On the Efficiency of Bankruptcy Law: Empirical Evidence in Spain

The current economic crisis is showing one of the main problems that many companies in financial distress have to face, namely, the impact of bankruptcy law in relation to companies and firms. This paper aims to analyze the bankruptcy law ex‐ante efficiency when companies are in financial distress. To test it out, two research questions are submitted: (i) Is solvency, the criterion used in the Spanish law, the best one to assess the relative significance of the main indicators, which determine bankrupt firms? (ii) Is the Spanish bankruptcy law efficient according to solvency or are there better criteria? To answer them, a logistic regression model is conducted. The sample embraces 1,387 firms in Spain, the data being obtained from 12 Commercial Justice Courts complemented with financial information. The main conclusion is that the solvency criterion is adequate to classify bankrupt companies although currently Spanish Bankruptcy law is not as efficient as it could be. Additionally, the relevant companies' indicators, which explain the financial distress procedure, are presented. Copyright © 2013 INSOL International and John Wiley & Sons, Ltd     

Information Risk and Fair Values: An Examination of Equity Betas

Using a sample of U.S. financial institutions, we exploit recent mandatory disclosures of financial instruments designated as fair value level 1, 2, and 3 to test whether greater information risk in financial instrument fair values leads to higher cost of capital. We derive an empirical model allowing asset‐specific estimates of implied betas, and find evidence that firms with greater exposure to level 3 financial assets exhibit higher betas relative to those designated as level 1 or level 2. We further find that this difference in implied betas across fair value designations is more pronounced for firms with ex ante lower‐quality information environments: firms with lower analyst following, lower market capitalization, higher analyst forecast errors, or higher analyst forecast dispersion. Overall, the results are consistent with a higher cost of capital for more opaque financial assets, but also suggest that differences in firms' information environments can mitigate information risk across the fair value designations.     

Foreign Banks in Poor Countries: Theory and Evidence

We study how foreign bank penetration affects financial sector development in poor countries. A theoretical model shows that when domestic banks are better than foreign banks at monitoring soft information customers, foreign bank entry may hurt these customers and worsen welfare. The model also predicts that credit to the private sector should be lower in countries with more foreign bank penetration, and that foreign banks should have a less risky loan portfolio. In the empirical section, we test these predictions for a sample of lower income countries and find support for the theoretical model.     

The use of financial accounting information and firm performance: an empirical quantification for farms

Many firms that do not have a formal obligation to prepare financial statements do not produce any accounting information voluntarily, either. However, as accounting information is generally believed to be useful for decision making, the reasons for this behaviour are unclear. In this paper we try to shed some light on this question and provide empirical evidence on the relationship between the use of financial reports and financial performance. In particular, we collected data on the use a sample of Catalan farmers made of the financial reports that were provided to them free by the European Farm Accountancy Data Network. We matched these observations with different financial indicators and found that the financial performance of farmers using the reports for decision-making purposes was significantly better than those who did not use the reports. These results suggest that financial reports can indeed be of use to managers. However, firms will only benefit if the expected gain in performance is sufficiently large to offset the costs of obtaining the reports. Although for the average farm the performance gain was probably sufficiently large to make accounting worthwhile, this was not the case for the smallest farms in our sample.     

Nonstationarity of efficient finance markets: FX market evolution from stability to instability

Real financial markets are uncertain on the shortest trading time scales, therefore trading translates into noise. We discuss the pair correlations of detrended returns necessary to understand financial markets. Efficient markets and equilibrium markets generate conflicting pair correlations. B. Eichengreen [B. Eichengreen, Globalizing Capital,: A History of the International Monetary System, Princeton, Princeton, 1998] argues that FX speculation was stabilizing before WWI. In contrast, our recent empirical analysis shows that we can model FX markets in our present era as nonstationary/unstable and efficient (meaning hard or impossible to beat). We can model pre-WWI non-efficient equilibrium FX dynamics from a closely related theoretical standpoint. Our main points are that deregulated markets are described by a nonstationary process with uncorrelated, nonstationary increments, and that a stationary market (equilibrium market) is mutually exclusive with an unregulated, efficient market. In short, stability and deregulation are mutually exclusive ideas. We review a simple, empirically deduced model of the unstable diffusive nature of volatile FX market dynamics. All information of interest is encoded in the variable diffusion coefficient defining the observed martingale process.     

Evaluating the effectiveness of model specifications and estimation approaches for empirical accounting-based valuation models

This study considers the effectiveness of different model specifications and estimation approaches for empirical accounting-based valuation models in the UK. Primarily, we are interested in the accounting determinants of market value and, in particular, whether accounting-based valuation models can be estimated that not only have in-sample explanatory power but also potentially can be used as a tool of financial statement analysis in developing useful estimates of value out-of-sample. This requires models to be estimated on one sample, and tested for effectiveness on a different sample. Then, issues of model specification arise, together with choosing between methods of estimating the empirical models, in identifying the effectiveness of each combination. Using the criteria of bias and accuracy to capture effectiveness, we suggest estimation methods and models that, overall, provide the most effective models in this context.     

