We show that during the weeks following the initiation of the COVID-19 pandemic, the United States equity market was inefficient. This is demonstrated by showing that utility maximizing agents over the time period ranging from mid-February to late March 2020 can generate statistically significant profits by utilizing only historical price and virus related data to forecast future equity ETF returns. We generalize Merton’s optimal portfolio problem using a novel method based upon a likelihood ratio in order to construct a dynamic trading strategy for utility maximizing agents. These strategies are shown to have statistically significant profitability and strong risk and performance statistics during the COVID-19 time-frame.

我们发现，在 COVID-19 大流行爆发后的几周内，美国股市效率低下。这通过以下事实得到证明：在 2020 年 2 月中旬到 3 月下旬的时间段内，效用最大化代理可以通过仅利用历史价格和病毒相关数据来预测未来股票 ETF 回报，从而产生统计上显着的利润。我们使用基于似然比的新颖方法概括了默顿的最优投资组合问题，以便为效用最大化代理构建动态交易策略。这些策略在 COVID-19 期间具有统计显着的盈利能力以及强大的风险和绩效统计数据。