This is a summary of links featured on Quantocracy on Monday, 07/23/2018. To see our most recent links, visit the Quant Mashup. Read on readers!
Machine Learning, Subset Resampling, and Portfolio Optimization [Flirting with Models]Portfolio optimization research can be challenging due to the plethora of factors that can influence results, making it hard to generalize results outside of the specific cases tested. That being said, building a robust portfolio optimization engine requires a diligent focus on estimation risk. Estimation risk is the risk that the inputs to the portfolio optimization process (i.e. expected
2D Asset Allocation Using PCA (Part 1) [CSS Analytics]Asset allocation is a complex problem that can be solved using endless variations of different approaches that range from theoretical like Mean-Variance to heuristic like Minimum Correlation or even tactical strategies. Another challenge is defining an appropriate asset class universe which can lead to insidious biases that even experienced practitioners can fail to grasp or appreciate.
ETFs, Smart Beta and Factor Exposure [Factor Research]Factor exposure analysis can be used to derive factor themes Smart beta ETFs offer relatively low factor exposure It is all about how factors are defined INTRODUCTION The Austrian energy drinks company Red Bull advertised for almost two decades that Red Bull gives you wings and improves a consumers concentration and reaction speed. Consumers in the US sued the company successfully in 2014