Investor Attention,Analyst Optimism,and Stock Price Crash Risk

This paper used the A-shares listed companies in China as samples,constructed a comprehensive indicator of investor attention,and conducted an empirical analysis on the correlations among investor attention,analyst optimism,and stock price crash risk.The results indicated that investor attention aggravates the stock price crash risk and has a positive effect on analyst optimism.Meanwhile,the analyst optimism plays a mediating role in the positive correlation between investor attention and stock price crash risk.In addition to that,institutional investor attention also has direct and indirect effects on the crash risk.

Corporate pledgeable asset ownership and stock price crash risk

We investigate how a firm’s corporate pledgeable asset ownership(CPAO)affects the risk of future stock price crashes.Using pledgeable asset ownership and crash risk data for a large sample of U.S.firms,we provide novel empirical evidence that a firm’s risk of a future stock price crash decreases with an increase in its pledgeable assets.Our main findings are valid after conducting various robustness tests.Further channel tests reveal that firms with pledgeable assets increase their collateral value,thereby enhancing corporate transparency and limiting bad news hoarding,resulting in lower stock price crash risk.Overall,the results show that having more pledgeable assets enables easier access to external financing,making it less likely that managers will hoard bad news.

Carbon emission trading system and stock price crash risk of heavily polluting listed companies in China:based on analyst coverage mechanism

This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.

The Effect of Information Disclosure Quality on Stock Price Crash Risk： Evidence from Listed Companies in China

From most prior studies, the effect of information disclosure quality on stock price crash risk is negative. However, using a large sample of Chinese firms for the period of 2001-2012, we fmd a nonlinear U-shape link between information disclosure quality and crash risk. Using synchronicity risk indicator R2 as the proxy of information transparency, we find that opaque In＇ms, as well as excessive transparent firms, are both more prone to stock price crashes. We identify one possible mechanism through which excessive transparency increases crash risk： excessive volatility augments volatility feedback effect and overreaction effect and then increases crash risk.

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG （Electro encephalography） （EEG-MPF and EEG-α/β）, ECG （Electrocradiogram） （RRV3）, t racking error, an d subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.

Modeling Crash Risk at Rail-Highway Grade Crossings by Track Class

The Federal Railroad Administration (FRA)’s Web Based Accident Prediction System (WBAPS) is used by federal, state and local agencies to get a preliminary idea on safety at a rail-highway grade crossing. It is an interactive and user-friendly tool used to make funding decisions. WBAPS is almost three decades old and involves a three-step approach making it difficult to interpret the contribution of the variables included in the model. It also does not directly account for regional/local developments and technological advancements pertaining to signals and signs implemented at rail-highway grade crossings. Further, characteristics of a rail-highway grade crossing vary by track class which is not explicitly considered by WBAPS. This research, therefore, examines and develops a method and models to estimate crashes at rail-highway grade crossings by track class using regional/local level data. The method and models developed for each track class as well as considering all track classes together are based on data for the state of North Carolina. Linear, as well as count models based on Poisson and Negative Binomial (NB) distributions, was tested for applicability. Negative binomial models were found to be the best fit for the data used in this research. Models for each track class have better goodness of fit statistics compared to the model considering data for all track classes together. This is primarily because traffic, design, and operational characteristics at rail-highway grade crossings are different for each track class. The findings from statistical models in this research are supported by model validation.

Personal data security and stock crash risk:Evidence from China’s Cybersecurity Law

Using China’s Cybersecurity Law(CSL)as an exogenous shock,I examine how personal data security affects stock crash risk.I find that the stock crash risk of treatment firms(which collect personal data)significantly decreases after the CSL,and such decrease is larger when firms face greater personal data breach risk and have less transparent information environments before the CSL.Furthermore,treatment firms increase their investment in personal data protection after the CSL.Finally,enhanced personal data security increases firm value and promotes firms’social responsibility to stakeholders.Overall,I provide evidence of the importance of data security for the digital economy from the perspective of capital market stability,which may present implica-tions for data security policy worldwide.

