Predictions of Short-Term Volatility in the U.S. Stock Market During Interest Rate Cut Cycles

Waifong Tian

doi:10.54254/2754-1169/2025.BJ28943

Advances in Economics, Management and Political SciencesOpen access

Predictions of Short-Term Volatility in the U.S. Stock Market During Interest Rate Cut Cycles

Research Article

Open Access

Predictions of Short-Term Volatility in the U.S. Stock Market During Interest Rate Cut Cycles

Waifong Tian ^1*

¹ China University of Mining and Technology-Beijing

^*Corresponding author: tian19859259777@gmail.com

Published on 5 November 2025

AEMPS Vol.238

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-519-6

ISBN (Online): 978-1-80590-520-2

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Abstract

With the increasing volatility of global capital markets, particularly in the stock market, efficiently forecasting price fluctuations has become a core issue in financial technology. The traditional AutoRegressive Integrated Moving Average (ARIMA) model performs well in fitting linear relationships but struggles to capture complex nonlinear behaviors. In contrast, the deep learning method Long Short-Term Memory (LSTM) is capable of handling nonlinear dependencies and long-sequence features. Based on the assumption that stock market data exhibit both linear and nonlinear characteristics, this study develops a hybrid LSTM-ARIMA model that integrates the strengths of both approaches: ARIMA is first employed to extract linear trends and generate residuals, which are then combined with technical indicators such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) and fed into LSTM to capture nonlinear fluctuations. Experiments are conducted using 30-minute frequency data of the Standard & Poor's (S&P) 500 index, adopting two integration strategies: dynamic weighting and stacking. The results indicate that during the Federal Reserve's interest rate cut cycle, the model outperforms the single model in terms of accuracy and stability in short-term volatility prediction. Specifically, stacking demonstrates stronger adaptability during policy shock periods by correcting residuals, whereas dynamic weighting, which relies on historical Mean Squared Error (MSE), proves slightly insufficient under regime shifts. This study provides empirical evidence and quantitative insights for financial time series forecasting and volatility management during interest rate cut cycles.

Keywords:

Federal Reserve interest rate cut cycles, hybrid LSTM-ARIMA model, dynamic weighting integration, stacking ensemble strategy.

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References

[1]. Kim, J. (2023) Interest Rate Cuts and Stock Market Volatility: Evidence From Global Monetary Cycles. Journal of Financial Economics, 148(2), 215-234.

[2]. Sâadaoui, F. & Rabbouch, H. (2024) Financial Forecasting Improvement With LSTM-ARFIMA Hybrid Models and Non-Gaussian Distributions. Technological Forecasting and Social Change, 198, 123539.

[3]. Saleti, S., Panchumarthi, L. Y., Reddy Kallam, Y., Parchuri, L. & Jitte, S. (2024) Enhancing Forecasting Accuracy With a Moving Average-Integrated Hybrid ARIMA-LSTM Model. SN Computer Science, 5, 704.

[4]. Petrozziello, A. (2022) Deep Learning for Complex Dynamic System Modeling: Advances and Applications. Journal of Computational Finance, 25(4), 33-58.

[5]. Hamiane, S., Ghanou, Y., Khalifi, H. & Telmem, M. (2024) Comparative Analysis of LSTM, ARIMA, and Hybrid Models for Forecasting Future GDP. Ingénierie des Systèmes d'Information, 29(3), 853-861.

[6]. Box, G. E. P. & Jenkins, G. M. (1970) Time Series Analysis: Forecasting and Control. Holden-Day.

[7]. Hochreiter, S. & Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.

[8]. Breiman, L. (1996) Bagging Predictors. Machine Learning, 24(2), 123-140.

[9]. Hybrid ML Models for Volatility Prediction in Financial Risk Management (2025) Expert Systems With Applications, 252, 125078.

[10]. MacQueen, J. (1967) Some Methods for Classification and Analysis of Multivariate Observations. In L. M. LeCam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297. University of California Press.

[11]. A Hybrid Approach of Wavelet Transform, ARIMA and LSTM Model for the Stock Index Futures (2023) Information Sciences, 640, 1456-1472.

[12]. Wolpert, D. H. (1992) Stacked Generalization. Neural Networks, 5(2), 241-259.

[13]. Michańków, J., Kwiatkowski, Ł. & Morajda, J. (2023) Combining Deep Learning and GARCH Models for Financial Volatility and Risk Forecasting. arXiv preprint, arXiv: 2310.01063.

[14]. Volatility Forecasting With Hybrid Neural Networks Methods for Risk-Controlled Strategy for Portfolio Allocation (2023) Expert Systems With Applications, 213, 120920.