Deep asset allocation for trend following investing

Trend following strategies are well-known to exhibit excellent excess return performance across a wide range of asset classes in various global markets. For the equity asset class in particular, while the securities selection part is relatively a straightforward procedure, the weight allocation part is more debatable and it has traditionally been identified with the equal-weighted allocation strategy. In this paper, we examine security's own return-based weight allocation strategy for trend following investing and find that this strategy generates superior returns to several well-established weight allocation schemes. In particular, if the true return of the holding period is used ex ante for weight allocation, it is found that this strategy can generate absolutely huge excess returns. Motivated by this finding, we investigate the efficacy of machine learning techniques for regression of securities' returns to improve the weight calculation in this framework. Empirical results indicate that deep learning provides the means of regression with which largest excess return gains are possible. In particular, it is demonstrated that the return-based weight allocation strategy defined by our proposed deep learning architecture produces substantial abnormal returns outperforming all other broadly recognised weight allocation schemes compared in this paper.