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Hindenburg Omen Predicts Crash - Should You Sell?

By John Jagerson, Analyst at Learning Markets

The Hindenburg omen is a technical signal in the news lately. The signal is triggered when a significant number of stocks (about 2.6%) are hitting 52 week highs and a significant number of stocks (2.6%) are also hitting 52 week lows. There are other criteria involved as well but you don't have to be a technician to understand why traders care about this strange divergence in price action. That kind of a bi-polar market movement is rare and indicates a lot of investor uncertainty. Of course, we already know that the market is currently very fragile and uncertainty is high, what the omen is really supposed to be able to predict are market crashes. We are interested in this signal right now because it completed yesterday... but should start selling your ( AAPL ), ( SPY ) or ( GOOG ) shares?

The omen includes additional criteria like volatility indicators, moving averages and a minimum number of 52 week divergences in a row. Critics would suggest that the pattern is over-fitted to historical data and that the actual predictiveness of the pattern is overstated. Looking at the data in the past - I would definitely agree. It makes for very dramatic news articles on Bloomberg or CNBC but it is probably not predictive enough to use by itself. Fortunately the criteria is easy to quantify and you can test it yourself very easily.

This doesn't mean that the market won't crash. Stocks seems quite fragile right now and we have been advising very robust risk control for a while, however, the Hindenburg omen is probably more of a coincidence than anything else. You can test this by building a model that copies the criteria and backtest it. What you will find is that very small changes to the criteria, data, time-periods or sample size will completely change your results. With certain criteria it is very predictive but a very small change can make it appear nearly random. This problem means that the model is not "robust." Statistics models are often not robust because there are large outliers (or surprises) in the data. Surprises and outliers are particularly large in the stock market.

I think this is a serious problem for traders. Too often they will apply a "probability" to a technical indicator without understanding the differences between market data and other kinds of data distributions. Systems and indicators that work really well on past data tend to fade as the market continues into the future because it was fitted to the past data without any tolerance to small changes in the market environment. You have heard the phrase "past performance is not necessarily indicative of future results?" That phrase is included in almost every prospectus you can find because its true. We have to be very careful about taking past data too far.

We don't need to get into too much detail to understand that if you were to sell on every technical sell or buy signal you would be trading way too often. The Hindenburg omen is interesting and is probably accurately showing a very high level of market uncertainty and anxiety in the market - which we already know but running for the exit every time a signal like this hits the news-wire will probably not do much more than increase your commissions. I would suggest that the same factors driving this indicator (risk and uncertainty) are reason enough to be trading very cautiously right now.

BASICSWhy Financial Writers Often Mislead

Learning Marketsoffers daily articles, videos and investing guides - for free - about everything from investing in stocks and options to trading currencies in the forex market and more. Visit LearningMarkets.com to learn more about investing and to interact with other investors just like you.Image Courtesy of estrategyblog

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.


The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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