The Capital Asset Pricing Model implies that assets with high beta should provide a higher rate of return than those with low beta. High beta assets are such because of a high degree of market exposure: a large amount of correlation with the overall market and high volatility. But, is it possible that high beta assets, with their high volatility, may outperform during market booms and then underperform during times of distress (relative to low beta assets)?
I can’t take credit for this idea, but I thought it was an interesting thought and worth some exploration.
This post also appeared on AlphaArchitect.com
So you’re a trend-follower. Great.
But here is a question:
What do you invest in when your rules suggest “risk off?”
Many investors suggest low duration cash or t-bills. Seems reasonable.
But is it optimal?
Perhaps we should invest in longer duration risk-off assets like 10-yr bonds? We investigate these questions and come to the conclusion that keeping it simple is probably the best solution — dump “risk-off” assets into truly low risk assets like cash or t-bills.
Ratios and normalized metrics are used regularly in the hard sciences, particularly when it comes to comparing scenarios and outcomes. The efficiency of a vehicle, for instance, is typically measured in miles per gallon, or the distance traveled per unit of energy. A Toyota Prius at about 50 MPG is without a doubt substantially more efficient compared to say a top fuel dragster.
The financial world has its equivalent of miles per gallon: the Sharpe Ratio, which combines both return and volatility into a single metric
The idea of buying stocks that are cheap and holding on as they appreciate in value over time is well aligned with the simple heuristic “buy low and sell high.” This central concept has created, for myself, a natural and intuitive pull towards value investing. The problem is that not all “cheap” stocks eventually go on to appreciate in value. Some are cheap for a reason–they have poor prospects and will likely end up in Wall Street’s corporate boneyard.
This post simultaneously appeared at AlphaArchitect.com
The idea that one can predict stock market movements is somewhat insane.
The major problem with stock market forecasting is the lack of evidence that it is possible. I am unaware of any market commentator that has been successful–on a consistent basis–at predicting the future direction of the market. Certainly, every once in a while a pundit or luminary may get something right, but it doesn’t occur often enough by the same party to demonstrate any significant level of skill. Throw enough darts at the dartboard, and your bound to hit a bulls-eye sooner or later. (For a humorous look at the track record of various pundits I suggest a piece by Michael Johnston for fundreference.com1)
In Part 1 I showed that as investment time increased there was, at least historically, a smaller probability of realizing a rebalancing bonus in 60/40 stock/bond portfolios. There was a lot that I left unaddressed at the time and I felt a need to develop a better understanding of the mechanics of the rebalancing bonus. Why does it work in some instances, but not in others? In other words, more was needed to demonstrate what actually drives the rebalancing bonus. A good place to start is with the work of Harry Markowitz, which showed that portfolio performance–both return and volatility–was mathematically related to three characteristics of the constituent assets
- Correlation with other assets
- Rate of return
Go to part 2
The rebalancing exercise that I performed with Shannon’s Demon implied that a premium may be obtained by rebalancing a portfolio of uncorrelated assets. These assets featured highly hypothetical performance with expected rates of return and volatility both well out-of-bounds of anything that’s likely to be seen in real world capital markets. The extreme nature of these make-believe stocks was used to illustrate what is possible.
Back to reality.