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Article
Publication date: 6 November 2019

Ying Liu, Geng Peng, Lanyi Hu, Jichang Dong and Qingqing Zhang

With the ascendance of information technology, particularly through the internet, external information sources and their impacts can be readily transferred to influence…

Abstract

Purpose

With the ascendance of information technology, particularly through the internet, external information sources and their impacts can be readily transferred to influence the performance of financial markets within a short period of time. The purpose of this paper is to investigate how incidents affect stock prices and volatility using vector error correction and autoregressive-generalized auto regressive conditional Heteroskedasticity models, respectively.

Design/methodology/approach

To characterize the investors’ responses to incidents, the authors introduce indices derived using search volumes from Google Trends and the Baidu Index.

Findings

The empirical results indicate that an outbreak of disasters can increase volatility temporarily, and exert significant negative effects on stock prices in a relatively long time. In addition, indices derived from different search engines show differentiation, with the Google Trends search index mainly representing international investors and appearing more significant and persistent.

Originality/value

This study contributes to the existing literature by incorporating open-source data to analyze how catastrophic events affect financial markets and effect persistence.

Details

Industrial Management & Data Systems, vol. 120 no. 2
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 23 January 2019

Ting Xue and Huiqi Liu

The development of Big Data and online searching engine provides a good opportunity for studying petition in China. This study has constructed a set of indices for…

Abstract

Purpose

The development of Big Data and online searching engine provides a good opportunity for studying petition in China. This study has constructed a set of indices for predicting petitions in China by using online searching engines and further explored the predicting role of economic, environment and public life risk perception in various petitions.

Design/methodology/approach

Based on the study of Xue and Liu (2017), this research first re-classified offline petition by human and cluster analysis in terms of social risk perception and built online searching indices of the two sets of petition by using data from “Google Trend” and “Baidu Index.” Second, it analyzed the predicting effect of social risk perception on online searching indices of petition by using Granger causality analysis. Finally, this study integrated the results and selected significant paths from social risk perception to the two sets of petition.

Findings

The study found that the re-classification made by human was more appropriate than the categories made by cluster analysis in terms of social risk perception. For the two sets of petition, the correlations between offline petition and Baidu Index of petition were both more significant than that of Google index. Moreover, economic and finance and resource and environment risk perception had a significant predicting effect on more than one kind of online searching indices of petition.

Originality/value

The results have demonstrated the important role of economic issues in China on predicting petitions of the economic kind, as well as other kinds. They have also reflected the dominant social contradictions and their relationship in modern China.

Details

Information Discovery and Delivery, vol. 47 no. 3
Type: Research Article
ISSN: 2398-6247

Keywords

Article
Publication date: 1 November 2022

Zhe Liu, Chong Huang and Benshuo Yang

This paper investigates the impact of investor attention on the COVID-19 concept stocks in China's stock market from the perspectives of the macroeconomy, the stock market…

Abstract

Purpose

This paper investigates the impact of investor attention on the COVID-19 concept stocks in China's stock market from the perspectives of the macroeconomy, the stock market and the COVID-19 pandemic.

Design/methodology/approach

On the basis of controlling the time effects and individual fixed effects, this paper studies the impact of investor attention on the COVID-19 concept stocks in China's stock market through a set of fixed effect panel data models. Among them, investor attention focuses on macroeconomy, stock market and the COVID-19 pandemic, respectively, while stock indicators cover return, volatility and turnover. In addition, this paper also examines the heterogeneity influence of investor attention on the COVID-19 concept stocks from the perspective of time and stock classification.

Findings

Findings indicate that the attention to macroeconomy does not have a statistically significant effect on the return, unlike the attention to stock market and COVID-19 incident. Three types of investor attention have significant positive effects on the volatility and turnover rate. During the outbreak of the domestic epidemic, the impact of investor attention was significantly higher than that during the outbreak of the epidemic overseas. A finer-grained analysis shows that the attention to stock market has significantly increased the return of preventive type and treatment type stocks, while diagnostic-related stocks have been most affected by the attention to COVID-19 incident.

Research limitations/implications

The major limitation of this work is the construction of investor attention. Although Baidu index is widely used, investor attention can be assessed more accurately based on more unstructured data. In addition, the effect of the COVID-19 can also be investigated in a longer time domain. Further research can be combined with the dynamics of the COVID-19 pandemic to more comprehensively evaluate its impact on the stock market.

Originality/value

The research proves that investor attention plays an important role in stock pricing and provides empirical evidence on the behavioral foundations of the conceptual sector of the stock market under uncertainty. It also has practical implications for regulators and investors interested in conducting accurate asset allocation and risk assessment.

