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Non Linear Analysis of S&P Index

100%
Hanias M., Magafas L., Konstantaki P.
Equilibrium. Quarterly Journal of Economics and Economic Policy
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2013
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vol. 8
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issue 4
125-135
EN
This paper applies non-linear methods to analyze and predict the daily open S&P index which is one of the most important stock index in the world. The aim of the analysis is to quantitatively show if the corresponding time series is a deterministic chaotic one and if one or more days ahead prediction can be achieved. These results make the present work a valuable tool for traders investors and funds.
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Deterministic chaos and forecasting in Amazon?s share prices

88%
Hanias M., Tsakonas S., Magafas L., Thalassinos E. I., Zachilas L.
Equilibrium. Quarterly Journal of Economics and Economic Policy
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2020
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vol. 15
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issue 2
253-273
EN
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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