|
on Market Microstructure |
| By: | Carol L. Clark |
| Abstract: | A small group of high-frequency algorithmic trading firms have invested heavily in technology to leverage the nexus of high-speed communications, mathematical advances, trading and high-speed computing. By doing so, they are able to complete trades at lightning speeds. High-frequency algorithmic trading strategies rely on computerized quantitative models that identify which type of financial instruments to buy or sell (e.g., stocks, options or futures), as well as the quantity, price, timing and location of the trades. These so-called black boxes are capable of reading market data, transmitting thousands of order messages per second to an exchange, cancelling and replacing orders based on changing market conditions and capturing price discrepancies with little or no human intervention. |
| Keywords: | Counterfeits and counterfeiting |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedhpd:pdp-2010-01&r=mst |
| By: | Alex Frino (University of Sidney); Maria Grazia Romano (University of Salerno and CSEF) |
| Abstract: | The article examines the impact of transaction costs on the trading strategy of informed institutional investors in a sequential trading market where traders can choose to transact a large or a small amount of stock. The analysis shows how the trading strategy of informed investors and the price impact of their trades depends on market conditions. The main prediction of the model is that institutional buyers are, on average, more aggressive than institutional sellers in bearish markets and less aggressive in bullish markets. Hence, the price impact is higher for purchases when market conditions are bearish, while it is higher for sales when market conditions are bullish. However, this asymmetry vanishes during strongly bearish or bullish phases, when information-based orders stop because the informational advantage of institutional investors becomes too small with respect to the transaction costs |
| Date: | 2010–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:sef:csefwp:252&r=mst |
| By: | Sophie Laruelle (PMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS : UMR7599 - Université Pierre et Marie Curie - Paris VI - Université Paris-Diderot - Paris VII); Charles-Albert Lehalle (Head of Quantitative Research - CALYON group); Gilles Pagès (PMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS : UMR7599 - Université Pierre et Marie Curie - Paris VI - Université Paris-Diderot - Paris VII) |
| Abstract: | Evolutions of the trading landscape lead to the capability to exchange the same financial instrument on different venues. Because of liquidity issues, the trading firms split large orders across several trading destinations to optimize their execution. To solve this problem we devised two stochastic recursive learning procedures which adjust the proportions of the order to be sent to the different venues, one based on an optimization principle, the other on some reinforcement ideas. Both procedures are investigated from a theoretical point of view: we prove a.s. convergence of the optimization algorithm under some light ergodic (or "averaging") assumption on the input data process. No Markov property is needed. When the inputs are i.i.d. we show that the convergence rate is ruled by a Central Limit Theorem. Finally, the mutual performances of both algorithms are compared on simulated and real data with respect to an "oracle" strategy devised by an "insider" who knows a priori the executed quantities by every venues. |
| Keywords: | Asset allocation, Stochastic Lagrangian algorithm, reinforcement principle, monotone dynamic system |
| Date: | 2009–10–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-00422427_v3&r=mst |
| By: | Fabien Guilbaud; Mohamed Mnif; Huy\^en Pham |
| Abstract: | This paper deals with numerical solutions to an impulse control problem arising from optimal portfolio liquidation with bid-ask spread and market price impact penalizing speedy execution trades. The corresponding dynamic programming (DP) equation is a quasi-variational inequality (QVI) with solvency constraint satisfied by the value function in the sense of constrained viscosity solutions. By taking advantage of the lag variable tracking the time interval between trades, we can provide an explicit backward numerical scheme for the time discretization of the DPQVI. The convergence of this discrete-time scheme is shown by viscosity solutions arguments. An optimal quantization method is used for computing the (conditional) expectations arising in this scheme. Numerical results are presented by examining the behaviour of optimal liquidation strategies, and comparative performance analysis with respect to some benchmark execution strategies. We also illustrate our optimal liquidation algorithm on real data, and observe various interesting patterns of order execution strategies. Finally, we provide some numerical tests of sensitivity with respect to the bid/ask spread and market impact parameters. |
| Date: | 2010–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1006.0768&r=mst |