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This study aims to examine the effects of firms’ accounting disclosure policies on stock price synchronicity and stock crash risk, using a sample including 13 emerging markets. Furthermore, this research investigates how these relationships are affected by country-level investor protection and firm-level governance rankings.

This paper uses accounting disclosure measures constructed based on survey questions by Credit Lyonnais Securities Asia (2001, CLSA). The accounting disclosure measure is used to explain the two dependent variables, stock price synchronicity and stock crash risk. The stock price synchronicity measure is defined as the logistic transformation of R2 following Hutton et al. (2009) and Jin and Myers (2006). R2 is taken from the estimation of an extended market model. The stock crash risk variable is measured as the frequency difference between extremely negative and positive stock return residues following Jin and Myers (2006). These stock return residues are taken from the estimation of an extended market model. Because the CLSA firm-level disclosure data are from 2000, this paper matches other data taken from the same year, for consistency. The final sample includes 204 observations in 13 emerging countries.

This paper finds that firms’ stocks are less synchronized with the entire market and have less crash risk if firms have superior accounting disclosure policies. These results suggest that the cost to collect firm-specific information may be decreased for investors if firms are more transparent. Thus, these firms’ stocks have more firm-specific information content. These results also suggest that management is less likely to hide some negative information and release such negative information suddenly in the future if firms have higher levels of accounting disclosure. Thus, these firms’ stocks are less likely to crash. In addition, the influences of firms’ accounting disclosure policies on stock price synchronicity and crash risk are more significant for firms with superior country-level investor protection and firm-level governance rankings. These results imply that external investors place more value on accounting disclosure by well-governed firms because firms with superior governance standards are less likely to intentionally disclose misleading information. Thus, these firms’ stocks can incorporate more firm-specific information and have less crash risk.

The current study is the first to show that the effects of accounting disclosure on stock price synchronicity and crash risk are more pronounced for firms with superior country-level investor protection and firm-level governance standards. Thus, this research extends the literature by providing a comprehensive picture of the influences of accounting disclosure on stock markets. In addition, the existing literature (Chen et al., 2006; Durnev et al., 2004) shows that firms with lower stock price synchronicity are associated with higher investment efficiency because managers invest based on the information in stock prices. Obviously, higher stock crash risk is highly related to higher bankruptcy risk for firms. Thus, examining the effects of accounting disclosure on stock price synchronicity and stock crash risk is of obvious importance to policy makers.

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 study examines the influence of stock liquidity on stock price crash risk and the moderating role of institutional blockholders in Vietnam’s stock market.

Crash risk is measured by the negative coefficient of skewness of firm-specific weekly returns (NCSKEW) and the down-to-up volatility of firm-specific weekly stock returns (DUVOL). Liquidity is measured by adjusted Amihud illiquidity. The two-stage least squares method is used to address endogeneity issues.

Using firm-level data from Vietnam, we find that crash risk increases with stock liquidity. The relationship is stronger in firms owned by institutional blockholders. Moreover, intensive selling by institutional blockholders in the future will positively moderate the relationship between liquidity and crash risk.

Since stock liquidity could exacerbate crash risk through institutional blockholder trading, firm managers should avoid bad news accumulation and practice timely information disclosures. Investors should be mindful of the risk associated with liquidity and blockholder trading.

We contribute to the literature by showing that the activities of blockholders could partly explain the relationship between liquidity and crash risk. High liquidity encourages blockholders to exit upon receiving private bad news.

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

Purpose – The purpose of this chapter is to review the methodological and empirical underpinnings of transport network screening, or management, as it relates to improving road safety. As jurisdictions around the world are charged with transport network management in order to reduce externalities associated with road crashes, identifying potential blackspots or hotspots is an important if not critical function and responsibility of transport agencies.

Methodology – Key references from within the literature are summarised and discussed, along with a discussion of the evolution of thinking around hotspot identification and management. The theoretical developments that correspond with the evolution in thinking are provided, sprinkled with examples along the way.

Findings – Hotspot identification methodologies have evolved considerably over the past 30 or so years, correcting for methodological deficiencies along the way. Despite vast and significant advancements, identifying hotspots remains a reactive approach to managing road safety – relying on crashes to accrue in order to mitigate their occurrence. The most fruitful directions for future research will be in the establishment of reliable relationships between surrogate measures of road safety – such as ‘near misses’ – and actual crashes – so that safety can be proactively managed without the need for crashes to accrue.

Research implications – Research in hotspot identification will continue; however, it is likely to shift over time to both closer to ‘real-time’ crash risk detection and considering safety improvements using surrogate measures of road safety – described in Chapter 17.

Practical implications – There are two types of errors made in hotspot detection – identifying a ‘risky’ site as ‘safe’ and identifying a ‘safe’ site as ‘risky’. In the former case no investments will be made to improve safety, while in the latter case ineffective or inefficient safety improvements could be made. To minimise these errors, transport network safety managers should be applying the current state of the practice methods for hotspot detection. Moreover, transport network safety managers should be eager to transition to proactive methods of network safety management to avoid the need for crashes to occur. While in its infancy, the use of surrogate measures of safety holds significant promise for the future.

This study aims to empirically explore the influence of managerial ability on crash risk and the moderating effect of financial constraints on this interrelationship.

Using a sample of listed corporations in the Egyptian Stock Exchange during 2018–2021, the authors test the hypotheses by using the measures and methods well established in prior literature. The authors also conduct multiple robustness analyses to ensure the validity of the empirical results.

The findings suggest that managerial ability can effectively inhibit crash risk. In addition, the authors report that financial constraints significantly dampen this relationship. Thus, financial restrictions play a striking role in hampering the managerial ability to prevent stock crashes. Furthermore, the authors document that the moderating role of severe financing constraints is more prominent during the Covid-19 pandemic period.

The originality of this study stems from the following considerations. First, this study enriches relevant studies on crash risk by providing evidence from one of the emerging markets in the Middle East; thereby, contrasting with those in developed economies. Second, to the best of the authors’ knowledge, this is the first study investigating the moderating impact of financing constraints on the managerial ability and crash risk nexus. Therefore, this work adds value to the extant knowledge by scrutinizing this important issue and providing novel empirical evidence.