Stock Trading via Feedback Control: Stochastic Model Predictive or Genetic?

TL;DR

This paper compares stochastic model predictive control and genetic algorithms for stock trading, evaluating robustness and performance with various prediction methods on DAX data.

Contribution

It introduces and compares eight trading controllers, including SMPC and genetic algorithms, under different prediction scenarios for stock trading.

Findings

SMPC controllers show robustness under worst-case predictions.

Genetic algorithms perform well with certain moving average strategies.

Dynamic hedging SMPC offers a promising alternative to traditional portfolio optimization.

Abstract

We seek a discussion about the most suitable feedback control structure for stock trading under the consideration of proportional transaction costs. Suitability refers to robustness and performance capability. Both are tested by considering different one-step ahead prediction qualities, including the ideal case, correct prediction of the direction of change in daily stock prices and the worst-case. Feedback control structures are partitioned into two general classes: stochastic model predictive control (SMPC) and genetic. For the former class three controllers are discussed, whereby it is distinguished between two Markowitz- and one dynamic hedging-inspired SMPC formulation. For the latter class five trading algorithms are disucssed, whereby it is distinguished between two different moving average (MA) based, two trading range (TR) based, and one strategy based on historical optimal (HistOpt) trajectories. This paper also gives a preliminary discussion about how modified dynamic hedging-inspired SMPC formulations may serve as alternatives to Markowitz portfolio optimization. The combinations of all of the eight controllers with five different one-step ahead prediction methods are backtested for daily trading of the 30 components of the German stock market index DAX for the time period between November 27, 2015 and November 25, 2016.