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Analyzing the Impact of COVID-19 on Short-Term Investment Behavior through Stochastic Oscillator Indicators

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  • Hsien-Ming Chou

Abstract

This research examines how the COVID-19 pandemic affects short-term investments, particularly focusing on the reliability of Stochastic Oscillator Indicators (SOI) used by investors. Utilizing an artificial intelligence learning prediction model, the study investigates the pandemic's impact on short-term investment strategies guided by SOI, comparing data from pre-pandemic and pandemic periods. The findings suggest that during the COVID-19 period, employing KD analysis with a nine-round turnover rate leads to higher forecast accuracy. Short-term investors tend to adopt a conservative approach, with higher turnover rates resulting in lower winning rates. Overall, the study challenges the notion that COVID-19 only affects medium and long-term investment habits, shedding light on its influence on daily investment profits and potentially increasing short-term profits for larger, conservative investors. Further research is warranted to explore the investor profile of conservative hedgers in more depth. Â

Suggested Citation

  • Hsien-Ming Chou, 2024. "Analyzing the Impact of COVID-19 on Short-Term Investment Behavior through Stochastic Oscillator Indicators," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 14(5), pages 1-6.
    • Handle: RePEc:spt:admaec:v:14:y:2024:i:5:f:14_5_6
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    1. Hsien-Ming Chou, 2024. "Predicting Turnover Rates for Short-Term Stock Index Investments Using Artificial Intelligence and Empirical Analysis," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 14(6), pages 1-18.

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