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.     

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.     

Mandatory disclosure,greenhouse gas emissions and the cost of equity capital: UK evidence of a U-shaped relationship

This paper examines the effects of disclosing greenhouse gas (GHG) information mandatorily on the cost of equity capital (COC) using a longitudinal unbalanced panel database of the United Kingdom's FTSE 350 firms for the period 2011–2016. We use a nonlinear panel quantile regression (PQR) model to examine the relationship between GHG disclosure (GHGD) and COC in the United Kingdom. This technique was supplemented by conducting a two-step generalised method of moment (GMM) estimation to address any concerns related to the potential existence of endogeneity problems. Our findings suggest that high-level GHGD appeared to be negatively associated with COC up to a certain level, which is known as the turning point; then, any increase in GHGD is likely to increase the COC. This means that the nonlinear association between GHGD and COC is evidenced in our study and takes a U shape. Likewise, our findings are associative of a moderating effect of the 2013 carbon disclosure regulation (CDR) on the GHGD–COC nexus. We argue that mandatory GHGD and GHG risk are linked so that those companies that are associated with higher GHG risk have a tendency to be better disclosers. Consequently, we urge regulators to design GHGD regulations in a way that mirrors corporate environmental risk and leads to a lower COC in order to align the interests of corporations with those of the society at large.     

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.     

老龄化与碳排放：消费效应还是生产效应？——基于2002-2017年中国省级面板数据的实证研究

人口老龄化是经济增长的一大阻碍,它对碳排放的影响是做出环境政策抉择的关键因素.以中国2002—2017年的省级面板数据为研究样本,从理论与实证两个层面分析人口老龄化与碳排放的关系及影响机制.一方面,构建包含碳排放的世代交叠模型(overlapping generations,OLG),发现老龄化主要通过消费效应和生产效应两个路径对碳排放产生影响;另一方面,从实证层面发现老龄化与碳排放之间存在倒"U"形关系,而且消费效应在人口老龄化与碳排放的关系中起到部分中介作用,生产效应的影响则不显著.     

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.     

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.     

碳排放交易制度的节能减排路径：效率提升还是结构转型？——来自中国省级数据的准自然实验

以中国2013年以来实行的碳排放交易试点政策作为准自然实验事件，基于2000-2017年中国30个省市的面板数据，运用双重差分法实证检验碳排放交易制度的节能减排效应及影响机制。研究发现，碳排放交易制度有利于实现中国经济“节能”与“减排”的双重目标，并通过了一系列稳健性检验；作用机制检验表明，能源效率提升和能源结构转型都是碳排放交易制度实现节能减排目标的重要路径；能源效率提升在碳排放交易制度实现“节能”和“减排”中分别发挥了10.19%和5.93%的作用，而能源结构转型分别发挥了48.87%和52.95%的作用。这意味着中国碳排放交易制度实现节能减排的主要动力来自能源结构转型，而非能源效率提升。能源结构转型涉及问题更加宏观和深层，这为中国加快推进节能减排进程，特别是完成2030年碳达峰和2060年碳中和的国际承诺提供政策启示。     

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.     

我国物流业转型升级研究综述

中国经济已经到了只有转型升级才能持续健康发展的关键阶段,产业转型升级在国家政策文件和研究文献中出现的词频较高。文章以我国物流业为研究对象,从现有涉及到物流业转型升级方面的文献挖掘有效信息,梳理我国物流业转型升级研究的历史脉络,从供应链与价值链、产业集群、现代科学技术应用和产业联动四个视角对现有的物流业转型升级文献进行了总结和归纳,并从物流业转型升级的动力研究、阶段性划分研究和演进规律研究三个方面指出了现有文献的研究不足和以后可能的研究方向。其中,供应链与价值链视角侧重于物流企业的纵向角度,产业集群视角侧重于物流企业的横向角度。文章对于物流企业战略设计、政府政策的制定和理论研究具有一定的参考作用。