Public information arrival: Price discovery and liquidity in electronic limit order markets

How information is translated into market prices is still an open question. This paper studies the impact of newswire messages on intraday price discovery, liquidity, and trading intensity in an electronic limit order market. We take an objective ex ante measure of the tone of a message to study the impacts of positive, negative, and neutral messages on price discovery and trading activity. As expected, we find higher adverse selection costs around the arrival of newswire messages. Negative messages are associated with higher adverse selection costs than positive or neutral messages. Liquidity increases around positive and neutral messages and decreases around negative messages. Available order book depth as well as the trading intensity increases around all news. Our results suggest that market participants possess different information gathering and processing capabilities and that negative news messages are particularly informative and induce stronger market reactions.     

Hidden liquidity: An analysis of order exposure strategies in electronic stock markets

Many stock exchanges choose to reduce market transparency by allowing traders to hide some or all of their order size. We study the costs and benefits of order exposure and test hypotheses regarding hidden order usage using a sample of Euronext-Paris stocks, where hidden orders represent 44% of the sample order volume. Our results support the hypothesis that hidden orders are associated with a decreased probability of full execution and increased average time to completion, and fail to support the alternate hypothesis that order exposure causes defensive traders to withdraw from the market. However, exposing rather than hiding order size increases average execution costs. We assess the extent to which non-displayed size is truly hidden and document that the presence and magnitude of hidden orders can be predicted to a significant, but imperfect, degree based on observable order attributes, firm characteristics, and market conditions. Overall, the results indicate that the option to hide order size is valuable, in particular, to patient traders.     

Liquidity,information, strategic trading in an electronic order book: New insights from the European carbon markets

The electronic limit order book (LOB hereafter) has rapidly become the primary way of trading European carbon assets over the 4 years of the EU ETS programme (2008–2012). In this first attempt of examining the informational content of an electronic order book, we evidence that order flow imbalances have a moderate capacity to predict short term price changes. However, we find that both LOB slope and immediacy costs help to forecast quote improvements and volatility in the next 30 min. Further, we explain why informed trading is highly influential and show that it consists in mixing order splitting strategies and posting fleeting orders once the asymmetric information is reduced (Rosu, 2009). Overall, the consolidated status of the order book mirrors a high level of market uncertainty and a low degree of informational efficiency. In this way, strategic trading can in itself explain some of order book properties, independently of the degree of traders’ sophistication and market competition.     

Informed traders and limit order markets

We consider a dynamic limit order market in which traders optimally choose whether to acquire information about the asset and the type of order to submit. We numerically solve for the equilibrium and demonstrate that the market is a “volatility multiplier”: prices are more volatile than the fundamental value of the asset. This effect increases when the fundamental value has high volatility and with asymmetric information across traders. Changes in the microstructure noise are negatively correlated with changes in the estimated fundamental value, implying that asset betas estimated from high-frequency data will be incorrect.     

Reduced form modeling of limit order markets

This paper proposes a parametric approach for stochastic modeling of limit order markets. The models are obtained by augmenting classical perfectly liquid market models with a few additional risk factors that describe liquidity properties of the order book. The resulting models are easy to calibrate and to analyse using standard techniques for multivariate stochastic processes. Despite their simplicity, the models are able to capture several properties that have been found in microstructural analysis of limit order markets. Calibration of a continuous-time three-factor model to Copenhagen Stock Exchange data exhibits, for example, mean reversion in liquidity as well as the so-called crowding out effect, which influences subsequent mid-price moves. Our dynamic models are also well suited for analysing market resilience after liquidity shocks.     

Investigating the nature of interaction between crypto-currency and commodity markets

This paper investigates the dynamic relationship and volatility spillovers between cryptocurrency and commodity markets using different multivariate GARCH models. We take into account the nature of interaction between these markets and their transmission mechanisms when analyzing the conditional cross effects and volatility spillovers. Our results confirm the presence of significant returns and volatility spillovers, and we identify the GO-GARCH (2,2) as the best-fit model for modeling the joint dynamics of various financial assets. Our findings show significant dynamic linkages and volatility spillovers between gold, natural gas, crude oil, Bitcoin, and Ethereum prices. We find that gold can serve as a safe haven in times of economic uncertainty, as it is a good hedge against natural gas and crude oil price fluctuations. We also find evidence of bidirectional causality between crude oil and natural gas prices, suggesting that changes in one commodity's price can affect the other. Furthermore, we observe that Bitcoin and Ethereum are positively correlated with each other, but negatively correlated with gold and crude oil, indicating that these cryptocurrencies may serve as useful diversification tools for investors seeking to reduce their exposure to traditional assets. Our study provides valuable insights for investors and policymakers regarding asset allocation and risk management, and sheds light on the dynamics of financial markets.     