A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress

Predicting financial distress has been and will remain an important and challenging issue. Many methods have been proposed to predict bankruptcies and detect financial crises, including conventional approaches and techniques involving artificial intelligence (AI). Financial distress information influences investor decisions, and investors depend on analysts’ opinions and subjective judgements in assessing such information, which sometimes results in investors making mistakes. In the light of the foregoing, this paper proposes a novel quarterly time series classifier, which reduces the sheer volume of high-dimensional data to be analysed and provides decision-makers with rules that can be used as a reference in assessing the financial situation of a company. This study employs the following six attribute selection methods to reduce the high-dimensional data: (1) the chi-square test, (2) information gain, (3) discriminant analysis, (4) logistic regression (LR) analysis, (5) support vector machine (SVM) and (6) the proposed Join method. After selecting attributes, this study utilises the rough set classifier to generate the rules of financial distress. To verify the proposed method, an empirically collected financial distress data-set is employed as the experimental sample and is compared with the decision tree, multilayer perceptron and SVM under Type I error, Type II error and accuracy criteria. Because financial distress data are quarterly time series data, this study conducts non-time series and time series (moving windows) experiments. The experimental results indicate that the LR and chi-square attribute selection combined with the rough set classifier outperform the listing methods under Type I, Type II error and accuracy criteria.     

Financial crisis,Value-at-Risk forecasts and the puzzle of dependency modeling

Forecasting Value-at-Risk (VaR) for financial portfolios is a crucial task in applied financial risk management. In this paper, we compare VaR forecasts based on different models for return interdependencies: volatility spillover (Engle & Kroner, 1995), dynamic conditional correlations (Engle, 2002, 2009) and (elliptical) copulas (Embrechts et al., 2002). Moreover, competing models for marginal return distributions are applied. In particular, we apply extreme value theory (EVT) models to GARCH-filtered residuals to capture excess returns.Drawing on a sample of daily data covering both calm and turbulent market phases, we analyze portfolios consisting of German Stocks, national indices and FX-rates. VaR forecasts are evaluated using statistical backtesting and Basel II criteria. The extensive empirical application favors the elliptical copula approach combined with extreme value theory (EVT) models for individual returns. 99% VaR forecasts from the EVT-GARCH-copula model clearly outperform estimates from alternative models accounting for dynamic conditional correlations and volatility spillover for all asset classes in times of financial crisis.     

On the robustness of the results of adoption date choice studies: The case of pension accounting

This paper examines the robustness of the results of adoption date choice studies to sample selection criteria, variable set, and the approach to model building. We use the new pension accounting standard (SFAS 87) in our examination. To this end, we develop a model of the relationship between the adoption date of the pension standard and the variables commonly used in adoption date studies. The variables are: (1) Firm size (2) income change prior to the adoption date and proxies for income management; (3) tightness of debt constraints; and (4) extent of the impact of the standard on financial statements.The results are consistent with the smoothing and compensation hypotheses, but not with the size hypothesis. Debt constraints did not have a significant effect on the selection of the adoption date; but, to the extent that the funding status of pension plans is a measure of the income effect of SFAS 87, the results are also consistent with the selection of adoption date to ease accounting-based debt constraints.Further tests indicate that the results of adoption date choice studies depend on the sample selection critiera, the variable set, and the approach to model building. Future researchers should be aware of the sensitivity of the results to these factors.     

Forecasting Financial Market Volatility Using a Dynamic Topic Model

This study employs big data and text data mining techniques to forecast financial market volatility. We incorporate financial information from online news sources into time series volatility models. We categorize a topic for each news article using time stamps and analyze the chronological evolution of the topic in the set of articles using a dynamic topic model. After calculating a topic score, we develop time series models that incorporate the score to estimate and forecast realized volatility. The results of our empirical analysis suggest that the proposed models can contribute to improving forecasting accuracy.     

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.     

Voluntary Disclosure of Sales and the Extent of Trade Credit in Small Private Companies

We examine the association between voluntary financial disclosure and the amount of obtained trade credit in a sample of small private Belgian companies. We argue that voluntary disclosure can help small private companies in mitigating information asymmetries that arise between the company and their suppliers. Using a propensity score matching procedure to control for selection bias, we find that voluntary financial disclosure by small and private companies is positively related to the level of trade credit. This is in line with the traditional view that asymmetric or incomplete information restricts access to external funds.     

Pricing of the currency risk in the Canadian equity market

Based on a three-factor international capital asset pricing model, we examine whether the world market, the local market and the currency risks are priced in the Canadian equity market. The analysis presented in this paper is based on data collected from 2003 to 2010. As the dataset also includes the period of global financial crisis, we examine the issue of risk pricing in the full sample as well as in before and after global financial crisis periods. Unlike most existing studies, the empirical results presented in this paper are based on (i) the quasi maximum likelihood estimation (QMLE) based multivariate GARCH-in-Mean specification and (ii) the generalized method of moments (GMM) techniques. Our empirical analysis based on weekly data on 58 largest Canadian firms indicates that the currency as well as the local and the world market risks are priced in the Canadian equity market. This result holds for all exchange currency rates proxies and in all sample periods. We find that the price of the world market, the local market and the currency risks is time-varying and the Canadian equity market is partially segmented.     

