Founded in 2008, GenFund Management (GFM) is an investment management firm that delivers superior risk-adjusted performance using proprietary, computer-based statistical algorithms and models. Noah Solomon, CEO and CIO, and Scott Miller, Managing Director, spoke with The Canadian Business Journal about the firm’s unique methodology and why it has proven successful.
GFM Investment Funds
GenFund Management offers a range of investment funds, including the GFM 130/30 Fund, the GFM Market Neutral Fund, and the GFM Dividend Income Fund. The objective of the GFM 130/30 and GFM Dividend Income Funds is to produce returns that are at least 30 per cent greater than the TSX Total Return Index, without any added volatility. This means that investors can receive considerably higher returns without taking on any incremental risk.
The GFM Market Neutral Fund aims to achieve returns of six to eight per cent per year with half the volatility of, and with no significant correlation to, the TSX Total Return Index (i.e., regardless of whether the TSX rises or declines).
Unique Investment Philosophy
Prior to launching GenFund Management, Solomon gained a wealth of investment experience with some of the largest global players in the financial industry, including Goldman Sachs. During his experience, Solomon applied statistical algorithms to vast quantities of historical fundamental data (valuation, profitability, etc.) for the purpose of identifying which characteristics of companies tend to be the most predictive in terms of forecasting relative returns.
GFM’s process is unabashedly systematic. There are no emotions involved in security selection or portfolio construction decisions. GFM’s proprietary models bring scientific discipline and mathematical rigour to what is an emotionally charged and chaotic investment process for most investors. Rather than fall victim to his own emotions, Solomon uses mathematical discipline to exploit opportunities.
GFM’s algorithms analyze approximately 150 data points for roughly 200 companies in each quarter going back about 20 years. These data points are income and balance sheet items (i.e. cash flow, debt, accruals, margins, etc.). The algorithms then ascertain which variables tend to possess the strongest and most stable power in terms of their abilities to forecast relative returns. Once the most predictive 60 factors have been identified, then a proprietary optimization technique is applied to assign them relative degrees of importance (how much weight each factor is assigned within the overall decision-making process).
Conservative Portfolio Structure
Each quarter, GFM’s proprietary models take the 200 most liquid TSX stocks and rank them from the most to the least attractive based on the aforementioned 60 variables. The top ranked stocks are purchased and the bottom ranked stocks are sold “short”. Typically, both long and short portfolios are each invested in 80 equally weighted positions (each stock is only 1.25 per cent of total exposure in each of the long and short portfolios).
Not Stock Pickers: GenFund Aims to Create the “Super Stock”
“We are not stock pickers in the classic sense,” Solomon detailed. “We are what I like to describe as portfolio engineers – more important than any single company is how each company fits in with the other companies in the portfolio. No one company has a perfect profile, but different companies have different strengths. We mathematically engineer portfolios of companies based on their ‘part worths’ to create the ‘super stock’ in portfolio form. In simple terms, GFM’s portfolios are scientifically designed based on guiding principles of maximizing exposure to those variables that drive returns while simultaneously minimizing exposure to those variables that cause volatility.”