Drug development is costly so drug makers need accurate estimates of sales potential. However, sales forecasts are often unreliable. Our study is unique in combining a large sample of drug classes with data on entry order and promotional spending to estimate peak market share while controlling...

We apply machine-learning techniques to predict drug approvals using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. To deal with missing data, we use imputation methods that allow us to...

Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on...

This paper examines whether financial analysts use the information contained in clinical trial disclosures to improve their forecast accuracy for pharmaceutical companies. Findings indicate that the improved clinical trial disclosures due to a quasi-regulation issued by the International...