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 conceptual model and research agenda for bidding,planning and delivering Major sport events that lever human rights

In this conceptual article, the authors propose a framework for how progressive human rights outcomes may be obtained in the context of bidding, planning and implementing major sport events (MSEs) through the implementation of four pathways, including good governance, the democratic participation of stakeholders, the formalisation of human rights agendas and the deployment of sensitive urban development. The authors argue that there is a need for adherence to internationally recognised standards, such as the Universal Declaration of Human Rights and the United Nations Guiding Principles on Business and Human Rights if rhetorical statements from MSE awarding bodies and host organisers are to be accountable to a wider set of actors. If researchers and practitioners want to address some of the critical issues related to human rights and MSEs, it is imperative that key actors working in the rights sphere are involved in shaping the research agenda and monitoring its implementation. Academics need to take a proactive approach aimed at achieving both theoretically grounded and practically relevant solutions, with engagement occurring over an extended period of time. This approach, avowedly political and concerned with genuine action, is a key way in which MSE stakeholders can be held to account for their actions in regard to human rights.     

Testing the Empirical Relevance of the ‘Saving for a Rainy Day’ Hypothesis in US Metro Areas

The joint implication of the consumption Euler equation and cointegration between income and consumption is that savings predict future income declines, the ‘saving for a rainy day’ hypothesis. The empirical relevance of this hypothesis plays a key role in discussions of fiscal policy multipliers, and it holds under the null that the permanent income hypothesis is true. We find little support for this hypothesis using time series data for the 100 largest US Metropolitan Statistical Areas for the period 1980q1–2015q4. Our approach is to test for cointegration and weak exogeneity between income and consumption, and by exploring the direction of Granger causality between the two time series. We find that income more often predicts consumption and saving than the converse. We also give evidence that house price changes played a role in US income and consumption dynamics, before, during and after the Great Recession.     

Terror per Capita

Studies on the correlates of terrorism usually analyze total numbers of attacks or victims per country. However, what we may ultimately care about in terms of policy recommendations is the likelihood of any individual being subject to the respective phenomenon. Thus, we propose and explore a simple alternative measure of terrorism: terror per capita. Studying terror per capita across 162 countries from 1970–2015, the associated correlates differ substantially in terms of sign, levels of statistical significance, and magnitude from those when analyzing total terror. We illustrate two cases in point, serving as proof of concept. First, democracy, often associated with more total terror, emerges as a marginally negative predictor of terror per capita. Second, a larger share of Muslims in society is associated with a positive and statistically significant link to total terror, but emerges as a negative predictor of terror per capita. We find similar changes in sign and statistical relevance for GDP per capita and language fractionalization as correlates of terrorism. Depending on the policy question, studying terror per capita can greatly enhance our understanding of terrorism drivers, especially when analyzing data across countries with vastly differing population sizes.     

LEADERSHIP IN CLIMATE CHANGE MITIGATION: CONSEQUENCES AND INCENTIVES

Initiatives in favor of unilateral action on climate change are frequently challenged by concerns over free riding. Nevertheless, we observe an increasing number of unilateral efforts at different administrative levels and in different parts of the world. Previous academic literature described various individual mechanisms where emissions abroad may increase or decrease as a reaction to unilateral emission reductions. In this paper, we collect a comprehensive set of both positive and negative reactions and analyze them in stylized models. This allows us to identify the most important characteristics that determine the potential of a leader to boost mitigation efforts abroad. We find that this potential depends on (i) a strong ability to generate knowledge through leadership, (ii) a high degree of credibility in the international community, and (iii) a similar economic structure to the most important emitters. While most effects are difficult to quantify, this comprehensive assessment suggests that leakage effects resulting from unilateral mitigation may well be outweighed by positive reactions.     

Revisiting the expatriate failure concept: A qualitative study of Scandinavian expatriates in Hong Kong

This article advances the understanding of expatriate failure, which remains a contested social phenomenon in international work life as well as scholarly research. The study challenges the definition of expatriate failure and its inherent biases, i.e., the epistemological primacy of the firm level and the failure/success binary. We argue that this qualitative study of 51 Scandinavian expatriates in Hong Kong can contribute to advancing theory on the expatriate failure concept by asking individual expatriates what constitutes failure to them. By applying social constructionist and social anthropological ideas to the expatriate failure concept debate, we develop the internationality thesis which demonstrates a discrepancy between the expatriates’ perceptions of successful international assignments and the actual nature of their lived lives; many expatriates desire to enrich their lives through experiencing an international/intercultural and adventurous lifestyle, but, in fact, living lives with limited intercultural exposure and interaction. We conclude by proposing a reconceptualisation of expatriate failure in terms of offering both a new definition and approach to researching expatriate failure in which time/duration, context, and geographical location need to be taken into account. We believe the new approach can overcome some of the empirical unsoundness of mainstream definitions.     

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.     

The Impact of Employment on Parental Coresidence

We examine the extent to which parents use housing and shared living arrangements as a form of risk‐sharing for their adult children, using detailed data on children and parents in the Health and Retirement Study for 1998–2012. On average, a young man moving from full‐time to nonemployment raises the likelihood of coresiding with a parent by 1.5 percentage points; moving from full‐time employment to being part‐time employed raises the likelihood of coresiding with a parent by 2 percentage points. The implied elasticity of parental coresidence with respect to the son's income is ‐1.1; for daughters, the elasticity is ‐0.5.     

Real Exchange Rate Misalignments in the Euro Area

Open Economies Review - Building upon a Behavioural Equilibrium Exchange Rate model, estimated at a quarterly frequency since 1999 on a broad sample of 57 countries, this paper assesses both the...