ABSTRACT
The study of behavioral finance is showing that profitable investment strategies can be implemented, alternatives to traditional analysis techniques, based on metrics on investors' mood. In this paper, we describe an algorithmic trading system that opens long (short) positions if the cumulative incidence at 7 days is minor (greater) than the cumulative incidence at 14 days, which implies a metric of the fear of COVID-19. The backtests run, using 2020 data, on five of the main European indices (AEX, CAC, DAX, IBEX, and MIB) show that the strategy is profitable, with ROI between 21% and 68% and profit factors ranging from 1.11 to 1.32. This is new evidence that accurate indicators of investors' mood (in this case the expansion of the COVID-19 pandemic) let us develop profitable alternative investment strategies based on behavioral finance. 2022– All Rights Reserved.
ABSTRACT
Much of the liquidity supply in modern markets comes from algorithmic traders (ATs). Prompted by concerns of fragility induced by such voluntary market making, we examine ATs' liquidity-provision role during the COVID-19 crisis. We find that amidst the turmoil as market liquidity declined, ATs did not (disproportionately) withdraw liquidity supply. Stocks with the highest algorithmic trading (AT) experienced lower liquidity reduction compared to stocks with the lowest AT activity. High AT stocks did not experience greater reduction in either competition for liquidity provision or price improvements than low AT stocks. Multiple tests indicate that high AT did not associate with any greater deterioration in price efficiency vis-à-vis low AT stocks. Stocks in the industries hardest hit by COVID-19 did not see any less AT competition for liquidity supply or price efficiency than stocks in the least affected ones. Overall, our results allay some concerns that the current levels of AT make markets more susceptible to liquidity withdrawal in times of crises. © 2022 Elsevier B.V.
ABSTRACT
We examine the impact of COVID-19 on market structure in the U.S. Specifically, we analyze the impact of both the COVID-19-induced market uncertainty period as well as the suspension of the NYSE floor on trading dynamics such as market fragmentation, algorithmic trading, and hidden liquidity in the market. During both the heightened market uncertainty and NYSE floor suspension periods, we find a significant increase in hidden liquidity yet significant decreases in both algorithmic trading and market fragmentation. However, despite withdrawing from the market during this period, remaining algorithmic traders appear to improve market quality. Our results indicate that COVID-19 had a significant impact on order routing, pre-trade transparency, and automated trading.
ABSTRACT
Much of the liquidity supply in modern markets comes from algorithmic traders (ATs). Prompted by concerns of fragility induced by such voluntary market making, we examine ATs’ liquidity-provision role during the COVID-19 crisis. We find that amidst the turmoil as market liquidity declined, ATs did not (disproportionately) withdraw liquidity supply. Stocks with the highest algorithmic trading (AT) experienced lower liquidity reduction compared to stocks with the lowest AT activity. High AT stocks did not experience greater reduction in either competition for liquidity provision or price improvements than low AT stocks. Multiple tests indicate that high AT did not associate with any greater deterioration in price efficiency vis-à-vis low AT stocks. Stocks in the industries hardest hit by COVID-19 did not see any less AT competition for liquidity supply or price efficiency than stocks in the least affected ones. Overall, our results allay some concerns that the current levels of AT make markets more susceptible to liquidity withdrawal in times of crises. © 2022 Elsevier B.V.