The day-of-the-week effect and conditional volatility: Sensitivity of error distributional assumptions

We test for reliable evidence of the day-of-the-week effect on both the mean and volatility for the S&P/TSX Canadian return index. Unlike previous studies, we permit several specifications for the error distribution — GARCH normal, Student's t, generalized error distribution, and double exponential distribution. Unlike other studies, we find that the day-of-the-week effect in both mean and conditional volatility is sensitive to the particular specification of the underlying distributions. We also find that using a regression analysis assuming a Student's t distribution is a better way to investigate this effect. Our evidence demonstrates the apparent fragility of previous empirical studies on calendar anomalies. Thus, our results serve as a warning that with financial data, the error distributional assumptions are critical to correctly identifying empirical regularities in the data.     

Price/Book Value Ratios and Equity Returns on the Tokyo Stock Exchange: Empirical Evidence of an Anomalous Regularity

This study examines the relationship between accounting data and financial market data for securities listed on the Tokyo Stock Exchange. We document, for the first time for a non-U.S. market, a significant price to book value ratio effect; i.e., Japanese equities with low price to book value ratios earn higher returns than those with high price to book value ratios, and this price to book value effect is stronger in January and June and for smaller firms. One implication of the international pervasiveness of these empirical regularities is that explanations for these effects that are based on unique institutional or accounting procedures are unlikely to be sufficient.     

Breaking down the barriers between econophysics and financial economics

This article highlights the current misunderstanding between economists and econophysicists by adopting the financial economists' viewpoint in order to explain why the works developed by econophysicists are not recognized in finance. Because both communities do not share the same scientific culture, and for the other reasons developed in the article, economists often consider econophysics as a strictly empirical field without theoretical justification. This paper shows the opposite; it also tries to facilitate the dialogue between econophysicists who often do not explain in details their theoretical roots and financial economists who are not familiar with statistical physics. Beyond this clarification, this paper also identifies what remains to be done for econophysicists to contribute significantly to financial economics: 1) development of a common framework/vocabulary in order to better compare and integrate the two approaches; 2) development of generative models explaining the emergence of power laws; and 3) development of statistical tests for the identification of such statistical regularities.     

Banking Mergers: The Impact of Financial Liberalization on the Taiwanese Banking Industry

The objective of this paper is to examine the nature of the Taiwanese banking sector and to analyze the impact of financial liberalization on the Taiwanese banking industry. We present empirical evidence to show that the recent wave of bank mergers observed in other countries is also suitable for Taiwan. Based on empirical results for overall economies of scale and expansion path subadditivity, Taiwanese banks should obtain the benefit of scale economies by merging with other banks rather than expanding by opening more branches. Furthermore, we show that the Relative Market Power hypothesis—which postulates that greater market shares lead to higher profitability—finds empirical support in Taiwanese banking data after financial reforms were enacted.     

信贷资源配置与非周期性产能过剩:微观数据的实证

厘清企业产能利用率的微观决定机制是探讨产能过剩长效治理机制的前提。现有研究虽然强调了中国重工业领域产能过剩的非周期性色彩,但缺乏微观数据实证的支持,也缺乏对不同因素实际影响的量化比较。为此,基于中国工业企业数据库的微观数据,选择钢铁行业作为研究样本,综合运用成本函数法的产能利用率测算以及产能利用率变化影响因素的微观计量,验证两个理论假设：制度政策性因素对于我国产能过剩形成具有更加显著的影响;信贷资源配置是企业产能利用率变化的决定变量,主导了我国重工业领域产能过剩的实际演变。进一步实证显示：金融抑制不仅会引发微观企业投资的预算软约束,还会扭曲微观企业行为。由此认为,我国钢铁行业乃至于其他重工业领域的产能过剩问题,改革比调控更加重要,而金融资源分配体制的改革则是重中之重。     

Statistical tests of distributional scaling properties for financial return series

Existing empirical evidence of distributional scaling in financial returns has helped motivate the use of multifractal processes for modelling return processes. However, this evidence has relied on informal tests that may be unable to reliably distinguish multifractal processes from other related classes. The current paper develops a formal statistical testing procedure for determining which class of fractal process is most consistent with the distributional scaling properties in a given sample of data. Our testing methodology consists of a set of test statistics, together with a model-based bootstrap resampling scheme to obtain sample p-values. We demonstrate in Monte Carlo exercises that the proposed testing methodology performs well in a wide range of testing environments relevant for financial applications. Finally, the methodology is applied to study the scaling properties of a data-set of intraday equity index and exchange rate returns. The empirical results suggest that the scaling properties of these return series may be inconsistent with purely multifractal processes.     

