Deep Portfolio Theory
By applying deep learning (DL) to classic portfolio optimisation, we show how to capture (or ‘learn’) non-linear features which are invisible to classic portfolio theory. We develop a self-contained four step routine of encode, calibrate, validate, and verify to improve ex-post performance in index tracking and benchmark outperformance.
Jan Hendrik Witte is a numerical analyst who has developed a number of new numerical algorithms in the area of optimal stochastic control. Since leaving academia, Witte has been working as an FX quant in finance. Witte is generally interested in the areas of numerical mathematical finance, systematic trading, and portfolio optimisation. Together with GreyMaths, Witte is building deep learning technologies for the use in trading and investing.