Research on the influence of equity pledge on stock price crash risk:based on financial shock of 2015 stock market crisis

The 2015 Chinese stock market crisis triggered liquidation because of equity pledge so that the leverage effect of the small probability event with severe results got intensive attention from investors.It is found that the effects of equity pledge on stock price crash risk reversed significantly before and after the 2015 stock market crisis.In the mechanism analysis,we further find that the equity pledge influenced the stock price crash risk by longer suspension and greater price fluctuation.The shareholding ratio of institutional investors and information environment also had a significant moderating effect on the influence of equity pledge on stock price crash risk.Alternative interpretation tests excluded the tunnel effect and pressure effect by shareholders and incentive effect by management.This study by analysing empirical data provides evidence on the change of investors’risk recognition,which is caused by financial shock,in the Chinese capital market.

How Does Debt Structure Influence Stock Price Crash Risk?

This paper uses the financial data of Chinese listed firms to explore the relationship between the debt structure, which is measured as the ratio of trade credit to bank loan, and future stock price crash risk. The empirical results show that the ratio of trade credit to bank loan is positively associated with the firm-specific crash risk while a good institutional environment reduces this positive relationship. In addition, considering the firm＇s ownership type, the authors find that the positive relationship between the debt structure and crash risk is more significant in the SOEs.

Critical audit matters and stock price crash risk

Using manually collected data on the number and category of critical audit matters(CAMs)in the period 2016–2017,we investigate the hitherto unexplored questions of whether CAMs affect firm-specific crash risk,how CAMs influence crash risk in the Chinese capital market,and recognize CAMs that contain incremental information.Our findings are as follows:(1)Crash risk decreases after implementing the new audit standard requiring the disclosure of CAMs;(2)CAMs release negative information and change the capital market information environment;(3)only corporateidiosyncratic CAMs contain incremental information;(4)crash risk is mitigated only by CAMs disclosed by companies with a high shareholding of institutional investors.The main conclusion of our study is a positive assessment of the new audit standard and of CAMs in terms of protecting the interests of investors and strengthening the stability of the capital market to provide a new perspective for supervising the implementation of the new audit standard.

Can executive mobility reduce the stock price crash risk?Evidence from China

The 21st century has witnessed increasing fluctuations in boththe real economy and financial markets around the world. Howto maintain the stability of the capital market has attracted moreserious attention. This paper examines the impact of executivemobility on the stock price crash risk at the company level. Basedon the data of A-share listed companies during 2010–2019, ourempirical results show that executive mobility can effectivelyreduce the stock price crash risk. Furthermore, our research confirms that employee salaries play an intermediary role in theimpact of executive mobility on the stock price crash risk. Thispaper has important implications for companies in emerging markets to optimise the structure of their executive teams.

Does a national industrial policy promote financial market stability?A study based on stock price crash risk

Whether the implementation of a national industrial policy can maintain stability in the financial market is a question of theoretical and practical significance. Using data from China’s non-financial listed firms from 2007 to 2020,we find that a national industrial policy lowers stock price crash risk. We find that the effect of an industrial policy on lowering stock price crash risk is more pronounced in regions with low levels of regional marketization and if firms have high external uncertainty, low total asset turnover, greater earnings management and receive small increments of long-term loans and fewer government subsidies, suggesting that industrial policies lower stock price crash risk by improving firm fundamentals and reducing external uncertainty,agency costs and information asymmetry.

Understanding the potential key risk factors associated with teen driver crashes in the United States: a literature review

This article is a compilation of teen driver crash contributing factors typically extractable from the crash data collection system in the United States.Tremendous research effort has been undertaken over the decades to comprehend teen driver crash risks,as teen drivers continue to be over-involved in crashes even when accounting for the driving exposure.This article presents the contexts of crash factors related to operating conditions,roadway,vehicle,and driver and their unique influences on teen driver crashes in terms of estimated risk,prevalence,and estimated likelihood mainly from descriptive and analytical studies.The key variables are selected based on the number of studies that considered each risk factor for analysis.The understanding of crash factors could be translated into graduated driver licensing and other teen driver safety programs.While the discussions were grounded in crash studies carried out in the United States,the insights gleaned from these studies hold the potential to offer valuable guidance to other countries.For example,the insights and discussions can serve as a catalyst for the development and improvement of driver education programs tailored to address the specific requirements and difficulties confronted by their teenage drivers.

