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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.

Understanding and mitigating stock price crash risk is vital for investors and regulators to ensure financial market stability. This study aims to unveil significant research trends and opportunities.

This study adopts the bibliometric and systematic review approach to analyse 485 Scopus-indexed articles through citation, keyword co-occurrence, bibliographic coupling, and publication analyses and delve into the depth of crash risk literature.

This bibliometric review reveals not only a surge in crash risk publications over the last decade but also delineates several emerging thematic threads within this domain. We identify seven distinct themes that have gained prominence in recent literature: bad news hoarding, board characteristics, capital market factors, corporate policies, ownership impact, corporate governance, and external environmental influences on crash risk. This thematic analysis provides a comprehensive overview of the evolving landscape of crash risk research and underscores the multifaceted nature of factors contributing to market instability.

This study makes a substantial contribution by furnishing a thorough examination of existing studies, pinpointing areas where knowledge is lacking, and shedding light on emerging trends and debates within the crash risk literature.

This study identifies current research trajectories and propels future exploration into agency perspectives, audit quality, and corporate disclosures within crash risk literature.

Investigation of the anomalies associated with crashes and jackpots in the Chinese stock market.

We propose a logit model to predict the events of crashes and jackpots in the Chinese stock market. The model introduces a new variable of the price-to-sales ratio and takes into account the market states, Up and Down.

The anomalies associated with crashes and jackpots are not related to variations in economic conditions, but are associated with limits to arbitrage. High-liquidity stocks have strong mispricing effects. The institutions’ speculative trading will push liquid stock prices further away from their fundamentals but avoid buying illiquid stocks with a higher probability of price crashes and jackpots.

We propose a logit model to predict the extreme events of both crash and jackpot in the Chinese stock market. Our model effectively disentangles from CRASHP and JACKP. Compared with the traditional model, it substantially enhances in-sample and out-sample predictions. Based on the predictions of the extreme events, we find two strong and robust pricing effects associated with ex ante CRASHP and JACKP in the Chinese stock market.

This study aims to examine whether mandatory audit partner rotation is associated with future stock price crash risk.

This study makes use of a regulatory change from the Ministry of Finance of China and the China Securities Regulation Commission, which requires mandatory rotation of audit partners since 2004, as a natural experiment to establish causality and applies a difference-in-difference research design.

Audit partner rotation leads to a significant decrease in future stock price crash risk in the departing partner’s final year of tenure preceding mandatory rotation, consistent with peer monitoring argument of mandatory rotation. Inconsistent with other arguments, including client-specific knowledge, fresh perspective and auditor independence, no significant effect takes a place in the incoming partner’s first year of tenure following mandatory rotation. Mechanism analysis documents that mandatory audit partner rotation reduces stock price crash risk by improving audit quality and constraining managerial empire building.

The results shed new light on the capital market consequence of mandatory audit partner rotation and the cause of stock price crash risk.

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.