This paper from Goldman Sachs made big headlines a couple of months back for forecasting an abysmal 3% nominal annual return for US stocks in the coming decade. For anyone who didn’t read GS’s analysis, the biggest contributor to that poor return was “market concentration”, or the market cap of the largest stocks relative to the […]
Data Quality
Vintage Economic Data: Part of Our Perpetual Drive to Better Results
Some of the strategies we track use economic data, like the unemployment rate, when making investment decisions. Like 99.99% of strategy backtests you’ll encounter, we’ve always taken the shortcut of basing our historical results on that economic data as it looks today. The problem is that introduces a degree of “lookahead bias”. Economic data is […]
When the Close Is Not Really the Close (A Geeky Discussion)
This post covers an issue rarely discussed in backtesting: the day’s last real-time price shown at 4pm ET often differs slightly from the day’s official closing price determined shortly after 4pm. This is not an Allocate Smartly issue; it’s an oddity of the exchanges. Every so often this difference can cause discrepancies between backtests based […]
Geek Note: How to Properly Lag Monthly Economic Data
We’ll be talking about Paul Novell’s flagship SPY-COMP strategy on the blog tomorrow. The strategy uses monthly economic data, like the kind available from the FRED database. We’ve covered a handful of strategies like this in the past (think Philosophical Economics’ Growth Trend-Timing). Whenever we do, we invariably get a ton of questions, because it […]
Beware Strategies That Fall Down on Good Data
Sources of long-term historical data are few and far between. Because it’s been generously provided for free, one of the most often used is data from Professor French (of Fama-French fame). Others include Shiller and Ibbotson. These datasets are fine for a first pass at testing out ideas, but they often don’t remotely match up […]
For the Nerds: Why We Conform All Strategies to a Common Set of Assets
This post gets a bit down into the weeds of how our backtest engine works, but we’ve received this question a few times recently, so we thought a blog post was in order for readers who are (like us) nerds. We conform all of the strategies that we track to a common set of assets […]
The Perils of Backtesting with Unrealistic Data
As readers hear us repeat often, our results tend to be less optimistic than you’ll find elsewhere. We do our best to show backtested results that are as realistic as possible (even though showing results that are as good as possible would probably be better for business). That’s partially a result of simple things, like accounting […]