Here's an oddball example. In 1977 Roger Ibbotson and Rex Sinquefield published a very famous paper, Stocks, Bonds, Bills, and Inflation: The Past (1926–1976) and the Future (1977–2000), which tackled the equity premium puzzle and was right but for the wrong reasons.
The paper's equity risk premium and therefore its estimated returns were too high but the paper got a lot of people into equities and for the 18 years 1982-2000 the major U.S. indices returned 18% per year in price appreciation. Ta da!
Mr Ibbotson sold his company to Morningstar, put the proceeds into his Zebra Asset Management and has a comfy chair at the Yale School of Management! Profit baby, profit.
Here's our headline story from Advisor Perspectives:
The Efficient Market Hypothesis posits that the stock market is efficient at incorporating publicly available information. If that is true, then it should be next to impossible to select stocks that will produce returns superior to the overall market’s returns, after making allowance for the cost of risk. But decades of academic research have turned up numerous “anomalies.” These anomalies appear to show that there have been systematic ways to choose stocks that will “beat the market,” and that success is attributable not just to a few individuals with idiosyncratic skills.
In some instances, it has been possible to show that the trading costs of taking advantage of these anomalies would wipe out any superior returns that they might produce. But that does not explain the persistence of most of them. One of the first anomalies to be discovered, and perhaps the best known, is the historical tendency of the stocks of small-capitalization companies to produce superior returns to the overall market (though certainly not at all times).
As the label “anomaly” suggests, the hypothesis of market efficiency is intuitively reasonable as a representation of the normal state of affairs, from which these phenomena are seen as departures. If many participants in the market know a strategy for beating the market, then they will use it until their trading drives up prices and the strategy ceases to provide value. The Efficient Markets Hypothesis posits that advantages are traded away almost instantaneously.
R. David McLean, of the University of Alberta, and his colleague Jeffrey Pontiff, of Boston College, have been conducting a large-scale study to see if the advantages of stock-market anomalies are traded away once they are published in academic papers and therefore become widely known, and if so, how quickly. McLean and Pontiff have not yet published their research, but McLean discussed it at a luncheon meeting of the Boston Security Analysts Society on April 15.
Three explanations for anomalies
McLean explained that there have been three responses to the documented existence of such anomalies.
One has been to say that they merely reflect “data mining” (also sometimes called “data snooping”). McLean referred to a 1998 paper by Eugene Fama, one of the fathers of the Efficient Markets Hypothesis, that made just this point. The argument is that if you test thousands of stock selection strategies, a few of them are bound to work through sheer luck. And if luck is the source of the winning strategies, hardly any of them can be expected to work during a time period outside the original test.
Another response, also identified with Fama, is that the success of the strategies is real, but the realized excess returns, called “alphas,” aren’t actually alphas. Rather, they are compensation for unrecognized systematic risks that are not included in risk models of the stock market but ought to be. This is the old problem of the “joint hypothesis” of market efficiency and any risk model: It is not possible to test the hypothesis of market efficiency independently of a model that provides estimates of the risks being incorporated into prices. (This response lies behind the Fama-French model that is an alternative to the Capital Asset Pricing Model.)
This is a conundrum. McLean said that he does not, however, have much sympathy for the argument, which is more a supposition that systematic risk should be able to explain away any regularity in the success of the stock-selection strategies. But he pointed out that one consequence of the argument is that, contrary to the first response, it implies that these strategies should be successful outside the time period of the original tests.
The third response is that these stock-selection strategies genuinely produce superior results. A likely consequence of this argument is that, because the strategies work, their effectiveness should decline, as they become widely known, to the point where they cease to work after taking into account trading costs.
The three responses to the evidence for successful stock-selection strategies imply three different possibly measureable results.HT: ValueWalk
How the research was conducted
McLean’s and Pontiff’s empirical investigation has three primary parts: identifying and replicating existing research on market anomalies, determining whether each anomaly continued to exist between the end of the period studied and the publication of the research and assessing whether, and to what degree, the anomaly continued to exist after the research was published. (The study does not consider market timing, only stock selection.)
McLean and Pontiff, after reviewing the academic literature, initially identified 95 published strategies that ostensibly predicted stock returns. (As their research has continued, they have accumulated more than 100, including some from unpublished papers.) In the full spirit of experimental investigation, they tried to replicate these studies and found that they could replicate the findings of only 78 of them within the spans of time of the original studies. They pulled these strategies mainly from three leading finance journals — McLean didn’t say which ones — with a few strategies drawn from other publications. The oldest paper that they drew upon was Blume and Husic’s report on the low-price anomaly, published in in the Journal of Finance in 1972. For replication of the studies, they used Compustat for accounting data, the Center for Research in Security Prices (CRSP) for price and return data, and the Institutional Brokers’ Estimate System (IBES) for analysts’ earnings estimates....MORE
