On the pattern recognition of Itô processes in market price data

Abstract

We introduce a highly error resistant method of extracting Itô processes as applied to market data. This method is inspired by an AI method known as Hough transforms (HT). The HT method has been used in extracting geometric shape patterns from noisy and corrupted image data. We use this method to extract simultaneously geometric Brownian motion trends and market parameters (volatility and mean) from simulated price histories and real-market price data. It turns out that this approach is an effective method of extracting market parameters and market processes for both simulated and real-world market price data.