Aggregational Gaussianity and barely infinite variance in financial returns

This paper aims at reconciling two apparently contradictory empirical regularities of financial returns, namely, the fact that the empirical distribution of returns tends to normality as the frequency of observation decreases (aggregational Gaussianity) combined with the fact that the conditional variance of high frequency returns seems to have a (fractional) unit root, in which case the unconditional variance is infinite. We provide evidence that aggregational Gaussianity and infinite variance can coexist, provided that all the moments of the unconditional distribution whose order is less than two exist. The latter characterizes the case of Integrated and Fractionally Integrated GARCH processes. Finally, we discuss testing for aggregational Gaussianity under barely infinite variance. Our empirical motivation derives from commodity prices and stock indices, while our results are relevant for financial returns in general.     

区域金融发展与经济增长的实证分析——基于中国地级市区数据与分位数回归方法

针对地级市区的金融发展与经济增长的关系,利用中国31个省区共336个样本地级市区2000-2006年的数据,基于分位数回归统计分析结果表明,在被解释变量经济增长的不同条件分位数处解释变最金融发展和控制变量对经济增长影响的差异和波动是统计显著的.与经典的条件均值回归相比,条件分位数回归实证分析能够揭示数据生成过程更加丰富的信息,这为对区域金融发展与经济增长关系进行时空特征整合的统计建模提供了有力支持.     

Gram–Charlier densities: a multivariate approach

This paper introduces a new family of multivariate distributions based on Gram–Charlier and Edgeworth expansions. This family encompasses many of the univariate semi-non-parametric densities proposed in financial econometrics as marginal of its different formulations. Within this family, we focus on the analysis of the specifications that guarantee positivity to obtain well-defined multivariate semi-non-parametric densities. We compare two different multivariate distributions of the family with the multivariate Edgeworth–Sargan, Normal, Student's t and skewed Student's t in an in- and out-of-sample framework for financial returns data. Our results show that the proposed specifications provide a reasonably good performance, and would therefore be of interest for applications involving the modelling and forecasting of heavy-tailed distributions.     

Investigating the leverage effect in commodity markets with a recursive estimation approach

This paper investigates the presence of the leverage effect in commodities, in comparison with financial markets. The EGARCH model with a Mixture of Normals distribution (EGARCH-MN) is used to capture (i) heavy tails and skewness in the conditional returns, and (ii) leverage effects and time-varying long-term component in the volatility specification. Besides, the estimation strategy relies on an innovative recursive (REC) method, which allows disentangling the leverage effect from the unconditional skewness as an empirical result. When applied to a broadly diversified dataset of assets during 1995–2012, the EGARCH-MN models offers state-of-the-art specifications with leverage and fat-tailed skewed densities, that allow to contrast the specific characteristics of commodities with traditional assets (equities, bonds, FX).     

Feedforward neural networks in the classification of financial information

Financial research has given rise to numerous studies in which, on the basis of the information provided by financial statements, companies are classified into different groups. An example is that of the classification of companies into those that are solvent and those that are insolvent. Linear discriminant analysis (LDA) and logistic regression have been the most commonly used statistical models in this type of work. One feedforward neural network, known as the multilayer perceptron (MLP), performs the same task as LDA and logistic regression which, a priori, makes it appropriate for the treatment of financial information. In this paper, a practical case based on data from Spanish companies, shows, in an empirical form, the strengths and weaknesses of feedforward neural networks. The desirability of carrying out an exploratory data analysis of the financial ratios in order to study their statistical properties, with the aim of achieving an appropriate model selection, is made clear.     

Turbulence in financial markets: the surprising explanatory power of simple cascade models

Price changes in financial markets have been found to share many of the features characterizing turbulent flows. In particular, a number of recent contributions have highlighted that time series from both stock and foreign exchange markets possess multifractal statistics, i.e. the scaling behaviour of absolute moments is described by a convex function. These findings have stimulated the application of certain cascade models from statistical physics to financial data. Extant work in this area has so far been confined to parameter estimation and visual comparison of empirical and theoretical scaling properties. The lack of rigorous statistical measures of goodness of fit in the literature on turbulence has, however, impeded a comparison of these new models with standard approaches in empirical finance. Here we try to fill this gap and provide a first assessment of two elementary cascade models based on elementary goodness-of-fit criteria. As it turns out, these relatively simple one-parameter models are not only capable of accommodating the multiscaling behaviour of price changes, but also provide a perplexingly good fit of the unconditional distribution of the data. In a double-blind test, we would, in fact, be unable to reject identity of the data-generating processes underlying empirical records and simulated data from stochastic cascades.     

The beginnings of accounting for capital consumption: disclosure practices in the British railway industry, 1830–55

Accounting for capital consumption has been one of the most vexed issues in the history of financial reporting. The early railway companies, whose ability to exploit the commercial opportunities available to them required unprecedented levels of capital expenditure, provided the first real arena for the development of possible solutions to the problem. Although accounting practices in the industry were subject to little regulation, some writers have asserted the existence of regularities in depreciation and replacement accounting practices (possibly driven by economic self-interest), although the evidential basis for these assertions has been slight. This paper provides the first assessment of the capital consumption accounting practices of companies in the railway industry, and of their regularities and patterns of change during the period 1830–55. to be derived from a substantial empirical base.     