Details

International Journal of Emerging Markets, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-8809

Keywords

Article
Publication date: 5 July 2021

Qiaoqi Lang, Jiqian Wang, Feng Ma, Dengshi Huang and Mohamed Wahab Mohamed Ismail

This paper verifies whether popular Internet information from Internet forum and search engine exhibit useful content for forecasting the volatility in Chinese stock market.

Abstract

Purpose

This paper verifies whether popular Internet information from Internet forum and search engine exhibit useful content for forecasting the volatility in Chinese stock market.

Design/methodology/approach

First, the authors’ study commences with several HAR-RV-type models, then the study amplifies them respectively with the posting volume and search frequency to construct HAR-IF-type and HAR-BD-type models. Second, from in-sample and out-of-sample analysis, the authors empirically investigate the interpretive ability, forecasting performance (statistic and economic). Third, various robustness checks are utilized to reconfirm the authors’ findings, including alternative forecast window, alternative evaluation method and alternative stock market. Finally, the authors further discuss the forecasting performance in different forecast horizons (h = 5, 10 and 20) and asymmetric effect of information from Internet forum.

Findings

From in-sample perspective, the authors discover that posting volume exhibits better analytical ability for Chinese stock volatility than search frequency. Out-of-sample results indicate that forecasting models with posting volume could achieve a superior forecasting performance and increased economic value than competing models.

Practical implications

These findings can help investors and decision-makers obtain higher forecasting accuracy and economic gains.

Originality/value

This study enriches the existing research findings about the volatility forecasting of stock market from two dimensions. First, the authors thoroughly investigate whether the Internet information could enhance the efficiency and accuracy of the volatility forecasting concerning with the Chinese stock market. Second, the authors find a novel evidence that the information from Internet forum is more superior to search frequency in volatility forecasting of stock market. Third, they find that this study not only compares the predictability of the posting volume and search frequency simply, but it also divides the posting volume into “good” and “bad” segments to clarify its asymmetric effect respectively.

Highlights

This study aims to verify whether posting volume and search frequency contain predictive content for estimating the volatility in Chinese stock market.

The forecasting model with posting volume can achieve a superior forecasting performance and increases economic value than competing models.

The results are robust in alternative forecast window, alternative evaluation method and alternative market index.

The posting volume still can help to forecast future volatility for mid- and long-term forecast horizons. Additionally, the role of posting volume in forecasting Chinese stock volatility is asymmetric.

Details

China Finance Review International, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-1398

Keywords

Article
Publication date: 15 June 2021

Tingting Zhang, Desheng Wei, Zhifeng Liu and Xihao Wu

This paper studies the effects of lottery preference on stock market participation at the macro level.

Abstract

Purpose

This paper studies the effects of lottery preference on stock market participation at the macro level.

Design/methodology/approach

The authors use the abnormal search volume intensity for lottery-related keywords from the Baidu search engine to capture retail investors' lottery preference. To measure stock market participation, they use five different macro-level measures from various angles. They perform the time series regression analysis in their empirical study.

Findings

First, the validation tests show that the lottery preference index in this study is reasonable. Further, the authors find that lottery preference increases people's propensity to enter and trade in the stock market. Besides, they find that the effect on trading behavior is asymmetric, that is, high lottery preference has a more significant impact on trading behavior than low lottery preference. However, lottery preference has no significant effect on the stockholding.

Originality/value

This paper contributes to the growing literature that examines the determinants of stock market participation and the role of lottery/gambling preference in the financial market. It also provides direct and novel evidence for Statman's (2002) conclusions about the similarity of lottery players and stock traders.

Details

China Finance Review International, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-1398

Keywords

Article
Publication date: 12 July 2022

Shutian Wang, Yan Lin, Yejin Yan and Guoqing Zhu

This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.

Abstract

Purpose

This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.

Design/methodology/approach

The long-run equilibrium relationship and short-run dynamic effects between the valence and volume of UGC, online search traffic and offline car sales are analyzed by applying the autoregressive distribution lag (ARDL) model.

Findings

The study found the following. (1) In the long-run relationship, the valence of online reviews on social media platforms is significantly negatively correlated with the sales of all models. However, in the short-run, the valence of online reviews has a significant positive correlation with all models in different lag periods. (2) The volume of online reviews is significantly positively correlated with the sales of all models in the long run. However, in the short run, the relationship between the volume of online reviews and the sales of lower-sales-volume cars is uncertain. There is a significant positive correlation between the volume of reviews and the sales of higher-sales-volume cars. (3) Online search traffic has a significantly negative correlation with the sales of all models in the long run. However, in the short run, there is no consistent conclusion on the relationship between online search traffic and car sales.

Originality/value

This study provides a reference for managers to use in their efforts to improve offline high-involvement product sales using online information.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 29 April 2022

Ye Wang, Fusheng Wang and Shiyu Liu

This paper aims to discuss whether the attention of investors to abnormalities can serve as a mechanism for the influence of online media coverage on earnings management.

