TUTCRIS - Tampereen teknillinen yliopisto


Investors, Information Arrivals, and Market Liquidity: Empirical Evidence from Financial Markets



KustantajaTampere University of Technology
ISBN (elektroninen)978-952-15-4231-2
ISBN (painettu)978-952-15-4211-4
TilaJulkaistu - 19 lokakuuta 2018
OKM-julkaisutyyppiG5 Artikkeliväitöskirja


NimiTampere University of Technology. Publication
ISSN (painettu)1459-2045


Well-functioning financial markets can be argued to benefit society widely. Investors, information arrivals, and market liquidity are all key aspects of financial markets. Without investors who trade, there would be no markets to begin with. Furthermore, information arrivals are important because information drives prices: new information may affect the valuation of assets traded. Finally, for prices to adjust efficiently to new information, the market needs to be sufficiently liquid, meaning that investors can trade when they want at a low transaction cost.

Earlier research on these topics exists, but the interrelations between these factors have not been studied in depth. The objective of this thesis is to improve our knowledge of the interrelations between investors, information arrivals, and liquidity in the context of financial markets. By addressing several research gaps related to these themes, this thesis aims to provide new empirical evidence in order to help the scientific community develop more reliable and robust models that describe the markets; in general, a better understanding of these topics and such interrelations may help improve regulations, exchange organizations, and investment management.

This thesis consists of an introductory part and four research papers (Articles I – IV). Article I uses logistic regression to study how Nokia’s Facebooks posts and related activities are associated with investors’ decisions to buy versus sell Nokia stock. In Article II, a framework from event studies is combined with high-frequency limit order book data to examine how liquidity in stock limit order books evolves around scheduled and nonscheduled company announcements. Article III applies regression analysis to identify the factors affecting the magnitude of order book liquidity shocks that company announcement releases cause in the limit order books. Finally, Article IV uses a unique data set to study the proportion of liquidity streams that a trader observes in a foreign exchange (FX) liquidity aggregator, as well as quantifies a trader’s theoretical improvements in the observed spread and the cost savings when comparing the current situation with the optimal combination of streams; the optimal combinations are solved using a genetic algorithm (GA).

Earlier literature has studied how news articles affect the trading of different investors, and this thesis contributes by providing evidence that the (potentially biased) information a company releases on social media affects the behaviors of different investors in the stock market differently. While the decisions of arguably less sophisticated investors—passive households and non-profit organizations—are associated with Facebook data, those of more sophisticated investors—financial institutions—seem to be independent of Facebook data.

Moreover, company announcements are found to cause significant changes in the stock limit order book liquidity, which is inconsistent with the finding of an earlier study using news data. In particular, scheduled announcement releases may improve liquidity to an abnormally high level, indicating that scheduled announcement releases resolve asymmetric information problems in the market, whereas the order book liquidity remains relatively low in many cases still an hour after the non-scheduled announcement releases The immediate liquidity shocks following the announcement releases are amplified by order book asymmetry prior to the announcements releases. Moreover, a fast reaction is a strong reaction (the faster the illiquidity peak is reached after the announcement release, the larger the peak usually is), and in case of non-scheduled announcement releases, recent losses amplify the liquidity shocks. The findings also indicate that liquidity measured over multiple order book price levels behaves quite differently compared to the conventional bid – ask spread calculated using data from the best order book levels, indicating that measuring liquidity just using top-of-the-book data may lead to misleading inferences.

Finally, the results show that in a liquidity aggregator, traders observe only a small proportion of liquidity streams available: on average, a trader observes 5.4 streams out of the total 165 streams provided by 42 liquidity providers (the maximum is 23 and the minimum is 1). However, traders observe relatively tight spreads already with four or five streams, and traders with more streams observe only marginal improvements in spread, if any. In theory, most traders could cut their observed spread by more than a half and save up to $0.18 basis points per e1 traded with the optimal combination of liquidity streams; in practice, however, traders may not be able to exploit the improvements because they are not free to choose just any streams in the aggregator, and if they would change the streams they observe, the liquidity providers would likely change their quoting behavior. Nevertheless, the novel empirical results can be used to assess the efficiency of the aggregator as a trading technology and the liquidity provision in the FX market, in general.


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