An intelligent hybrid system for forecasting stock and forex trading signals using optimized recurrent flann and case-based reasoning

Abstract

An accurate prediction of future stock market trends is a bit challenging as it requires a profound understanding of stock technical indicators, including market-dominant factors and inherent process mechanism. However, the significance of better trading decisions for a successful trader inspires researchers to conceptualize superior model employing the novel set of techniques. In light of this, an intelligent stock trading system utilizing dynamic time windows with case-based reasoning (CBR), and recurrent function link artificial neural network (FLANN) optimized with Firefly algorithm is designed. The idea of using CBR module is to offer a dynamic window search to assist the recurrent FLANN architecture for superior fine-tuning the trading operations. This integrated stock trading system is intended to pick the buy/sell window of target stock to maximize the profit. To demonstrate the applicability of the projected system, the time-series stock data from IBM, Oracle and in currency Euro to INR and USD to INR exchange data on daily closing stock prices are used for simulation. The performance of the proposed model is assessed using error measures such as mean absolute error and mean absolute percent error. Furthermore, the experimental results obtained with/without using CBR is exhibited for different stock and Forex trading data.