Characteristics of the order flow through an electronic open limit order book

We estimate and examine certain characteristics of the order flow through an electronic open limit order book, using order (not trade) data. In doing this, we bring out new evidence on order flow from a market with microstructure different from that of the NYSE. We find that the proportion of informed orders is less than 10%, lower than previous estimates. Informed traders choose smaller orders than uninformed traders, but do not materially differ in their choice of limit or market orders. The proportion of informed investors is similar between good and bad news days. Finally, there are U-shaped intraday patterns in order arrival, and the information content of the order flow appears to follow this pattern across the day.     

Nonlinearities in stochastic clocks: trades and volume as subordinators of electronic markets

This paper discusses the possibility of recovering normality of asset returns through a stochastic time change, where the appropriate economic time is determined through a simple parametric function of the cumulative number of trades and/or the cumulative volume. The existing literature argues that the re-centred cumulative number of trades could be used as the appropriate stochastic clock of the market under which asset returns are virtually Gaussian. Using tick-data for FTSE-100 futures, we show that normality is not always recovered by conditioning on the re-centred number of trades. However, it can be shown that simply extending the approach to a nonlinear function can provide a better stochastic clock of the market.     

Measuring the resiliency of an electronic limit order book

An electronic limit order book is resilient when it reverts to its normal shape promptly after large trades. This paper suggests a continuous-time impulse response function based on intensities, which formalizes resiliency in terms of a time-frame and probability of order book replenishment. This is then estimated for trading on an LSE order book, using an appropriate parametric model which views orders and cancellations as a mutually-exciting ten-variate Hawkes point process. Consistent with findings in the related literature, in over 60 per cent of cases, the order book does not replenish reliably after a large trade. However, if it does replenish, it does so with a fairly fast half life of around 20 s. Various other dynamics are quantified.     

Analysing liquidity and absorption limits of electronic markets with volume durations

This paper focuses on the liquidity of electronic stock markets applying a sequential estimation approach of models for volume duration with increasing threshold values. A modified ACD model with a Box–Tukey transformation and a flexible generalized beta distribution is proposed to capture the changing cluster structure of duration processes. The estimation results with German XETRA data reveal the market's absorption limit for high volumes of shares, expanding the time costs of illiquidity when trading these quantities.     

A behavioural model of investor sentiment in limit order markets

By incorporating behavioural sentiment in a model of a limit order market, we show that behavioural sentiment not only helps to replicate most of the stylized facts in limit order markets simultaneously, but it also plays a unique role in explaining those stylized facts that cannot be explained by noise trading, such as fat tails in the return distribution, long memory in the trading volume, an increasing and non-linear relationship between trade imbalance and mid-price returns, as well as the diagonal effect, or event clustering, in order submission types. The results show that behavioural sentiment is an important driving force behind many of the well-documented stylized facts in limit order markets.     

Non-constant rates and over-diffusive prices in a simple model of limit order markets

Abstract

Using simple particle models of limit order markets, we argue that the mid-term over-diffusive price behaviour is due to the variability of market order and limit order rates. Several rules for rate changes are considered. We obtain analytical results for bid-ask spread properties, Hurst plots and price increment correlation functions.     

Dynamic interaction networks in modelling and predicting the behaviour of multiple interactive stock markets

The behaviour of multiple stock markets can be described within the framework of complex dynamic systems. A representative technique of the framework is the dynamic interaction network (DIN), recently developed in the bioinformatics domain. DINs are capable of modelling dynamic interactions between genes and predicting their future expressions. In this paper, we adopt a DIN approach to extract and model interactions between stock markets. The network is further able to learn online and updates incrementally with the unfolding of the stock market time-series. The approach is applied to a case study involving 10 market indexes in the Asia Pacific region. The results show that the DIN model reveals important and complex dynamic relationships between stock markets, demonstrating the ability of complex dynamic systems approaches to go beyond the scope of traditional statistical methods. Copyright © 2009 John Wiley & Sons, Ltd.     

