How the individual investors took on big data: The effect of panic from the internet stock message boards on stock price crash

Yang, Xiaolan; Zhu, Yu; Cheng, Teng Yuan

doi:10.1016/j.pacfin.2019.101245

Cited by 34 publications

(30 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies have investigated the strict effect of investor panic on stock prices during the COVID-19 health crisis. Yang et al (2020) affirm that the panic index can be employed to forecast the stock market crash. Another study indicates that confirmed cases of COVID-19, uncertainty and investor panic sentiment have negative effects on the abnormal returns of stock indices in Asian countries ( Liu et al, 2020 ).…”

Section: Literature and Theoretical Backgroundmentioning

confidence: 89%

The impact of COVID-19 news, panic and media coverage on the oil and gold prices: An ARDL approach

2021

View full text Add to dashboard Cite

Section: Literature and Theoretical Backgroundmentioning

confidence: 89%

The impact of COVID-19 news, panic and media coverage on the oil and gold prices: An ARDL approach

2021

View full text Add to dashboard Cite

“…Opinion mining is a method for analyzing opinions by collecting and processing documents from various datasets [22]- [23] [24]. Opinion mining analyzes the opinions, evaluations, attitudes, and emotions of people who express their impressions and thoughts about specific topics posted on social networks and websites [25], [26].…”

Section: A Opinion Miningmentioning

confidence: 99%

Detecting a Risk Signal in Stock Investment Through Opinion Mining and Graph-Based Semi-Supervised Learning

2020

View full text Add to dashboard Cite

The objective of this study is to develop an algorithm to support a decision-making process in stock investment through opinion mining and graph-based semi-supervised learning. For this purpose, this research addresses the following core processes: (1) filtering fake information, (2) assessing credit risk and detecting risk signals, and (3) predicting future occurrences of credit events through sentiment analysis, word2vec, and graph-based semi-supervised learning. First, financial data, including news, texts in social network services, and financial statements, were collected. Among these data, fake information such as rumors and fake news was filtered by author analysis and a rule-based approach. Second, credit risk was assessed by opinion mining and sentiment analysis for both social data and news in the form of a sentiment score and the trend of documents for each stock. A signal for a credit event was then detected by the degree of assessed risk. Consequently, the possibility of credit events such as delisting and bankruptcy in the near future was forecast based on the risk signal using logistic regression. This research illustrated the real case of a company to validate the applicability of the proposed approach. The results of this study can help investors monitor a large amount of historically accumulated data and detect hidden signals of risk events ahead of time.

show abstract

“…Yao et al [ 23 ] constructed an investor attention index by the posts from the Eastmoney Stock Forum and Hexun Stock Forum. Yang et al [ 22 ] used the textual information from Eastmoney Stock Forum to build a panic sentiment index, and studied the impact of textual sentiment on crash risk. The emotion dictionaries are widely used for the computation of sentiment scores.…”

Section: Introductionmentioning

confidence: 99%

A study of the impact of COVID‐19 on the Chinese stock market based on a new textual multiple ARMA model

et al. 2022

Statistical Analysis

View full text Add to dashboard Cite

Coronavirus 2019 (COVID‐19) has caused violent fluctuation in stock markets, and led to heated discussion in stock forums. The rise and fall of any specific stock is influenced by many other stocks and emotions expressed in forum discussions. Considering the transmission effect of emotions, we propose a new Textual Multiple Auto Regressive Moving Average (TM‐ARMA) model to study the impact of COVID‐19 on the Chinese stock market. The TM‐ARMA model contains a new cross‐textual term and a new cross‐auto regressive (AR) term that measure the cross impacts of textual emotions and price fluctuations, respectively, and the adjacent matrix which measures the relationships among stocks is updated dynamically. We compute the textual sentiment scores by an emotion dictionary‐based method, and estimate the parameter matrices by a maximum likelihood method. Our dataset includes the textual posts from the Eastmoney Stock Forum and the price data for the constituent stocks of the FTSE China A50 Index. We conduct a sliding‐window online forecast approach to simulate the real‐trading situations. The results show that TM‐ARMA performs very well even after the attack of COVID‐19.

show abstract

How the individual investors took on big data: The effect of panic from the internet stock message boards on stock price crash

Cited by 34 publications

References 22 publications

The impact of COVID-19 news, panic and media coverage on the oil and gold prices: An ARDL approach

The impact of COVID-19 news, panic and media coverage on the oil and gold prices: An ARDL approach

Detecting a Risk Signal in Stock Investment Through Opinion Mining and Graph-Based Semi-Supervised Learning

A study of the impact of COVID‐19 on the Chinese stock market based on a new textual multiple ARMA model

Contact Info

Product

Resources

About