Số 236 | Tháng 11/2025
Tâm lý cực đoan và hành vi bầy đàn trên thị trường tiền điện tử: Bằng chứng thực nghiệm từ dữ liệu âm nhạc
Phạm Thị Ngọc Dung, Nguyễn Thị Hồng Hạnh
Tóm tắt:
Mục tiêu nghiên cứu: Nghiên cứu này phân tích hành vi bầy đàn (HVBĐ) trên thị trường tiền điện tử (TTTĐT) và vai trò điều tiết của tâm lý cực đoan (TLCĐ).
Thiết kế nghiên cứu/phương pháp/tiếp cận: Tâm lý nhà đầu tư được đo lường thông qua dữ liệu cảm xúc từ 200 bài hát phổ biến trên Spotify, kết hợp với thông tin của 361 loại tiền điện tử (TĐT) trong giai đoạn 2017–2023. HVBĐ được xác định qua chỉ số Cross-Sectional Absolute Deviation (CSAD), sử dụng mô hình hồi quy tĩnh và mô hình chuyển đổi chế độ.
Kết quả nghiên cứu chính: Kết quả cho thấy HVBĐ hiện diện đáng kể, đặc biệt trong giai đoạn thị trường biến động cao. Mô hình tĩnh cho thấy tâm lý tiêu cực làm gia tăng HVBĐ, trong khi tâm lý tích cực không có ảnh hưởng rõ rệt. Trong mô hình chuyển đổi chế độ, tác động của tâm lý không đáng kể, hàm ý rằng biến động thị trường là yếu tố chi phối chính.
Giá trị đóng góp mới: Việc sử dụng dữ liệu âm nhạc cung cấp một hướng tiếp cận mới để đo lường cảm xúc và hành vi phi lý trí trong thị trường tài sản số.
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Extreme Investor Sentiment and Herding in the Cryptocurrency Market: Empirical Insights from Music-Based Sentiment Data
Abstract:
Purpose: This study analyzes herding behavior in the cryptocurrency market and the moderating role of extreme investor sentiment.
Design/methodology/approach: Investor sentiment is measured using emotional data derived from 200 popular songs on Spotify, combined with market data from 361 cryptocurrencies during the 2017–2023 period. Herding behavior is identified through the Cross-Sectional Absolute Deviation (CSAD) index, employing both static regression and regime-switching models.
Findings: The findings reveal a significant presence of herding, especially during periods of high market volatility. The static model shows that negative sentiment amplifies herding behavior, whereas positive sentiment has no clear effect. In the regime-switching model, sentiment appears statistically insignificant, implying that market volatility is the dominant factor.
Originality/value: The use of music-based emotional data offers a novel approach for capturing investor sentiment and irrational behavior in digital asset markets.