Research on macroeconomic indicators and stock market correlation analysis based on machine learning

Haocheng Tian

doi:10.54254/2755-2721/87/20241611

Research Article

Open access

Published on 31 July 2024

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Tian,H. (2024). Research on macroeconomic indicators and stock market correlation analysis based on machine learning. Applied and Computational Engineering,87,179-184.

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Research on macroeconomic indicators and stock market correlation analysis based on machine learning

Haocheng Tian *^,1,

¹ Financial Engineering, University of Southern California, Los Angeles, 90007, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/87/20241611

Abstract

The stock market is known as the barometer of the national economy, and macroeconomic factors have an important impact on the volatility of the stock market. Therefore, considering the impact of macroeconomic factors on the sustainability of stock market volatility will help to capture the time-varying characteristics of volatility persistence, so as to significantly improve the estimation and forecasting effect of volatility. In this work, the generalized autoregressive conditional heteroskedasticity-mixing data sampling (GARCH-MIDAS) model is adopted, which combines the advantages of the GARCH model in short-term volatility modeling and the advantages of MIDAS regression in integrating macroeconomic variables of different frequencies. The GARCH model provides an accurate depiction of intraday volatility in financial markets by capturing the dynamic nature of short-term volatility. MIDAS regression introduces long-term macroeconomic factors into volatility modeling by integrating macroeconomic data with different sampling frequencies, thus making up for the shortcomings of traditional measurement methods in frequency matching. By combining these two methods, the GARCH-MIDAS model can more comprehensively reflect the dynamic changes of stock market volatility, considering both the impact of short-term market volatility and the role of long-term macroeconomic factors, thus providing a more accurate and in-depth analytical tool for volatility prediction and risk management. The results show that the GARCH-MIDAS model can significantly improve the accuracy of volatility forecasting, and provide more reliable decision support for investors, policymakers and economists.

Keywords

Macroeconomic Indicators, Stock Market, Correlation Analysis, Long-short Term, Machine Learning.

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References

[1]. Khan, Muhammad Kamran, et al. "Stock market reaction to macroeconomic variables: An assessment with dynamic autoregressive distributed lag simulations." International Journal of Finance & Economics 28.3 (2023): 2436-2448.

[2]. Chang, Bisharat Hussain, et al. "Macroeconomic variables and stock indices: an asymmetric evidence from quantile ARDL model." South Asian Journal of Business Studies 10.2 (2021): 242-264.

[3]. Hashmi, Shabir Mohsin, and Bisharat Hussain Chang. "Asymmetric effect of macroeconomic variables on the emerging stock indices: A quantile ARDL approach." International Journal of Finance & Economics 28.1 (2023): 1006-1024.

[4]. Boukhatem, Jamel, Zied Ftiti, and Jean Michel Sahut. "Bond market and macroeconomic stability in East Asia: a nonlinear causality analysis." Annals of Operations Research 297.1 (2021): 53-76.

[5]. Sun, Hongxiang, Zhongkai Yao, and Qingchun Miao. "Design of macroeconomic growth prediction algorithm based on data mining." Mobile Information Systems 2021.1 (2021): 2472373.

[6]. Ma, Yaming, Ziwei Wang, and Feng He. "How do economic policy uncertainties affect stock market volatility? Evidence from G7 countries." International Journal of Finance & Economics 27.2 (2022): 2303-2325.

[7]. Nagao, Ryoya, Yoshihiro Kondo, and Yoshiyuki Nakazono. "The macroeconomic effects of monetary policy: Evidence from Japan." Journal of the Japanese and International Economies 61 (2021): 101149.

[8]. Bhargava, Vivek, and Daniel Konku. "Impact of exchange rate fluctuations on US stock market returns." Managerial finance 49.10 (2023): 1535-1557.

[9]. Li, Yun Daisy, Talan B. İşcan, and Kuan Xu. "The impact of monetary policy shocks on stock prices: Evidence from Canada and the United States." Journal of international money and finance 29.5 (2010): 876-896.

[10]. Kim, Yunmi, and Charles R. Nelson. "Pricing stock market volatility: does it matter whether the volatility is related to the business cycle?." Journal of Financial Econometrics 12.2 (2013): 307-328.

[11]. Ghysels, Eric, Pedro Santa-Clara, and Rossen Valkanov. "The MIDAS touch: Mixed data sampling regression models." (2004).

[12]. Engle, Robert F., Eric Ghysels, and Bumjean Sohn. "Stock market volatility and macroeconomic fundamentals." Review of Economics and Statistics 95.3 (2013): 776-797.

Cite this article

Tian,H. (2024). Research on macroeconomic indicators and stock market correlation analysis based on machine learning. Applied and Computational Engineering,87,179-184.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://www.confcds.org/

ISBN：978-1-83558-585-6(Print) / 978-1-83558-586-3(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.87

ISSN：2755-2721(Print) / 2755-273X(Online)

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