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This study examines the importance of income income-shifting arrangements of US multinational corporations (MNCs) on future stock price crash risk.

This study employs a sample of 7,641 corporation-year observations over the 2005–2017 period and uses ordinary least squares regression analysis.

The authors find that the income-shifting arrangements of MNCs are positively and significantly associated with stock price crash risk after controlling for corporate tax avoidance and other known determinants of stock price crash risk in the regression model. This result is robust to alternative measures of stock price crash risk and income-shifting, and several endogeneity tests. The authors also observe that income-shifting arrangements increase stock price crash risk both directly and indirectly through the information opacity channel. Finally, in cross-sectional analyses, the authors find that the positive association between income-shifting and stock price crash risk is more pronounced for MNCs that use tax haven subsidiaries and have weak corporate governance mechanisms.

The authors provide new empirical evidence that MNCs will likely face significant capital market consequences regarding their income-shifting arrangements.

This paper examines how a firm's hierarchical complexity, which is determined by the way it organizes its subsidiaries across the hierarchical levels, can impact its stock price crash risk.

The authors employ a measure of hierarchical complexity that captures the depth and breadth of how subsidiaries are organized within a firm. This measure is calculated using information about firms' subsidiaries extracted from the Bureau van Dijk (BvD) database that allows the authors to construct each firm's hierarchical structure. The data sample includes 2,461 USA firms for the period from 2012 to 2017 (11,006 firm-year observations). Univariate tests and panel regression are used for the main analysis. Two-stage-least-squares (2SLS) instrumental variable regression and various other tests are employed for robustness check.

The results show a positive relationship between hierarchical complexity and stock price crash risk. This relationship is amplified in firms with a greater number of subsidiaries that are hierarchically distanced from the parent company as well as in firms with a greater number of foreign subsidiaries in countries with weaker rule of law.

This paper is the first to investigate the impact hierarchical complexity has on crash risk. The results highlight the role that a firm's organizational structure can have on asset pricing behavior.

This study focuses on listed and over-the-counter (OTC) companies in the Taiwan Stock Exchange. It found that an increase in the ownership proportion of institutional investors (INs), including foreign investors, investment trusts, and dealers can enhance the informativeness of stock prices. The relationship between these factors follows an inverted U-shaped pattern, indicating that excessively high ownership ratios can actually lead to a decrease in the informativeness of stock prices. Additionally, increasing the ownership proportions of foreign investors and investment trusts can reduce the risk of stock price collapse, while dealers show no significant relationship in this regard. This study also reveals that the technical variable of the price deviation rate is an important explanatory factor for post-collapse returns. It is positively correlated with the magnitude of the price decline after a collapse, meaning that stocks with weaker pre-collapse performance experience larger post-collapse declines. When the data during the 2020 pandemic period are excluded, changes in foreign ownership ratios show a significant positive correlation with postcrash returns in both the long and short term. The significant correlation in the short term may be due to a high proportion of foreign ownership. Any reduction in this could put pressure on stock prices, and retail investors may follow suit and sell-off, using foreign investors as a reference. The significant correlation in the long term might be due to foreign investors themselves possibly also trying to avoid the pressure that their own short-term sell-offs could exert on stock prices. The changes in the ownership ratios of investment trusts and dealers indicate that medium and long-term changes have a significant impact on postcrash returns, while the changes in the major players' ownership show no significant correlation. When data from 2020 are included in the analysis, the significance of all INs decreases.

Consumer behavior has evolved because of technological development. Nowadays, consumers carry out the different stages of the decision-making process by combining multiple devices which has been defined as multi, cross and omnichannel behavior. These behaviors have attracted the attention of academics and become a hot topic in literature. As a result, vast amounts of studies on the subject need to be revised and clarified. Thus, the aim of this chapter is to synthetize the primary academic literature that analyzes multi, cross and omnichannel behavior from the consumer point of view. To do that, first, the main concepts (multi, cross and omnichannel) and their differences are clarified. Second, the major findings of channel mix literature regarding the topics, channel scope and theories are exposed and described. Third, the opportunities and future lines of research are presented. This chapter contributes to the literature by clarifying the conceptualization of multi, cross and omnichannel behaviors; offering a complete picture of the main topics, channel approaches and theories addressed in channel mix literature; and presenting future research opportunities and open research questions in a channel mix context that could serve as a starting point to build further research.

