Relative Strength Spread

July 23, 2013

The chart below is the spread between the relative strength leaders and relative strength laggards (universe of mid and large cap stocks).  When the chart is rising, relative strength leaders are performing better than relative strength laggards.    As of 7/22/2013:


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212 Years of Price Momentum

July 23, 2013

Christopher Geczy, University of Pennsylvania, and Mikhail Samonov,, recently published the world’s longest backtest on momentum (1801 – 2012).  This is a truly fascinating white paper.  So much of the testing on momentum has been done on the CRSP database of U.S. Securities which goes back to 1926.  Now, we can get a much longer-term perspective on the performance of momentum investing.


We assemble a dataset of U.S. security prices between 1801 and 1926, and create an out-of-sample test of the price momentum strategy, discovered in the post-1927 data. The pre-1927 momentum profits remain positive and statistically significant. Additional time series data strengthens the evidence that momentum is dynamically exposed to market beta, conditional on the sign and duration of the tailing market state. In the beginning of each market state, momentum’s beta is opposite from the new market direction, generating a negative contribution to momentum profits around market turning points. A dynamically hedged momentum strategy significantly outperforms the un-hedged strategy.

Yep, momentum worked then too!  As pointed out in the white paper, those looking for a return factor that outperforms every single year will not find it with price momentum (or any other factor), but momentum has a long track record of generating excess returns.

So much for the theory that ideas in investing tend to streak, get overinvested, then die.  Some, like momentum (or value), go in an out of favor, but they have generated very robust returns over long periods of time.

HT: Abnormal Returns and Turnkey Analyst

Past performance is no guarantee of future returns.

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How to Pick a Winner

July 23, 2013

Scientific American takes a look at the best way to select a winner:

Given the prevalence of betting and the money at stake, it is worth considering how we pick sides. What is the best method for predicting a winner? One might expect that, for the average person, an accurate forecast depends on the careful analysis of specific, detailed information. For example, focusing on the nitty-gritty knowledge about competing teams (e.g., batting averages, recent player injuries, coaching staff) should allow one to predict the winner of a game more effectively than relying on global impressions (e.g., overall performance of the teams in recent years). But it doesn’t.

In fact, recent research by Song-Oh Yoon and colleagues at the Korea University Business School suggests that when you zero in on the details of a team or event (e.g., RBIs, unforced errors, home runs), you may weigh one of those details too heavily. For example, you might consider the number of games won by a team in a recent streak, and lose sight of the total games won this season. As a result, your judgment of the likely winner of the game is skewed, and you are less accurate in predicting the outcome of the game than someone who takes a big picture approach. In other words, it is easy to lose sight of the forest for the trees.

So often people that consider employing relative strength strategies, which measure overall relative price performance of securities rather than delving into the weeds with various accounting level details, feel like they must not be doing an adequate job of analyzing the merits of a given security.  As pointed out in  this research, the best results came from focusing on less data, not more.

Whether trying to select a winner in sports or in the stock market, it is important to remember that “detailed analysis fog the future.”

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