LAUSR

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.