Corporate Social Responsibility Reporting, Pyramidal Structure And Political Interference: Evidence From China

This paper attempts to investigate the relation between pyramidal structure and corporate social responsibility (CSR) reporting quality and the effect of political interference on the relation. Based on 1388 Chinese A-share listed firms during 2010-2012, this paper demonstrates that the separation between control and ownership rights is significantly and positively related to the CSR reporting quality in the state-owned firms (SOFs), while negatively related to the CSR reporting quality in the non-state-owned firms (NSOFs). Results also indicate that the pyramidal layer between the bottom firms and their top ultimate owners is negatively related to CSR reporting quality, particularly significant for the NSOFs. Our research enriches the corporate governance literature by giving insights into the mechanism of pyramidal structure in corporate reporting, and extends the understanding of political interference in the CSR field. This study has public policy implications for China as well as a number of other countries in the Asia–Pacific region.

Low-Rank Tensor Recovery with Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization

The nuclear norm and Schatten-$p$ quasi-norm of a matrix are popular rank proxies in low-rank matrix recovery. Unfortunately, computing the nuclear norm or Schatten-$p$ quasi-norm of a tensor is NP-hard, which is a pity for low-rank tensor completion (LRTC) and tensor robust principal component analysis (TRPCA). In this paper, we propose a new class of rank regularizers based on the Euclidean norms of the CP component vectors of a tensor and show that these regularizers are monotonic transformations of tensor Schatten-$p$ quasi-norm. This connection enables us to minimize the Schatten-$p$ quasi-norm in LRTC and TRPCA implicitly. The methods do not use the singular value decomposition and hence scale to big tensors. Moreover, the methods are not sensitive to the choice of initial rank and provide an arbitrarily sharper rank proxy for low-rank tensor recovery compared to nuclear norm. We provide theoretical guarantees in terms of recovery error for LRTC and TRPCA, which show relatively smaller $p$ of Schatten-$p$ quasi-norm leads to tighter error bounds. Experiments using LRTC and TRPCA on synthetic data and natural images verify the effectiveness and superiority of our methods compared to baseline methods

Monotone Cubic B-Splines

We present a method for fitting monotone curves using cubic B-splines with a monotonicity constraint on the coefficients. We explore different ways of enforcing this constraint and analyze their theoretical and empirical properties. We propose two algorithms for solving the spline fitting problem: one that uses standard optimization techniques and one that trains a Multi-Layer Perceptrons (MLP) generator to approximate the solutions under various settings and perturbations. The generator approach can speed up the fitting process when we need to solve the problem repeatedly, such as when constructing confidence bands using bootstrap. We evaluate our method against several existing methods, some of which do not use the monotonicity constraint, on some monotone curves with varying noise levels. We demonstrate that our method outperforms the other methods, especially in high-noise scenarios. We also apply our method to analyze the polarization-hole phenomenon during star formation in astrophysics. The source code is accessible at \texttt{\url{https://github.com/szcf-weiya/MonotoneSplines.jl}}