英文标题：
《Optimal multi-asset trading with linear costs: a mean-field approach》
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作者：
Matt Emschwiller, Benjamin Petit, Jean-Philippe Bouchaud
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最新提交年份：
2020
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英文摘要：
Optimal multi-asset trading with Markovian predictors is well understood in the case of quadratic transaction costs, but remains intractable when these costs are $L_1$. We present a mean-field approach that reduces the multi-asset problem to a single-asset problem, with an effective predictor that includes a risk averse component. We obtain a simple approximate solution in the case of Ornstein-Uhlenbeck predictors and maximum position constraints. The optimal strategy is of the \"bang-bang\" type similar to that obtained in [de Lataillade et al., 2012]. When the risk aversion parameter is small, we find that the trading threshold is an affine function of the instantaneous global position, with a slope coefficient that we compute exactly. We relate the risk aversion parameter to the desired target risk and provide numerical simulations that support our analytical results.
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中文摘要：
在二次交易成本的情况下，使用马尔可夫预测的最优多资产交易是很容易理解的，但当这些成本为1美元时，仍然难以解决。我们提出了一种均值场方法，该方法将多资产问题简化为单个资产问题，并具有一个包含风险规避成分的有效预测因子。在Ornstein-Uhlenbeck预测和最大位置约束的情况下，我们得到了一个简单的近似解。最佳策略为“bang-bang”类型，类似于【de Lataillade等人，2012年】中获得的策略。当风险厌恶参数很小时，我们发现交易阈值是瞬时全局位置的仿射函数，具有我们精确计算的斜率系数。我们将风险规避参数与期望的目标风险联系起来，并提供支持我们分析结果的数值模拟。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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