Counter Propagation neural networks

Technical Trading, Neural Networks and the All-Mighty Bid-Ask Spread
Full-size image (329 K) I haven't posted in awhile. In the evenings I either write on my blog or I write code. Lately I've been mostly writing code instead. I hope to post some results of some of the experiments I've done later, but in the meantime here is a summary of the stuff I've been working on over the past month or so.
* I finally got my option data in a usable form. I used this to backtest various simple volatility trading strategies involving dynamic hedging. I was mainly interested in finding some useful long-gamma scalping strategies. Nothing really interesting came about from this. The main utility of these experiments was that it was a good practical test of my data access and cleaning routines. Bad data points were a big problem. This project forced me to create more-robust data-scrubing routines which will be invaluable later on.
Generally the long-gamma trading ideas I tested showed piss-poor results. I was surprised in general how poorly hedging the options with stock seemed to work. One revealing experiment was my first one. I tested buying an ATM option and replicating it with black-scholes, hedging the the delta with stock using the end-of-day prices for both the options and the stock. One would expect such a basic strategy would generally not make significant profits

Source: Volatility Rider

Elsevier Modeling of structure-mutagenicity relationships: counter propagation neural network approach using calculated structural descriptors [An article from: Analytica Chimica Acta]
Book (Elsevier)

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2003-11-16 10:13:22 by Mozi

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