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.     

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.     

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.     

货币政策、企业家信心与上市公司投资效率*

企业、企业家等微观主体对货币政策的反应，在较大程度上影响货币政策的效果。货币政策可通过对企业家信心的影响，进而对公司投资效率产生作用。基于中国2007—2016年40个季度A股上市公司样本的实证研究表明：企业家信心在货币政策传导过程中有不可忽视的作用。企业家信心增强或者货币政策宽松时会加剧过度投资，减轻投资不足；企业家信心减弱或者货币政策紧缩时可以有效抑制过度投资，但会加剧投资不足。其中，国有企业对企业家信心和货币政策松紧的敏感性更强。进一步，货币政策对企业投资效率的影响，也会通过货币政策和企业家信心的交互作用而发挥作用。这种作用在国有企业和非国有企业之间无显著差异。     

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.     

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。     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.