Stationarity - Stationarity: The Best-Kept Secret in...

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Stationarity: The Best-Kept Secret in Trading Success Brett N. Steenbarger, Ph.D. www.brettsteenbarger.com Note: The following article was written on 12/28/03 specifically for the Trading Psychology website. I believe the concepts in the article help to explain why 90% of the material written for traders and/or presented in workshops is invalid. Every trader is familiar with what Victor Niederhoffer calls the “ever-changing cycles” within the market. Just as a pattern makes itself evident in the market, the pattern shifts to a new configuration. For example, the market may trade for a while within a range, offering nice buy and sell signals with a 14 period RSI. Then, abruptly, the market will break out and an overbought RSI will stay overbought or oversold for a prolonged period as the market makes a trending move. It is because of these ever-changing cycles that traditional tools of technical analysis cannot be successfully applied in a purely mechanical fashion. The website Barchart.com has a nice feature where they track trading signals from such standard tools as moving averages. Over time, it is clear from their tracking that the signals do not perform better than random chance. For a while the signals will prove profitable, only to degrade once the cycles change. It is ironic that traders spend considerable time researching better indicators and models while giving little thought to the time frame over which these trading tools might be valid. If, indeed, the market consists of ever-changing cycles, then any system or indicator is apt to degrade in its performance over time. In fact, if one waits for an indicator or system to develop a fine historical track record, the odds are good that their useful life are limited. What can a trader do in the face of such uncertainty? Stationarity The statistician’s term for ever-changing cycles is stationarity. A number series is stationary if the process that generated the series has been constant. Clifford Sherry, in his excellent text The Mathematics of Technical Analysis explains, “A stationary time series is one in which the underlying rules that generate the time series do not change over time.” (p. 9). My favorite example is the Las Vegas casino. Let’s say that you are playing blackjack and think that you have a superior card-counting strategy that will help you make money. By counting the number of picture cards vs. other cards that have been
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dealt, you can assess the probability of drawing a picture card on subsequent hands, tilting odds in your favor. Such a strategy will work as long as the number of decks employed by the dealer is constant. If, however, the dealer intermittently and secretly changes the number of decks in the shoe, the card counting strategy would be imperiled. If the gambler assumed that twelve cards worth 10 or higher were left in the deck because eight had been dealt, the assumption would be faulty if two decks instead of one were being used. By changing the rules for dealing cards, the dealer creates a distribution that is nonstationary. Clifford Sherry notes the importance of nonstationarity for traders:
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Stationarity - Stationarity: The Best-Kept Secret in...

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