Religion and stock price crash risk:Evidence from China

This paper investigates whether religious traditions influence firm-specific crash risk in China.Using a sample of A-share listed firms from 2003 to 2013,we provide evidence that the more intense the religious environment,the lower the stock price crash risk,implying that religion plays an important role in Chinese corporate governance.Further,we find that(1) religion affects stock price crash risk by reducing earnings management and the management perk problem;(2) different religions have different effects,and Taoism,in particular,is unrelated to crash risk; and(3) the effects of religion are more pronounced with higher quality corporate governance and a stronger legal environment.Religion constrains the management agency problem,thus reducing stock price crash risk in China.Our paper enriches the literature on stock price crash risk and religion,and on new economic geography.

IMPACT和CRASH模型对创伤性颅脑损伤患者预后评估价值的比较研究

背景国际颅脑损伤预后临床测试研究(IMPACT)和重型颅脑损伤后皮质类固醇的随机化研究(CRASH模型是国际上具有影响力的创伤性颅脑损伤(TBI)预后预测模型,需要继续开发,完善和持续的外部验证,以确保对不同环境的普适性。目的同时在中国TBI人群中进行验证IMPACT和CRASH模型的预后评估价值并进行比较。方法选取2017—2019年在郑州大学附属郑州中心医院内接受治疗的TBI患者210例为研究对象,收集纳入患者的基本信息。随访观察患者14 d存活情况和6个月格拉斯哥预后评分(GOS),随访截止时间为2021年6月,终止事件为中途失访。绘制受试者工作特征曲线(ROC曲线)评估IMPACT和CRASH模型对TBI患者预后的预测效能,计算ROC曲线下面积(AUC)。采用Brier评分评价模型的校准度。结果患者平均年龄(54.0±17.4)岁,分别绘制IMPACT模型与CRASH模型预测TBI患者预后的ROC曲线,结果显示IMPACT核心模型、CT模型、实验室模型预测TBI患者6个月GOS预后不良的AUC分别为0.807(95%CI=0.747~0.866,P<0.001)、0.843(95%CI=0.789~0.897,P<0.001)、0.845(95%CI=0.793~0.897,P<0.001),Brier评分分别为0.179、0.164、0.161;IMPACT核心模型、CT模型、实验室模型预测TBI患者6个月死亡的AUC分别为0.868(95%CI=0.816~0.919,P<0.001)、0.896(95%CI=0.851~0.941,P<0.001)、0.892(95%CI=0.850~0.935,P<0.001),Brier评分分别为0.151、0.144、0.136。CRASH基本模型、CT模型预测TBI患者6个月GOS预后不良的AUC分别为0.747(95%CI=0.682~0.813,P<0.001)、0.766(95%CI=0.703~0.829,P<0.001),Brier评分分别为0.306、0.308;CRASH基本模型、CT模型预测TBI患者14 d死亡的AUC分别为0.791(95%CI=0.723~0.860,P<0.001)、0.797(95%CI=0.728~0.865,P<0.001);Brier评分分别为0.348、0.383。结论对于TBI患者的预后,IMPACT模型整体较CRASH模型显示出较好的预测能力。

Study of the Discount on Private Placements and Risk of Stock Market Crash in Listed Companies

With the gradual completion of the split-share structure reform,private placement has gradually become the mainstream of refinancing. One of the points that the practical and theoretical circles are widely concerned about is that the private placement price is often higher than the market price at the time of the private placement. High discounts are often accompanied by the transmission of benefits,and the increase in insider information will lead to the risk of a stock market crash? This paper intends to use the data of A-share listed companies from 2006 to 2015 to empirically study the relationship between the discount on private placements and the risk of stock market crash. At the same time,this paper examines whether the degree of information asymmetry plays a regulatory role in the relationship between the discount on private placements and the risk of stock market crash. This paper provides a certain reference for the regulatory authorities to improve the relevant laws and regulations in the private placement,and to provide a certain reference for the protection of the interests of small and medium-sized investors.

