In this paper,we show that the increasing popularity of machine learning improves market efficiency.By analysing the performance of a set of popular machine learning-based investment strategies,we find that profits fr...In this paper,we show that the increasing popularity of machine learning improves market efficiency.By analysing the performance of a set of popular machine learning-based investment strategies,we find that profits from these strategies experience significant declines since the wide adoption of machine learning techniques,especially for profits based on the more preferred method of neural networks.These declines mainly come from long legs.Using the‘machine learning’Google search index as a proxy for machine learning-based trading intensity,we find that returns from the neural networks-based long–short and long-only strategies are weaker following high levels of machine learning intensity,while no relation is found between machine learning intensity and the short-only neural networks-based strategy.展开更多
基金supported by the Key Programme of National Natural Science Foundation of China(NSFC)[Grant No.72233003].
文摘In this paper,we show that the increasing popularity of machine learning improves market efficiency.By analysing the performance of a set of popular machine learning-based investment strategies,we find that profits from these strategies experience significant declines since the wide adoption of machine learning techniques,especially for profits based on the more preferred method of neural networks.These declines mainly come from long legs.Using the‘machine learning’Google search index as a proxy for machine learning-based trading intensity,we find that returns from the neural networks-based long–short and long-only strategies are weaker following high levels of machine learning intensity,while no relation is found between machine learning intensity and the short-only neural networks-based strategy.
