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The aim is to explore how inflation targeting influences stock market volatility drivers using machine learning techniques.

April 11, 2026International Journal of Financial Engineering0 citations

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The moderating role of inflation targeting in stock market volatility drivers: Machine learning insights into macro-financial channels

IDIchrak DridiManouba University MBMohamed Malek BelhoulaManouba University

Key Points

The aim is to explore how inflation targeting influences stock market volatility drivers using machine learning techniques.
Analyzed data from 16 inflation targeting and 16 non-inflation targeting emerging markets from 1995 to 2024.
Utilized macroeconomic data, policy signals, and country-specific factors to assess market responses.
Employed 11 machine learning techniques, including regularization, decision trees, and neural networks.
Applied GARCH-type models to capture volatility characteristics and dynamics.
Ranked features using mean SHAP importance and interaction techniques to uncover interactions.
Identified distinct volatility drivers in inflation targeting versus non-inflation targeting markets.
Non-IT markets show sharp reactions to market signals, influenced by external factors and institutional quality.
IT regimes provide greater stability with structured macroeconomic fundamentals, mitigating short-term shocks.
Volatility dynamics in IT regimes are shaped by predictable, lagged fundamentals rather than sentiment-driven fluctuations.

Abstract

Purpose: This study explores the moderating role of inflation targeting (IT) regime in shaping stock market volatility drivers, leveraging a machine learning (ML) approach to elucidate the complex interplay between monetary policy regimes and macro-financial channels. By analyzing key macro-financial interactions, we aim to provide actionable insights for policymakers and investors in emerging markets. Methodology: This study analyzes 16 IT and 16 non-IT emerging markets (1995:Q1–2024:Q1) using macroeconomic data, policy signals, country-specific factors, and 11 ML techniques (regularization, trees, neural networks) alongside various GARCH-type models. Furthermore, both mean SHAP importance and SHAP interaction techniques are used to rank features and uncover macro-financial channel interactions. Findings: Findings reveal distinct volatility drivers across regimes. Non-IT markets react sharply to immediate market signals and external vulnerabilities, amplified by macro-financial channels, notably institutional quality interacting with global risk. Conversely, IT regimes exhibit greater stability through structured macroeconomic fundamentals and disciplined policy frameworks that buffer short-term shocks. The IT regime’s predictability transforms volatility dynamics, replacing sentiment-driven fluctuations with lagged fundamentals and external risk factors, fostering more resilient financial environments compared to non-IT contexts. Practical implications: Non-IT policymakers should strengthen institutions and monitor external balances to mitigate volatility, while considering IT adoption. IT countries must maintain credible regimes with flexible exchange rates to stabilize markets and boost confidence. Originality/Value: This study pioneers optimal volatility measures and ML methods for emerging markets, using SHAP to analyze macro-financial channels and IT’s moderating role, advancing theory and guiding policy and investment strategies.

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The moderating role of inflation targeting in stock market volatility drivers: Machine learning insights into macro-financial channels

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