This paper analyzes the economic determinants of the voluntary revaluation of assets by Spanish companies, within the framework of the Royal Decree 7/1996, of June 13th. The study focuses on an accounting decision that combines discretionary components with several others which are strictly regulated, as companies could voluntarily decide whether or not to revaluate their assets, but were not free to decide when or how to do it. The restatement was allowed for a specific period and according to a certain method. Therefore, it was not possible to introduce value estimates in the process. Consequently, this represents an excellent scenario to assess the extent to which the revaluation decision may be explained by the classical theories of accounting choice. Results from univariate tests reveal that asset revaluations were aimed both, at increasing contracting efficiency and, at signaling future profitability. Multivariate tests intended to identify the factors that jointly explain the revaluation decision show that, after controlling for uncertainty, companies restating their balance sheets were those with the higher profitability in subsequent periods, even though the decision is conditioned by the existence of proprietary costs. We found no evidence that political costs, tax savings or leverage have explanatory power for the revaluation decision. Our results are robust to alternative functional specifications and statistical tests. We provide evidence that the quality of earnings is higher in companies restating their fixed assets both, in terms of persistence and in that they use accounting discretion less opportunistically.

The recent proposal of various non-parametric models to solve prediction problems in banking has provided powerful estimators. This paper uses two non parametric models, Classification and Regression Trees (CART) and Multivariate Adaptative Regression Splines (MARS), in the eurobond market to measure the accuracy of both these methods in determining issue price in the primary market. Also the research included a comparative analysis with a parametric model, lineal regression estimated by Ordinary Least Squares (OLS). Furthermore, the learning-generalization dilemma is tackled by cross-validation in order to establish the most efficient algorithm structure for the solution of our problem. The study concludes that CART algorithm is best at banking and state sector spread prediction, while MARS algorithm and OLS estimates are more accurate at banking issues.

Los hedge funds, o fondos de cobertura, han despertado la atencion de los inversores con una aureola de misticismo y oscurantismo, y se han convertido en un sector de mas de $1.000.000.000.000 a nivel mundial. En este articulo se intenta simplemente presentar algunos hechos sobre el sector, y reflexionar sobre algunos de los mitos. En este articulo revisaremos la industria, sus productos, riesgos y plantearemos una manera de pensar sobre los hedge funds, desde el punto de vista del invesor, que tiene en cuenta sus diferencias respecto a las inversiones tradicionales.