Downloads: 71

Files in This Item:
File Description SizeFormat 
DP1082.pdf796.7 kBAdobe PDFView/Open
Title: Efficient Market Hypothesis Test with Stock Tweets and Natural Language Processing Models
Authors: Mao, Bolin
Chu, Chenhui
Nakashima, Yuta
Nagahara, Hajime
Keywords: Efficient Market Hypothesis Test
Daily Stock Price Prediction
Stock Tweet
Natural Language Processing
Issue Date: Sep-2022
Publisher: Institute of Economic Research, Kyoto University
Journal title: KIER Discussion Paper
Volume: 1082
Start page: 1
End page: 30
Abstract: The efficient market hypothesis (EMH) plays a fundamental role in modern financial theory. Previous empirical studies have tested the weak and semi-strong forms of EMH with typical financial data, such as historical stock prices and annual earnings. However, few tests have been extended to include alternative data such as tweets. In this study, we use 1) two stock tweet datasets that have different features and 2) nine natural language processing (NLP)-based deep learning models to test the semi-strong form EMH in the United States stock market. None of our experimental results show that stock tweets with NLP-based models can prominently improve the daily stock price prediction accuracy compared with random guesses. Our experiment provides evidence that the semi-strong form of EMH holds in the United States stock market on a daily basis when considering stock tweet information with the NLP-based models.
Related Link:
Appears in Collections:KIER Discussion Paper (English)

Show full item record

Export to RefWorks

Export Format: 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.