JOURNAL ARTICLE
How do intangible assets and financial constraints affect stock returns in Vietnam before and during the COVID‐19 pandemic?
Published In: International Journal of Finance & Economics, 2025, v. 30, n. 1. P. 315 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Duong, Khoa Dang; Huynh, Tran Ngoc; Truong, Linh Thi Diem 3 of 3
Abstract
We are the first to determine the effect of intangible intensity (INTANG) on cross‐sectional stock returns after controlling financial constraints in the Vietnam stock market. Our sample includes 37,938 firm‐month observations from 488 non‐financial firms from October 2008 to February 2021. We employ Fama and MacBeth regressions and portfolio analysis methodologies to estimate the impact of intangible assets and financial constraints on stock returns. Our findings show that a percentage increase in INTANG empowers stock returns by 0.922%. Meanwhile, the cross‐sectional stock returns decrease by 0.506% when the financial constraints index increases by a percentage point. Moreover, the results suggest that intangible assets in the entire sample and before COVID‐19 empower the stock return cross‐sectionally. Our findings are robust after employing alternative INTANG proxies. Our findings support the risk‐based explanation, the pecking order theory, and prior literature. Our findings suggest governments should promote intellectual property and copyright regulations to encourage Small and Medium Enterprises (SMEs) to expand intangible assets. Furthermore, investors can utilize our suggested models to construct their portfolios efficiently. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Finance & Economics. 2025/01, Vol. 30, Issue 1, p315
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:1076-9307
- DOI:10.1002/ijfe.2916
- Accession Number:182048716
- Copyright Statement:Copyright of International Journal of Finance & Economics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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