Valuing Volunteers: Expanding the Relevance and Reliability Debate

Volunteers’ inputs to public and other not‐for‐profit organisations are significant. However, while statisticians gather data on volunteering, accountants do so less often due to a debate as to whether the relevance of recognising volunteer input in financial reports outweighs the reliability of that data and the costs of its derivation. This paper presents a survey of voluntary organisations that publish financial reports. It explores whether the promise of funding for volunteer effort has been matched with increasing financial reporting of volunteering. The results show there is little recognition of volunteer labour in financial reports, reflecting the unresolved relevance‐reliability debate in the accounting profession.     

The Changing Relationship Between CAMEL Ratings and Bank Soundness during the Indonesian Banking Crisis

During the recent Southeast Asian financial crisis, numerous banks failed quickly and unexpectedly. This study uses a unique data set provided by Bank Indonesia to examine the changing financial soundness of Indonesian banks during this crisis. Bank Indonesia's non-public CAMEL ratings data allow the use of a continuous bank soundness measure rather than ordinal measures. In addition, panel data regression procedures that allow for the identification of the appropriate statistical model are used.We argue the nature of the risks facing the Indonesian banking community calls for the addition of a systemic risk component to the Indonesian ranking system. The empirical results show that during Indonesia's stable economic periods, four of the five traditional CAMEL components provide insights into the financial soundness of Indonesian banks. However, during Indonesia's crisis period, the relationships between financial characteristics and CAMEL ratings deteriorate and only one of the traditional CAMEL components—earnings—objectively discriminates among the ratings. The panel data results indicate systemic economy-wide forces must be explicitly considered by the rating system.     

Does corporate governance transparency affect the accuracy of analyst forecasts?

Using country‐level proxies for corporate governance transparency, this paper investigates how differences in transparency across 21 countries affect the average forecast accuracy of analysts for the country's firms. The association between financial transparency and analyst forecast accuracy has been well documented in previous published literature; however, the association between governance transparency and analyst forecast accuracy remains unexplored. Using the two distinct country‐level factors isolated by Bushman et al. (2004 ), governance transparency and financial transparency, we investigate whether corporate governance information impacts on the accuracy of earnings forecasts over and above financial information. We document that governance transparency is positively associated with analyst forecast accuracy after controlling for financial transparency and other variables. Furthermore, our results suggest that governance‐related disclosure plays a bigger role in improving the information environment when financial disclosures are less transparent. Our empirical evidence also suggests that the significance of governance transparency on analyst forecast accuracy is higher when legal enforcement is weak.     

On the Optimal Relation between the Properties of Managerial and Financial Reporting Systems

We develop a theoretical model of the firm that links properties (stewardship vs. valuation focus) of financial reporting regimes with the informational properties of optimal managerial accounting systems. We show that, contrary to the standard textbook proposition, properties of management and financial accounting systems are not independent. Significantly, we provide an explicit connection between exogenous and observable properties of a firm's financial reporting system and the quality of the managerial accounting system on which manager(s) base real economic decisions. As the quality of those economic decisions can also be inferred from publicly available data, our theory generates new opportunities for empirical managerial accounting research on large nonproprietary samples. Further, by being able to identify enhanced performance due to improved managerial accounting information, our theory provides opportunities to gain a better understanding of the link between particular managerial accounting practices and the quality of the information produced.     

Functional gradient descent for financial time series with an application to the measurement of market risk

The estimation and forecast of the volatility matrix are two of the main tasks of financial econometrics since they are essential ingredients in many practical applications. Unfortunately the use of classical multivariate methods in large dimensions is difficult because of the curse of dimensionality. We present a general semiparametric technique, based on functional gradient descent (FGD) and able to overcome most problems associated with a multivariate GARCH-type estimation. By testing the accuracy of the volatility estimates for the measurement of market risk on real data we provide empirical evidence of the strong predictive potential of the FGD approach, also in comparison to other standard methods.     

Dynamic interdependencies among the housing market,stock market,policy uncertainty and the macroeconomy in the United Kingdom

In this study, we examine the dynamic interdependencies among the housing market, stock market, policy uncertainty and the macroeconomy in the United Kingdom, over the period 1997 M1–2015 M02. The findings of this study suggest the following empirical regularities. First, the transmission of various types of shocks contributes significantly to economic fluctuations in the United Kingdom. Second, spillovers show large variations over time. Third, in the wake of the global financial crisis, spillovers have reached unprecedented levels. Specifically, we find large spillovers of shocks from the housing market, stock market and economic policy uncertainty to inflation, economic growth and monetary policy stance. These results illustrate the contagion from the housing and financial crisis to the real economy and the policy reaction to stabilize the economy.