Here is a quickie primer on AISs or immune system inspired algorithms. Essentially AISs do a good job of figuring out what is ‘itself’ or ‘normal’ and what is ‘non-self’, ‘alien’ or ‘abnormal’ in a system. Antigens are components that find pathogens (antigens find patterns that are normal to them, the non-normal things then are pathogens).
Some ~2 or 3 years ago, MIT’s Journal of Evolutionary Computation had a ‘special issue’ on AISs. There were several interesting publications in that particular issue, and it led me on a minor reading splurge regarding AISs. At the time, I had been heavily focused on evolutionary and other bio-inspired algorithms and their applications for automated trading systems. There are a ton of algorithms in this class, many of them have mappings to various sub-problems in automated trading.
As far as I can find, there is no work out there currently that applies AISs to finding mis-priced options, and it seems quite a good fit. Searching over a sea of options to sort out which one seem to be aberrations may be a good match. AISs are relatively efficient algorithms compared to alternatives. Interestingly – it would not even necessarily involve your own option pricing model – you would be basically letting the AIS sort out what seem to be ‘normal’ pricings and what seem to stand out as odd.
This is obviously one component of the puzzle, sorting out if the option should be bought or sold is another component. This particular technique could also be applied to any other derivative market with ample liquidity, not just options.
Just some nonsense that has been meandering around my brain for a week or so, so I figured I would write it up here.