ABSTRACT

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.

摘要

众所周知，趋势跟踪策略在全球各个市场的各种资产类别中都表现出出色的超额回报表现。特别是对于股权资产类别，虽然证券选择部分是一个相对简单的程序，但权重分配部分更值得商榷，传统上被确定为等权重分配策略。在本文中，我们研究了证券自身基于回报的权重分配策略，用于趋势跟踪投资，并发现该策略比几种成熟的权重分配方案产生了更高的回报。特别是，如果事先使用持有期的真实回报对于权重分配，发现这种策略可以产生绝对巨大的超额收益。受这一发现的启发，我们研究了机器学习技术在证券回报回归中的有效性，以改进该框架中的权重计算。实证结果表明，深度学习提供了一种回归方法，通过这种方法可以获得最大的超额收益收益。特别是，证明了由我们提出的深度学习架构定义的基于回报的权重分配策略产生了大量的异常回报，优于本文中比较的所有其他广泛认可的权重分配方案。