新三板是我国多层次资本市场的重要组成部分,其对提高挂牌及上市企业质量能否真正发挥作用值得关注和研究.本文以 2014-2021 年期间在A股首次公开募股的上市公司为研究样本,把有新三板挂牌经历的公司作为"预科班"企业,比较研究"预科班"企业与非"预科班"企业在首次公开募股上市后的表现差异.实证结果表明:第一,"预科班"企业上市首日公开募股溢价显著高于非"预科班"企业,平均差距达 43 个百分点;第二,"预科班"企业上市一年后的业绩优于非"预科班"企业,非"预科班"企业在公开募股上市后业绩整体出现下滑,但"预科班"企业业绩则相对保持稳定;第三,"预科班"企业上市后财务稳健性要强于非"预科班"企业.上述研究结果证实了在新三板挂牌的"预科班"经历能够提高上市公司的质量.为此,应进一步发挥好新三板作为企业上市"预科班"的功能,加强新三板市场的机制建设,畅通新三板与主板之间的转板通道.

舞弊是企业内部治理的顽疾,正日益成为全球性的焦点问题.利用2021年"企业反舞弊联盟"的问卷调查数据,基于舞弊三角理论,分析中国企业反舞弊现状及其成因.研究显示:企业反舞弊形势仍然严峻;舞弊案件主要发生在销售与采购环节;舞弊者中29岁左右的人占比最大;女性舞弊者远远低于男性,但有上升趋势;高层舞弊者人数不多,但造成的损失重大;大部分舞弊者事前与顾客或供应商的关系异常;舞弊损失金额与舞弊行为持续时间正相关;内部举报是舞弊发现的第一渠道;审计新技术的应用有助于更早发现舞弊;最重要的舞弊证据是会计数据;解雇是企业惩罚舞弊者的最常见手段;不能将舞弊者法办的主要原因是缺乏足够的证据.进一步分析发现:机会是发生舞弊的第一要素,其中内部控制问题排名第一;借口是发生舞弊的第二要素,其中舞弊带来的后果不严重是最主要的借口;贪婪在压力因素中排位最前.在此基础上,基于舞弊成因数据分析,有针对性地提出政策建议.

Like the chief executive officer (CEO), the chief financial officer (CFO) is an important corporate player. However, compared to the role of CEOs, research on the factors influencing corporate innovation has paid very little attention to the role of CFOs. Based on the perspective of role theory, we measure CFO role performance by organizational identification to explore the role of CFOs in corporate innovation. Employing the availability of CFO organizational identification data from a survey of listed firms in China, we find that: (1) CFO organizational identification is negatively associated with innovation output in state-owned enterprises (SOEs) and positively associated with innovation output in non-state-owned enterprises (non-SOEs); (2) corporate misconduct experience positively moderates the relationship between CFO organizational identification and innovation in SOEs; (3) CFO financial industry experience positively moderates the relationship between CFO organizational identification and innovation in non-SOEs. Our results show that CFOs play the supervisor role in innovation in SOEs and the supporter role in innovation in non-SOEs. Our research provides theoretical and practical references for companies to sustainably drive innovation.

Previous research on corporate governance has extensively explored the motives of corporate fraud. However, this research has paid little attention to employees, the real executors of fraud, resulting in the psychological and behavioral decision-making process of employees who commit fraud in enterprises becoming a “black box” that has not yet been opened. Based on the theory of planned behavior, our study integrates the existing research findings on driving factors of employee fraud and anti-fraud practical experience, extracts the key factors of employee fraud motive, and develops a multidimensional scale of employee fraud motive. The exploratory factor analysis (EFA) generates three subscales, comprising 14 items, measuring attitude, subjective norm and perceived behavioral control of employee fraud motive. The confirmatory factor analysis (CFA) supports the reliability, discriminant validity and convergent validity of the new scale. The multiple regression results show that the score of employee fraud motive is positively correlated with the amount of employee fraud occurrence, indicating that the predictive validity of the scale holds. Overall, the scale developed in our study displays good reliability and validity, and is worth spreading.

Corporate financial distress is related to the interests of the enterprise and stakeholders. Therefore, its accurate prediction is of great significance to avoid huge losses from them. Despite significant effort and progress in this field, the existing prediction methods are either limited by the number of input variables or restricted to those financial predictors. To alleviate those issues, both financial variables and non-financial variables are screened out from the existing accounting and finance theory to use as financial distress predictors. In addition, a novel method for financial distress prediction (FDP) based on sparse neural networks is proposed, namely FDP-SNN, in which the weight of the hidden layer is constrained with L_1/2 regularization to achieve the sparsity, so as to select relevant and important predictors, improving the predicted accuracy. It also provides support for the interpretability of the model. The results show that non-financial variables, such as investor protection and governance structure, play a key role in financial distress prediction than those financial ones, especially when the forecast period grows longer. By comparing those classic models proposed by predominant researchers in accounting and finance, the proposed model outperforms in terms of accuracy, precision, and AUC performance.