I wrote a paper with a colleague- Jason Teed- for the NAAIM competition. The concept was to apply basic machine-learning algorithms to generate adaptive portfolio allocations using traditional inputs such as returns, volatility and correlations. In contrast to the seminal works on Adaptive Asset Allocation (Butler,Philbrick, Gordillo) which focused on creating allocations that adapted to changing historical inputs over time, our paper on Adaptive Portfolio Allocations (APA) focuses on how to adaptively integrate these changing inputs versus using an established theoretical framework. The paper can be found here: Adaptive Portfolio Allocations Paper. A lot of other interesting papers were submitted to the NAAIM competition and the rest of them can be found here. The method of integration of these portfolio inputs by APA into a final set of portfolio weights is not theory or model driven like MPT, but instead is based upon learning how they interact to produce optimal portfolios from a sharpe ratio perspective. The results show that a traditional mean-variance/Markowitz/MPT framework under-performs this simple framework in terms of maximizing the sharpe ratio. The data further implies that traditional MPT makes far too many trades and takes on too many extreme positions as a function of how it is supposed to generate portfolio weights. This occurs because the inputs- especially the returns- are very noisy and may also demonstrate non-linear or counter-intuitive relationships. In contrast, by learning how the inputs map historically to optimal portfolios at the asset level, the resulting allocations drift in a more stable manner over time. This simple learning framework proposed can be substantially extended with a more elegant framework to produce superior results to those in the paper. The methodology for multi-asset portfolios was limited to an aggregation of pairwise portfolio allocations for purposes of simplicity for readers. The paper didn’t win (or even place for that matter), but like many contributions made on this blog it was designed to inspire new ideas rather than sell cookie-cutter solutions or sound too official or academic. At the end of the day there is no simple ABC recipe or trading system that can survive indefinitely in the ever-changing nature of the markets. There is no amount of rigor,simulation, or sophistication that is ever going to change that. As such, the hope was to provide insight into how to harness a truly adaptive approach for the challenging task of making portfolio allocations.