PITFALLS IN CROSS‐SECTION STUDIES WITH INTEGRATED REGRESSORS: A SURVEY AND NEW DEVELOPMENTS

In cross‐section studies, if the dependent variable is I(0) but the regressor is I(1), the true slope must be zero in the resulting “unbalanced regression.” A spuriously significant relationship may be found in large cross‐sections, however, if the integrated regressor is related to a stationary variable that enters the DGP but is omitted from the regression. The solution is to search for the related stationary variable, in some cases the first difference of the integrated regressor, in other cases, a categorical variable that can take on limited number of values which depend on the integrated variable. We present an extensive survey, new developments, and applications particularly in finance.     

Shareholder Activism and Voluntary Disclosure Initiation: The Case of Political Spending

Demand for disclosures on environmental, social, and governance (ESG) issues has increased dramatically. Using corporate political spending disclosures as our setting, we conduct a detailed inquiry of 541 political spending‐related shareholder proposals from 2004 to 2012 to highlight the role of shareholder activism as a mechanism to motivate ESG disclosure. Unlike earlier studies, we examine both proposals that went to a vote and proposals that were withdrawn by the activist, allowing us to assess more comprehensively the success of shareholder activism. We find that 20 percent of firms targeted by disclosure proposals begin disclosing in the subsequent year, although implementation rates vary by proposal type—8 percent for proposals subject to a vote versus 56 percent for proposals withdrawn. The sponsor is also important: unions and public pension funds are less likely than other activists to target firms with agency problems and are less successful in having proposals withdrawn, and the implementations they obtain are viewed more negatively by the broader investor base. Our findings highlight shareholder proposals as one mechanism through which investors can successfully express their preferences for corporate disclosure policies. Given activists' long‐standing interest in environmental and social disclosure policies, we believe our findings generalize to a broader set of ESG disclosures.     

Green Public Procurement: A Neglected Tool in the European Green Deal Toolbox?

Intereconomics - Public procurement amounts to around 14% of European Union GDP and, given this size, could well represent an important tool to foster the green transition. However, green public...     

Banks’ deferred tax assets during the financial crisis

Review of Quantitative Finance and Accounting - Prior studies have demonstrated that the net deferred tax liabilities of industrial firms are valued by market participants in a manner consistent...     

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.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

INSURANCE‐INDUCED MORAL HAZARD: A DYNAMIC MODEL OF WITHIN‐YEAR MEDICAL CARE DECISION MAKING UNDER UNCERTAINTY

This study quantifies the moral hazard effect of health insurance on medical expenditure by estimating a dynamic model of within‐year medical care consumption that allows for insurance selection, endogenous health transitions, and individual uncertainty about medical care prices in an environment where insurance has nonlinear cost‐sharing features. The results suggest that moral hazard accounts for 53.1%, on average, of total annual medical expenditure when insured. This estimate is significantly different, and generally larger, than that produced by an alternative model that is representative of the annual medical care decision‐making models commonly found in the literature.     

Costs and potential funding of expanded public pension coverage in Asia

Public pension burdens in most emerging Asian economies are still relatively small. However, there are a number of reasons to believe that they will increase markedly in the coming years. First, many Asian economies will face rapidly ageing populations, which will raise pension and other old‐age‐related spending dramatically. Second, as economies develop, political pressures to expand the coverage of public pensions and raise pension benefits will likely increase. The first objective of this paper is to identify the potential fiscal burden of public pensions in 23 emerging Asian economies, based on econometric models and forecasts of GDP and demographic trends. Using two different methodologies yields estimated increases in the average share of public pension expenditures in GDP of 1.0 percentage point and 3.6 percentage points by 2030 compared with current levels. We believe the latter estimate is more realistic. The second objective is to recommend policies to provide adequate funding for public pension needs, including enhancing the efficiency of social insurance programs, improving the balance of revenues and expenditures, implementing more explicit fiscal rules and frameworks, and establishing stronger fiscal surveillance at the national and regional levels.