An Exploratory Study of Geospace Perturbations Using Financial Analysis Tools in the Context of Complex Systems

• Main Authors: Pavlos I. Zitis, Stelios M. Potirakis, Georgios Balasis, Konstantinos Eftaxias
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Geosciences
• Subjects: natural hazards; complex systems; magnetosphere; magnetic storms; <i>D_st</i>; technical analysis
• Online Access: https://www.mdpi.com/2076-3263/11/6/239

In the frame of complex systems research, methods used to quantitatively analyze specific dynamic phenomena are often used to analyze phenomena from other disciplines on the grounds that are governed by similar dynamics. Technical analysis is considered the oldest, currently omnipresent, method for financial markets analysis, which uses past prices aiming at the possible short-term forecast of future prices. This work is the first attempt to explore the applicability of technical analysis tools on disturbance storm time (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>D</mi><mrow><mi>s</mi><mi>t</mi></mrow></msub></mrow></semantics></math></inline-formula>) index time series, aiming at the identification of similar features between the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>D</mi><mrow><mi>s</mi><mi>t</mi></mrow></msub></mrow></semantics></math></inline-formula> time series during magnetic storms (MSs) and asset price time series. We employ the following financial analysis tools: simple moving average (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>M</mi><mi>A</mi></mrow></semantics></math></inline-formula>), Bollinger bands, and relative strength index (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>S</mi><mi>I</mi></mrow></semantics></math></inline-formula>), formulating an analysis approach based on various features, appearing in financial time series during high volatility periods, that could be found during the different phases of the evolution of an MS (onset, main development, and recovery phase), focusing on the temporal sequence they occur. The applicability of the proposed analysis approach is examined on several MS events and the results reveal similar behavior with the financial time series in high volatility periods. We postulate that these specialized data analysis methods could be combined in the future with other statistical and complex systems time series analysis methods in order to form a useful toolbox for the study of geospace perturbations related to natural hazards.