Title: Reservoir computing vs. neural networks in financial forecasting

Authors: Spyros P. Georgopoulos; Panagiotis Tziatzios; Stavros G. Stavrinides; Ioannis P. Antoniades; Michael P. Hanias

Addresses: School of Science and Technology, International Hellenic University, Thermi Campus, Thessaloniki, Greece ' School of Science and Technology, International Hellenic University, Thermi Campus, Thessaloniki, Greece ' School of Science and Technology, International Hellenic University, Thermi Campus, Thessaloniki, Greece ' Physics Department, Aristotle University of Thessaloniki, Thessaloniki, Greece ' Department of Physics, International Hellenic University, Kavala Campus, St. Lucas, Kavala, Greece

Abstract: Stock market prediction techniques are a major research area, thus, extracting time-dependent patterns for the existing predictive models is of major significance. In this work, we compare forecasting performance of the nonlinear model of recurrent neural networks (RNN) in two implementations, LSTM and CNN-LSTM, to the relatively novel approach of reservoir computing (RC), and in specific, the particular class of the echo state networks (ESN). This comparison focuses on exploiting data latent dynamics, in performing efficient training and high quality predictions of the evolution of real-world financial data. Applying a multivariate scheme to a stock market index without any stationarity techniques, a definite precedence of the ESN-RC over both types of RNN's in computational efficiency as well as prediction quality, emerges. Finally, the implemented approach is friendly to the trader, since specific values of a stock market timeseries provide with a frame allowing for in time forecasting, under real-world circumstances.

Keywords: deep learning; neural networks; reservoir computing; machine learning; time series analysis; financial-economic forecasting; algorithmic comparisons.

DOI: 10.1504/IJCEE.2023.127283

International Journal of Computational Economics and Econometrics, 2023 Vol.13 No.1, pp.1 - 22

Received: 10 Mar 2021
Accepted: 13 Jul 2021
Published online: 30 Nov 2022 *