从资本结构到交易结构——探究企业金融微观结构

资本结构及融资行为是公司金融领域的重要论题,数十年来研究成果丰硕,但对商业实践真正有指导意义的成果却凤毛麟角。本文指出了到目前为止的资本结构研究的主要方向及其不足,提出了企业金融微观结构概念。本文认为,商学院的公司资本结构研究应该区别于经济学,深入到金融工具交易结构的微观层面,才能更深刻、真实地揭示金融实践原理,更好地指导金融实践。     

Machine learning methods in finance: Recent applications and prospects

We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: (i) the construction of superior and novel measures, (ii) the reduction of prediction error, and (iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest many benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance.     

Sustainable finance. A critical realist perspective

This article seeks to respond to certain epistemological challenges which are posed to the traditional methods of inquiry used in finance by the emergence of sustainable finance. We first underline the qualitative changes that have been induced in the finance function by the imperatives of sustainable finance. Then, through the prism of critical realism, we analyse it to reveal a contradiction, which we attribute to a misconceived social ontology in academic finance. Finally, we outline methodological avenues which might allow us to overcome these difficulties and pave the way for a more realist and pluralist approach to finance research in the 21st century.     

Significance testing in empirical finance: A critical review and assessment

This paper critically reviews the practice of significance testing in modern finance research. Employing a survey of recently published articles in four top-tier finance journals, we find that the conventional significance levels are exclusively used with little consideration of the key factors such as the sample size, power of the test, and expected losses. We also find that statistically significant results reported in many surveyed papers become questionable, if Bayesian method or revised standards for evidence were instead used. We observe strong evidence of publication bias in favour of statistical significance. We propose that substantial changes be made to the current practice of significance testing in finance research, in order to improve research credibility and integrity.     

Corporate finance theory: Introduction to special issue

Theoretical research in corporate finance is critical for our understanding of real-world phenomena, for interpreting empirical results, and for deriving policy implications. We discuss the benefits and limitations of research in corporate finance theory and link them to the nine articles in this special issue on “Corporate Finance Theory.” We provide a perspective on the nine articles in this special issue, and outline our perception of how future research may evolve. We also review several themes that emerge out of the articles, which we think deserve more attention from theorists going forward: interactions between financial markets and corporate finance and dynamic models of corporate decisions, such as capital structure and managerial compensation.     

Fintech and access to finance

This article surveys research on the effects of digitalization on access to finance. We focus the review on access through fintech. We review the growth of three main fintech technologies, fintech lending (incl. peer-to-peer lending), crowdfunding and initial coin offerings. We discuss existing evidence on how fintech affects access to finance for firms and investors and consider the regulatory challenges it poses. We incorporate the papers in this special issue, underlining their significant contributions to our understanding of the digitalization of finance and its effects. Finally, we discuss the challenges of research in the digital finance area and propose some new avenues for future research.     

The Stability of Factor Models of Interest Rates

The daily term structure of interest rates is filtered to reducethe influence of cross-correlations and autocorrelations onits factors. A three-factor model is fitted to the filtereddata. We perform statistical tests, finding that factor loadingsare unstable through time for daily data. This finding is notdue to the presence of outliers nor to the selected number offactors. Such an instability problem can be solved when applyingthe factor analysis on multivariate scaled residuals, filteredusing a nonparametric technique based on functional gradientdescent.     

宏观经济政策与微观企业行为——拓展会计与财务研究新领域

本文首先回顾国际主流会计学术研究近年来遇到的挑战与质疑,然后提出以宏观经济政策与微观企业行为互动为基础的会计与财务研究框架,并以货币政策变动对企业融资、会计选择、经营业绩的影响为例说明这个框架对会计与财务研究可能产生的影响。本文认为结合宏观经济政策与微观企业行为互动作用的研究可能拓展会计与财务研究的新领域。     

The External Funding of Academic Finance Research

We analyze the external funding of academic finance research. We show that funding is uncommon, particularly for U.S.‐based faculty, and is related to predictable attributes of an author's reputational capital. Further, when research is funded we find it is associated with better articles, as measured by publication in the most prestigious journals and the receipt of increased citations over time. Our study has relevance for every stakeholder in the university's research mission in finance.     