On the short-term predictability of exchange rates: A BVAR time-varying parameters approach

In this paper we propose a Bayesian vector autoregressive model with time-varying parameters (BVAR-TVP) to examine the short-term predictability of exchange rates. An important contribution of the paper is the application of the BVAR-TVP model, for the first time, to daily data using information from financial markets. Another contribution is the production of forecasts in real time at the very short horizon of one-trading day-ahead typically used by traders and investors in financial markets. We employ financial criteria and recently developed statistical tests to assess the exchange rate predictability. We find that the BVAR-TVP model outperforms the random walk for all exchange rates. These forecast gains are due primarily to the time-variation of coefficients, and secondly to information from other financial markets. It is shown that international investors could have made statistically significant excess profits if they had followed an inter-day trading strategy based on the buy/sell signals generated by the model’s one-day-ahead exchange rate forecasts, even after allowing for transaction costs and risk factors.     

Multiple Large Shareholders and Financial Reporting Quality: Evidence from China

In this paper, we examine the effect of multiple large shareholders (MLS) on financial reporting quality. Using a sample of Chinese listed firms over the period 2007–2018, we find that firms with MLS tend to have lower financial reporting quality. Our findings are robust to an array of robustness tests, including controlling for possible omitted variables, a Heckman two-step sample selection model, and a difference-in-differences analysis based on a propensity score matched sample. We further show that the effect of MLS on financial reporting quality is attenuated for firms followed by more analysts, cross-listed on the Hong Kong Stock Exchange, and held by institutional blockholders. Finally, we find that agency problems appear to be the possible underlying mechanisms through which MLS lower financial reporting quality.     

Information dissemination on intellectual capital in mergers and acquisitions: purchase price allocations,press releases and business press

In order to reduce information asymmetries in relation to a firm's current decisions and long-term strategy, firms must consistently provide information to stakeholders. This paper investigates intellectual capital (IC) information disclosed in mergers and acquisitions (M&A) provided through three different disclosure channels (voluntary press releases, related newspaper articles and subsequent mandatory corporate disclosures in the notes to the financial statements). For a sample of 215 randomly selected US and European M&As, we analyse 215 press releases, 1025 newspaper articles and 215 purchase price allocations. Our findings suggest that IC disclosure in press releases is not perceived as informative and qualitative forward-looking IC information in voluntary corporate disclosures appears to lack credibility. Moreover, we empirically demonstrate interdependencies across the three disclosure channels. The business press seems to filter IC information provided in press releases. The amount of IC disclosure in the notes to the financial statements is positively associated with prior IC disclosure in newspaper articles, but negatively associated with IC disclosure in press releases. The managements of acquirer firms appear to pay attention to news coverage and public opinion. However, both voluntary and mandatory corporate disclosures appear to substitute rather than complement each other.     

Moral hazard,adverse selection,and health expenditures: A semiparametric analysis

Theoretical models predict asymmetric information in health insurance markets may generate inefficient outcomes due to adverse selection and moral hazard. However, previous empirical research has found it difficult to disentangle adverse selection from moral hazard in health care consumption. We propose a two‐step semiparametric estimation strategy to identify and estimate a canonical model of asymmetric information in health care markets. With this method, we can estimate a structural model of demand for health care. We illustrate this method using a claims‐level data set with confidential information from a large self‐insured employer. We find significant evidence of moral hazard and adverse selection.     

Monitoring Financial Distress in a High-Stress Financial World: The Role of Option Prices as Bank Risk Metrics

The current financial crisis offers a unique opportunity to investigate the leading properties of market indicators in a stressed environment and their usefulness from a banking supervision perspective. One pool of relevant information that has been little explored in the empirical literature is the market for bank’s exchange-traded option contracts. In this paper, we first extract implied volatility indicators from the prices of option contracts on financial firms’ equity. We then examine empirically their ability to predict financial distress by applying survival analysis techniques to a sample of large US financial firms. We find that market indicators extracted from option prices significantly explain the survival time of troubled financial firms and do a better job in predicting financial distress than other time-varying covariates typically included in bank failure models. Overall, both accounting information and option prices contain useful information of subsequent financial problems and, more importantly, their combination produces good forecasts in a high-stress financial world.     

Comparability: An analytic examination

Comparability is often invoked as an evaluative criterion in regulatory and financial reporting controversies. This article explores the issue by first developing a formal model of comparability that is consistent with the prior information economics literature regarding normative criteria. This model is then used in a restricted setting to explore how such criteria might eventually be used to establish preference relationships.     

基于径向基神经网络的非寿险公司财务预警研究

选取我国保险行业中40家非寿险公司2006~2008年的混合非平衡面板数据作为研究样本,采用径向基神经网络模型从偿付能力、盈利能力、成长能力三个角度对非寿险公司财务预警进行研究。实证表明,此模型可以为管理层的财务预警提供更有有效的信息,并且在我国保险业具有样本量小,有效数据少的情况下,有着良好的运用前景。