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Article
Publication date: 1 December 2022

Fang Sun and Xiangjing Wei

In this paper, the impact of stock-based compensation and further the joint effects of stock-based compensation and investor sentiment on pension discount rate choice is examined.

Abstract

Purpose

In this paper, the impact of stock-based compensation and further the joint effects of stock-based compensation and investor sentiment on pension discount rate choice is examined.

Design/methodology/approach

The hypotheses is tested using fixed effects models and instrumental variable analysis where pension discount rate is the dependent variable, and stock-based compensation and investor sentiment are our variables of interest.

Findings

It was found that pension discount rate is negatively associated with managers' stock-based compensation. Further analysis indicates that managers with larger stock-based compensation tend to adjust down their pension discount rates in higher (smaller) degree, responding to high (low) investor sentiment.

Practical implications

The findings provide important insights into how managers use pension discount rates to engage in earnings management. Understanding these relationships has implications for interpreting pension numbers reported in the financial statements and designing pension accounting rules that minimize the possibility that managers take advantage of the complexity associated with pension accounting to influence the reported earnings and executive compensation. Moreover, the findings suggest the need for increased attention from boards of directors, auditors and regulators to reported pension liabilities and service costs, especially for firms paying higher proportion of stock-based compensation to managers and during periods of high investor sentiment.

Originality/value

The findings contribute to the extant literature by identifying the joint impacts of stock-based compensation and investor sentiment as incentives for pension discount rate manipulation. The empirical results of this study also have important implications for corporate governance and regulation.

Details

Managerial Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0307-4358

Keywords

Open Access
Article
Publication date: 28 November 2022

Ruchi Kejriwal, Monika Garg and Gaurav Sarin

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been…

Abstract

Purpose

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

Design/methodology/approach

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Findings

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

Originality/value

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

Details

Vilakshan - XIMB Journal of Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0973-1954

Keywords

Article
Publication date: 24 November 2022

Nao Li, Xiaoyu Yang, IpKin Anthony Wong, Rob Law, Jing Yang Xu and Binru Zhang

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based…

24

Abstract

Purpose

This paper aims to classify the sentiment of online tourism-hospitality reviews at an aspect level. A new aspect-oriented sentiment classification method is proposed based on a neural network model.

Design/methodology/approach

This study constructs an aspect-oriented sentiment classification model using an integrated four-layer neural network: the bidirectional encoder representation from transformers (BERT) word vector model, long short-term memory, interactive attention-over-attention (IAOA) mechanism and a linear output layer. The model was trained, tested and validated on an open training data set and 92,905 reviews extrapolated from restaurants in Tokyo.

Findings

The model achieves significantly better performance compared with other neural networks. The findings provide empirical evidence to validate the suitability of this new approach in the tourism-hospitality domain.

Research limitations/implications

More sentiments should be identified to measure more fine-grained tourism-hospitality experience, and new aspects are recommended that can be automatically added into the aspect set to provide dynamic support for new dining experiences.

Originality/value

This study provides an update to the literature with respect to how a neural network could improve the performance of aspect-oriented sentiment classification for tourism-hospitality online reviews.

研究目的

本文旨在从方面级对在线旅游-酒店评论的情感进行分类。提出了一种基于神经网络模型的面向方面的情感分类新方法。

研究设计/方法/途径

本研究使用集成的四层神经网络构建面向方面的情感分类模型:BERT 词向量模型、LSTM、IAOA 机制和线性输出层。该模型在一个开放的训练数据集和从东京餐厅推断的 92,905 条评论上进行了训练、测试和验证。

研究发现

与其他神经网络相比, 该模型实现了显着更好的性能。研究结果提供了经验证据, 以验证这种新方法在旅游酒店领域的适用性。

研究原创性

该研究提供了有关神经网络如何提高旅游酒店在线评论的面向方面的情感分类性能的新文献。

研究研究局限

应该识别更多的情感从而来更加细化衡量旅游酒店体验, 并推荐新的方面/维度可以被自动添加到方面集中, 为新的用餐体验提供动态支持。

Details

Journal of Hospitality and Tourism Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1757-9880

Keywords

Article
Publication date: 14 January 2019

Tracy Tuten and Victor Perotti

The purpose of this study is to illustrate the influence of media coverage and sentiment about brands on user-generated content amplification and opinions expressed in…

6639

Abstract

Purpose

The purpose of this study is to illustrate the influence of media coverage and sentiment about brands on user-generated content amplification and opinions expressed in social media.

