiently solve the regularized objective functions and a new data-driven procedure to determine structural dimension and tuning parameters, without the need to invert a large covariance matrix. Simulations and a real data analysis are offered to demonstrate the promise of our proposal in ultrahigh-dimensional settings. Supplementary materials for this article are available online.</p

Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models

<p>The Tweedie GLM is a widely used method for predicting insurance premiums. However, the structure of the logarithmic mean is restricted to a linear form in the Tweedie GLM, which can be too rigid for many applications. As a better alternative, we propose a gradient tree-boosting algorithm and apply it to Tweedie compound Poisson models for pure premiums. We use a profile likelihood approach to estimate the index and dispersion parameters. Our method is capable of fitting a flexible nonlinear Tweedie model and capturing complex interactions among predictors. A simulation study confirms the excellent prediction performance of our method. As an application, we apply our method to an auto-insurance claim data and show that the new method is superior to the existing methods in the sense that it generates more accurate premium predictions, thus helping solve the adverse selection issue. We have implemented our method in a user-friendly R package that also includes a nice visualization tool for interpreting the fitted model.</p