Integrating conceptual perspectives from social exclusion, thinking style and context effects, this study aims to examine how different types of social exclusion influence attraction and compromise effects.

Eight studies were conducted. To establish the causal relationship between social exclusion types and context effects, this study uses experimental designs in all studies.

The attraction effect is stronger when consumers feel rejected than ignored, whereas the compromise effect is stronger when they feel ignored than rejected. Consumers who feel rejected increase their propensity to think holistically, which in turn leads to their choice preferences for asymmetrically dominant options. Conversely, those who feel ignored increase their propensity to think analytically, which in turn leads to their choice preferences for compromise options.

The findings show that consumer preferences for one option over the other alternatives in choice contexts are susceptible to subtle differences in the manner that exclusion is communicated. The studies are limited to recall tasks and scenarios that previous research has shown to be effective. Future research may use actual exclusion to corroborate this study’s findings.

Marketing practitioners may benefit from this study’s findings when it comes to an increase in the relative share of their target brand against their competitor brands by introducing a third option.

To the best of the authors’ knowledge, this research is the first to provide evidence that exclusion communicated in an explicit manner produces the attraction effect, whereas exclusion communicated in an implicit manner produces the compromise effect. Given that threatening situations often influence individuals’ preferences and choices, how social exclusion shapes cognitive processes is an empirical question worthy of investigation.

This study aims to revisit the relationship between Shariah-compliant firms and earnings management. Specifically, the authors examine whether Shariah-certified firms have lower earnings management than non-Shariah-certified firms and how often a firm must hold its certification to observe considerably reduced earnings management. This study also explores how senior management ethnic dualism affects the association of Shariah certification and earnings management.

The authors analyze the hypothesized association between Shariah certification and earnings management using a panel regression model and several robustness tests, including the Heckman selection model. The sample consists of 547 nonfinancial firms listed on the Bursa Malaysia stock exchange, with 5,478 firm-year observations over the 2001–2016 sample period.

Shariah certification is found to mitigate earnings management, particularly for firms that consistently retain their Shariah status. The longer firms retain their Shariah certification continually, the lower the earnings management. Additionally, the results indicate that the negative impact of Shariah certification on earnings management is driven by ethnic duality when a specific ethnic group dominates the top management.

Firms’ commitment to religious-based screening and continuation of certification plays a significant role in improving earnings quality. Firms are committed to abiding by the Shariah code of conduct instead of using the Shariah status for reputation purposes to attract investors.

For investors, the continuous compliance status is a crucial indicator of a firm’s commitment to comply with Shariah principles and to mitigate earnings management. Regarding policy implications, Shariah-compliance guidelines can constrain earnings manipulation, especially among firms lacking ethnic diversity.

The study shows that Shariah certification must be maintained consecutively to reduce earnings management. Shariah certification’s governance function is crucial in ethnically homogeneous firms, primarily when one ethnic group dominates the senior management.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

Since stock return and volatility matters to investors, this study proposes to incorporate the textual sentiment of annual reports in stock price crash risk prediction.

Specific sentences gathered from management discussions and their subsequent analyses are tokenized and transformed into numeric vectors using textual mining techniques, and then the Naïve Bayes method is applied to score the sentiment, which is used as an input variable for crash risk prediction. The results are compared between a collection of predictive models, including linear regression (LR) and machine learning techniques.

The experimental results find that those predictive models that incorporate textual sentiment significantly outperform the baseline models with only accounting and market variables included. These conclusions hold when crash risk is proxied by either the negative skewness of the return distribution or down-to-up volatility (DUVOL).

It should be noted that the authors' study focuses on examining the predictive power of textual sentiment in crash risk prediction, while other dimensions of textual features such as readability and thematic contents are not considered. More analysis is needed to explore the predictive power of textual features from various dimensions, with the most recent sample data included in future studies.

The authors' study provides implications for the information value of textual data in financial analysis and risk management. It suggests that the soft information contained within annual reports may prove informative in crash risk prediction, and the incorporation of textual sentiment provides an incremental improvement in overall predictive performance.