87 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 7 DOI: 10.4018/978-1-4666-1870-1.ch007 INTRODUCTION Financial engineering is a rapidly expanding research area. Trading systems based on com- putational intelligence techniques for financial asset management, notably in the areas of equities trading and risk management for derivatives like options and swaps has received considerable in- terest from both researchers and financial traders. Neural networks (NN) have been used extensively for market forecasting (White, 1988; Chiang et al., 1996). More recently, time-delay, recurrent and probabilistic NNs have been used to analyze the predictive capability of the networks using “live Chai Quek Nanyang Technological University, Singapore Zaiyi Guo Nanyang Technological University, Singapore Douglas L. Maskell Nanyang Technological University, Singapore A Novel Fuzzy Associative Memory Architecture for Stock Market Prediction and Trading ABSTRACT In this paper, a novel stock trading framework based on a neuro-fuzzy associative memory (FAM) ar- chitecture is proposed. The architecture incorporates the approximate analogical reasoning schema (AARS) to resolve the problem of discontinuous (staircase) response and ineffcient memory utilization with uniform quantization in the associative memory structure. The resultant structure is conceptually clearer and more computationally effcient than the Compositional Rule Inference (CRI) and Truth Value Restriction (TVR) fuzzy inference schemes. The local generalization characteristic of the associative memory structure is preserved by the FAM-AARS architecture. The prediction and trading framework exploits the price percentage oscillator (PPO) for input preprocessing and trading decision making. Numerical experiments conducted on real-life stock data confrm the validity of the design and the per- formance of the proposed architecture.