GMI Forecast Remains Strong, US Stocks Lag

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GLOBAL MARKET INDEX,GMI,PORTFOLIO

The Global Market Index (GMI) maintains its high return outlook for the second consecutive month, driven by strong performance across major asset classes except US equities.

The unchanged forecast, based on three models defined below, reflects a second month with the highest return outlook in recent history for this multi-asset-class global benchmark. GMI’s long-term projection remained steady at a 7.2% annualized total return. GMI is an unmanaged benchmark that holds all the assets in the global market.

As in recent months, US equities remain the lone downside outlier for expected return relative to the market’s history and the various asset classes that comprise GMI. The average forecast for American shares is still printing well below its trailing 10-year performance. The implication: US shares are expected to earn noticeably softer results in the years ahead vs. the market’s realized return over the past decade. By contrast, the rest of the major asset classes continue to post return forecasts above their trailing 10-year records. On that basis, the case for a globally diversified portfolio looks more attractive compared to the past decade. GMI represents a theoretical benchmark for the “optimal” portfolio that’s suited for the average investor with an infinite time horizon. Accordingly, GMI is useful as a tool for customizing asset allocation and portfolio design to match an investor’s expectations, objectives, risk tolerance, etc. GMI’s history suggests that this passive benchmark’s performance is competitive with most active asset-allocation strategies, especially after adjusting for risk, trading costs and taxes. It’s likely that some, most or possibly all of the forecasts above will be wide of the mark in some degree. GMI’s projections, however, are expected to be somewhat more reliable vs. the estimates for its components. Predictions for the specific markets (US stocks, commodities, etc.) are subject to greater volatility and tracking error compared with aggregating the forecasts into the GMI estimate, a process that may reduce some of the errors through tim

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