Regulatory Protection and Opportunistic Bankruptcy*

We document controlling shareholder (insider) opportunism in an insolvency regime that uses an accounting rule to determine bankruptcy eligibility. Our study sheds light on managerial incentives induced by weak investor protection laws. Using unique data on bankrupt firms from an emerging market, consistent with our prediction, we show insiders intentionally manage earnings downward to understate firm net worth so as to be able to file for bankruptcy. Downward pre-bankruptcy earnings management is associated with more payments to insiders and weaker performance, post-filing. A battery of tests suggests our results cannot be fully explained as an artifact of financial distress. Rather, they are consistent with insiders exploiting weak investor protection to extract private benefits at the expense of lenders and outside shareholders. Our study serves as a cautionary tale for all insolvency regimes that use a balance sheet test in an environment with weak creditor protection.     

Pathwise CVA regressions with oversimulated defaults

We consider the computation by simulation and neural net regression of conditional expectations, or more general elicitable statistics, of functionals of processes $(X,Y)$. Here an exogenous component Y (Markov by itself) is time-consuming to simulate, while the endogenous component X (jointly Markov with Y) is quick to simulate given Y, but is responsible for most of the variance of the simulated payoff. To address the related variance issue, we introduce a conditionally independent, hierarchical simulation scheme, where several paths of X are simulated for each simulated path of Y. We analyze the statistical convergence of the regression learning scheme based on such block-dependent data. We derive heuristics on the number of paths of Y and, for each of them, of X, that should be simulated. The resulting algorithm is implemented on a graphics processing unit (GPU) combining Python/CUDA and learning with PyTorch. A CVA case study with a nested Monte Carlo benchmark shows that the hierarchical simulation technique is key to the success of the learning approach.     

社会化商务沟通如何提高B2B企业净推荐值？

文章聚焦于探讨和解释社会化商务沟通的核心特质及其对企业净推荐值影响。基于社会交换理论，研究一运用扎根理论发现社会化商务沟通具有广关联性、泛可见性与富载体性三个核心特质，同时提炼出净推荐值这一关系绩效的核心要素。研究二通过问卷调查进一步证实了社会化商务沟通的核心特征，并通过回归分析验证了社会化商务沟通通过关系强度影响净推荐值的作用机制，同时揭示了IT可供性感知在广关联性、泛可见性、富载体性与关系强度之间关系中发挥的负向调节作用以及合同详尽性在关系强度与净推荐值之间关系中发挥的负向调节作用。文章有助于深入认识社会化商务沟通对净推荐值的影响机制，并有效指导企业的关系绩效管理。     

聚氨酯多孔弹性混合料水稳定性影响因素研究

聚氨酯多孔弹性混合料(PPEM)由聚氨酯黏结剂、橡胶颗粒和石料组成，相较于普通沥青混合料具有空隙率大、橡胶颗粒掺量高等特点。由于PPEM是大孔隙结构，使得雨天雨水易流入路面内部，进而引发水损害。为此,基于浸水马歇尔试验、冻融劈裂试验和浸水飞散试验，分别测试不同聚氨酯黏结剂、橡胶颗粒替换量、级配3种工况下PPEM的水稳定性。结果表明：对比分析不同聚氨酯黏结剂多孔弹性混合料，其中PU-III多孔弹性混合料水稳定性最好，PU-IV多孔弹性混合料水稳定性最差；当橡胶颗粒替换量不断提高时，PPEM的马歇尔残留稳定度和抗拉强度比呈线性增长，说明橡胶颗粒对PPEM水稳定性的提高有促进作用；PPEM所用级配的空隙率与其水稳定性呈负增长，水稳定性随空隙率增大而减小。