Deterministic chaos and forecasting in Amazon’s share prices

Michael Hanias (), Stefanos Tsakonas (), Lykourgos Magafas (), El Thalassinos () and Loukas Zachilas ()
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Michael Hanias: International Hellenic University, Greece
Stefanos Tsakonas: University of Thessaly, Greece
Lykourgos Magafas: International Hellenic University, Greece
Loukas Zachilas: University of Thessaly, Greece

Equilibrium. Quarterly Journal of Economics and Economic Policy, 2020, vol. 15, issue 2, 253-273

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.

Keywords: time series; chaos theory; econophysics; forecasting (search for similar items in EconPapers)
JEL-codes: C53 C63 G17 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pes:ierequ:v:15:y:2020:i:2:p:253-273

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