We propose a simple approach to bridge between portfolio theory and machine learning. The outcome is an out-of-sample machine learning efficient frontier based on two assets, high risk and low risk. By rotating between the two assets, we show that the proposed frontier dominates the...

This paper considers the optimal hedge ratio problem under estimation risk. Due to incomplete information, the decision-maker evaluates the opportunity cost of hedging using exchange-traded funds or notes (ETF/Ns). Using a back-testing procedure over the last five years and 13 different hedging...

Naive asset allocation and other ad-hoc techniques are commonly practiced by fund managers in the industry. Such strategies, however, are deemed mean-variance (MV) sub-optimal according to modern portfolio theory. Nonetheless, taking estimation risk into considerations, such practices are...

AI/ML models are used for many financial applications ranging from portfolio selection to efficient credit allocation. However, the drawback to applying these models in practice is that performance (i.e., predictive power) is generally inversely related to model complexity. In this chapter, we...

Estimation constitutes a major challenge in the implementation of mean-variance portfolios. To overcome this, we propose a partial index-tracking strategy that aims to mitigate estimation error ex-ante. Theoretically, we minimize the mean-square error of the proposed strategy by shrinking the...

We propose a joint distribution that decomposes asset returns into two independent components: an elliptical innovation (Gaussian) and a systematic non-elliptical latent process. The paper provides a tractable approach to estimate the underlying parameters and, hence, the assets' exposures to...