Research Projects
Financial institutions’ disclosures in times of crisis
The recent financial crisis had major economic, political and social impact across the world. Financial institutions are at the center of this storm, blamed for igniting the crisis and drastically suffering its effects in the form of bad financial performance and damaged public reputation. The effects of the crisis are a worldwide phenomenon, but they were particularly pronounced in Portugal, where the banking sector was severely affected. It is important for policy-makers, investors, lenders and society to understand the changes that crises cause in the financial sector and the mechanisms used to recover. This project aims to shed light on these issues. In particular we study the effects of the 2008 crisis on information disclosed by the banks as information is essential for the well-functioning of capital markets and economic development.  Two issues make the study of financial institutions unique: the complexity of the business and specific regulations. In addition, there is a growing trend of information disclosed, due to an increase in regulation and public scrutiny. What information was disclosed, before and after the crisis? How do investors and other agents perceive that information? How does it affect capital markets? And banks reputation? Understanding these issues is essential to economic agents and policy-makers in order to make strategic choices that lead to the path of economic development.  Disclosed information, i.e. the amount and quality of information disclosed (Diamond and Verrecchia, 1991), can be quantitative and financially-oriented or qualitative and oriented to non-financial stakeholders. This project analyzes the economic effects of banks’ disclosure of financial information and information related to corporate social responsibility (CSR) activities in a long time-series (2005 to 2015) that covers the crisis period and the period of changes in the banking regulation (i.e. European Capital Requirement Directive (CRD), 2014). We start by d...
Project Information
2018-01-15
2018-01-15
Project Partners
Capital market effects of financial reporting regulation
Over the last years capital markets around the world have been shaken by a series of dramatic events. Corporate scandals, banking failures, and over-leveraged firms and nations made the news headlines in the last decade. These events have triggered a public demand for strict regulation on corporations (BusW_2001,Merkel_2010).Voicing the demands, the US regulators has recently issued important rules affecting firms’ financial reports. The aim of these rules is to promote efficient capital markets. However, regulation is costly. Regulators are often unaware of firms’ incentives and lack the means to enforce the rules (Coase_1960). The aim of this project is to investigate whether these regulatory interventions result in net capital markets benefits. Particularly we study three regulatory interventions in firms’ financial reporting: rules directing the Securities and Exchange Commission(SEC) to review regularly firm’s financial reports (SOX408_2002); bankruptcy rules that mandate firms that emerge from bankruptcy to apply fresh start accounting-FSA (FASB852_2009); and rules requesting firms to disclosure internal control weaknesses (ICW) in their financial reports (SOX302_2002,SOX404_2002). We test whether the introduction of these rules help capital markets agents in their economic decisions, namely: 1) whether institutional investors disinvest in firms that have been identified by the SEC reviews as having deficiencies in financial reports; 2)whether the post-bankruptcy performance of firms that apply FSA is better than the post-restructuring performance of firms that do not apply FSA; 3)whether investors incorporate information about internal control problems in their long-run investment decisions. While these regulatory interventions have generated intense debate among practitioners and academics in the US, research on the topics is scarce, mainly in the first two cases. One of the reasons is the lack of ready-to-use data. We take advantage of that and hand-coll...
Project Information
2013-06-01
2016-01-30
Project Partners
Determinants and economic consequences of non-GAAP financial reporting in Europe
Public announcements of firm’s earnings made by managers typically disclose more information than GAAP earnings (earnings calculated according to mandatory accounting rules or Generally Accepted Accounting Principles). Frequently managers choose to disclose other financial measures (referred to as Non-GAAP). An internal study by EFRAG (European Financial Reporting Advisory Group is an EU body for financial reporting issues) in 2006 finds that 68% top firms disclose non-GAAP measures in the U.K. That percentage is 62% for Germany and 76% for France. In the U.S. non-GAAP reporting is also a common practice (Marques, 2006). Managers argue that non-GAAP numbers represent the true permanent earnings of the firm which is not captured by the rigid GAAP rules. Critics of these numbers call it ‘earnings before the bad stuff’ and contra-argue that they mislead users of financial information. Academic research finds evidence of both hypotheses. Black and Christensen (2007) show that managers opportunistically choose non-GAAP measures while Brown and Sivakumar (2003) suggest that non-GAAP information is value relevant to investors.   In the U.S. the voluntary disclosure of non-GAAP measures was regulated by the Securities and Exchange Commission (SEC) in 2003. The SEC intention was to reduce potentially misleading information. As a consequence the frequency of these disclosures decreased significantly (Marques 2006).   In Europe, with the exception of the U.K., there are no rules on non-GAAP disclosure. In most European countries managers do not need to explain non-GAAP numbers suggesting that European managers’ have a higher degree of discretion over financial information than their US counterparts. A pilot study by Isidro and Marques (2008) indicates that non-GAAP disclosures are frequent in Europe and its content varies across firms and countries. Concerned about the lack of consistency and clarify of these measures European regulators are considering the introduction of r...
Project Information
2010-04-01
2013-06-30
Project Partners
Developing and extending regime switching models in finance and accounting
Finance and accounting are two research fields in management that have witnessed very important methodological developments in recent years. For example in Finance, the sophistication of the financial industry requires advanced models to solve problems of portfolio and risk management. Despite the progress, practioneers still face many challenges. For instance, it is well known that financial and economic variables present upward and downward trends. A simple but actual illustration is to think about recessions and expansions or in bull and bear markets. An econometric tool that has been developed to address market regimes is the regime switching model (RSM). It has been applied successfully to many time series data such as interest rates and stock returns. Regime switching models have nevertheless shortcomes. First, because it requires complex maximum likelihood optimization procedures, when applied to the joint estimation of many variables the number of estimated parameters easily reaches dozens, becoming unfeasible to estimate. Moreover, when applied to panel data (for example, more than one stock market indices) RSM assumes by construction the same regime at a given time point to all observed values, not allowing differentiating diverse market behaviour. Recently, the RSM have been extended to allow for different regime switching of variables (recognizing heterogeneity in variables) to panel data (Dias et al. (2008, 2009)). However, when applying this methodology to finance and accounting variables, it does not account some well known patterns in financial data like persistence and volatility clustering. This project aims to extend previous developments by incorporating a Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) structure in modeling the conditional variances with clustering and regime switching, simultaneously. Both types of models allow capturing general nonlinear structures in data by incorporating unobserved heterogeneity (and struc...
Project Information
2010-01-01
2013-07-11
Project Partners