The San Bernardino County Employees’ Retirement Association implemented a simple innovation 15 years ago that’s added nearly US$1 billion to the fund’s total value as of Aug. 31, 2020.
That innovation is “informed rebalancing,” said Arun Muralidhar, adjunct professor of finance at George Washington University, when speaking at the Canadian Investment Review’s Investment Innovation Conference in November.
Informed rebalancing is a process of dynamically connecting a pension fund’s asset allocation to current market conditions, rather than resorting to infrequent and mechanical rebalancing only after allocations have reached the edge of their portfolio range.
The pension fund, which has more than US$10 billion in assets under management, implemented the strategy after back-testing the program using historical periods against buying and holding, range-based rebalancing, calendar-based rebalancing and volatility-based rebalancing strategies.
“All the additional ideas that they had tested clearly did not add value when they were looking back . . . after they [ran] the program,” Muralidhar said. “Whereas the informed rebalancing actually generated, on a back-tested basis, something like one per cent per annum. But it did so not only with positive alpha, but also with lower drawdowns.”
When investors allow asset allocations to drift within portfolio ranges, they’re essentially “taking bets,” Muralidhar said.
Those implicit bets hit investors hard in February 2020. If they hadn’t rebalanced after the previous market rally, they were overweight in equities just as prices tumbled in the coronavirus-related market volatility. At the bottom of the cycle those investors were suddenly underweight in equities, but didn’t use the buying opportunity and missed the next market rally.
“Rather than letting a portfolio drift arbitrarily within these ranges, a little bit like a drunk driver driving on a highway alongside the guardrail, why not instead dynamically link the asset allocation to current market conditions?” Muralidhar said.
“In other words, adopt the same processes that your managers are using in selecting stocks and bonds, and apply those same techniques to the asset allocation decision, because it has a significant impact on your overall returns. Asset allocation contributes 80 to 90 per cent of a fund’s total risk. What’s nice about this process is you can make sure that you continue to stay within the board-approved strategic policy ranges, but now you’ve got a more informed process.”
He recommended using four factors as decision-making inputs: short-term fixed price data on all assets, such as comparing the moving average of the stock market’s last five days versus the last 50 days; short- to medium-term market sentiment indicators such as volatility, credit spreads and sector rotation; longer-term valuation indicators such as dividend yields and price-to-earnings ratios; and medium-term economic indicators such as interest rates and commodity prices.
“If you combine the short-term indicators . . . with the long-term indicators essentially you can create a diversified process,” Muralidhar said. “If you apply it across multiple assets as well, you can diversify the risk of being wrong.”
Muralidhar said funds can use futures to move their asset allocation between high-level asset classes and the strategy can be implemented as a futures overlay on top of the entire fund. “That’s how it does not disrupt the existing portfolio manager lineup and stays within the broad policy.”
The process should show funds which asset classes they should be overweight in and by how much, with the sum of all those allocations adding up to zero — meaning the strategy has rebalanced favourable assets against those expected to underperform.
This will add alpha on the total fund and potentially lower overall risk, Muralidhar said.