While partial moments like semivariance, lower and upper partial moments have seen acceptance by both academics and investment professionals, there are some who consider these measures to be ad hoc measures of investment performance. This paper seeks to provide the academic foundation for the...

Minimizing the classical definition of risk should be a counterintuitive venture as the explanatory nature of historical metrics' construction challenges their ability to serve a predictive purpose on a non-stationary process. We uncover an ill conceived bias in these metrics and discover they...

Function approximation is at the heart of machine learning. Given a dataset comprised of inputs and outputs, we assume that there is an unknown underlying function that is consistent in mapping inputs to outputs in the target domain and resulted in the dataset. We then use supervised learning...

This quick note is intended to further inform the reader on the inherent flexibility of partial moments. We are not re-inventing a wheel. Conditional probability is conditional probability, nothing has changed. What we have uniquely done is represent the partial moment equivalences to these...