The new wave of AI-powered luxury brands online shopping experience: The role of digital multisensory cues and customers’ engagement

Academic literature retains a dearth of empirical evidence of the cutting-edge aspect of artificial intelligence (AI)-powered digital assistance and digital multisensory cues, despite the prospect of these factors on real-life customers' luxury brand online shopping experience. Thus, the aim of this study is to examine the significant pathway and effects of AI-powered digital assistance toward customers’ luxury brand online shopping experience. Drawing on S–O-R (Stimulus, organism, and response) and TRAM (Technology Readiness and Acceptance Model) paradigm, a multi-method research design was deployed to investigate constructs. Firstly, semi-structured interviews were utilized to explore customers' online behavior under the luxury brands and information technology aspect. Secondly, survey data were collected and analyzed by using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The PLS-based analysis of quantitative data confirmed the exploratory insights of qualitative findings, establishing the connections of AI-powered digital assistance, customer engagement, and customers' luxury brand online shopping experience. Research findings also suggest that customer engagement plays a mediation role in the relationship between AI-powered digital assistance and customers' luxury brand online shopping experience. Besides, digital multisensory cues moderate the relationship between AI-powered digital assistance and customer engagement. Further, fsQCA complements the findings of PLS-SEM that reveal the significant combination of factors that lead to the perceptions of customers' luxury brand online shopping experience.     

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.     

欧盟视阈下在线平台中个人数据的增值税处理

近年来,在线平台的迅猛发展推动了全球经济数字化加速转型,但同时也引发了包括增值税政策适用在内的诸多挑战.通过研究《欧盟增值税指令》和欧州法院的判例法,以及分析个人数据与在线平台运行之间的关系可以推断出,个人数据是在线平台提供访问服务构成的增值税应税范围内的供应,且与消费者是否向在线平台支付金钱形式的对价无关.在实践中,使用客观价值方法来确定在线服务供应的应税金额存在实际困难,而使用直接联系标准解决易货交易问题,会导致增值税税基在本质上得到扩张.因此,建议当前不要将在线平台访问服务的供应视为一项应税交易.此外,对于商家为获得消费者数据而向平台付款的情形,因其不符合直接联系标准,也不应被视为服务对价.     

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.     

浅谈基于大数据背景下的数字经济发展——以数字电网为例

我们已经进入了一个全新的大数据信息化时代,充满了更多的机遇与挑战。大数据及数字经济的出现极大程度地改变了传统技术及工作思维的方式。数字经济是继工业和农业之后的又一大经济形态,为社会的经济增长提供了新的动力。通过分析电网行业传统的科学技术及作业方式,论文简要阐述了基于大数据信息化发展下电网的影响,并认为信息化技术的发展对于数字经济有着至关重要的影响及重要性。最后论文讨论了数字经济发展存在的问题及方向。     

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.     

中国零售业自营与联营问题的流通经济学分析

零售业自营与联营之争由来已久，在当前供给侧结构性改革、完善促进消费体制机制以及零售业寻求创新发展的背景下，有必要对这一问题展开进一步的理论探讨。文章结合马克思流通经济理论指出，自营和联营的本质区别在于是商业资本还是产业资本承担商品流通职能，自营和联营并不是非此即彼的互斥关系，但如果零售业完全倚重其中一种模式并在全社会推向极端，则需要反思商业资本和产业资本是否各得其所和高效分工。在中国流通体制变革中，虽然联营模式在特定历史时期帮助众多零售企业渡过生存危机并推进内资零售快速扩张，但发展至今，已呈现零售业普遍联营的情况，使零售业整体面临着制约创新发展的新问题。数字经济为零售业回归自营提供了有利契机，零售企业应充分利用新技术摆脱联营制的路径依赖，依托数字化开展深度自营并全面提升流通效率。     

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.