An analysis of “exactly distributed” test statistics for multivariate event tests

In this paper we derive a rule that identifies when exact tests may be used in the context of the multivariate regression model. Our derivation extends distribution theory reported in Rao (1973) and leads to the specification of exact tests for several event study hypothesis forms of interest to accounting and finance researchers. For tests where the event parameter is constrained to be equal across firms, we show that an infinite set of exact tests is available, of which the well known portfoliot-test is a special case. We conduct simulations using data from the CRSP Daily Returns file, and find that several test statistics, including exactly distributed statistics derived using the multivariate regression model, significantly over-reject the hypotheses examined.     

Topics and trends in finance research: What is published,who publishes it and what gets cited?

In this study we provide a detailed examination of the subject matter of finance research and its institutional features as it has evolved over the past two decades. Drawing on novel approaches from data science, we examine the content of more than 30,000 published papers. Overall, we find a striking lack of diversity in the topics investigated and the methodological approaches used. Almost all finance research is conducted using techniques from economics and mathematics, with virtually no use made of qualitative methods or interdisciplinary approaches. Looking at the developments of the discipline over time, we document an increase in the volume of corporate finance research and a variation in the topics covered following the financial crisis, although these changes appear to be reactive and trivial rather than paradigm shifts. We also provide a cartography of research in finance and its citation-based impact by the location of authors. Leading finance research is concentrated in elite US institutions, and has a disproportionately strong citation-based impact. Compared with other business and management sub-fields, citations in finance are heavily skewed towards the top journals as the latter generate almost twice as much impact as the lower rated outlets.     

A semi-parametric approach to risk management

Abstract

The benchmark theory of mathematical finance is the Black–Scholes–Merton (BSM) theory, based on Brownian motion as the driving noise process for stock prices. Here the distributions of financial returns of the stocks in a portfolio are multivariate normal. Risk management based on BSM underestimates tails. Hence estimation of tail behaviour is often based on extreme value theory (EVT). Here we discuss a semi-parametric replacement for the multivariate normal involving normal variance–mean mixtures. This allows a more accurate modelling of tails, together with various degrees of tail dependence, while (unlike EVT) the whole return distribution can be modelled. We use a parametric component, incorporating the mean vector μ and covariance matrix Σ, and a non-parametric component, which we can think of as a density on [0,∞), modelling the shape (in particular the tail decay) of the distribution. We work mainly within the family of elliptically contoured distributions, focusing particularly on normal variance mixtures with self-decomposable mixing distributions. We discuss efficient methods to estimate the parametric and non-parametric components of our model and provide an algorithm for simulating from such a model. We fit our model to several financial data series. Finally, we calculate value at risk (VaR) quantities for several portfolios and compare these VaRs to those obtained from simple multivariate normal and parametric mixture models.     

CAFR 1999–2021, the past two decades and a look ahead

The China Accounting and Finance Review (CAFR) was jointly established in 1999 by the Hong Kong Polytechnic University and Tsinghua University. Over the past 22 years, CAFR has published original papers in accounting and finance with a focus on China-related research. In this article, we review the journal’s publishing patterns and the impactful articles it has published, with the aim of better understanding past research on China-related issues and recent publication patterns and trends as well as developing new insight that may inspire future submissions. We divide past CAFR articles by topic into six groups: (i) information disclosure; (ii) auditing; (iii) corporate governance; (iv) market efficiency; (v) corporate finance; and (vi) miscellaneous. We use these categories as the basis of our review for articles published before 2020. We also summarize articles by their regional setting, research methodology, and authors’ university affiliation. We then highlight the contributions of a few impactful CAFR articles that are actively cited in both the Chinese and English literature. We complement the literature review by going over China’s financial stability research in JFS. We also compare CAFR with other major accounting and finance journals in the Asia-Pacific region. CAFR stands out by welcoming research using a diversity of regional settings and research topics. Finally, we discuss the new editorial strategies that began in 2020. Under the new editorial policy, CAFR now publishes more non-China and more cross-disciplinary studies than it used to. We review several recent publications to demonstrate the change. Going forward, we intend to call for the publication of more high-quality papers in accounting and finance that are not restricted to a region, area, or methodology providing new insights into accounting and finance.     

Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications

This article presents a general framework for identifying andmodeling the joint-tail distribution based on multivariate extremevalue theories. We argue that the multivariate approach is themost efficient and effective way to study extreme events suchas systemic risk and crisis. We show, using returns on fivemajor stock indices, that the use of traditional dependencemeasures could lead to inaccurate portfolio risk assessment.We explain how the framework proposed here could be exploitedin a number of finance applications such as portfolio selection,risk management, Sharpe ratio targeting, hedging, option valuation,and credit risk analysis.     

25 years and counting of corporate finance: What have we learned and where are we going?

This Special Issue pays tribute to the Journal of Corporate Finance (JCF) and its cutting-edge research. We do this by taking stock of the trends of research published in the Journal over the last 25 years, reviewing areas being researched currently, and offering some insight into fruitful areas of corporate finance research going forward. To that end, the Special Issue includes a blend of articles that represent the past, present, and future of corporate finance research. In particular, we highlight areas of corporate finance research that may be promising and offer insights on the potential of JCF going forward.     

开发性金融支持产业升级的效应分析

开发性金融是一种特殊的金融资源配置机制,是连接政府与市场,实现两种机制优势内在结合的有效途径,成为解决市场失灵和政府失灵的有效方式。本文研究了开发性金融支持产业升级的作用机理,借鉴日本开发性金融经验与教训,通过分析我国开发性金融实践对产业升级的支持效应,总结提出在当前现代化赶超的关键性历史阶段,开发性金融是弥补我国投融资体制不足和推动产业升级的重要力量,并就其进一步发展提出对策建议。     

Making sense and transparency in finance literature: Evidence from trends in readability

Transparency continues to interest finance scholarship, as regards not just to financial reporting, but to a host of areas. Concomitantly, there is a growing emphasis on the transparency of the finance research process, with journals initiating requirements for uploading data and codes. However, little consideration is given to the transparency of finance scholarly texts, despite new emphasis by academic institutions and accreditation bodies on articles having an impact on practitioners. We use textual analysis to investigate the readability of articles in a selection of finance journals. Results evidence that academic articles are becoming less readable. Whether readability straightforwardly implies transparency is unclear, still, we consider these issues alongside our findings. Our study should be of great interest to those concerned with the state of finance scholarship.     

Voting with Their Feet: In Which Journals Do the Most Prolific Finance Researchers Publish?

The financial economics literature has experienced rapid growth over the past 40 years, triggering a dramatic increase in the number of journals. We employ a new method to analyze the current pecking order of finance journals. Specifically, we analyze the publication records of prolific authors to provide evidence regarding the perceived quality of a set of 23 high‐impact finance journals. Assuming these scholars target the “best” research outlets, their publication records can reveal information about their subjective rankings of the next‐best alternatives to the traditional elite finance journals. The results suggest that prolific authors are most likely to target outlets that have raised their profile in recent years (e.g., Financial Management and Financial Analysts Journal) and new specialized finance journals (e.g., Journal of Financial Markets, Journal of Corporate Finance, and Journal of Financial Intermediation) when publishing outside the set of elite journals.     

正规金融与非正规金融:机制、效率与实证挑战

正规金融与非正规金融的定位和关系,不仅是重要的学术问题,也是影响间接金融体系改革方向的关键。本文从机制视角建立学理框架,从信息和履约两个基础维度,讨论正规金融与非正规金融的相对效率及决定因素,并通过系统梳理相关文献,指出现有实证研究的成果与挑战;进而结合中国特征,讨论如何提高正规金融与非正规金融的效率,最后提出未来研究方向。本文认为,正规金融与非正规金融因信息和履约机制不同而各有其独立存在价值,其相对效率取决于不同经济发展阶段的社会网络基础和征信、法律体系,现有实证文献对两者作用的分歧主要源于分类、样本局限和内生性处理。本文从机制视角进行分析,体现了正规金融和非正规金融的金融属性,为探索相关领域学术研究和政策制定提供启示。     

Nonlinear Models in Corporate Finance Research: Review,Critique, and Extensions

Since the work of Morck, Shleifer and Vishny (1988), nonlinear model specification has gained more attention in corporate finance research. In this paper, we provide a detailed review of the previous studies that have examined nonlinear relations in corporate finance. We review the theory and evidence in these studies and discuss the advantages and disadvantages of the various methodologies used to detect nonlinearity. We also suggest two possible methodological extensions, which we apply in the empirical analysis of R&D investment and firm value.