Great post QG - I think one could scan for those securites that observer non-normal historical spiking behaviour. IMHO this is the easiest way to filter for badly behaved / volatilie / non-normally distributed stocks. I refer to the approach in Microsoft Excel for Stock and Option Traders: Jeff Augen. There is a great tool in that book which allows traders to pick a stock and to see its historical price movement in SD terms - From there, one can easily count the number of times a stock spikes by more than, say, 4-5 SD's in a year. They then get ranked by their annual 'spikiness'. In theory, those stocks options prices can never price in their behaviour because the BS model assumes that 4-5 SD spikes never happen. One must obviously exclude earnings spikes but the tool is super easy to code up in python and deploy as a web app. Shout if you want mine and I'll upload it to Discord.
I had a chance to take a good look at what you submitted to the Discord and it's really impressive stuff!
That's also an interesting angle, I could definitely see that as a good way to get exposure to "underpriced" vol. I would imagine that the prior shocks would have been due to legitimate information catalysts that would be unlikely to repeat and/or hard to predict, but it could still be interesting to see if that could be a way of systematically identifying stocks most likely to have extreme outlier moves. Definitely upload it to the Discord when you're ready, every extra shared resource goes a long way haha.
Great post QG - I think one could scan for those securites that observer non-normal historical spiking behaviour. IMHO this is the easiest way to filter for badly behaved / volatilie / non-normally distributed stocks. I refer to the approach in Microsoft Excel for Stock and Option Traders: Jeff Augen. There is a great tool in that book which allows traders to pick a stock and to see its historical price movement in SD terms - From there, one can easily count the number of times a stock spikes by more than, say, 4-5 SD's in a year. They then get ranked by their annual 'spikiness'. In theory, those stocks options prices can never price in their behaviour because the BS model assumes that 4-5 SD spikes never happen. One must obviously exclude earnings spikes but the tool is super easy to code up in python and deploy as a web app. Shout if you want mine and I'll upload it to Discord.
Thanks Alun!
I had a chance to take a good look at what you submitted to the Discord and it's really impressive stuff!
That's also an interesting angle, I could definitely see that as a good way to get exposure to "underpriced" vol. I would imagine that the prior shocks would have been due to legitimate information catalysts that would be unlikely to repeat and/or hard to predict, but it could still be interesting to see if that could be a way of systematically identifying stocks most likely to have extreme outlier moves. Definitely upload it to the Discord when you're ready, every extra shared resource goes a long way haha.
Thanks!