An Interview with Josh Rosen, CMT, a New York Stock Exchange Specialist with Kellogg Group, conducted by Molly Schilling.

NOTE: This interview will be printed in two parts in the September and October issues of Technically Speaking. Josh Rosen blogs daily at TheTrendline.blogspot.com.

Molly Schilling: So, Josh, here we are at the famous Bobby Van Restaurant on Wall Street after a difficult trading day (June 22, 2006). You work as a Specialist on the floor at the New York Stock Exchange — what a challenging job…What was it like today? 

Josh Rosen: It was very interesting today – it seems that a lot of things are shifting right now in the market in general…Old adages aren’t necessarily working…The summer market was supposed to slow up — light trading and low volatility — we’ve seen low volatility in the summer for the past three years.

But, all of a sudden, we have a situation where 

Adapted from “Developing Neural Models with Tradecision,” available at www.tradecision.com.

Even the simplest definitions of neural networks can be confusing. For example, “Neural networks learn to associate values in the output column with the underlying patterns they detect within historical input data.” A neural network is trained on past data to find patterns (inter-dependencies) between the input features and desired target. After an artificial neural network is trained, it uses historically detected patterns to make future forecasts. When you update the data in an already trained neural network or feed new data into it, the network starts looking for patterns that are similar to those it has already found to be associated with certain price movements. After that, it makes a forecast based on the existing similarities with the known price patterns. Neural networks are not a technical indicator, fundamental valuation, regression algorithm or magic super-technology.

The main advantage of neural networks