Design/methodology/approach

This study used a mixed-method approach, using a brand situation as a case example, including sentiment analysis of social media conversations and sentiment analysis of media coverage. This study tracks the diffusion of a false claim about the brand via online media coverage, subsequent spreading of the false claim via social media and the resulting impact on sentiment toward the brand.

Findings

The findings illustrate the influence of digital mass communication sources on the subsequent spread of information about a brand via social media channels and the impact of the social spread of false claims on brand sentiment. This study illustrates the value of social media listening and sentiment analysis for brands as an ongoing business practice.

Research limitations/implications

While it has long been known that media coverage is in part subsequently diffused through individual sharing, this study reveals the potential for media sentiment to influence sentiment toward a brand. It also illustrates the potential harm brands face when false information is spread via media coverage and subsequently through social media posts and conversations. How brands can most effectively correct false brand beliefs and recover from negative sentiment related to false claims is an area for future research.

Practical implications

This study suggests that brands are wise to use sentiment analysis as part of their evaluation of earned media coverage from news organizations and to use social listening as an alert system and sentiment analysis to assess impact on attitudes toward the brand. These steps should become part of a brand’s social media management process.

Social implications

Media are presumed to be impartial reporters of news and information. However, this study illustrated that the sentiment expressed in media coverage about a brand can be measured and diffused beyond the publications’ initial reach via social media. Advertising positioned as news must be labeled as “advertorial” to ensure that those exposed to the message understand that the message is not impartial. News organizations may inadvertently publish false claims and relay information with sentiment that is then carried via social media along with the information itself. Negative information about a brand may be more sensational and, thus, prone to social sharing, no matter how well the findings are researched or sourced.

Originality/value

The value of the study is its illustration of how false information and media sentiment spread via social media can ultimately affect consumer sentiment and attitude toward the brand. This study also explains the research process for social scraping and sentiment analysis.

Details

Qualitative Market Research: An International Journal, vol. 22 no. 1
Type: Research Article
ISSN: 1352-2752

Keywords

Article
Publication date: 30 September 2022

Franziska Ploessl and Tobias Just

To investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study…

Abstract

Purpose

To investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study aims to examine the relationship between news coverage or news sentiment and residential real estate prices in Germany at a regional level.

Design/methodology/approach

Using methods in the field of natural language processing, in particular word embeddings and dictionary-based sentiment analyses, the authors derive five different sentiment measures from almost 320,000 news articles of two professional German real estate news providers. These sentiment indicators are used as covariates in a first difference fixed effects regression to investigate the relationship between news coverage or news sentiment and residential real estate prices.

Findings

The empirical results suggest that the ascertained news-based indicators have a significant positive relationship with residential real estate prices. It appears that the combination of news coverage and news sentiment proves to be a reliable indicator. Furthermore, the extracted sentiment measures lead residential real estate prices up to two quarters. Finally, the explanatory power increases when regressing on prices for condominiums compared with houses, implying that the indicators may rather reflect investor sentiment.

Originality/value

To the best of the authors’ knowledge, this is the first paper to extract both the news coverage and news sentiment from real estate-related news for regional German housing markets. The approach presented in this study to quantify additional qualitative data from texts is replicable and can be applied to many further research areas on real estate topics.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 7 October 2022

Liping Liao and Zhijiang Wu

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the…

Abstract

Purpose

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.

Design/methodology/approach

This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.

Findings

The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.

Practical implications

The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.

