Issues surrounding worker classification go back decades in the tax context. Given the rise of the gig-economy, these issues have resurfaced with urgency, with Uber and other gig-economy service providers making headlines for the past several years for allegedly misclassifying workers. The...

When trying to interpret estimated parameters the researcher is interested in the (relative) importance of the individual predictors. However, if the predictors are highly correlated, the interpretation of coefficients, e.g. as economic multipliersʺ, is not applicable in standard regression or...

The scientific community has demonstrated that for the bankruptcy prediction, different techniques have different advantages on different data sets and different feature selection approaches. This subject has attracted a lot of research interests as it is one of the major preoccupation of...

The prime aim of machine learning is to develop techniques or methods which automatically detect patterns in the given data, and later to make use the discovered pattern to predict future or other results of interest. Machine learning practitioners around the globe are trying to model the huge...

We develop a discrete choice recursive model that classifies companies with and without dividend reinvestment plans with 72.0% accuracy. Misclassified companies are more likely to switch their plan status within the next five years, suggesting that financial statements foreshadow changes in plan...