  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.
