Algorithmic Trading Based on the Incidence of Covid-19 in Europe(Pages 536-545)
Raúl Gómez Martínez*, María Luisa Medrano García and Francisco Díez Martín
Universidad Rey Juan Carlos, Madrid.
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.
COVID-19, cumulative incidence, algorithmic trading, index futures, behavioral finance.
How to Cite:
Raúl Gómez Martínez, María Luisa Medrano García and Francisco Díez Martín. Algorithmic Trading Based on the Incidence of Covid-19 in Europe. [ref]: vol.20.2022. available at: https://refpress.org/ref-vol20-a61/
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