从推文到交易：分析公众情绪对土耳其股市表现的影响
From Tweets to Trades: Analyzing the Influence of Public Mood over Stock Market Performance in Turkiye
查看机构详情
- Yıldız Technical University（耶尔德兹技术大学）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本研究利用土耳其X平台帖子，通过微调Transformer模型分析公众情绪与股市关系，发现情绪影响价格波动幅度而非方向，且存在领域异质性。
中文摘要 AI 辅助
目的：本研究考察特定领域的公众情绪是否与股市动态相关，以及这些关系是否因沟通领域和市场条件而异。它将公众情绪与投资者情绪区分开来，并调查异质的公众沟通来源是否与市场行为表现出不同的关系。设计：本研究分析了2022年1月至2023年12月期间由176个精选X账户发布的610,422条帖子，涵盖政治与政府、经济与金融以及媒体与社会三个领域。帖子使用微调后的土耳其语Transformer模型在三种特定领域机制和一种合并机制下进行分类。公众情绪指标按日、周和月频率构建，并与BIST100和BIST30市场指标一起，在全时段和选定市场条件下，使用相关性分析、格兰杰因果检验、向量自回归和脉冲响应分析进行检验。发现：公众情绪与股市收益的方向无关，但与价格波动的幅度相关，尤其是媒体与社会领域以及合并沟通。这些关系在更长的聚合频率下变得更强。预测关系集中在经济与金融沟通中，而其幅度和方向随市场条件变化，特别是在2023年选举期间。合并指标在很大程度上反映了最活跃的沟通领域。原创性：本研究通过将沟通领域的异质性纳入公众情绪与市场动态的分析中，为行为金融研究做出贡献。它还展示了聚合异质来源如何掩盖公众沟通与金融市场之间的特定领域关系。
英文摘要
Purpose: This study examines whether domain-specific public mood is associated with stock-market dynamics and whether these relationships vary across communication domains and market conditions. It distinguishes public mood from investor sentiment and investigates whether heterogeneous sources of public communication exhibit different relationships with market behaviour. Design: The study analyses 610,422 posts published by 176 curated X accounts between January 2022 and December 2023, covering Politics and Government, Economy and Finance, and Media and Society. Posts are classified using fine-tuned Turkish transformer models under three domain-specific and one pooled regime. Public mood measures are constructed at daily, weekly, and monthly frequencies and examined alongside BIST100 and BIST30 market measures using correlation, Granger causality, vector autoregression, and impulse response analyses across the full period and selected market conditions. Findings: Public mood is not associated with the direction of stock-market returns but is associated with the magnitude of price movements, particularly for Media and Society and pooled communication. These relationships become stronger at longer aggregation frequencies. Predictive relationships are concentrated in Economy and Finance communication, while their magnitude and direction vary across market conditions, particularly during the 2023 election period. The pooled measure largely reflects the most active communication domain. Originality: The study contributes to behavioral-finance research by incorporating communication - domain heterogeneity into the analysis of public mood and market dynamics. It also demonstrates how aggregating heterogeneous sources can obscure domain-specific relationships between public communication and financial markets.