Preventing crash in stock market:The role of economic policy uncertainty during COVID‑19

This paper investigates the impact of economic policy uncertainty(EPU)on the crash risk of US stock market during the COVID-19 pandemic.To this end,we use the GARCHS(GARCH with skewness)model to estimate daily skewness as a proxy for the stock market crash risk.The empirical results show the significantly negative correlation between EPU and stock market crash risk,indicating the aggravation of EPU increase the crash risk.Moreover,the negative correlation gets stronger after the global COVID-19 outbreak,which shows the crash risk of the US stock market will be more affected by EPU during the epidemic.

Analyzing Crash-Prone Drivers in Multiple Crashes for Better Safety Educational and Enforcement Strategies

Crash-prone drivers should be effectively targeted for various safety education and regulation programs because their over-involvement in crashes presents a big adverse effect on highway safety. By analyzing seven years of crash data from Louisiana, this paper investigates crash-prone drivers’ characteristics and estimates their risk to have crashes in the seventh year based on these drivers’ crash history of the past six years. The analysis results show that quite a few drivers repeatedly had crashes;seven drivers had 13 crashes in seven years;and the maximum number of crashes occurring in a single year to a single driver is eight. The probability of having crash(es) in any given year is closely related to a driver’s crash history: less than 4% for drivers with no crash in the previous six years;and slightly higher than 30% for drivers with nine or more crashes in the previous six years. Based on the results, several suggestions are made on how to improve roadway safety through reducing crashes committed by drivers with much higher crash risk as identified by the analysis.

基于深度学习的投资者情绪挖掘及其对股价崩盘风险的影响

本文基于网络爬虫挖掘东方财富股吧中个股的发帖文本,搭建卷积神经网络和长短时记忆神经网络特征融合模型(LSTM-CNN),对样本股的股吧发帖文本进行情感识别,构建投资者情绪指标并分析了其对股价崩盘风险的影响效应及其机制。实证发现,当期投资者情绪对下一期股价崩盘风险存在显著的正向影响效应,投资者情绪高涨加剧了未来股价崩盘风险;不同市场环境下,情绪对股价崩盘风险影响具有不对称性,熊市状态下投资者情绪对崩盘风险的正向影响效应更为明显。进一步的异质性分析表明,规模较小、股权集中度较低、卖空限制大、公司所在地市场化水平低的样本公司中投资者情绪对股价崩盘风险的影响更为明显。此外,我们发现股票流动性是投资者情绪影响股价崩盘风险的一个重要的中介变量。研究结论有助于从投资者情绪视角来解释股价崩盘风险的形成机理,丰富了对股价崩盘风险影响因素的认识。

企业提高ESG表现能够降低股价崩盘风险吗?

本文以2011—2020年沪深A股上市公司为对象,运用企业ESG表现对股价崩盘风险影响的处理效应模型(Treatment effect model)考察ESG表现对股价崩盘风险的影响。研究结果表明,企业提高ESG表现会抑制股价崩盘风险,符合“价值假说”。影响机制检验表明,媒体与分析师关注度正向调节ESG表现对股价崩盘风险的抑制作用,发挥“互补效应”;企业提高ESG表现通过缓解委托代理冲突抑制股价崩盘风险。进一步分析发现,ESG表现对股价崩盘风险的抑制作用仅在非国有企业和所处地区市场化水平较低的企业中成立,且抑制股价崩盘风险是ESG表现提升企业价值的重要路径。上述结论为推动上市公司履行ESG投资责任、完善风险管理机制和实现企业长期价值提供了经验参考。