Detecting turning points of stock markets in China and the United States

ABSTRACT

Choosing the appropriate method to identify turning points is critical for investors and policymakers. We propose a novel hybrid model combining dual long memory with structural breaks in the mean to identify the turning points of the stock market in China and the United States. The results show that models accounting for long memory and structural break perform well in the turning point detection. Notably, the proposed hybrid model generates superior in-sample turning point matching and out-sample forecasts for SSECI over those obtained from the competing ARFIMA models. In contrast, DJIA is better explained by the dual memory model with structural breaks in volatility. Finally, our analysis extends to encompass FTSE, HSI, IBOVESPA, and SENSEX30. The results demonstrate the superior performance of the hybrid model in markets exhibiting fractal characteristics. Such heterogeneity indicates differences in investor risk preference across stock markets and offers elaborate international evidence to the adaptive market hypothesis.

KEYWORDS:

• Trend-cycle decomposition
• long memory
• turning points
• structural breaks

JEL CLASSIFICATION:

• C22
• G15
• F37

Acknowledgements

We wish to express our deepest appreciation to the editors and the anonymous reviewers. We gratefully acknowledge the financial support of the National Natural Science Foundation of China (No. 71773035 and No. 72301101), Humanities and Social Science Fund of Ministry of Education of China (No. 22YJC790079), and Natural Science Foundation of Hunan Province (No. 2023JJ40454). We are responsible for any errors.

Disclosure statement

There are no conflicts of interest to declare.

Data availability statement

The data that support the findings of this study are available in the WIND database.

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/00036846.2024.2305613.

Notes

¹ As the return $y_t$ is in logarithm, when the differencing order of the stock price series is less than 1, the differencing order of returns will naturally yield values less than 0.

² Following Hanna (2018), the preset value of ${\tau }_{\mathit{tu}}^1$ is set to 3.

³ Following Hanna (2018), the value of ${\tau }_{\mathit{tu}}^2$ is set to 3.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (No. 71773035 and No. 72301101), Humanities and Social Science Fund of Ministry of Education of China (No. 22YJC790079), and Natural Science Foundation of Hunan Province (No. 2023JJ40454).