Repurchasing Shares on a Second Trading Line

This paper studies a unique buyback method allowing firms to reacquire their own shares on a separate trading line where only the firm is allowed to buy shares. This temporary trading platform is opened concurrently with the original trading line on the stock exchange. This share repurchase method is called the Second Trading Line and has been extensively used by Swiss companies since 1997. This type of repurchase is unique for two reasons. First, unlike open market programs, the repurchasing company does not trade under the cover of anonymity. Second, all transactions made by the repurchasing firm are publicly available in real time to every market participant. This is a case of instantaneous disclosure which contrasts sharply with other markets characterized by delayed or no disclosure. Using actual repurchase data from all buybacks implemented through second trading lines, we find that managers exhibit timing ability for the majority of programs. We also document that the daily repurchase decision is statistically associated with short-term price changes. However, we reject the opportunistic repurchase hypothesis and find no evidence that managers exploit their information advantage when reacquiring shares. We also find that repurchases on the second trading line have a beneficial impact on the liquidity of repurchasing firms (i.e., higher trading volumes, smaller bid-ask spreads, and thicker total depths). Exchanges and regulators may consider the second trading line an attractive share reacquisition mechanism because of its transparency and positive liquidity effects.Share Repurchases;Disclosure Environment;Information Asymmetry;Liquidity

Acute Muscular Sarcocystosis: An International Investigation Among Ill Travelers Returning From Tioman Island, Malaysia, 2011-2012

A large outbreak of acute muscular sarcocystosis (AMS) among international tourists who visited Tioman Island, Malaysia, is described. Clinicians evaluating travelers returning ill from Malaysia with myalgia, with or without fever, should consider AMS in their differential diagnosi

Opening October DataFest “Love your data, share your data”

The DataFest opening ceremony will start with welcoming remarks by Prof. dr. Pursey Heugens (ERIM Scientific Director) followed by the keynote speaker Prof. dr. Christophe Pérignon (HEC, Paris) who will talk to us about Cascad, the first certification agency for scientific code & data

Why common factors in international bond returns are not so common

This paper analyzes the common factor structure of US, German, and Japanese Government bond returns. Unlike previous studies, we formally take into account the presence of country-specific factors when estimating common factors. We show that the classical approach of running a principal component analysis on a multi-country dataset of bond returns captures both local and common influences and therefore tends to pick too many factors. We conclude that US bond returns share only one common factor with German and Japanese bond returns. This single common factor is associated most notably with changes in the level of domestic term structures. We show that accounting for country-specific factors improves the performance of domestic and international hedging strategies

The level and quality of Value-at-Risk disclosure by commercial banks

In this paper we study both the level of Value-at-Risk (VaR) disclosure and the accuracy of the disclosed VaR figures for a sample of US and international commercial banks. To measure the level of VaR disclosures, we develop a VaR Disclosure Index that captures many different facets of market risk disclosure. Using panel data over the period 1996–2005, we find an overall upward trend in the quantity of information released to the public. We also find that Historical Simulation is by far the most popular VaR method. We assess the accuracy of VaR figures by studying the number of VaR exceedances and whether actual daily VaRs contain information about the volatility of subsequent trading revenues. Unlike the level of VaR disclosure, the quality of VaR disclosure shows no sign of improvement over time. We find that VaR computed using Historical Simulation contains very little information about future volatility