Asset Allocation Insights

  1. Edward Chancellor is a member of the asset allocation team at GMO LLC. Prior to joining GMO he worked as a financial journalist, receiving the 2007 George Polk Award. Chancellor is the author of acclaimed books on the financial markets, including Crunch Time for Credit (2005) and Devil Take the Hindmost: A History of Financial Speculation (1999). In an interview last week with Paul Amery, editor of, Chancellor talked about his firm’s approach to asset allocation. Edward, how does GMO approach asset allocation from a theoretical perspective?

    Chancellor: Our asset allocation approach is very much valuation-driven. Many allocation models are based on historical asset class returns and correlations, perhaps followed by an optimisation process. While this approach is fine in theory, in practice it tends to lead to disaster, since it ignores the fact that asset class returns are primarily a function of starting valuations.

    For example, ten years ago there was great euphoria about equities, everyone was reading Jeremy Siegel’s “Stocks for the Long Run”, and all this was based on hundred-year historical returns from share investment. But if you decomposed the historical equity return you could see that the starting point was a low double-digit price/earnings ratio, and two thirds of the overall return came from dividends. Given that the dividend yield a decade ago was less than half the long-term average, and that valuations were much higher than those in the past, forward-looking returns were much less attractive than those you could expect from extrapolating history.

    The study of market bubbles and busts, from both a historical and behavioural perspective, reinforces confidence in our valuation models and enables us to add a certain contrarianism to our investment approach. So, a decade ago, the observation of various kinds of euphoria in the markets would have added to the conviction that we were indeed in a bubble.

    It’s always worth examining asset allocation claims that are based on historical returns with some scepticism. Take commodities, for example. There’s been a lot of hype about raw materials as an alternative asset class over the last seven or eight years. Promoters of this idea point to the returns on the GSCI index from 1970 onwards and the low correlation of commodity returns with those on other assets. But it turns out that a large part of the overall commodity index return came in the 1970s, and a major component was due to roll yield – rolling from one commodity futures contract to the next while the market was in backwardation. Now we have the opposite situation, where an investor faces contango and a cost in rolling forward. The component of a commodity index return that came from investing in treasury bills has also now largely disappeared. Finally, the low correlation argument took a hit last year when commodities fell with everything else.

    If the last year has demonstrated anything, it’s the importance of a dynamic asset allocation process, shifting funds to asset classes where the risk/reward trade-off is best, based upon forward-looking valuations rather than historical returns.

    This approach helped GMO’s asset allocation group to highlight negative expected returns from US equities in 1999 and, conversely, very high expected returns from emerging market equities in the same period, at a time when people were still highly sceptical of this asset class following the Asia crisis and Russian default.

    More recently, during the credit bubble period, we observed that the price of all risk assets – equities, high yield, emerging market debt, even commodities – was being bid up, and Ben Inker, who heads the asset allocation team, produced a chart in 2007 showing that the more risk you took on, the less return you were getting. Conventional asset allocation models at that time were showing that you should increase your exposure to private equity and hedge funds, for example. Because Ben used forward-looking returns, he was able to show that the efficient frontier – the line matching risk and return, which should theoretically be upward-sloping – had become flat, and in fact cash had the highest risk-adjusted return.
  • Luke Handt

    Luke Handt is a seasoned cryptocurrency investor and advisor with over 7 years of experience in the blockchain and digital asset space. His passion for crypto began while studying computer science and economics at Stanford University in the early 2010s.

    Since 2016, Luke has been an active cryptocurrency trader, strategically investing in major coins as well as up-and-coming altcoins. He is knowledgeable about advanced crypto trading strategies, market analysis, and the nuances of blockchain protocols.

    In addition to managing his own crypto portfolio, Luke shares his expertise with others as a crypto writer and analyst for leading finance publications. He enjoys educating retail traders about digital assets and is a sought-after voice at fintech conferences worldwide.

    When he's not glued to price charts or researching promising new projects, Luke enjoys surfing, travel, and fine wine. He currently resides in Newport Beach, California where he continues to follow crypto markets closely and connect with other industry leaders.