Our last post on rebalancing struck an equivocal note. We ran a thousand simulations using historical averages across different rebalancing regimes to test whether rebalancing produced better absolute or risk-adjusted returns. The results suggested it did not. But we noted many problems with the tests—namely, unrealistic return distributions and correlation scenarios. We argued that if we used actual historical data and sampled from it, we might resolve many of these issues.
Back in the rebalancing saddle! In our last post on rebalancing, we analyzed whether rebalancing over different periods would have any effect on mean or risk-adjusted returns for our three (equal, naive, and risky) portfolios. We found little evidence that returns were much different whether we rebalanced monthly, quarterly, yearly, or not at all. Critically, as an astute reader pointed out, if these had been taxable accounts, the rebalancing would likely have been a drag on performance.
We’re taking a break from our series on portfolio construction for two reasons: life and the recent market sell-off. Life got in the way of focusing on the next couple of posts on rebalancing. And given the market sell-off we were too busy gamma hedging our convexity exposure, looking for cheap tail risk plays, and trying to figure out when we should go long the inevitable vol crush. Joking. We’re not even sure what any of that means.
In our last post, we introduced benchmarking as a way to analyze our hero’s investment results apart from comparing it to alternate weightings or Sharpe ratios. In this case, the benchmark was meant to capture the returns available to a global aggregate of investable risk assets. If you could own almost every stock and bond globally and in the same proportion as their global contribution, what would your returns look like?
In our last post, we looked at one measure of risk-adjusted returns, the Sharpe ratio, to help our hero decide whether he wanted to alter his portfolio allocations. Then, as opposed to finding the maximum return for our hero’s initial level of risk, we broadened the risk parameters and searched for portfolios that would at least offer the same return or better as his current portfolio and would also allow him to find a “comfortable” asset allocation.
In our last post, we started building the intuition around constructing a reasonable portfolio to achieve an acceptable return. The hero of our story had built up a small nest egg and then decided to invest it equally across the three major asset classes: stocks, bonds, and real assets. For that we used three liquid ETFs (SPY, SHY, and GLD) as proxies. But our protagonist was faced with some alternative scenarios offered by his cousin and his co-worker; a Risky portfolio of almost all stocks and a Naive portfolio of 50/50 stocks and bonds.
Say you’ve built a little nest egg thanks to some discipline and frugality. And now you realize that you should probably invest that money so that you’ve got something to live off of in retirement. Or perhaps you simply want to earn a better return than stashing your cash underneath your bed, I mean your savings account. How do you choose the assets? What amount of money should you put into each asset?
The oil-to-gas ratio was recently at its highest level since October 2013, as Middle East saber-rattling and a recovering global economy supported oil, while natural gas remained oversupplied despite entering the major draw season. Even though the ratio has eased in the last week, it remains over one standard deviation above its long-term average. Is now the time to buy chemical stocks leveraged to the ratio? Or is this just another head fake foisted upon unsuspecting generalists unaccustomed to the vagaries of energy volatility?
In our previous post we ran two investing strategies based on Apple’s last twelve months price-to-earnings multiple (LTM P/E). One strategy bought Apple’s stock when its multiple dropped below 10x and sold when it rose above 20x. The other bought the stock when the 22-day moving average of the multiple crossed above the current multiple and sold when the moving average crossed below. In both cases, annualized returns weren’t much different than the benchmark buy-and-hold, but volatility was, resulting in significantly better risk-adjusted returns.
In our last post on valuation, we looked at whether Apple’s historical mutiples could help predict future returns. The notion was that since historic price multiples (e.g., price-to-earnings) reflect the market’s value of the company, when the multiple is low, Apple’s stock is cheap, so buying it then should produce attractive returns. However, even though the relationship between multiples and returns was significant over different time horizons, its explanatory power was pretty low.