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on Market Microstructure |
| By: | Rama Cont (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS : UMR7599 - Université Paris VI - Pierre et Marie Curie - Université Paris VII - Paris Diderot); Arseniy Kukanov (Columbia University - Columbia University) |
| Abstract: | To execute a trade, participants in electronic equity markets may choose to submit limit orders or market orders across various exchanges where a stock is traded. This decision is influenced by the characteristics of the order flow and queue sizes in each limit order book, as well as the structure of transaction fees and rebates across exchanges. We propose a quantitative framework for studying this order placement problem by formulating it as a convex optimization problem. This formulation allows to study how the interplay between the state of order books, the fee structure, order flow properties and preferences of a trader determine the optimal placement decision. In the case of a single exchange, we derive an explicit solution for the optimal split between limit and market orders. For the general problem of order placement across multiple exchanges, we propose a stochastic algorithm for computing the optimal policy and study the sensitivity of the solution to various parameters using a numerical implementation of the algorithm. |
| Keywords: | order routing, limit order book, limit order market, liquidity, optimal order execution, transaction costs, market microstructure |
| Date: | 2012–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-00737491&r=mst |
| By: | Pierre Collin-Dufresne; Vyacheslav Fos |
| Abstract: | We extend Kyle's (1985) model of insider trading to the case where liquidity provided by noise traders follows a general stochastic process. Even though the level of noise trading volatility is observable, in equilibrium, measured price impact is stochastic. If noise trading volatility is mean-reverting, then the equilibrium price follows a multivariate 'stochastic bridge' process, which displays stochastic volatility. This is because insiders choose to optimally wait to trade more aggressively when noise trading activity is higher. In equilibrium, market makers anticipate this, and adjust prices accordingly. More private information is revealed when volatility is higher. In time series, insiders trade more aggressively, when measured price impact is lower. Therefore, execution costs to uninformed traders can be higher when price impact is lower. |
| JEL: | D4 D8 D80 D82 D83 D84 G0 G00 G1 G10 G12 G14 |
| Date: | 2012–10 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:18451&r=mst |
| By: | Pierre Collin-Dufresne; Vyacheslav Fos |
| Abstract: | Using a comprehensive sample of trades by Schedule 13D filers, who possess valuable private information when they accumulate stocks of targeted companies, this paper studies whether several liquidity measures reveal the presence of informed trading. The evidence suggests that when Schedule 13D filers trade aggressively, both high-frequency and low-frequency measures of stock liquidity indicate a higher stock liquidity. Importantly, measures that have been used as direct proxies for adverse selection, such the Kyle (1985) lambda, the Easley et al. (1996) pin measure, and the Amihud (2002) illiquidity measure, suggest that the adverse selection is lower when informed trading takes place. The evidence is consistent with informed traders being more aggressive when measured stock liquidity is high. |
| JEL: | G0 G00 G1 G10 G12 G14 G3 G34 |
| Date: | 2012–10 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:18452&r=mst |
| By: | Cecilia Caglio; Stewart Mayhew |
| Abstract: | Revenues generated from the sales of consolidated data represent a substantial source of income for U.S. stock exchanges. Until 2007, consolidated data revenue was allocated in proportion to the number of reported trades. This allocation rule encouraged market participants to break up large trades and execute them in multiple pieces. Exchanges devised revenue-sharing and rebate programs that rewarded order-flow providers, and encouraged algorithmic traders to execute strategies involving large numbers of small trades. We provide evidence that data revenue allocation influenced the trading process, by examining trading activity surrounding various events that changed the marginal data revenue per trade. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2012-65&r=mst |
| By: | Lönnbark, Carl (Department of Economics, Umeå University) |
| Abstract: | The empirically most relevant stylized facts when it comes to modeling time varying financial volatility are the asymmetric response to return shocks and the long memory property. Up till now, these have largely been modeled in isolation though. To more flexibly capture asymmetry also with respect to the memory structure we introduce a new model and apply it to stock market index data. We find that, although the effect on volatility of negative return shocks is higher than for positive ones, the latter are more persistent and relatively quickly dominate negative ones. |
| Keywords: | Financial econometrics; GARCH; news impact; nonlinear; risk prediction; time series |
| JEL: | C12 C51 C58 G10 G15 |
| Date: | 2012–10–03 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:umnees:0849&r=mst |
| By: | Walther, A. |
| Abstract: | In financial markets with asymmetric information, traders may have an incentive to forgo profitable deals today in order to preserve their informational advantage for future deals. This sort of manipulative behaviour has been studied in markets with one informed trader (Kyle 1985, Chakraborty and Yilmaz 2004). The effect is slower social learning. Using an extension of Glosten and Milgrom’s (1985) trading model, we study this effect in markets with N informed traders. As N grows large, each trader’s price impact subsides, and so does manipulation in equilibrium. However, the impact of manipulation on social learning can be increasing in N. As N increases, each trader individually manipulates less. But nonetheless, the increased number of manipulative actions introduces enough noise to exacerbate the impact of manipulation on learning. |
| Keywords: | Price manipulation, asset pricing, asymmetric information, Glosten-Milgrom model |
| JEL: | D80 D82 G10 G14 |
| Date: | 2012–10–08 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:1242&r=mst |