Deterministic chaos and forecasting in Amazon’s share prices

Keywords: time series, chaos theory, econophysics, forecasting

Abstract

Research background: The application of non-linear analysis and chaos theory modelling on financial time series in the discipline of Econophysics.

Purpose of the article: The main aim of the article is to identify the deterministic chaotic behavior of stock prices with reference to Amazon using daily data from Nasdaq-100.

Methods: The paper uses nonlinear methods, in particular chaos theory modelling, in a case study exploring and forecasting the daily Amazon stock price.

Findings & Value added: The results suggest that the Amazon stock price time series is a deterministic chaotic series with a lot of noise. We calculated the invariant parameters such as the maxi-mum Lyapunov exponent as well as the correlation dimension, managed a two-days-ahead forecast through phase space reconstruction and a grouped data handling method.

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Published
2020-06-24
How to Cite
Hanias, M., Tsakonas, S., Magafas, L., Thalassinos, E. I., & Zachilas, L. (2020). Deterministic chaos and forecasting in Amazon’s share prices. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 253-273. https://doi.org/10.24136/eq.2020.012
Section
Articles