Albert J. Menkveld

Bio: Albert J. Menkveld is an academic researcher from VU University Amsterdam. The author has contributed to research in topics: Market liquidity & High-frequency trading. The author has an hindex of 32, co-authored 99 publications receiving 5993 citations. Previous affiliations of Albert J. Menkveld include Duisenberg School of Finance & Tinbergen Institute.

Does Algorithmic Trading Improve Liquidity

Abstract: Algorithmic trading has sharply increased over the past decade Does it improve market quality, and should it be encouraged? We provide the first analysis of this question The NYSE automated quote dissemination in 2003, and we use this change in market structure that increases algorithmic trading as an exogenous instrument to measure the causal effect of algorithmic trading on liquidity For large stocks in particular, algorithmic trading narrows spreads, reduces adverse selection, and reduces trade-related price discovery The findings indicate that algorithmic trading improves liquidity and enhances the informativeness of quotes

Does Algorithmic Trading Improve Liquidity

Abstract: Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes. TECHNOLOGICAL CHANGE HAS REVOLUTIONIZED the way financial assets are traded. Every step of the trading process, from order entry to trading venue to back office, is now highly automated, dramatically reducing the costs incurred by intermediaries. By reducing the frictions and costs of trading, technology has the potential to enable more efficient risk sharing, facilitate hedging, improve liquidity, and make prices more efficient. This could ultimately reduce firms’ cost of capital. Algorithmic trading (AT) is a dramatic example of this far-reaching technological change. Many market participants now employ AT, commonly defined as the use of computer algorithms to automatically make certain trading decisions, submit orders, and manage those orders after submission. From a starting point near zero in the mid-1990s, AT is thought to be responsible for

Information Asymmetry and Asset Prices: Evidence from the China Foreign Share Discount

Abstract: We examine the effect of information asymmetry on equity prices in the local A- and foreign B-share market in China. We construct measures of information asymmetry based on market microstructure models, and find that they explain a significant portion of cross-sectional variation in B-share discounts, even after controlling for other factors. On a univariate basis, the price impact measure and the adverse selection component of the bid-ask spread in the A- and B-share markets explains 44% and 46% of the variation in B-share discounts. On a multivariate basis, both measures are far more statistically significant than any of the control variables.

Information Asymmetry and Asset Prices: Evidence from the China Foreign Share Discount

Abstract: We examine the effect of information asymmetry on equity prices in the local A- and foreign B-share market in China. We construct measures of information asymmetry based on market microstructure models, and find that they explain a significant portion of cross-sectional variation in B-share discounts, even after controlling for other factors. On a univariate basis, the price impact measure and the adverse selection component of the bid-ask spread in the A- and B-share markets explains 44% and 46% of the variation in B-share discounts. On a multivariate basis, both measures are far more statistically significant than any of the control variables. THE EXTENT OF INFORMATION ASYMMETRY in the international equity market has become a very important topic. The question of whether domestic investors have better information than foreign investors has also become increasingly controversial. For instance, a few papers argue that domestic investors have a linguistic and cultural advantage (Brennan and Cao (1997), Choe, Kho, and Stulz (2001), and Hau (2001)), others argue that foreign investors have an informational advantage because they possess a significant amount of investment experience and expertise (Seasholes (2000), Grinblatt and Keloharju (2000),

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Does Algorithmic Trading Improve Liquidity

Abstract: Algorithmic trading has sharply increased over the past decade Does it improve market quality, and should it be encouraged? We provide the first analysis of this question The NYSE automated quote dissemination in 2003, and we use this change in market structure that increases algorithmic trading as an exogenous instrument to measure the causal effect of algorithmic trading on liquidity For large stocks in particular, algorithmic trading narrows spreads, reduces adverse selection, and reduces trade-related price discovery The findings indicate that algorithmic trading improves liquidity and enhances the informativeness of quotes

Market Liquidity: Asset Pricing with Liquidity Risk

Abstract: This paper solves explicitly a simple equilibrium model with liquidity risk. In our liquidityadjusted capital asset pricing model, a security s required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with the market return and liquidity. In addition, a persistent negative shock to a security s liquidity results in low contemporaneous returns and high predicted future returns. The model provides a unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels and provide evidence of flight to liquidity. r 2005 Elsevier B.V. All rights reserved.

High-Frequency Trading and Price Discovery

Abstract: We examine the role of high-frequency traders (HFTs) in price discovery and price efficiency. Overall HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors, both on average and on the highest volatility days. This is done through their liquidity demanding orders. In contrast, HFTs' liquidity supplying orders are adversely selected. The direction of HFTs' trading predicts price changes over short horizons measured in seconds. The direction of HFTs' trading is correlated with public information, such as macro news announcements, market-wide price movements, and limit order book imbalances.