Asymmetric effects of housing wealth on college enrolment

We investigate the impact of housing wealth, credit availability and financial distress on college enrolment decisions. We find that housing wealth is negatively related to enrolment in public schools and positively related to enrolment in private schools. This evidence suggests that, on average, students substituted away from private schools towards public institutions during the recent financial crisis.     

开放式创新社区中用户交互反馈对个体创新贡献度的影响

开放社区中的用户交互反馈是用户创新活动的重要组成部分，基于需求满足理论，研究其他用户反馈对个体创新贡献度的影响。结合典型开放式产品设计社区——Local Motors平台，采用数据挖掘方法，从数量、质量和情感3个方面研究开放式创新社区中FOU对个体创新贡献度的影响。结果表明，反馈数量和质量均对个体创新贡献度有显著影响，其中反馈次数对个体创新贡献度有显著正向影响，反馈率和反馈人员多样性对个体创新贡献度有显著负向影响，反馈长度和反馈支持文件对个体创新贡献度没有影响，正向情感倾向在用户反馈和个体创新贡献度之间无任何调节作用。     

Recognition versus disclosure of finance leases: Evidence from Japan

This study examines whether credit market participants—bond investors and credit rating agencies—treat recognized and disclosed finance leases differently when assessing firms’ credit risk in Japan. I use firms’ credit risk, measured by bond spreads and credit ratings, to investigate the relations between recognized versus disclosed finance lease obligations and firms’ credit risk following the adoption of Statement No. 13, Accounting Standard for Lease Transactions. For a sample of firms issuing new bonds, I find that, unlike recognized finance leases, disclosed finance leases are not associated with bond spreads. Moreover, the associations between recognized versus disclosed finance leases and bond spreads are substantially different. Conversely, recognized and disclosed finance leases are associated with credit ratings and are processed similarly when credit ratings are determined. Taken together, my results suggest that the sophistication of capital market participants influences their credit risk assessments of recognized versus disclosed finance leases.     

Dynamic response of bank-intermediated trade finance to a global liquidity shock

This paper investigates the bank-level responses of a bank-intermediated instrument of trade finance to a negative global liquidity shock in Korea. Using a factor-augmented vector autoregression approach, the results show that there exists significant heterogeneity in bank-level trade-finance responses to a global liquidity shock. In addition, we find that the source of the heterogeneity is the bank-level foreign currency liquidity condition; banks with a better foreign currency liquidity condition may dampen the negative impact of a global liquidity shock on trade-finance.     

Does job passion enhance job embeddedness? A moderated mediation model of work engagement and abusive supervision

Academic and industrial attention has been paid to the job embeddedness as an important predictor of employee actual turnover. Studies have examined the influence of job embeddedness as an antecedent, mediator, and moderator. However, there have been few investigations of antecedents of job embeddedness from the perspective of employee characteristics. Therefore, the current study bridged this research gap and investigated the associations among job passion, work engagement, abusive supervision, and job embeddedness. Data from 278 full-time hotel employees indicated that harmonious passion was not associated with job embeddedness. Moreover, obsessive passion shared a positive relationship with job embeddedness. Both harmonious and obsessive passion had an indirect effect on job embeddedness through work engagement. Abusive supervision moderated only the indirect effect of obsessive passion on job embeddedness via work engagement. A new model is proposed based on our findings to explain factors that contribute to job embeddedness.     

Overconfidence and tax avoidance: The role of CEO and CFO interaction

We investigate how overconfident CEOs and CFOs may interact to influence firms’ tax avoidance. We adopt an equity measure to capture overconfident CEOs and CFOs and utilize multiple measures to identify companies’ tax-avoidance activities. We document that CFOs, as CEOs’ business partners, play an important role in facilitating and executing overconfident CEOs’ decisions in regard to tax avoidance. Specifically, we find that companies are more likely to engage in tax-avoidance activities when they have both overconfident CEOs and overconfident CFOs, compared with companies that have other combinations of CEO/CFO overconfidence (e.g., an overconfident CEO with a non-overconfident CFO), which is consistent with the False Consensus Effect Theory. Our study helps investors, regulators, and policymakers understand companies’ decision-making processes with regard to tax avoidance.     

Market impact: a systematic study of the high frequency options market

This paper deals with a fundamental subject that has seldom been addressed in recent years, that of market impact in the options market. Our analysis is based on a proprietary database of metaorders—large orders that are split into smaller pieces before being sent to the market—on one of the main Asian markets. In line with our previous work on the equity market [Said, E., Bel Hadj Ayed, A., Husson, A. and Abergel, F., Market impact: A systematic study of limit orders. Mark. Microstruct. Liq., 2018, 3(3&4), 1850008.], we propose an algorithmic approach to identify metaorders, based on some implied volatility parameters, the at the money forward volatility and at the money forward skew. In both cases, we obtain results similar to the now well-understood equity market: Square-Root Law, Fair Pricing Condition and Market Impact Dynamics.     

产业共性技术政府支持性研发外包合同影响因素研究

建立产业共性技术研发外包博弈模型，从政府视角研究以共性技术产出最大化为目标的最优政府支持性研发外包合同，并对最优外包合同影响因素进行分析。研究表明：足够多的政府预算是共性技术政府支持性最优研发外包合同存在的前提，最优合同条款随政府预算的增大而增多，企业保留收益增大倒逼政府预算增加；企业最优知识技术投入与共性技术价值系数以及从研发中分享的收益份额正相关，与政府支付的固定研发费用负相关；政府最优知识技术投入与固定研发费用及单位投入成本系数负相关；企业最优投入与政府投入成本系数负相关，与政府投入对研发成功的贡献系数正相关，且企业最优投入随其研发能力的增强而加大。最后，根据研究结论，从政府预算、项目前景及支持性资源分配3个方面提出共性技术研发外包合同实施的政策建议。     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.