What it means
The indicator was popularised by a sports journalist in the 1970s. The rule says that if a team from the older National Football League side wins, the market will rise over the year, and if a team from the American Football League side wins, it will fall.
Over some stretches of history the rule seemed to work, which is why it kept being repeated. Statisticians treat it as a classic case of a spurious correlation, which is a pattern that appears in the data by chance and has no real connection behind it.
The stock market rises in most years, so any rule that tends to say "up" will often be right. Given enough rules to test, some will appear to work simply by luck.
For finance professionals, its value is educational. It shows how easy it is to find a pattern in a long list of data and mistake it for insight, a habit sometimes called data mining or data dredging.
It also reminds analysts to ask whether a relationship has a believable reason behind it, and whether it holds up on fresh data. The indicator is mainly of interest to commentators at the start of a year, who like to mention it for fun.
Serious investors do not base portfolio decisions on it, and professional forecasts rely on earnings, interest rates, valuations and the economy. Treating a trivial pattern as a signal risks real money.
The nuance is that a strong historical hit rate does not equal predictive power. A rule must be tested against a sensible baseline, such as always predicting that the market will rise, before it can be said to add value.
In this case, the simple baseline performs nearly as well, and the sport has nothing to do with the economy.
In practice
Real-world examples.
Example
A financial news presenter mentions the indicator at the start of the year as a bit of fun. The analyst sitting beside the presenter reminds viewers that it has no economic basis and should not influence any investment decision.
Example
A university lecturer uses the indicator in a statistics class to show how spurious correlations arise. Students are asked to find other unrelated series that line up with market returns, and quickly find several.
Example
A cautious private investor is tempted to sell shares after the "wrong" team wins. His adviser shows that holding a diversified portfolio through the year would normally be a better plan than reacting to a trivia-based signal.
Formula
Calculation
Hit rate = number of correct predictions / number of years tested
Compare the rule with a baseline that always predicts "up". Suppose that in a 50-year sample the market rose in 35 years. The always-up baseline is right 35 times, which is 35 / 50 = 70%. Suppose the indicator is right in 38 of the 50 years, a hit rate of 38 / 50 = 76%. The indicator beats the baseline by only 3 years, or 6 percentage points, which on such a small sample is easily explained by luck.Case study
Seen in the real world.
Sterling Oak Advisors is an illustrative, fictional wealth management firm that runs a short training course for new analysts each January. One exercise asks them to test the Super Bowl Indicator against a plain baseline.
The trainees find that over a long sample the indicator is right a little more often than a coin flip, but about the same as saying the market always rises. They then test twenty other unrelated patterns, such as hemline lengths and weather in a major city, and find that several seem to work.
The head of research uses the result to make a point. In this illustrative case, the lesson is that a pattern needs a credible reason and fresh evidence, and that analysts who forget this can end up giving clients advice based on coincidence.
Watch out
Common mistakes.
- Treating a past hit rate as proof that the indicator predicts the market, when there is no link between a sports result and company earnings.
- Forgetting to compare the rule with a simple baseline, such as always predicting that the market will rise.
- Searching through many patterns and presenting only the ones that worked, which is data dredging.
Questions
People also ask.
Is the Super Bowl Indicator reliable?
No, it is a coincidence that has fitted some periods and failed in others, and it has no economic mechanism behind it.
What is a spurious correlation?
It is a statistical relationship between two things that happens by chance or because of a hidden third factor, and not because one causes the other.
Should I use it to choose investments?
No, investment decisions should rest on factors such as your goals, time horizon, costs, diversification and the fundamentals of what you buy.
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