Financial Econometrics provides the academic underpinnings of Modern Portfolio Theory* (MPT), an evolution in the way to quantify the relationship between risks, returns and investor utility. The origins for GSI’s MPT program dates back to 1983 at the University of British Columbia when John Poulter was introduced to the concept while studying Econometrics and completing his first investment management industry exams.

From Theory to Practice
In 1985, Connor Clark and Company adopted quantitative techniques using MPT and other statistical investment methods. The modeling at Connor Clark Included mean-variance routines common to MPT and also new modeling for individual company selection.

The individual company selection models were among the first in North America to use Graham and Dodd and Warren Buffet like investment principles and apply them to the new world of electronic information management and a developing field of investment finance called Quantitative Analysis.

Our capabilities continued evolving through 1997 when our systems and quantitative platform became central to the investment management process at Cumberland Private Wealth Management, at that time, one of Canada’s most progressive investment management companies.

Bridging the Gap Between Qualitative and Quantitative
One of the challenges faced in the early years of our program’s development arose from the fact that many high net worth management firms are qualitatively oriented investment managers. Quantitative techniques are steeped in mathematics; statistics and qualitative management is typically one part art, one part science. It is the blending of these two typically opposing investment regimes that is one of our processes most powerful achievements. 

Understanding where qualitative market assessments are best used and how quantitative techniques can be used to compliment risk management is a relatively unique skill. 

Quantitative risk assessment is primarily a mathematical affair. Relying on it solely doesn’t provide a full or accurate picture. That’s why we believe future returns benefit immensely from a combination of in-depth qualitative and quantitative examination. 

Enhancing the Investment Tools
Our asset allocation models are scenario-dependent;  our optimizers rely on our qualitative macro-outlook concerning big picture events. Our implementation of our scenario outlook is wrapped in quantitative techniques to ensure that we are implementing our outlook most efficiently.  Our quantitative portfolio systems ensure portfolios are optimally suited to our market forecasts.

Our company selection models rely on the quantitative implementation of time-test qualitative notions of company strengths and value. We adhere to solid fundamental measurements for reviewing a company’s inherent strengths. Our model draws heavily from the decades old tenants of practitioners like Ben Graham and David Dodd** whose seminal work on investment research remains the cornerstone of value investing. With tens of thousands of potential investment candidates in the global markets, applying quantitative techniques to surface value and measure our portfolios profiles is the most efficient way to proceed.  

We build our own tools
Our modeling has been presented at both the IVY School of Business and the Toronto Society of Chartered Financial Analysts. Our company selection models are currently patent-pending and are distributed through a number of financial internet service providers. Including  McGraw Hills Standard and Poor’s. 

As authors of our own quantitative modeling and its implementation within a qualitative framework, we believe that we are well suited to deliver the best possible outcomes from our unique investment process. Our system is designed to apply industry leading investment analytics to provide high value investment solutions whether they be traditional portfolios of individual securities or index/ ETF based TAA programs.