《Incorporating Signals into Optimal Trading》
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作者:
Charles-Albert Lehalle and Eyal Neuman
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最新提交年份:
2018
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英文摘要:
Optimal trading is a recent field of research which was initiated by Almgren, Chriss, Bertsimas and Lo in the late 90\'s. Its main application is slicing large trading orders, in the interest of minimizing trading costs and potential perturbations of price dynamics due to liquidity shocks. The initial optimization frameworks were based on mean-variance minimization for the trading costs. In the past 15 years, finer modelling of price dynamics, more realistic control variables and different cost functionals were developed. The inclusion of signals (i.e. short term predictors of price dynamics) in optimal trading is a recent development and it is also the subject of this work. We incorporate a Markovian signal in the optimal trading framework which was initially proposed by Gatheral, Schied, and Slynko [21] and provide results on the existence and uniqueness of an optimal trading strategy. Moreover, we derive an explicit singular optimal strategy for the special case of an Ornstein-Uhlenbeck signal and an exponentially decaying transient market impact. The combination of a mean-reverting signal along with a market impact decay is of special interest, since they affect the short term price variations in opposite directions. Later, we show that in the asymptotic limit were the transient market impact becomes instantaneous, the optimal strategy becomes continuous. This result is compatible with the optimal trading framework which was proposed by Cartea and Jaimungal [10]. In order to support our models, we analyse nine months of tick by tick data on 13 European stocks from the NASDAQ OMX exchange. We show that orderbook imbalance is a predictor of the future price move and it has some mean-reverting properties. From this data we show that market participants, especially high frequency traders, use this signal in their trading strategies.
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中文摘要:
最优交易是最近的一个研究领域,由Almgren、Chriss、Bertsimas和Lo在90年代末发起。其主要应用是分割大型交易订单,以最大限度地降低交易成本和流动性冲击导致的价格动态潜在扰动。初始优化框架基于交易成本的均值-方差最小化。在过去15年中,开发了更精细的价格动态建模、更现实的控制变量和不同的成本函数。在最优交易中包含信号(即价格动态的短期预测)是最近的发展,也是这项工作的主题。我们在Gathereal、Schied和Slynko最初提出的最优交易框架中加入了马尔可夫信号,并提供了关于最优交易策略存在性和唯一性的结果。此外,对于Ornstein-Uhlenbeck信号和指数衰减瞬态市场冲击的特殊情况,我们推导了一个显式奇异最优策略。均值回复信号与市场影响衰减的组合特别令人感兴趣,因为它们会影响相反方向的短期价格变化。随后,我们证明了在渐近极限下,当瞬时市场冲击变为瞬时时,最优策略变为连续策略。这一结果与Cartea和Jaimungal提出的最优交易框架是一致的【10】。为了支持我们的模型,我们分析了纳斯达克OMX交易所13只欧洲股票的9个月逐笔数据。我们表明,订单簿不平衡是未来价格变动的预测因子,并且具有一些均值回复特性。从这些数据我们可以看出,市场参与者,尤其是高频交易者,在他们的交易策略中使用了这种信号。
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分类信息:
一级分类:Quantitative Finance 数量金融学
二级分类:Trading and Market Microstructure 交易与市场微观结构
分类描述:Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构,流动性,交易和拍卖设计,自动化交易,基于代理的建模和做市
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一级分类:Quantitative Finance 数量金融学
二级分类:Mathematical Finance 数学金融学
分类描述:Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法,包括随机、概率和泛函分析、代数、几何和其他方法
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