Sentiment Matters in Stock Market: Construction of Sentiment Index Using Machine Learning

• 발행기관: 한국계량경제학회
• 저자명: Seiwan Kim YooJeong Choi Jisu Hwang Yanxin Lu
• 간행물 정보: 『JOURNAL OF ECONOMIC THEORY AND ECONOMETRICS』Vol.35 No.4, 87~112쪽, 전체 26쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2024.12.31

국문 초록

This study employs machine learning to analyze news article sentiment, developing a stock market sentiment index (SSI) based on this analysis. By examining the textual data from news articles, which constitute unstructured data, we aimed to capture the prevailing sentiments among market participants across the financial market. Specifically, this study utilizes The BERT model to decipher the psychological sentiment embedded in the articles through its contextualized understanding of the tone and language patterns. The variables tested included the risk aversion estimate, calculated using the VKOSPI and Bekaert’s method for assessing risk aversion. The empirical analysis involving the SSI, VKOSPI, and risk aversion reveals a significant negative impact of SSI on VKOSPI and risk aversion. We further find that the news sentiment index (NSI) and SSI simultaneously exhibit a converging trend.

목차

1. INTRODUCTION

2. LITERATURE REVIEW

3. DATA

4. CONSTRUCTION OF STOCK MARKET SENTIMENT INDEX

5. ROBUSTNESS OF STOCK MARKET SENTIMENT INDEX

6. CONCLUSION

REFERENCES

키워드

Machine learning stock market sentiment index risk aversion