Project portfolio benefits (PPBs) are typically regarded as a key feature for successful project portfolios in practice. However, there is little prescriptive knowledge that offers guidance on how to tackle the realisation and improvement of PPBs. To narrow this research gap, we develop a...

Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, this paper establishes a new central limit theorem (CLT) for a linear spectral statistic (LSS) of high dimensional sample correlation matrices for the case where the...

This paper studies the information content of a subtle writing style, the expression of confidence. We use unsupervised machine learning to generate new lexicons for three aspects of a writer's level of confidence in expressing opinions: certainty, intensity of expression, and emotional...

I use classification-based machine-learning methods to decompose 32 anomaly payoffsinto risk exposures and mispricing. The component driven by risk earns statistically insignificantreturns, despite its efficacy in explaining the time-series variation in anomaly payoffs.The mispricing component...