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The equity trading landscape all over the world has changed dramatically in recent years. We have witnessed the advent of new trading venues and significant changes in the market shares of existing ones. We use an extensive panel dataset from the European equity markets to analyze the market shares of five categories of lit and dark trading mechanisms. Market design features, such as minimum tick size, immediacy and anonymity; market conditions, such as liquidity and volatility; and the informational environment have distinct implications for order routing decisions and trading venues' resulting market shares. Furthermore, these implications differ distinctly for small and large trades, probably because traders jointly optimize their trade size and venue choice. Our results both confirm and go beyond current theoretical predictions on trading in fragmented markets.
Algorithmic trading engines versus human traders – do they behave different in securities markets?
(2009)
After exchanges and alternative trading venues have introduced electronic execution mechanisms worldwide, the focus of the securities trading industry shifted to the use of fully electronic trading engines by banks, brokers and their institutional customers. These Algorithmic Trading engines enable order submissions without human intervention based on quantitative models applying historical and real-time market data. Although there is a widespread discussion on the pros and cons of Algorithmic Trading and on its impact on market volatility and market quality, little is known on how algorithms actually place their orders in the market and whether and in which respect this differs form other order submissions. Based on a dataset that – for the first time – includes a specific flag to enable the identification of orders submitted by Algorithmic Trading engines, the paper investigates the extent of Algorithmic Trading activity and specifically their order placement strategies in comparison to human traders in the Xetra trading system. It is shown that Algorithmic Trading has become a relevant part of overall market activity and that Algorithmic Trading engines fundamentally differ from human traders in their order submission, modification and deletion behavior as they exploit real-time market data and latest market movements.
Advances in technology and several regulatory initiatives have led to the emergence of a competitive but fragmented equity trading landscape in the US and Europe. While these changes have brought about several benefits like reduced transaction costs, regulators and market participants have also raised concerns about the potential adverse effects associated with increased execution complexity and the impact on market quality of new types of venues like dark pools. In this article we review the theoretical and empirical literature examining the economic arguments and motivations underlying market fragmentation, as well as the resulting implications for investors' welfare. We start with the literature that views exchanges as natural monopolies due to presence of network externalities, and then examine studies which challenge this view by focusing on trader heterogeneity and other aspects of the microstructure of equity markets.
Die Anpassung der EU-Richtlinie über Märkte für Finanzinstrumente (MiFID II) und die Einführung einer begleitenden Verordnung (MiFIR) im Jahr 2014 werden erhebliche Auswirkungen auf die Finanzmärkte in Europa haben und zu einer grundlegenden Neuordnung der Finanzmarktstrukturen führen. Ausgehend von einer Diskussion der Zielerreichung der ursprünglichen Richtlinie (MiFID I) aus dem Jahr 2004 werden im vorliegenden Artikel die Zielsetzungen und Maßnahmen der Neuregelung beleuchtet. Wesentliche Elemente im Hinblick auf Marktstrukturen und den Wertpapierhandel sind die Einführung einer neuen Handelsplatzkategorie, des organisierten Handelssystems („Organised Trading Facility“; OTF), sowie die Ausweitung der bislang für Aktien geltenden Transparenzvorschriften auf weitere Finanzinstrumente. Zudem werden eine Handelsverpflichtung für Aktien und Derivate sowie eine Clearingpflicht für Derivate, die auf geregelten Märkten gehandelt werden, neu eingeführt. Schließlich werden der algorithmische Handel und der Hochfrequenzhandel auf europäischer Ebene reguliert, wobei die Regelungen weitgehend dem 2013 eingeführten deutschen Hochfrequenzhandelsgesetz angelehnt sind. Im Ausblick wird zunächst der weitere Prozess der Regulierung skizziert (insbesondere die sog. Level II-Maßnahmen). Abschließend werden mögliche Auswirkungen von MiFID II und MiFIR auf die Marktstruktur und den Wertpapierhandel aufgezeigt.
Regulatory impact analysis (RIA) serves to evaluate whether regulatory actions fulfill the desired goals. Although there are different frameworks for conducting RIA, they are only applicable to regulations whose impact can be measured with structured data. Yet, a significant and increasing number of regulations require firms to comply by communicating textual data to consumers and supervisors. Therefore, we develop a methodological framework for RIA in case of unstructured data based on textual analysis and apply it to a recent financial market regulation: MiFID II.