The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets

Cryptocurrencies have gained a lot of attention since Bitcoin was first proposed by Satoshi Nakamoto in 2008, highlighting the potential to play a significant role in e-commerce. However, relatively little is known about cryptocurrencies, their price behaviour, how quickly they incorporate new information and their corresponding market efficiency. To extend the current literature in this area, we develop four smart electronic Bitcoin markets populated with different types of traders using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We apply the STGP technique to historical data of Bitcoin at the one-minute and five-minute frequencies to investigate the formation of Bitcoin market dynamics and market efficiency. Through a plethora of robust testing procedures, we find that both Bitcoin markets populated by high-frequency traders (HFTs) are efficient at the one-minute frequency but inefficient at the five-minute frequency. This finding supports the argument that at the one-minute frequency investors are able to incorporate new information in a fast and rationale manner and not suffer from the noise associated with the five-minute frequency. We also contribute to the e-commerce literature by demonstrating that zero-intelligence traders cannot reach market efficiency, therefore providing evidence against the hypothesis of Hayek (1945; 1968). One practical implication of this study is that we demonstrate that e-commerce practitioners can apply artificial intelligence tools such as STGP to conduct behaviour-based market profiling.     

The contribution of market makers to liquidity and efficiency of options trading in electronic markets

This paper examines the contribution of market makers to the liquidity and the efficiency of the options market in a unique setup of an order-driven computerized trading system, in which market makers and other participants operate under equitable conditions. The main findings are: (1) liquidity increased – a 60% increase in trading volume and a 35% decrease of bid–ask spreads; (2) the efficiency of shekel–euro options trading improved – deviations from put–call parity decreased significantly by 12%, and skewness decreased by about 30%. We also find that the net cost to the exchange is out weighted by the benefit to the trading public and that the presence of market makers encouraged trading between other participants far beyond their own trading.     

Symmetric ignorance: The cost of anonymous lemons

Rules that restrict information required in negotiated private transactions have spurred a vast increase in the scope of anonymous financial markets, particularly in the United States. The subtle costs of the information-restricting rules raise questions about the social value of “completing” anonymous markets that would not naturally survive and did not historically exist.     

Flying under the radar: The real effects of anonymous trading

Using unique data on TSX Attributed Trading and a new proxy of Tobin's Q that accounts for intangible capital (Peters and Taylor, 2017), we investigate the impact of anonymous trading (AT) on managers' ability to use feedback conveyed by stock prices to improve investment efficiency. We show that AT reduces investment efficiency and that both anonymous buyer-initiated and seller-initiated trades have comparable effects. The negative effect of AT on managerial learning from stock prices is significant only for tangible investments and when disagreement among anonymous traders is high. Taken together, our new evidence indicates that AT distorts investment sensitivity to Tobin's Q, plausibly because anonymity attracts additional (uninformed) liquidity trading, which negatively impacts the effectiveness of asset prices in aggregating private information and in revealing fundamentals.     

The impact of the COVID-19 pandemic and Russia-Ukraine war on multiscale spillovers in green finance markets: Evidence from lower and higher order moments

This study analyzes the impact of the COVID-19 pandemic and the Russia-Ukraine war on the connectedness of lower-order moments (returns and volatility) and higher-order moments (skewness and kurtosis) in the markets of green bonds, clean energy, wind, solar, and sustainability indexes. To compare the spillover effects of these moments, we use the Diebold and Yilmaz and Barunik and Krehlik methods. Our findings show that the total spillover effect of lower-order moments is higher than that of higher-order moments in the time domain. In the frequency domain, the total return and skewness spillover are primarily concentrated in the short term, whereas the total volatility spillover is mainly concentrated in the long term. Furthermore, we observe that the spillover effect of the Russia-Ukraine war on the green finance market is mild, while the COVID-19 pandemic has a significant and unprecedented influence on the spillover of both lower- and higher-order moments in this market. Additionally, we note that before the COVID-19 outbreak, the total kurtosis spillover was irregular, but it became concentrated in the long term after the outbreak. Moreover, the continuation of COVID-19 has had an unprecedented and long-lasting impact on the kurtosis and skewness of the green bond market.