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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.
We analyze limit order book resiliency following liquidity shocks initiated by large market orders. Based on a unique data set, we investigate whether high‐frequency traders are involved in replenishing the order book. Therefore, we relate the net liquidity provision of high‐frequency traders, algorithmic traders, and human traders around these market impact events to order book resiliency. Although all groups of traders react, our results show that only high‐frequency traders reduce the spread within the first seconds after the market impact event. Order book depth replenishment, however, takes significantly longer and is mainly accomplished by human traders’ liquidity provision.
THE SPEED OF TRADING, AND IN PARTICULAR HIGH-FREQUENCY TRADING, IS ONE OF THE MOSTLY DEBATED ISSUES AMONG REGULATORS AND MARKET PARTICIPANTS. NEVERTHELESS, SEVERAL ACADEMIC STUDIES HAVE SHOWN THAT HIGH-FREQUENCY TRADERS USING LOW-LATENCY INFRASTRUCTURE PROVIDE ADDITIONAL LIQUIDITY THEREBY REDUCING TRANSACTION COSTS IN ORDINARY TIMES OF TRADING. WE STUDY WHETHER HIGH-FREQUENCY TRADERS ALSO CONTRIBUTE TO THE RECONSTRUCTION OF THE ORDER BOOK AFTER LIQUIDITY SHOCKS CAUSED BY LARGE ORDERS.
AGAINST THE BACKGROUND OF FRAGMENTED EUROPEAN EQUITIES TRADING, MARKET OPERATORS HAVE EMPLOYED DIFFERENT STRATEGIES TO INCREASE LIQUIDITY ON THEIR MARKET RELATIVE TO OTHER TRADING VENUES. ONE OF THESE STRATEGIES IS TO INCENTIVIZE LIQUIDITY PROVIDERS VIA FEE REBATES. THIS ARTICLE PRESENTS AN EMPIRICAL INVESTIGATION OF THE INTRODUCTION OF THE XETRA LIQUIDITY PROVIDER PROGRAM AT DEUTSCHE BÖRSE AND ITS IMPACT ON LIQUIDITY AND TRADING VOLUME ON THE INTRODUCING MARKET ITSELF AND ON THE CONSOLIDATED EUROPEAN MARKET.
ALGORITHMIC DECISION MAKING PLAYS AN IMPORTANT ROLE IN FINANCIAL MARKETS. ONE SOURCE OF INFORMATION FOR SUCH ALGORITHMS IS THE SENTIMENT OF SOCIAL MEDIA MESSAGES AND NEWS ARTICLES CONCERNING A LISTED COMPANY. YET, CURRENT TOOLS DO NOT DISTINGUISH BETWEEN POPULAR AND LESS POPULAR NEWS AND IT IS UNCLEAR WHETHER METHODOLOGIES BASED ON DATA ANALYTICS CAN BE APPLIED ON SMALL DATASETS OF LESS POPULAR COMPANIES. THEREFORE, WE ANALYZE WHETHER THE IMPACT OF MEDIA SENTIMENT ON FINANCIAL MARKETS IS INFLUENCED BY TWO LEVELS OF INVESTOR ATTENTION AND WHETHER THIS IMPACTS ALGORITHMIC DECISION MAKING.