Over the past two decades, Keller Partners has developed a statistical methodology that scores the performance potential of individual securities. This analytical engine operates exclusively on technical factors, i.e., market-generated inputs and is designed to monitor the activity of informed investors.
Favorably-ranked securities become the building blocks of our Active Large-Cap Portfolios. By design, these 10×10 Portfolios prioritize the management of volatility, specifically portfolio drawdown. They also seek additional return (alpha) through through the investment selection process. The mandates are protection and performance — in that order.
This approach can often deliver both, but our first priority is always capital preservation as it was in 2022. Our portfolios are drawn from a base universe of 120 eligible issues composed of the largest, most liquid issues that trade on US markets. These 120 stocks recently represented over 60% of the market capitalization of the US equity market.
We mathematically evaluate every issue for its potential to experience significant price change in either direction, — what we term “repricing events” — over the ensuing 6-18 months. The analytical engine generates roughly 5-7 price signals a year per issue, reflecting the relative attractiveness of the issue. Given the priority we give to risk management, the process typically improves risk-adjusted metrics such as the Sharpe Ratio, especially in declining markets. Here is a four-year illustration of these technical factor signals for Apple Inc.
The portfolio construction process aligns itself with these signals. Over time, the resulting 10×10 portfolios tend capture a few unusual winners and, equally important, avoid owning some significant losers.
We have evolved these non-traditional selection and portfolio management concepts over the past 10 years. Today, the 10×10 portfolio engine is capable of managing portfolios autonomously (without human intervention) for extended periods of time.
Several years ago, in a collaboration with a major US technology firm, we were able to program and implement a long-term simulation of this autonomous, active, large-cap portfolio process. This exercise covered rough 6500 trading days and found the current process and decision rules to be robust over several bull/bear market cycles. Selection and decision rules were kept constant over the two decades+ of the simulation, and they are the same ones we use today.
