Temporal Portfolio Theory – Introduction
Modern Portfolio Theory (MPT) is now over 65 years old and was developed long before computers were available for analyzing daily market data. MPT’s development was inherently limited to long-term statistical analysis of market data and portfolios. Without time domain (temporal) data analysis, there could be no daily trend/momentum data analysis; which limited MPT’s algorithm investing to simplistic buy-and-hold diversification models. It’s time to extend MPT’s framework to embrace time domain data (momentum) and the many signal processing technologies that have since been developed, perfected, and successfully deployed for Ethernet, WIFI, seismic sensors, and image processing. It has long been proven by both academic studies and industry studies alike that market data contains trend information that can improve the probability of making better investment choices. Temporal Portfolio Theory extends MPT by integrating the cross-disciplinary sciences of Matched Filter Theory, Differential Signal Processing, Fuzzy Logic, and Holistic Risk Management within a layered Portfolio-of-Strategies framework to measurably improve both risk and return performance. The primary components of Temporal Portfolio Theory are outlined below.
This is not your granddad’s portfolio theory!