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.
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?
In our last post on covered calls we introduced the CBOE’s buy-write index (or BXM), whose underlying is the S&P500 index. We looked at some of the historical data, made a few comparisons between the index and the S&P, and noted that there was a report that analyzed the buy-write index. In this post, we’ll look at some of the findings from that report, which can be found on the CBOE’s website.
One of the simplest options strategies is known as the covered call. For this strategy, an investor who already owns a stock elects to sell (or write) an option contract to surrender that stock at a specified price (known as the strike) at some point in the future (also known as expiration). The sale of the contract generates income for the investor, not unlike when an insurance company receives premiums from selling an insurance contract.
When we were testing random correlations and weighthings in our last post on diversification, we discovered that randomizing correlations often increased portfolio risk. Then, when we randomized stock weightings on top of our random correlations, we began to see more cases in which one would have better off not being diversified. In other words, the percentage of portfolios whose risk exceeded the least risky stock began to rise. By chance, the least risky stock (in terms of the lowest volatility), also happened to enjoy the highest risk-adjusted return, so our random selection of stock returns might be a bit anomalous.