As of 11/20/2024   Indus: 43,408 +139.53 +0.3%     Trans: 17,002 -26.31 -0.2%     Utils: 1,055 +1.25 +0.1%     Nasdaq: 18,966 -21.33 -0.1%     S&P 500: 5,917 +0.13 +0.0%	YTD  +15.2%    +6.9%    +19.7%    +26.3%    +24.1%	Targets    Overview: 11/12/2024     46,000 or 43,000 by 12/01/2024   18,000 or 16,600 by 12/01/2024   1,075 or 1,000 by 12/01/2024   20,000 or 18,400 by 12/01/2024   6,100 or 5,800 by 12/01/2024
Chart Pattern Indicator: on 11/18/24

As of 11/20/2024   Indus: 43,408 +139.53 +0.3%     Trans: 17,002 -26.31 -0.2%     Utils: 1,055 +1.25 +0.1%     Nasdaq: 18,966 -21.33 -0.1%     S&P 500: 5,917 +0.13 +0.0%	YTD  +15.2%    +6.9%    +19.7%    +26.3%    +24.1%
Targets    Overview: 11/12/2024     46,000 or 43,000 by 12/01/2024   18,000 or 16,600 by 12/01/2024   1,075 or 1,000 by 12/01/2024   20,000 or 18,400 by 12/01/2024   6,100 or 5,800 by 12/01/2024
Chart Pattern Indicator: on 11/18/24

Bulkowski on Price Volatility

I looked at 10 years of my trades and found that 2x volatility for stocks in the range of $0.90 to $1.90 (a volatility reading, not a stock price) gives the best results. A stock too volatile or one showing too little volatility hurts trading performance.

Price Volatility: Background

Volatility is the average of the high-low price range of each day over the prior month (21 entries) multiplied by 2. I multiplied volatility by 2 to keep it consistent with how I use it for stops, but it is otherwise unnecessary.

Price Volatility: Methodology

I measured the 2x volatility of each stock on the buy date and logged how the trade performed. I used data from 1/1/1997 to 6/22/2007. This range included both bull and bear markets. I created three results: the entire 10.5-year period, 5, and 5.5-year periods with nearly equal number of trades (over 100 for both 5 and 5.5-year periods). The first period from 1/1/1997 to 1/1/2002 incorporated most of the bear market of 3/24/2000 to 10/10/2002, as posted by the S and P 500 index.

Price Volatility: Results

10.5-years from January 1997 to June 2007

Using the median 2x volatility of $1.13 showed that high volatility trades made 5.25 times as much money as low volatility ones.

In sample: 5 years from January 1997 to January 2002

The median 2x volatility was $1.29 and high volatility trades beat low volatility trades by 2.69 to 1.

Out of sample: 5.5 years from January 2002 to June 2007

The median 2x volatility was $1.04 and high volatility trades beat low volatility ones by 2.58 to 1.

The in sample and out of sample results were similar. Trades using high volatility stocks resulted in performance that was about 2.5 times more profitable than low volatility ones, despite the in sample results incorporating most of a bear market.

Next, I did a frequency distribution of volatility versus performance and found that volatility too high also hurt performance. The chart shows the sum of profits or losses from trades with low and high volatility readings over the 10-year period. The thinking is that there is a range of 2x volatility that improves the performance of both low and high volatility trades. The area between the red lines is where 2x volatility results in good profit per trade. Performance deteriorates outside of the red lines.

The results described here are for stocks only. Check the results of your trades and see what range works best for you.

-- Thomas Bulkowski

See Also

• This study says that high volatility before a breakout is good.
• Noise. Find out how long stocks and ETFs trend.
• Scaling in. Adding to a position may not pay.
• Scaling out. Should you sell a portion of your position?
• Trade review. Reviewing trades can lead to profits.
• Zero-cost averaging. What is it?

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