Abstract

Purpose

This paper aims to discuss whether the attention of investors to abnormalities can serve as a mechanism for the influence of online media coverage on earnings management.

Design/methodology/approach

Based on Baidu index data of China’s A-share listed companies between 2014 and 2018, this paper studies influencing mechanism of online media reports on earnings management from the perspective on abnormal investor attention.

Findings

The results show that internet media reports can impose pressure on managers of companies by inducing abnormal focus of the public on listed companies and further force the latter to generate more actions on the management of earnings. It is the abnormal rather than normal investor attention that mediates network media reports and earnings management.

Practical implications

This research enriches and refines the theory on influencing mechanism of media effects on earnings management and provides significant empirical evidence for future researches. Meanwhile, the conclusion of the research is of great practical importance for instructing listed firms dealing with media reports, guiding rational investment of investors and intensifying precision regulation of regulators.

Originality/value

By categorizing abnormal investor attention into active spontaneous abnormal attention which is not guided by media report and passive guided abnormal attention which is guided by media reports, the authors clarify the difference between the two categories. The result indicates that it is only the latter that is the influential mechanism of media report on earnings management.

Details

Nankai Business Review International, vol. 13 no. 3
Type: Research Article
ISSN: 2040-8749

Keywords

Article
Publication date: 31 May 2021

Mingming Hu, Mengqing Xiao and Hengyun Li

While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ…

Abstract

Purpose

While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting.

Design/methodology/approach

Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting.

Findings

Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal.

Practical implications

Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management.

Originality/value

This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data.

Details

International Journal of Contemporary Hospitality Management, vol. 33 no. 6
Type: Research Article
ISSN: 0959-6119

Keywords

Open Access
Article
Publication date: 10 October 2022

Wenjun Jing, Xuan Liu, Linlin Wang and Yi He

Aiming at the lack of explanatory power of traditional industrial organization theory in cross-border competition, by introducing the idea of ecological niche, the authors…

Abstract

Purpose

Aiming at the lack of explanatory power of traditional industrial organization theory in cross-border competition, by introducing the idea of ecological niche, the authors aim to explore the competitive situation of platform-based enterprises when they operate in multiple fields.

Design/methodology/approach

With the help of ecological niche theory, construct the niche width and niche overlap index of typical enterprises in the platform economy, and find out the advantages and the intensity of competition through comparative analysis.

Findings

In an environment of cross-border competition, large enterprises have significant competitive advantages, and the fierce competition is concentrated among medium-sized enterprises.

Originality/value

The conclusions of this paper not only provide new insights for explaining the phenomenon of cross-border competition in the platform economy, but also provide theoretical reference for the anti-trust enforcement practice in the platform economy.

Details

Journal of Internet and Digital Economics, vol. 2 no. 2
Type: Research Article
ISSN: 2752-6356

Keywords

Article
Publication date: 8 September 2022

Xingwei Li, Xiang Liu, Yicheng Huang, Jingru Li, Jinrong He and Jiachi Dai

The green innovation behavior of construction enterprises is the key to reducing the construction industry's carbon emissions and realizing the green transformation of the…

Abstract

Purpose

The green innovation behavior of construction enterprises is the key to reducing the construction industry's carbon emissions and realizing the green transformation of the construction industry. The purpose of this study is to reveal the evolutionary mechanism of green innovation behavior in construction enterprises.

Design/methodology/approach

This study is based on resource-based theory, Porter's hypothesis and signaling theory. First, a measurement model of the green innovation behavior of construction enterprises was constructed from three aspects: environmental regulation, enterprise resources and public opinion through hierarchical analysis. Then, the state values of the measurement model of green innovation behavior of construction enterprises were calculated through the time series data from 2011–2018. Finally, the Markov chain model was used to predict the evolutionary trend of green innovation behavior of construction enterprises, and the accuracy of the prediction effect of the Markov chain model was verified using the time series data of 2019.

Findings

The Markov chain model of green innovation behavior of construction enterprises constructed in this study has high accuracy. This model finds that the transition of the growth state of green innovation behavior in China's construction industry is fluid and predicts the evolution trend of the innovation behavior of construction enterprises. In the future, the green innovation behavior of construction enterprises has a probability of 70.17% to be in a continuous growth state and 40.27% to be in a rapid growth state.

Originality/value

Based on the Markov chain model of green innovation behavior of construction enterprises, this study finds that the transition of the growth state of green innovation behavior of construction enterprises in China has the characteristics of liquidity. In addition, it reveals the development process of the green innovation behavior of construction enterprises from 2011–2018 and predicts the evolution trend of the green innovation behavior of construction enterprises.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

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