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.
Stock analysts are usually separated into two philosophical camps: fundamental or technical. The fundamental analyst uses financial statements, economic forecasts, industry knowledge, and valuation to guide his or her investment process. The technical analyst uses prices, charts, and a whole host of “indicators”. In reality, few stock analysts are purely fundamental or technical, usually blending a combination of the tools based on temperament, experience, and past success. Nonetheless, at the end of the day, the fundamental analyst remains most concerned with valuation, while the technical focuses on price action.
In a previous post we compared the results from employing a 200-day moving average tactical allocation strategy to a simple buy-and-hold investment in the S&P500. Over the total period, the 200-day produced a higher cumulative return as well as better risk-adjusted returns. However, those metrics did erode over time until performance was essentially in line or worse since 1990.
While there’s still some more work to do on understanding the drivers of performance for the 200-day strategy.
Only three months ago, market pundits were getting lathered up about the potential for an inverted yield curve. We discussed that in our post Fed up. But a lot has changed since then.
One oft-used measure of the yield curve, the time spread (10-year Treasury yields less 3-month yields), has inverted (gone negative).
The NY Fed’s yield curve model sets the probability of recession 12-months hence above 31%, up from over 27% in May.
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.
Don’t hold your breath. We’re taking a break from our deep dive into diversification. We know how you couldn’t wait for the next installment. But we thought we should revisit our previous post on investing strategies to mix things up a bit. Recall we investigated whether employing a 200-day moving average tactical allocation would improve our risk-return proflie vs. simply holding a large cap index like the S&P500.
What we learned when we calculated rolling twenty-year cumulative returns was that the moving average strategy outperformed the S&P 500 76% of the time.
In our last post, we took a detour into the wilds of correlation and returned with the following takeaways:
Adding assets that are not perfectly positively correlated to an existing portfolio tends to lower overall risk in many cases.
The decline in risk depends a lot on how correlated the stocks are in the existing portfolio as well as how the additional stocks correlate with all the existing assets.
In our last post, we asked the simple question of whether an investor is better off being diversified if he or she doesn’t know in advance how a stock is likely to perform. We showed some graphs that suggested diversification lowered risk (or, more precisely, volatility), but this came at the expense of accepting less than maximal returns. We then showed that a diversified portfolio was able to produce better risk-adjusted returns on 8 out of 10 of the stocks we had randomly generated.
“Diversify, diversify, diversify!” Mantra, call-to-arms, or warning. Whether you’re an amateur or professional, a student or professor, a pedestrian or pundit you’ve been told that diversification is patently good when it comes to investing. Golly, it makes sense. Don’t bet it all black. Don’t own just one stock. Even grandma knows this. After all, she told you not to put all your egss in one basket. Then again she also told you about the Easter Bunny, who did just that.