External funding of major capital projects in the UK Higher Education sector: issues of demand,supply and market timing?

The aim of this paper is to consider the sources of finance used to support major capital expenditure in the UK Higher Education sector and to reflect on any differences between traditional corporate finance theory and practice in the UK university sector. Utilising both HESA data returns and published annual accounts, an in-depth analysis using a logit structure is carried out on data from the top 63 UK universities over the period 2014–2017, to establish the range of funding sources adopted for major capital projects, all set within the context of the UK macro environment and a period of low interest rates. The research also carries out a survey of funders to understand the decision criteria used by lenders active in the Higher Education sector and a survey of university finance directors to determine the use of the funds, the reasons behind past lending decisions and to ascertain likely future demand for finance to fund major capital projects.     

中国数字经济空间关联网络及其影响因素分析

基于2013年和2020年29个省的数据，用因子模型测算各省数字经济发展水平，在此基础上使用修正引力模型构建数字经济空间关联网络，并深入分析此网络特征。结果表明：中国数字经济发展水平存在差异，东部地区高于中西部地区；中国数字经济空间关联网络密度不断增大；北京、山东、江苏一直是网络的重要输出省份，近年来西部地区在网络中的作用越来越明显；地理位置相邻、贸易开放度和产业结构对网络的形成有显著影响。     

国有企业如何做好投资项目财务风险管理

作为国有企业经营管理的重要组成部分,财务风险管理对于投资项目经济效益的保障具有积极作用。论文在阐述国有企业投资项目财务风险管理必要性的基础上,就现阶段财务风险管理的问题展开分析,并指出国有企业投资项目财务风险管理的策略。期望能进一步提升财务风险管理质量,确保投资项目资金运行的安全性,进而促进国有企业的进一步发展。     

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.     

Determinants of individual investment decisions in investment‐based crowdfunding

We investigate determinants of investment decisions in investment‐based (equity and bond) crowdfunding campaigns, using a novel investment‐, investor‐ and campaign‐level database, where equity refers to investments in entrepreneurial start‐ups and bonds to large real estate projects. We find that investors who have higher social interactions invest more. Social interactions are important in an equity crowdfunding context but do not affect participation in bond investments. This is consistent with the view that investors' social networks help reduce information asymmetry. Women invest less in the riskiest (equity) investments but more in safer ones (bonds). These findings are better explained by differences in risk aversion than differences in overconfidence between men and women. Overall, the findings contribute to the understanding of how investment‐based crowdfunding can be a viable source of entrepreneurial finance and how entrepreneurs' campaign decisions affect investor participation in this new form of entrepreneurial finance.     

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.     

Managerial optimism: New observations on the unifying theory

Managerial optimism theory is behavioral finance's greatest achievement. It explains two prominent features of corporate financial behavior – over‐investment and pecking‐order capital structure preferences – that otherwise require two different theories with mutually incompatible assumptions about managerial loyalties to shareholder‐value maximization. After reviewing the development of managerial optimism as a unifying theory, I use a simple change of measure to transform risk‐averse optimism to risk‐neutral probabilities that can be pessimistic or optimistic depending on wealth changes. This unexplored feature has implications for, among other things, pay for performance when managers are excessively optimistic.     

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 emergence of new Islamic economic and business moralities

This article aims to explore the sources of the observed transformation in the embeddedness of economic, business, and financial practices of Muslim individuals in comparison to premodern period Muslims. It argues that the predomination of instrumental reasoning in modern times, as opposed to substantive morality in everyday practice, is one of the main reasons behind the transformation of embeddedness of Muslim individuals. Instrumental reasoning, being the dominant methodology, leads to diminished submergence in social relations; that is not limited to interpersonal relationships, but further extended to the core religious acts. How such an emergent economic and business morality is reconciled with the Islamic substantive morality is examined. It is argued that “transformation of exception into norm” is the main method used to reconcile instrumental reasoning with Islamic law in fulfilling religious obligations, at least in terms of fulfilling the form and in complying with the necessities of modern life. This has led to the emergence of new economic and business moralities.