Quantitative Strategy
An investment strategy defined by a set of rules, behaviors, and procedures designed to guide an investor’s selection of an investment portfolio. This non-discretionary approach tends to remove any emotional response or biases that a person may experience when buying or selling investments.
This investing technique is typically employed by the most sophisticated, technically advanced hedge funds and institutional investors, where machine learning algorithms and supercomputers are deployed to identify predictable patterns within financial data.
History rhymes roughly; it doesn’t repeat neatly.
Adaptive Asset Allocation | Target Volatility Enhanced Momentum
AAA|TVM strategy is founded on the principles of Adaptive Asset Allocation: A Primer, by Adam Butler, Michael Philbrick, Rodrigo Gordillo, and David Varadi. It constitutes an extensive evolution and expansion of the original AAA Primer framework. Besides using momentum, volatilities, and correlations of the historical returns to determine weights while targeting specific level of portfolio’s volatility and drawdowns, advanced concepts ranging from Risk Parity to alternative volatility and momentum measures were implemented.
Strategy offers dynamic exposure to global market opportunities across regional equity, fixed income, credit, currencies, real estate, and commodities. The result is an evolving portfolio seeking strength and diversification in all market conditions.
AAA|TVM launched in 2016
AAA|TV research in 2015
AAA research in 2014
Sector Selection | ISM
SS|ISM is a U.S. centric, non-discretionary Long/Short equity strategy where signals are inferred from coincident information about activity in the U.S. economy contained in Institute of Supply Management (ISM) reports. The algorithm applies rigorous quantitative methods and mathematical transformations to form sector return predictions and systematically allocates capital using SPDRS Sector ETFs.
SS|ISM.3 launched in July 2017
SS|ISM.2 launched in 2015
SS|ISM research in 2013
Elastic Asset Allocation AGGRESSIVE | DEFENSIVE
Both strategies use a geometrical weighted average of the historical returns, volatilities, and correlations, using elasticities as weights.
EAA|A & EAA|D to be launched in 2017
EAA research in 2017
Additional Considerations
The main reason quantitative strategies work is that they are based on discipline. Successful strategies can pick up on trends in their early stages as the computers constantly run scenarios to locate inefficiencies before others do. The models are capable of analyzing a very large group of investments simultaneously, where the traditional analyst may be looking at only a few at a time.
Their weakness is that they rely on historical data for their success. While quant-style investing has its place in the market, it's important to be aware of its shortcomings and risks. To be consistent with diversification principles, it's a good idea to treat quantitative strategies as an investing style and combine it with traditional strategies to achieve proper diversification.
In ever-changing markets, we value agility that allows our strategies to adapt, while staying true to our core principles of rigorous research, robust solutions, and organic growth. In a nutshell, we are operate at the intersection of finance, automation, and statistics; and we believe that we have necessary skills, experience, and infrastructure to tackle those problems.