300 Ledgewood Pl, Ste. 101,
In this substantial market volatility, it is important to provide a reminder of the Chapoquoit model’sinvestment objective and decision-making process. Chapoquoit Dynamic Portfolios is a tactically long-term strategy.
Although rebalanced monthly, the Chapoquoit model’s focus is on achieving its objective over market cycles, not over short periods of time.
Investors chose the Chapoquoit strategy because they have a longer-term investment horizon. As a reminder, the Chapoquoit objective is to minimize portfolio losses while targeting an appropriate risk return along the efficient frontier over market cycles. Since the model is driven by monthly changes in macroeconomic and market factors, those factors play out over full market cycles, not over short periods of time. The model makes small changes in allocations each month to achieve its objectives over the cycle. Changes in allocations are a function of changes in all factors as they totally impact the universe of the Chapoquoit universe of investment, not just a single set of investments.
1. Chapoquoit investors must keep their eye on the longer-term investment horizon to realize the investment objective provided by:
• A well-diversified portfolio of market sectors• Monthly rebalancing of allocations influenced by changes in macroeconomic factors
The Chapoquoit model is a rules-based strategy that takes the behavioral (emotional) decision making out of the investment decision. It took five years of rigorous research to determine the ideal set of diversified low-correlated ETFs along with the most effective macroeconomic and market factors to control asset allocations. A rigorous research study using out-of-sample portfolio performance from 1993 to 2012 provided compelling confidence in the potential of the Chapoquoit model quantitative learning process. This process learns from causative monthly conditions using data as far back as 1973.
Each new month’s portfolio allocations results from an analysis of the addition of the newmonth’s set of monthly factor and ETF universe data to historical data series. The new data is analyzed with the historical data as far back as 1973, i.e., under all the equity and fixed income cycle changes since at least the late 1970s. Considering all of data history, the objective is to allocate to ETFs to minimize portfolio losses while targeting an appropriate risk return over market cycles. Focusing on short term volatility will only create unappreciated investment discomfort.
2. As experienced in March and April 2020, other allocation changes could occur if the model detects a disconnect between market behavior and macroeconomic and market factors. When the disconnect is detected, the model could experience a circuit-breaker trigger, which will cause the portfolio allocations to move to a defensive portfolio for a relatively short period of time.