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A Novel Fuzzy Associative Memory Architecture for Stock Market Prediction and Trading

In this paper, a novel stock trading framework based on a neuro-fuzzy associative memory FAM architecture is proposed. The architecture incorporates the approximate analogical reasoning schema AARS to resolve the problem of discontinuous staircase response and inefficient memory utilization with uniform quantization in the associative memory structure. The resultant structure is conceptually clearer and more computationally efficient 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 confirm the validity of the design and the performance of the proposed architecture.