Optimal trading with alpha predictors

Passerini, Filippo; Vázquez, Samuel E.

doi:10.21314/jois.2016.070

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Much of the previous work in this area assumes that agents have complete and correct knowledge of all model parameters ( [34], [15], [16], [18], [85], [64], [86], [32]). Others consider the possibility that their agents' models have the correct form; however, the agents must gradually learn the values of certain unobserved features ( [17], [31], [43], [54], [62], [48]).…”

Section: Background and Contributionsmentioning

confidence: 99%

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Mini-Flash Crashes, Model Risk, and Optimal Execution

Bayraktar

Munk

2017

SSRN Journal

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Abstract. Oft-cited causes of mini-flash crashes include human errors, endogenous feedback loops, the nature of modern liquidity provision, fundamental value shocks, and market fragmentation. We develop a mathematical model which captures aspects of the first three explanations. Empirical features of recent mini-flash crashes are present in our framework. For example, there are periods when no such events will occur. If they do, even just before their onset, market participants may not know with certainty that a disruption will unfold. Our mini-flash crashes can materialize in both low and high trading volume environments and may be accompanied by a partial synchronization in order submission.Instead of adopting a classically-inspired equilibrium approach, we borrow ideas from the optimal execution literature. Each of our agents begins with beliefs about how his own trades impact prices and how prices would move in his absence. They, along with other market participants, then submit orders which are executed at a common venue. Naturally, this leads us to explicitly distinguish between how prices actually evolve and our agents' opinions. In particular, every agent's beliefs will be expressly incorrect.As far as we know, this setup suggests both a new paradigm for modeling heterogeneous agent systems and a novel blueprint for understanding model misspecification risks in the context of optimal execution.

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Section: Background and Contributionsmentioning

confidence: 99%

“…A possible extension of our work could replace (4.2) with one of the more recent models considered in the literature on optimal trading problems with a learning aspect ( [17], [54], [43], [64], [86], [62], [48]). …”

Section: Preliminariesmentioning

confidence: 99%

Mini-Flash Crashes, Model Risk, and Optimal Execution

Bayraktar

Munk

2017

SSRN Journal

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“…(3.7) 1 Almgren & Lorenz provide further details regarding the interpretation of (3.1) ( [8]). A possible extension of our work could replace (3.1) with one of the more recent models considered in the literature on optimal trading problems with a learning aspect ( [8], [21], [14], [25], [36], [24], [17]).…”

Section: Agents and The Execution Pricementioning

confidence: 99%

Mini-Flash Crashes, Model Risk, and Optimal Execution

Bayraktar

Munk²

2018

Mark. Microstructure Liq.

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Oft-cited causes of mini-flash crashes include human errors, endogenous feedback loops, the nature of modern liquidity provision. We develop a mathematical model which captures aspects of these explanations. Empirical features of recent mini-flash crashes are present in our framework. For example, there are periods when no such events will occur. If they do, even just before their onset, market participants may not know with certainty that a disruption will unfold. Our miniflash crashes can materialize in both low and high trading volume environments and may be accompanied by a partial synchronization in order submission.Instead of adopting a classically-inspired equilibrium approach, we borrow ideas from the optimal execution literature. Each of our agents begins with beliefs about how his own trades impact prices and how prices would move in his absence. They, along with other market participants, then submit orders which are executed at a common venue. Naturally, this leads us to explicitly distinguish between how prices actually evolve and our agents' opinions. In particular, every agent's beliefs will be expressly incorrect.2010 Mathematics Subject Classification. Primary 91G80; Secondary 60H30, 60H10, 34M35.

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“…The penalty is imposed to account for a version of temporary price impact similar to walking the LOB, and they include a permanent price impact which reverts to zero if there are no trades. Passerini and Vazquez (2016) numerically study a continuous-time, finite horizon, version of Gârleanu and Pedersen (2013), and account for crossing the spread or posting limit orders. Our approach differs in five main aspects: (i) our setup is in continuous-time, (ii) the execution horizon is finite, (iii) the agent solves an execution problem where prices are co-integrated (rather than having independent predictable components), (iv) the agent's MOs have permanent and temporary impact, and (v) the MOs of other market participants also have permanent price impact.…”

Section: Introductionmentioning

confidence: 99%

Trading co‐integrated assets with price impact

Cartea

Gan

Jaimungal

2018

Mathematical Finance

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Executing a basket of co-integrated assets is an important task facing investors. Here, we show how to do this accounting for the informational advantage gained from assets within and outside the basket, as well as for the permanent price impact of market orders (MOs) from all market participants, and the temporary impact that the agent's MOs have on prices. The execution problem is posed as an optimal stochastic control problem and we demonstrate that, under some mild conditions, the value function admits a closed-form solution, and prove a verification theorem. Furthermore, we use data of five stocks traded in the Nasdaq exchange to estimate the model parameters and use simulations to illustrate the performance of the strategy. As an example, the agent liquidates a portfolio consisting of shares in Intel Corporation and Market Vectors Semiconductor ETF. We show that including the information provided by three additional assets (FARO Technologies, NetApp, Oracle Corporation) considerably improves the strategy's performance; for the portfolio we execute, it outperforms the multiasset version of Almgren-Chriss by approximately 4-4.5 basis points. K E Y W O R D Salgorithmic trading, co-integration, co-movements, cross-price impact, optimal execution, price impact 542

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Optimal trading with alpha predictors

Cited by 6 publications

References 28 publications

Mini-Flash Crashes, Model Risk, and Optimal Execution

Mini-Flash Crashes, Model Risk, and Optimal Execution

Mini-Flash Crashes, Model Risk, and Optimal Execution

Trading co‐integrated assets with price impact

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