Author(s)

S. N. Onyango & M. Ingleby

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. Keywords: Itô processes, geometric Brownian motion, artificial intelligence (AI), Hough transforms, price histories, market processes, pattern recognition. 1 Introduction Artificial Intelligence (AI) is the application of science to the development of systems that appear to manifest intelligence of the human or mammalian sort. It is a research field where computer science intersects with other fields such as, philosophy, psychology, linguistics, finance and economics among others. The characteristics of a successful recogniser of patterns corrupted by noise are robustness of the following different types: ● resistance to statistical errors in the capturing process (‘Gaussian noise’), ● resistance to extreme errors (‘impulsive noise’), ● resistance to loss of data (‘pattern occlusion’). There is a growing literature on attempts to apply multi-layer artificial neural nets (ANNs) to financial market data (Kingdon [8]; Zapranis and Refenes [16]).

Keywords

Itô processes, geometric Brownian motion, artificial intelligence (AI), Hough transforms, price histories, market processes, pattern recognition.