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

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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 map** 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.

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Availability of data and material

The datasets analyzed and experimented during the current study are available at https://in.finance.yahoo.com/quote/, which openly available. The source of datasets is highlighted in Subsection 2.3.

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Authors and Affiliations

  1. School of Computer Engineering, KIIT University, Bhubaneswar, 751024, India

    Subhranginee Das & Biswajit Sahoo

  2. Department of Computer Science and Engineering, CMR College of Engineering &Technology, Hyderabad, 501401, India

    Sarat Chandra Nayak

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  1. Subhranginee Das
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  2. Sarat Chandra Nayak
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  3. Biswajit Sahoo
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Correspondence to Sarat Chandra Nayak.

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Das, S., Nayak, S.C. & Sahoo, B. Towards Crafting Optimal Functional Link Artificial Neural Networks with Rao Algorithms for Stock Closing Prices Prediction. Comput Econ 60, 1–23 (2022). https://doi.org/10.1007/s10614-021-10130-9

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