Call auction transparency and market liquidity: Evidence from China

This paper uses the natural experiment offered by the Shanghai Stock Exchange to investigate the impact of opening call auction transparency on market liquidity. We find that the dissemination of indicative trade information during the pre-open call auction session leads to an overall improvement in stock liquidity in the continuous trading session. Bid-ask spreads narrow in the first trading hour because adverse selection risk fell significantly and there is less price volatility in the continuous market. This effect is greater for actively traded securities than illiquid securities. Our findings are robust for different lengths of sample period, different lengths of trading hours after market open, and stocks that had (and had not) reformed the share split structure during our research period.

The role of optimistic news stories in IPO pricing

This study investigates the influence of optimistic news stories on first-day pricing of initial public offerings (IPOs) in Australia between 1995 and 2005. Unlike the United States, Australia has no quiet-period regulation limiting the dissemination of information from media before IPO listing dates. We find that optimistic news stories are negatively associated with IPO underpricing. Results from a relative valuation model show that IPOs which received positive news stories ahead of the first trading day are not overpriced relative to their industry benchmarks. These results suggest that optimistic news stories mitigate information asymmetry and adverse selection problems. However, optimistic news stories do not appear to inflate the share price on the first day of trading. Our findings suggest that regulation mandating a 'quiet period' before the commencement of trading in IPOs is neither necessary nor desirable in the Australian environment.

Latency reduction and market quality: the case of the Australian Stock Exchange

This study investigates the role of latency in market quality in the Australia Securities Exchange following the introduction of the Integrated Trading Platform (ITS) and ASXTrade. We find that the reduction in system latency from 70. ms to 30. ms (ITS) improved liquidity. However, the lower latency has not had a long-lasting downward effect on spreads, as there was no discernible reduction in trading costs when institutional traders already had access to lower-latency co-locations. We contribute to the literature by reporting that low latency improves market liquidity, but privileged participants that have access to trading information prior to others may induce greater information asymmetry and adverse selection.

Analyst coverage and financing decisions

We provide evidence that analyst coverage affects security issuance. First, firms covered by fewer analysts are less likely to issue equity as opposed to debt. They issue equity less frequently, but when they do so, it is in larger amounts. Moreover, these firms depend more on favorable market conditions for their equity issuance decisions. Finally, debt ratios of less covered firms are more affected by Baker and Wurgler’s(2002) “external finance-weighted” average market-to-book ratio. These results are consistent with market timing behavior associated with information asymmetry, as well as behavior implied by dynamic adverse selection models of equity issuance.

The effect of auditor quality on financing decisions

We present a model and provide empirical evidence showing that auditor quality affects the financing decisions of companies, and that higher audit quality reduces the impact of market conditions on client financial decisions and capital structure. Consistent with our analytical predictions, we find that companies audited by Big 6 firms are more likely to issue equity as opposed to debt than are those audited by small audit firms. We also find that companies audited by Big 6 auditors are able to make larger equity issues than are those audited by small auditors, but the difference narrows when market conditions improve. Additional results show that the debt ratios of companies decrease less in response to favorable market conditions when auditor quality is high, at least over the medium term.

The missing evidence: a systematic review of patients' experiences of adverse events in health care

PURPOSE: Preventable patient harm due to adverse events (AEs) is a significant health problem today facing contemporary health care. Knowledge of patients' experiences of AEs is critical to improving health care safety and quality. A systematic review of studies of patients' experiences of AEs was conducted to report their experiences, knowledge gaps and any challenges encountered when capturing patient experience data. DATA SOURCES: Key words, synonyms and subject headings were used to search eight electronic databases from January 2000 to February 2015, in addition to hand-searching of reference lists and relevant journals. STUDY SELECTION: Titles and abstracts of publications were screened by two reviewers and checked by a third. Full-text articles were screened against the eligibility criteria. DATA EXTRACTION: Data on design, methods and key findings were extracted and collated. RESULTS: Thirty-three publications demonstrated patients identifying a range of problems in their care; most commonly identified were medication errors, communication and coordination of care problems. Patients' income, education, health burden and marital status influence likelihood of reporting. Patients report distress after an AE, often exacerbated by receiving inadequate information about the cause. Investigating patients' experiences is hampered by the lack of large representative patient samples, data over sufficient time periods and varying definitions of an AE. CONCLUSION: Despite the emergence of policy initiatives to enhance patient engagement, few studies report patients' experiences of AEs. This information must be routinely captured and utilized to develop effective, patient-centred and system-wide policies to minimize and manage AEs.

Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes

Cardiac autonomic neuropathy (CAN) poses an important clinical problem, which often remains undetected due difficulty of conducting the current tests and their lack of sensitivity. CAN has been associated with growth in the risk of unexpected death in cardiac patients with diabetes mellitus. Heart rate variability (HRV) attributes have been actively investigated, since they are important for diagnostics in diabetes, Parkinson's disease, cardiac and renal disease. Due to the adverse effects of CAN it is important to obtain a robust and highly accurate diagnostic tool for identification of early CAN, when treatment has the best outcome. Use of HRV attributes to enhance the effectiveness of diagnosis of CAN progression may provide such a tool. In the present paper we propose a new machine learning algorithm, the Multi-Layer Attribute Selection and Classification (MLASC), for the diagnosis of CAN progression based on HRV attributes. It incorporates our new automated attribute selection procedure, Double Wrapper Subset Evaluator with Particle Swarm Optimization (DWSE-PSO). We present the results of experiments, which compare MLASC with other simpler versions and counterpart methods. The experiments used our large and well-known diabetes complications database. The results of experiments demonstrate that MLASC has significantly outperformed other simpler techniques.