The paper aims to construct an optimal trading strategy for procuring a large but fixed volume of a risky asset. The proposed approach splits a large block of trade into smaller packages to minimize the execution cost of the trade. In this study, we suggest an incipient price dynamics for the asset where we express its current market price as a convex combination of the price impervious to our anterior trade in the asset and the execution price carrying the impact of our anterior trade in it. We propose to apply the regression techniques to estimate the unaffected price of the asset. The formulated model is a convex quadratic optimization problem and thus computationally tractable. We evaluate the performance of the proposed model on five stocks namely, Apple, Microsoft, Coca-Cola, Amazon, and Netflix and conclude that the proposed model consistently achieves a lower execution cost than the one obtained from some other models existing in the literature.

本文旨在构建一种最佳交易策略，以获取大量但固定数量的风险资产。所提出的方法将一大块交易分成更小的包，以最小化交易的执行成本。在这项研究中，我们建议资产的初始价格动态，我们将其当前市场价格表示为不受我们之前的资产交易影响的价格和承受我们之前的交易影响的执行价格的凸组合。我们建议应用回归技术来估计资产未受影响的价格。所制定的模型是一个凸二次优化问题，因此在计算上易于处理。我们在苹果、微软、可口可乐、亚马逊、