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We examine intra-day market reactions to news in stock-specific sentiment disclosures. Using pre-processed data from an automated news analytics tool based on linguistic pattern recognition we extract information on the relevance as well as the direction of company-specific news. Information-implied reactions in returns, volatility as well as liquidity demand and supply are quantified by a high-frequency VAR model using 20 second intervals. Analyzing a cross-section of stocks traded at the London Stock Exchange (LSE), we find market-wide robust news-dependent responses in volatility and trading volume. However, this is only true if news items are classified as highly relevant. Liquidity supply reacts less distinctly due to a stronger influence of idiosyncratic noise. Furthermore, evidence for abnormal highfrequency returns after news in sentiments is shown. JEL-Classification: G14, C32
This paper focuses on dynamic interactions of equity prices among theoretically related assets. We explore the existence of intraday non-linearities in the FTSE 100 cash and futures indices. We test whether the introduction of the electronic trading systems in the London Stock Exchange in 1997 and in the London International Financial Futures and Options Exchange (LIFFE) in 1999 has eliminated the non-linear dynamic relationship in the FTSE 100 markets. We show that the introduction of the electronic trading systems in the FTSE 100 markets has increased the efficiency of the markets by enhancing the price discovery process, namely by facilitating the increase of the speed of adjustment of the futures and cash prices to departures of the mispricing error from its non-arbitrage band. Nevertheless, we conclude that the automation of the markets has not completely eliminated the non-linear properties of the FTSE 100 cash and futures return series. JEL Classification: G12, G14, G15