基于欧洲Covid-19发病率的算法交易

Q4 Economics, Econometrics and Finance
R. Martínez, M. García, F. Martín
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引用次数: 0

摘要

行为金融学的研究表明,基于投资者情绪的指标,可以实施有利可图的投资策略,替代传统的分析技术。在本文中,我们描述了一种算法交易系统,如果7天的累积发病率小于(大于)14天的累积发病率,则该系统将开设多头(空头)头寸,这意味着对COVID-19的恐惧的度量。使用2020年的数据,在五个主要欧洲指数(AEX, CAC, DAX, IBEX和MIB)上进行回测,结果表明该策略是有利可图的,投资回报率在21%到68%之间,利润系数在1.11到1.32之间。这是新的证据,表明投资者情绪的准确指标(在本例中是COVID-19大流行的扩大)使我们能够基于行为金融学制定有利可图的替代投资策略。2022 -保留所有权利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algorithmic Trading Based on the Incidence of Covid-19 in Europe
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.
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来源期刊
Review of Economics and Finance
Review of Economics and Finance Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
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