This study empirically examines the impact of changes in substantial shareholdings ahead of 450 Australian takeover offers between the years 2000 and 2009. Previous studies have attributed a significant proportion of the price run-up effect in takeover targets to insider-trading behaviour. This study examines the contribution of a broad range of public information sources that are known to typically generate market anticipation, including the acquisition of toeholds ahead of takeover announcements. Our findings show no significant pre-bid run-up for takeover targets after considering these sources. We conclude from these results that previous findings attributing pre-bid share price run-up to illegal insider trading may overstate the existence of such conduct.

Both fairness and efficiency are important considerations in market design and regulation, yet many regulators have neither defined nor measured these concepts. We develop an evidencebased policy framework in which these are both defined and measured using a series of empirical proxies. We then build a systems estimation model to examine the 2003-2011 explosive growth in algorithmic trading (AT) on the London Stock Exchange and NYSE Euronext Paris. Our results show that greater AT is associated with increased transactional efficiency and reduced information leakage in top quintile stocks. For less liquid stocks, manipulation at the close declines. We also document the tradeoff between reduced spreads and increased manipulation or information leakage following the introduction of MiFID1.

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Matching algorithms are important for well‐functioning financial markets. This paper examines the 2007 change by LIFFE, to move from pure pro‐rata to time pro‐rata allocation for the Euribor, Short Sterling, and Euroswiss futures contracts. We show that the removal of pure pro‐rata matching reduces market depth but suggest that this outcome improves execution quality for market participants. Our results are consistent with suggestions in the literature that the former regime creates incentives for traders to “drown” the order book with large orders and that the addition of a time element to this algorithm alters their behavior. We provide evidence that traders increase the amount of order splitting in the new framework, consistent with local optimizing, but argue that this may hinder overall market efficiency. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 35:522–541, 2015

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