VIDYA and the Nasdaq (Part 1)

11/09/00 12:24:43 PM

by Dennis D. Peterson

VIDYA, the variable index dynamic average, provides the means to dynamically change the period of an exponential moving average. Its application to the Nasdaq provides some interesting insight to Nasdaq behavior.

Security: $compq
Position: N/A

In 1994 Tushar S. Chande and Stanley Kroll authored the book "The New Technical Trader" that introduced new insights and new indicators. At the beginning of the book they show an example of a stock that starts with a top, downtrends with a retracement, bottoms, then goes up and forms a price channel. It is a stock price history built from a random number generator. It does get your attention even if you've seen similar examples before. Chande and Kroll have made their point-- you can't ignore the purely statistical nature of the market.

Most indicators work because they reflect momentum. Momentum is arguably the emotional reaction of investors and can be bullish, bearish or neutral. Statistics on the other hand are pure math. There's nothing subjective about them except in our interpretation of their meaning.

VIDYA adjusts the period of an exponential moving average using price volatility. The approach is to increase the period when volatility is low and decrease the period when volatility is high. In the extreme you might have a period of 50 days to use for today if recent history shows low volatility, and in a week or so you might be using 10 days for the period if volatility has increased recently. So there are two considerations when using VIDYA. First, what is the appropriate period to measure volatility, and secondly what do you want to use as the "base period" for the exponential moving average? You'll end up modifying the "base period" length depending on the amount of volatility.

I used two measures of volatility, the Chande Momentum Oscillator (CMO), and the ratio of the standard deviation of price for recent periods to a historical standard deviation of price. The formulas for VIDYA CMO and the ratio of standard deviations are lengthy and will be shown in Part II of this article.

Figure 1: Nasdaq Composite with VIDYA bands calculated using the Chande Momentum Oscillator (CMO) in the lower chart, and the ratio of standard deviations in the upper chart.

Graphic provided by: MetaStock.

VIDYA is applied as follows: when a close is above an upper band it indicates a bullish trend, when the close is below the lower band it indicates a bearish trend, and when the close is between the two bands the trend is unsure and could be a possible reversal. Since VIDYA lengthens its period very quickly as soon as volatility decreases, it moves into price channels quickly. For an uptrend or downtrend a single close into the band does not indicate a reversal. In fact it may take several. However in keeping with the bullish and bearish signals, an uptrend that has a close below the lower band is very likely a reversal, or for a downtrend a close above the upper band is likely a reversal.

Using the ratio of standard deviations and the CMO gave slightly different results. I used a 12-period CMO, and the standard deviations input was also 12, meaning a recent standard deviation was calculated for the most recent 12 days and the historical reference standard deviation was calculated using 24 days. The results were similar in their VIDYA channels. VIDYA, using standard deviations (Figure 1 upper chart), had five potential reversals-- points A, B, C, D, E-- using the rule above (close above upper band while in a downtrend - close below lower band while in a uptrend). VIDYA, using CMO, had four potential reversals-- points B', C', D', E'. Standard deviations did better in signaling the reversals but in the course of being quicker to respond also had a false signal at A, a typical tradeoff in technical analysis.

Dennis D. Peterson

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Comments

Date:�/ /Rank:�5Comment:�

Date:�/ /Rank:�3Comment:�The idea of the EMA is to take care of the volatility...So why is the added value of the VIDYA ? Pascal

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VIDYA and the Nasdaq (Part 1)

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