Forecasting Stock Market Volatility: A Sentiment Based Approach

• 영문명: Forecasting Stock Market Volatility: A Sentiment Based Approach
• 발행기관: 한국계량경제학회
• 저자명: Gyujin Choi Chang Sik Kim
• 간행물 정보: 『JOURNAL OF ECONOMIC THEORY AND ECONOMETRICS』Vol.35 No.1, 29~57쪽, 전체 29쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2024.03.30

영문 초록

This paper examines the impact of investor sentiment on stock market volatility using a natural language processing classification method applied to a large-scale dataset of social network data. We also apply numerous forecasting techniques not only including conventional linear models, but also different machine learning models and compare its results. Among various economic and sentiment features, we employ the least absolute shrinkage and selection operator (Lasso) for linear models and a tree-based nonlinear variable selection method to demonstrate the critical role of sentiment measures in market volatility. The results show that sentiment variables are identified to be one of the most important variables in relationship with stock market volatility and improve the future prediction of volatility when considered.

목차

1. INTRODUCTION

2. LITERATURE REVIEW

3. DATA

4. MODEL AND EMPIRICAL RESULTS

5. CONCLUSION

6. APPENDIX: DATA DESCRIPTION

REFERENCES

키워드

Forecasting market volatility investor sentiment machine learning VIX Index