Corporate governance and the informativeness of disclosures in Australia:a re-examination

We re-examine the association between corporate governance and disclosures reported by Beekes and Brown (2006), using an extended time series of Australian data. Since the ASX corporate governance guidelines were introduced in 2003, firms generally have increased their disclosure frequency and demonstrated an improvement in the timeliness of bad news relative to good news, indicating a levelling of disclosure practices and greater transparency. Better governed firms have become more cautious in their disclosure practices. However they continue to be more balanced with respect to good and bad news timeliness. Changes to disclosure laws have also influenced company practices

Financial integration and the transparency of firms in emerging capital markets

We examine the association between financial integration and capital market transparency of emerging-market firms. We use four intra-year price timeliness measures derived from the Beekes and Brown (2006, 2007) methods as indicators of the firm’s transparency. The sample comprises 57,465 firm-year observations on listed companies in 24 emerging economies over the period 1995-2010. As expected, we find that greater financial integration is associated with greater transparency, and that the effect is more pronounced when the news about the firm is bad. Using structural equation modelling (SEM), we find evidence of a mechanism through which financial integration enhances the information environment: improved corporate governance

Reweighted lp Constraint LMS-Based Adaptive Sparse Channel Estimation for Cooperative Communication System

This paper studies the issue of sparsity adaptive channel reconstruction in time-varying cooperative communication networks through the amplify-and-forward transmission scheme. A new sparsity adaptive system identification method is proposed, namely reweighted norm ( < < ) penalized least mean square（LMS）algorithm. The main idea of the algorithm is to add a norm penalty of sparsity into the cost function of the LMS algorithm. By doing so, the weight factor becomes a balance parameter of the associated norm adaptive sparse system identification. Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper bounds provided which serve as a sufficient condition for the LMS channel estimation of the precise reweighted norm. With the upper bounds, we prove that the ( < < ) norm sparsity inducing cost function is superior to the reweighted norm. An optimal selection of for the norm problem is studied to recover various sparse channel vectors. Several experiments verify that the simulation results agree well with the theoretical analysis, and thus demonstrate that the proposed algorithm has a better convergence speed and better steady state behavior than other LMS algorithms

SCAN: Semantic Communication with Adaptive Channel Feedback

In existing semantic communication systems for image transmission, some images are generally reconstructed with considerably low quality. As a result, the reliable transmission of each image cannot be guaranteed, bringing significant uncertainty to semantic communication systems. To address this issue, we propose a novel performance metric to characterize the reliability of semantic communication systems termed semantic distortion outage probability (SDOP), which is defined as the probability of the instantaneous distortion larger than a given target threshold. Then, since the images with lower reconstruction quality are generally less robust and need to be allocated with more communication resources, we propose a novel framework of Semantic Communication with Adaptive chaNnel feedback (SCAN). It can reduce SDOP by adaptively adjusting the overhead of channel feedback for images with different reconstruction qualities, thereby enhancing transmission reliability. To realize SCAN, we first develop a deep learning-enabled semantic communication system for multiple-input multiple-output (MIMO) channels (DeepSC-MIMO) by leveraging the channel state information (CSI) and noise variance in the model design. We then develop a performance evaluator to predict the reconstruction quality of each image at the transmitter by distilling knowledge from DeepSC-MIMO. In this way, images with lower predicted reconstruction quality will be allocated with a longer CSI codeword to guarantee the reconstruction quality. We perform extensive experiments to demonstrate that the proposed scheme can significantly improve the reliability of image transmission while greatly reducing the feedback overhead

Alleviating Distortion Accumulation in Multi-Hop Semantic Communication

Recently, semantic communication has been investigated to boost the performance of end-to-end image transmission systems. However, existing semantic approaches are generally based on deep learning and belong to lossy transmission. Consequently, as the receiver continues to transmit received images to another device, the distortion of images accumulates with each transmission. Unfortunately, most recent advances overlook this issue and only consider single-hop scenarios, where images are transmitted only once from a transmitter to a receiver. In this letter, we propose a novel framework of a multi-hop semantic communication system. To address the problem of distortion accumulation, we introduce a novel recursive training method for the encoder and decoder of semantic communication systems. Specifically, the received images are recursively input into the encoder and decoder to retrain the semantic communication system. This empowers the system to handle distorted received images and achieve higher performance. Our extensive simulation results demonstrate that the proposed methods significantly alleviate distortion accumulation in multi-hop semantic communication

Financial integration and emerging markets capital structure

a b s t r a c t This paper investigates the impact of country-level financial integration on corporate financing choices in emerging economies. Examining 4477 public firms from 24 countries, we find that corporate leverage is positively related to credit market integration and negatively related to equity market integration. As integration proceeds to higher levels, high-growth firms seem to obtain more debt than low-growth firms; large firms seem to obtain more debt -especially long-term debt -and issue more equity than small firms. Also, there is evidence that firms are able to borrow more funds in countries with more efficient legal systems during integration process