Financial TV networks are filled with investors offering their insights into where markets will move in the future. Will there be a recession in 2020? Will the S&P hit new records? The reality is no one knows.
I recently traveled through the Highlands of Scotland, whose poet Robert Burns in 1785 wrote the well-known line: “The best-laid plans of mice and men often go astray.” That line, in a verse about a plowman churning up a field mouse’s nest, is preceded by the not-so-famous but equally prescient: “But mouse, thou art not alone in proving foresight may be vain.”
So why, when our powers of prediction are so inadequate, do we rely heavily on recent past experiences to inform our decisions about future outcomes, such as portfolio construction?
From 1978 through September 2009, the Sharpe Ratio for a typical 60-40 portfolio was 0.45. For the same portfolio in the 10 years since, the ratio, which tells investors how much excess return they can expect to earn for the additional risk they’re taking, is 1.25.
Because the Sharpe Ratio is almost three times higher than its historical norm since 2009, while bonds have performed strongly and equity and interest rate volatility has been low, portfolio optimization tools now routinely recommend a 60-40 split.
That’s akin to driving while only looking in the rear-view mirror, because it fails to take into account constantly shifting probabilities.
That’s especially so when signals are mixed. With 40% of S&P 500 companies reporting third-quarter results, 80% had a positive earnings per share surprise and 64% had revenue that exceeded estimates, according to FactSet.
That helped to push the S&P 500 to a record high close on Oct. 28, yet two days later, even after third-quarter real GDP came in at a higher-than-expected annualized 1.9%, the Federal Reserve cut rates by 0.25% for a third straight time in a bid to boost business investment and exports. Two days after that, the Labor Department said the economy added 128,000 jobs in October, far surpassing the 85,000 expectation.
Given such conflicting data, it would seem logical to look beyond the recommendations of optimization tools and create portfolios that perform in more volatile and uncertain circumstances. Further, rather than “predicting outcomes,” use scenario analysis to inform your outcome.