Towards Crafting Optimal Functional Link Artificial Neural Networks with Rao Algorithms for Stock Closing Prices Prediction

Subhranginee Das (), Sarat Chandra Nayak () and Biswajit Sahoo ()
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Subhranginee Das: KIIT University
Sarat Chandra Nayak: CMR College of Engineering &Technology
Biswajit Sahoo: KIIT University

Computational Economics, 2022, vol. 60, issue 1, No 1, 23 pages

Abstract: Abstract Quite a good number of population-based meta-heuristics based on mimicking natural phenomena are observed in the literature in resolving varieties of complex optimization problems. They are widely used in search of the optimal model parameters of artificial neural networks (ANNs). However, efficiencies of these are mostly dependent on fine tuning algorithm-specific parameters. Rao algorithms are metaphor-less meta-heuristics which do not need any algorithm-specific parameters. Functional link artificial neural network (FLANN) is a flat network and possesses the ability of mapping input–output nonlinear relationships by using amplification in input vector dimension. This article attempts to observe the efficacy of Rao algorithms on searching the most favorable parameters of FLANN, thus forming hybrid models termed as Rao algorithm-based FLANNs (RAFLANNs). The models are evaluated on forecasting five stock markets such as NASDAQ, BSE, DJIA, HSI, and NIKKEI. The RAFLANNs performances are compared with that of variations of FLANN (i.e., FLANN based on gradient descent, multi-verse optimizer, monarch butterfly optimization and genetic algorithm) and conventional models (i.e., MLP, SVM and ARIMA). The proposed models are found better in terms of prediction accuracy, computation time and statistical significance test.

Keywords: Stock market forecasting; Rao algorithms; Functional link artificial neural network; Genetic algorithm; Monarch butterfly optimization; Financial time series forecasting (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10614-021-10130-9

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