Chapoquoit Dynamic Portfolios Strategy

A Review of the Chapoquoit Dynamic Portfolios Strategy

In this substantial market volatility, it is important to provide a reminder of the Chapoquoit model’s
investment 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 new
month’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

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