Polynomial Based Functional Link Artificial Recurrent Neural Network adaptive System for predicting Indian Stocks

Abstract

A low complexity Polynomial Functional link Artificial Recurrent Neural Network (PFLARNN) has been proposed for the prediction of financial time series data. Although different types of polynomial functions have been used for low complexity neural network architectures earlier for stock market prediction, a comparative study is needed to choose the optimal combinations of the nonlinear functions for a reasonably accurate forecast. Further a recurrent version of the Functional link neural network is used to model more accurately a chaotic time series like stock market indices with a lesser number of nonlinear basis functions. The proposed PFLARNN model when trained with the well known gradient descent algorithm produces reasonable accuracy with a choice of range of weight parameters of the network. However, to improve the accuracy of the forecast further, the weight parameters of the recurrent functional neural network are optimized using an evolutionary learning algorithm like the differential evolution (DE). A comparison with other well known neural architectures shows that the proposed low complexity neural model can provide significant prediction accuracy for one day advance and speed of convergence using the International Business Machines Corp. (IBM) stock market indices.

An Intelligent Hybrid System for Forecasting Stock and Forex Trading Signals using Optimized Recurrent FLANN and Case-Based Reasoning

“…Unlike multilayer perceptron (MLP), FLANN system doesn't have any hidden layer, which makes this kind of neural net simple to understand, since learning methods adopted for training this kind of model is not sophisticated. In FLANN, the dimension of the input space can be amplified by extending the input vector with intensifying impression of the input nodes [31]. The amplified input features from the input layer are being supplied to the network such that weighted sums of inputs are provisioned and the net input is computed.…”

Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

“…Wu and Duan [83] compare network structures of different neural networks in predicting the price trend of the Chinese stock market and conclude that the dynamic relationship between investors and market volatility can be thoroughly illustrated. Bebarta et al [99] propose a model that selects optimum nonlinear combinations of Indian stock forecasting. e model proposed by the authors makes use of an evolutionary algorithm to optimize the weight parameters of different functional expansions to improve forecast accuracy.…”

Economic and Financial Performance of the Brazilian Pulp and Paper Industry

“…There are already several studies on the use of computational intelligence on time series forecasting. Bebarta et al [3] presents a model of recurrent neural networks using technical indicators to predict future prices in the Indian stock market. Pommeranzenbaum [30] through Artificial Neural Networks proposes a prediction model of the price series of the Ibovespa index.…”