Originality/value

The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

Details

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

Keywords

Open Access
Article
Publication date: 6 September 2022

Dyliane Mouri Silva de Souza and Orleans Silva Martins

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

Abstract

Purpose

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

Design/methodology/approach

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

Findings

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

Originality/value

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

Details

Revista de Gestão, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1809-2276

Keywords

Article
Publication date: 1 September 2022

Qilan Li, Zhiya Zuo, Yang Zhang and Xi Wang

Since the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population…

Abstract

Purpose

Since the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population inflow to urban areas introduces nontrivial social conflicts between urban natives and migrant workers. This study aims to investigate the most discussed topics about migrant workers on Sina Weibo along with the corresponding sentiment divergence.

Design/methodology/approach

An exploratory-descriptive-explanatory research methodology is employed. The study explores the main topics on migrant workers discussed in social media via manual annotation. Subsequently, guided LDA, a semi-supervised topic modeling approach, is applied to describe the overall topical landscape. Finally, the authors verify their theoretical predictions with respect to the sentiment divergence pattern for each topic, using regression analysis.

Findings

The study identifies three most discussed topics on migrant workers, namely wage default, employment support and urban/rural development. The regression analysis reveals different diffusion patterns contingent on the nature of each topic. In particular, this study finds a positive association between urban/rural development and the sentiment divergence, while wage default exhibits an opposite relationship with sentiment divergence.

Originality/value

The authors combine unique characteristics of social media with well-established theories of social identity and framing, which are applied more to off-line contexts, to study a unique phenomenon of migrant workers in China. From a practical perspective, the results provide implications for the governance of urbanization-related social conflicts.

Details

Internet Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1066-2243

Keywords

Article
Publication date: 8 August 2022

Maya Deori, Vinit Kumar and Manoj Kumar Verma

The consumption of news from social media is the new trend, still news channels are the authentic source to transmit relevant news to audiences. Social media has gradually…

Abstract

Purpose

The consumption of news from social media is the new trend, still news channels are the authentic source to transmit relevant news to audiences. Social media has gradually left an impact on the audience but the news channels have upgraded and providing various news services online on social media websites. The present study aims to study the type of news videos uploaded by the top five Hindi TV news channels on their YouTube channels with an aim to see which type of videos spark interest for YouTube viewers.

Design/methodology/approach

By applying the techniques of content analysis, sentiment analysis and text mining the study aims to measure the average sentiments, top words and the trend of selected popular terms in the comments on uploaded news videos by the top five Hindi news channels over a period of one year.

Findings

Results of the study indicate that the news channels are uploading more news videos about crime and investigation, politics, health and protests while uploading fewer news videos covering travel, science and technology, and religion. While the viewers of the participating news channels are more interested in giving their thoughts or opinions in the form of comments on news videos concerning crime, politics, protests and health or that these videos inspire conversation on YouTube.

Research limitations/implications

The findings might be of interest to content managers of news channels to understand the interest of their audience.

Originality/value

The study's distinctiveness resides in the approach utilised to collect data and analyse the results in order to better understand the online behaviour of news channel audiences.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2022-0007

Details

Online Information Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1468-4527

Keywords

Book part
Publication date: 23 February 2016

Francis P. Barclay, C. Pichandy, Anusha Venkat and Sreedevi Sudhakaran

Do public opinion and political sentiments expressed on Twitter during election campaign have a meaning and message? Are they inferential, that is, can they be used to…

Abstract

Purpose

Do public opinion and political sentiments expressed on Twitter during election campaign have a meaning and message? Are they inferential, that is, can they be used to estimate the political mood prevailing among the masses? Can they also be used to reliably predict the election outcome? To answer these in the Indian context, the 2014 general election was chosen.

Methodology/approach

Tweets posted on the leading parties during the voting and crucial campaign periods were mined and manual sentiment analysis was performed on them.

Findings

A strong and positive correlation was observed between the political sentiments expressed on Twitter and election results. Further, the Time Periods during which the tweets were mined were found to have a moderating effect on this relationship.

Practical implications

This study showed that the month preceding the voting period was the best to predict the vote share with Twitter data – with 83.9% accuracy.

Social implications

Twitter has become an important public communication tool in India, and as the study results reinstate, it is an ideal research tool to gauge public opinion.

Details

Communication and Information Technologies Annual
Type: Book
ISBN: 978-1-78560-785-1

Keywords

1 – 10 of over 20000