Unveiling the impact of irrelevant answers on analyst forecast errors: A topic modeling approach

IF 9.8 1区 经济学 Q1 BUSINESS, FINANCE International Review of Financial Analysis Pub Date : 2025-06-01 Epub Date: 2025-02-25 DOI:10.1016/j.irfa.2025.104041
Mengshu Hao , Yang Xu , Peiyao Yuan , Kecai Chen
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Abstract

This study explores the influence of irrelevant answers during earnings communication conferences on analyst forecast errors. Utilizing the LDA method to quantify text-based answer irrelevance pertaining to various topics, we uncover that the degree of irrelevant responses concerning product-related issues positively correlates with analyst forecast errors, while those related to the firm's financial performance and corporate governance do not significantly correlate with them. This causal relationship is robustly confirmed by a comprehensive series of endogeneity tests and robustness checks. Additionally, our cross-sectional analysis reveals that our main findings are more pronounced in firms with higher operational complexity and weaker information environments, supporting our hypothesis that analysts encounter greater challenges in identifying and interpreting irrelevant answers regarding product information, thereby leading to reduced forecast accuracy.
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揭示不相关答案对分析师预测误差的影响:主题建模方法
本研究探讨盈余沟通会议中不相关答案对分析师预测误差的影响。利用LDA方法量化与各种主题相关的基于文本的答案不相关性,我们发现与产品相关问题相关的不相关性程度与分析师预测误差呈正相关,而与公司财务绩效和公司治理相关的不显著相关。这种因果关系得到了一系列内生性检验和稳健性检验的有力证实。此外,我们的横断面分析显示,我们的主要发现在运营复杂性较高和信息环境较弱的公司中更为明显,这支持了我们的假设,即分析师在识别和解释与产品信息相关的答案时遇到更大的挑战,从而导致预测准确性降低。
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来源期刊
CiteScore
10.30
自引率
9.80%
发文量
366
期刊介绍: The International Review of Financial Analysis (IRFA) is an impartial refereed journal designed to serve as a platform for high-quality financial research. It welcomes a diverse range of financial research topics and maintains an unbiased selection process. While not limited to U.S.-centric subjects, IRFA, as its title suggests, is open to valuable research contributions from around the world.
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