Receding Horizon Optimization of Large Trade Orders

Gerardo Alfonso,Teodoro Alamo,A Daniel Carnerero,Daniel R Ramirez

doi:10.1109/access.2021.3075700

Gerardo Alfonso, Teodoro Alamo + Show 2 more

Open Access

https://doi.org/10.1109/access.2021.3075700

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Abstract

The optimal execution of stock trades is a relevant and interesting problem as it is key in maximizing profits and reducing risks when investing in the stock market. In the case of large orders, the problem becomes even more complex as the impact of the order in the market has to be taken into account. The usual solution is to split large orders into a set of smaller suborders that must be executed within a prescribed time window. This leads to the problem of deciding when in the time window execute each suborder. There are popular ways of executing the trading of these split orders like those which try to track the “Time Weighted Average Price” and the “Volume Weighted Average Price”, usually called TWAP and VWAP orders. This paper presents a strategy to optimize the splitting of large trade orders over a given time window. The strategy is based on the solution of an optimization problem that is applied following a receding horizon approach. This approach reduces the impact of prediction errors due to the uncertain market dynamics, by using new values of the price time series as they are available as time goes on. Suborder size constraints are taken into account in both market and limit orders. The strategy relies on price and traded volume forecast but it is independent of the prediction technique used. The performance index weighs not only the financial cost of the suborders, but also the impact on the market and the forecasting accuracy. A tailored optimization algorithm is proposed for efficiently solving the corresponding optimization problem. Most of the computations of the algorithm can be parallelized. Finally, the proposed approach has been tested through a case study composed by stocks of the Chinese A-share market.

Highlights

Stock trading is becoming an increasingly complex field as investors try to maximize their profits and reduce their risks with increased emphasis in recent years on optimal execution
It is clear that the forecasting errors induce negative effects in both the price and execution percentage attained, but even using forecasted data the proposed strategy beats both TWAP and VWAP limit orders while meeting the price limit
Limit orders are more complex than market orders, it is interesting to examine in more detail the results shown in table 5

Summary

INTRODUCTION

Stock trading is becoming an increasingly complex field as investors try to maximize their profits and reduce their risks with increased emphasis in recent years on optimal execution. Particle swarm optimization has been used by [13] in a high-frequency trading system based on moving averages, used to determine the trading sequence that maximizes the net returns over a series of consecutive time steps This optimization technique has been used by [14] to train a kernel-based nonlinear predictor that was applied to forecast the VWAP price in the Shanghai market. Time series methods have been used in [21], where an autoregressive fractionally integrated moving average model is used to forecast intraday trading volumes in the Chinese equity market, and its application to VWAP tracking, obtaining better results than static approaches. We propose a data-based method that relies on price and volume forecasts, and the use of dynamic optimization over a finite horizon, to obtain optimal sequences of suborders to fulfil large trade orders.

PROBLEM STATEMENT

CASE STUDY

Findings

CONCLUSION