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on Market Microstructure |
| By: | Charles-Albert Lehalle |
| Abstract: | A great deal of academic and theoretical work has been dedicated to optimal liquidation of large orders these last twenty years. The optimal split of an order through time (`optimal trade scheduling') and space (`smart order routing') is of high interest \rred{to} practitioners because of the increasing complexity of the market micro structure because of the evolution recently of regulations and liquidity worldwide. This paper translates into quantitative terms these regulatory issues and, more broadly, current market design. It relates the recent advances in optimal trading, order-book simulation and optimal liquidity to the reality of trading in an emerging global network of liquidity. |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1302.4592&r=mst |
| By: | Robert Azencott; Arjun Beri; Yutheeka Gadhyan; Nicolas Joseph; Charles-Albert Lehalle; Matthew Rowley |
| Abstract: | Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance, a stochastic control, an impulse control or a statistical learning viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order. We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method in the post trade analysis of algorithms can be taken advantage of to automatically adjust their trading action. |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1302.6363&r=mst |
| By: | Claudia Foroni; Massimiliano Marcellino |
| Abstract: | The development of models for variables sampled at different frequencies has attracted substantial interest in the recent econometric literature. In this paper we provide an overview of the most common techniques, including bridge equations, MIxed DAta Sampling (MIDAS) models, mixed frequency VARs, and mixed frequency factor models. We also consider alternative techniques for handling the ragged edge of the data, due to asynchronous publication. Finally, we survey the main empirical applications based on alternative mixed frequency models. |
| Keywords: | mixed-frequency data, mixed-frequency VAR, MIDAS, nowcasting,forecasting |
| JEL: | E37 C53 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:eui:euiwps:eco2013/02&r=mst |
| By: | Turan G. Bali (McDonough School of Business, Georgetown University); Lin Peng (Zicklin School of Business, Baruch College); Yannan Shen (Zicklin School of Business, Baruch College); Yi Tang (Schools of Business, Fordham University) |
| Abstract: | This paper investigates how the stock market reacts to firm level liquidity shocks. We find that negative and persistent liquidity shocks not only lead to lower contemporaneous returns, but also predict negative returns for up to six months in the future. Long-short portfolios sorted on past liquidity shocks generate a raw and risk-adjusted return of more than 1% per month. This economically and statistically significant relation is robust across alternative measures of liquidity shocks, different sample periods, and after controlling for various risk factors and firm characteristics. Furthermore, the documented effect is stronger for small stocks, stocks with low analyst coverage and institutional holdings, and for less liquid stocks. Our evidence suggests that the stock market underreacts to firm level liquidity shocks, and that this underreaction can be driven by investor inattention as well as illiquidity. |
| Keywords: | Stock returns, liquidity shocks, stock market reactions, underreaction, investor attention. |
| JEL: | G10 G11 G12 G14 C13 |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:koc:wpaper:1304&r=mst |