Market persistence amidst financial crisis: an Indian investigation

This study examines persistence behavior of the Indian Stock Market during financial crises over the past decade (2012–2022). The investigation period encompasses significant events such as economic depression of 2013, demonetization and implementation of the Goods and Services Tax (2016–2017), the US-China Trade War (2018), and the COVID-19 pandemic (2021–2022). This study focuses on the dynamic persistence of various stocks by measuring the Hurst Exponent (HE) using the sliding window method and identifying herding patterns in the financial market. The technique is novel because it explains the time-varying degree of persistence during financial crises, anticipating future trends in the financial returns. The results classify stocks as either mean-reverting or trending. Moreover, the study also compares persistence for shorter and longer time windows, where the presence of higher persistence is observed as the window size is reduced. Furthermore, the majority of the stocks show high persistence in all crisis periods, with the highest value of HE during the economic downturn (2012–2014) and the post-COVID-19 era (2021–2022). Financial advisors can refer to HE values, assisting the investors with decisions such as ‘buy’, ‘sell’ or ‘hold’. Furthermore, a higher HE value can become an early warning signal for impending market stress in the form of bubbles or bursts. This study investigates persistence behavior of the Indian Stock Market during financial crises during 2012–2022. It measures persistence by computing both static and dynamic Hurst Exponent (H.E.) using rescaled range method. Computed results classify the stocks as mean-reverting, trending or random. It also reveals the time varying degree of persistence of each stock. Financial advisors can use the Hurst exponent for momentum trading. Advisers can benefit clients by providing buy and hold strategies, investing in less risky stocks, or increasing the fund for fixed returns to gain assured returns. Individual equity investors can use this knowledge to manage their funds, control their long-term and short-term investment goals, and their returns by applying a better hedging strategy for a safe and secured future.

Publication Details

Journal
Figshare
Published
2024-12-13
DOI
https://doi.org/10.6084/m9.figshare.28023334.v1
Primary Topic
Stock Market Forecasting Methods
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article
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Market persistence amidst financial crisis: an Indian investigation

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Stock Market Forecasting Methods
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Market persistence amidst financial crisis: an Indian investigation

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Abstract

This study examines persistence behavior of the Indian Stock Market during financial crises over the past decade (2012–2022). The investigation period encompasses significant events such as economic depression of 2013, demonetization and implementation of the Goods and Services Tax (2016–2017), the US-China Trade War (2018), and the COVID-19 pandemic (2021–2022). This study focuses on the dynamic persistence of various stocks by measuring the Hurst Exponent (HE) using the sliding window method and identifying herding patterns in the financial market. The technique is novel because it explains the time-varying degree of persistence during financial crises, anticipating future trends in the financial returns. The results classify stocks as either mean-reverting or trending. Moreover, the study also compares persistence for shorter and longer time windows, where the presence of higher persistence is observed as the window size is reduced. Furthermore, the majority of the stocks show high persistence in all crisis periods, with the highest value of HE during the economic downturn (2012–2014) and the post-COVID-19 era (2021–2022). Financial advisors can refer to HE values, assisting the investors with decisions such as ‘buy’, ‘sell’ or ‘hold’. Furthermore, a higher HE value can become an early warning signal for impending market stress in the form of bubbles or bursts. This study investigates persistence behavior of the Indian Stock Market during financial crises during 2012–2022. It measures persistence by computing both static and dynamic Hurst Exponent (H.E.) using rescaled range method. Computed results classify the stocks as mean-reverting, trending or random. It also reveals the time varying degree of persistence of each stock. Financial advisors can use the Hurst exponent for momentum trading. Advisers can benefit clients by providing buy and hold strategies, investing in less risky stocks, or increasing the fund for fixed returns to gain assured returns. Individual equity investors can use this knowledge to manage their funds, control their long-term and short-term investment goals, and their returns by applying a better hedging strategy for a safe and secured future.

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