Tail Effects and Industry Heterogeneity in Stock Return Responses to Macroeconomic Shocks: Evidence from Quantile Regression

DOI

10.2991/978-94-6239-701-9_98How to use a DOI?

Keywords

Quantile regression; Stock return; Industry heterogeneity; Quantile heterogeneity; Market volatility

Abstract

This paper examines Google (GOOGL), Chevron (CVX), and Southern Copper (SCCO) from the US technology, energy, and basic materials industries using monthly data from January 2016 to December 2025. We investigate the heterogeneous effects of market return, oil price return, VIX, inflation indicators, and PMI changes on stock returns. As traditional OLS regression relies on normality assumptions and cannot capture tail effects, quantile regression is applied to estimate coefficients at 19 quantiles. Wald tests and Holm-corrected pairwise tests are used to check the significance of quantile heterogeneity. Results show that key factors have significant quantile-dependent and industry-heterogeneous effects on stock returns. Systematic market factors show universal impacts, market volatility depends on extreme market conditions, and economic sentiment displays a common “right-tail strengthening” pattern. The findings provide empirical support for investors and managers, and enrich asset pricing research.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jintong Li
PY  - 2026
DA  - 2026/07/30
TI  - Tail Effects and Industry Heterogeneity in Stock Return Responses to Macroeconomic Shocks: Evidence from Quantile Regression
BT  - Proceedings of the 2026 11th International Conference on Social Sciences and Economic Development (ICSSED 2026)
PB  - Atlantis Press
SP  - 960
EP  - 970
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-701-9_98
DO  - 10.2991/978-94-6239-701-9_98
ID  - Li2026
ER  -