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.     

The ‘Blogosphere’ as a platform for interpretative phenomenological analysis: the case of TEFL tourism

The use of the Internet is becoming increasingly integral to the day-to-day operations of society, facilitating an interconnected and globalised world, where geographic barriers are no longer a defining criteria for communication. This opens the researcher up to a realm of research opportunities, which to-date have been under-exploited. This paper contributes to the wider body of literature addressing the concept of Online research, demonstrating in practice how it can be utilised in the context of interpretative phenomenological analysis. It is argued that netnography is akin to interpretative phenomenological analysis (IPA) in that it accounts for an individual’s personal perception or account of an event or state, as opposed to attempting to produce an objective record. This paper outlines how blog analysis can be utilised in the context of IPA, demonstrating how this worked in practice during the initial exploratory research phase of the teaching English as a foreign language tourism phenomenon.     

Understanding the trend to mobile-only internet connections:A decomposition analysis

A growing portion of internet users rely solely on mobile devices such as smartphones for their online access. The percentage of “mobile-only” households increased from 9% in 2011 to 20% in 2015, more than doubling in only four years. As this shift continues, it leads to the question of what factors are driving the rise in mobile-only adoption. Using nationally representative data, this study uses logistic regressions and a decomposition technique to understand the trend. The decomposition reveals that a significant portion (65%) of the growth was due to an increase in the download speeds of mobile networks. An increased acceptance of mobile-only access by households aged 55 and older was also partly responsible. Understanding (and developing a response to) the trend towards mobile-only adoption will be important as organizations and governments continue to work to close the digital divide.     

Nicknames as identity badges: How self-reflective nicknames can facilitate users’ online social interactions

An Internet nickname is essential in facilitating online interpersonal interactions. It is a pivotal element of interaction that provides other users with an initial impression and affects the decisions and behaviors of users during their online interactions. Although the existing literature has found that nicknames are relevant to users’ psychology and behavior, direct research on the motivation for and the effect of choosing a particular Internet nickname has been very limited. Borrowing from relevant theories on avatars, this paper summarizes the motivations behind Internet nickname choices in terms of three aspects: virtual exploration, social navigation and contextual adaptation. Moreover, from the perspective of collective self-esteem (CSE) and group identification, this research explores the influence mechanism between the motivations underlying Internet nickname choices and users’ online social interactions. A total of 394 samples were obtained from online communities in China to test our research model. We found that virtual exploration, social navigation and contextual adaptation are three motivations for creating Internet nicknames, and they have direct effects on the online social interactions of users. CSE has a full mediating effect between virtual exploration and online social interaction and acts as a mediated variable between social navigation and online social interaction. Group identification fully mediates the relationship between social navigation and online social interaction as well as that between contextual adaptation and online social interaction. The conclusion of this paper provides not only a new perspective for the study of Internet nicknames but also a potentially feasible way to stimulate users’ online social interactions.     

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.     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

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.