[00:02] much a stock's price has moved over time, whether it's one day, five days, 10 days, whatever, is to measure how many standard deviations it's changed.
[00:14] Remember, standard deviation is a statistical measure of how much you know, how much something has moved away from an average price. So, or an average level, okay? Now, when it comes to stocks, um a
[00:30] standard deviation is based on the the stock's volatility. And for the stock's volatility. And for volatility, I use the options' implied Um here I have an example of Palantir, PLNT, PLTR. Just loaded up here. And
[00:46] when I look at these these options, these overall options, um on this right-hand side, you know, the August 7th options, 133% volatility, um August weekly options, um with 11 days, they have 93% volatility.
[01:02] You can use those, certainly. Um one of the other things I like to do too to estimate what the volatility was at a previous time though to see how many how many standard deviations something has changed is to go to the charts.
[01:17] Okay? So, let me let me clarify one point that I can use these volatilities to project forward. How much do I think a how many how many standard deviations
[01:29] do I think a stock might change in the future. But for the past, I go back to the charts. Okay? And I have loaded up here the implied volatility level, the overall implied volatility level for, in this
[01:43] case, PLTR, Palantir. Okay? How do I do that? that? Well, what I do is scroll back to the date in question. And let's go, yeah, we're on one day here.
[01:56] we're on one day here. Um let's go back to this date. Okay? Um 6/30, so June 30th. And on June 30th, the closing price of And on June 30th, the closing price of Palantir was 116.67.
[02:10] left-hand corner. And I'll also look down straight down to here, this level right here, to this blue line. And the blue The value of the blue line is 0.62. What
[02:24] does that mean? It means that Palantir's overall volatility was 62%. And then the >> [snorts] >> 0.62 for 62%. So, the formula that I use
[02:37] is actually pretty simple. The only tricky part is is adjusting volatility. And then what I do is I take the previous day's volatility. I want to see how many standard deviations Palantir has moved since
[02:52] since June 30th. Or any day, really. But in this case, June 30th. What I do is I take the volatility on June 30th, 6.62. And I multiply it by the the the square
[03:06] And I multiply it by the the the square root of the number of days between 6/30 and the current date. And what I do is I just count the bars. I'll say 1 2 3 4 5
[03:18] just count the bars. I'll say 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23. So, 23 days. 23 trading days.
[03:31] And because I'm looking at trading days, not calendar days, I use 262 days in a not calendar days, I use 262 days in a in a year. There are approximately 262 trading days in any given year. Between 252 262, up or down a couple of days.
[03:47] But I use 262. So, I adjust volatility point 62 So, I adjust volatility point 62 by the square root of 23 divided by 262
[03:59] by the square root of 23 divided by 262 to give me a an adjusted volatility. Volatility is expressed here and is an annual number, but I don't want a year's worth of of standard deviation. I want 23 days worth of standard deviation. I
[04:13] want 23 days worth of volatility. So, I adjust it by the square root of 23 divided by 262 and then I multiply that result, that time adjusted volatility, by the previous day's close. So, back on June
[04:30] 30th, Palantir closed at 116.67. I multiply that adjusted volatility by 116.67 to get what the expected number of
[04:42] standard deviations Palantir might move based on that previous day's based on that previous day's information, based on 60 62% volatility and based on 116.67 closing price.
[04:56] The next step, I just take the current price of Palantir, which is 125.29. I subtract the previous day's close of or the previous date's close, 116.67.
[05:09] and divide it by that standard deviation number that I just calculated. That tells me not just the dollar change, which is fine, um but again, a a $10 move in a $125 stock is a lot
[05:26] smaller percentage than a $10 move in a $1,000 stock. So, yes, I can I can adjust or or normalize things by looking at percent changes, but that ignores volatility. Adding in the volatility component by
[05:42] looking at the number of standard deviations a stock price has changed sort of evens everything out. It turns apples, grapes, and pears into oranges, so I can compare oranges. Okay, pick your fruit of choice. So,
[05:58] that's how I do it. It's a fairly straightforward forward calculation. If you want, you can put this formula into Excel. You can put it into Python, you see them, as you pull them off the chart, and get an answer very quickly.
[06:14] That's what I do. So, it is it is I think a useful estimate. It It is It's a useful metric to see how much a stock price has changed. Now, none of this is
[06:27] a trade recommendation. If you use a standard deviation price change to you do a trade, that's fine, but that's on you. And remember, if you do, don't take any more risk than you are comfortable with.