Deterministic chaos and forecasting in Amazon?s share prices

Hanias, M. P.; Tsakonas, Stefanos; Magafas, L.; Thalassinos, Eleftherios; Zachilas, Loukas

doi:10.24136/eq.2020.012

Cited by 4 publications

(5 citation statements)

References 33 publications

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“…Afolabi et al (2019) studied the relationship between leverage and financial performance of Nigerian firms between the years 2007-2016). The random effect generalized least squares method revealed a positive and significant effect between leverage and profitability (Hanias et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 97%

The Impact of Leverage on Financial Performance of Tunisian Quoted Firms

Mohamed,

Amira

2023

IJEBA

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Purpose: This study attempts to understand the relationship between financial leverage and financial perofmrance of Tunsian firms. Design/Methodology/Approach: By appling a panel research method for a sample of 30 firms quoted in Tunsiian stock exchange for the period of 2016-2021. Findings: The study found that leverage has a positive impact on ROA but negative impact on ROE. Practical Implications: Financial leverage is important to meet the requirements of firms and increase the investments. Leverage indicates the possible probalbility of growth and the ambitions of the directors. Originality/Value: Financial performance is necessary to ameliorate the management of the firm by increasing the revenues and decrease the expenses.

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Section: Literature Reviewmentioning

confidence: 97%

The Impact of Leverage on Financial Performance of Tunisian Quoted Firms

Mohamed,

Amira

2023

IJEBA

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“…Considering this limitation, other approaches have been proposed like the nonlinear analysis of the first differences or their equivalent, i.e., the numerical approximation of the derivative of the studied time series. In this case coincidence in the calculation of the correlation dimension between the initial time series and its first differences, provide a trustworthy conclusion of the deterministic or stochastic nature of the studied time series [20]. Finally, it should be mentioned that the deterministic or stochastic nature of a system is decided by combining various metrics, according to different methodologies [21].…”

Section: Introductionmentioning

confidence: 93%

Distinction of Chaos from Randomness Is Not Possible from the Degree Distribution of the Visibility and Phase Space Reconstruction Graphs

Angelidis,

Goulas,

Bratsas

et al. 2024

Entropy

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We investigate whether it is possible to distinguish chaotic time series from random time series using network theory. In this perspective, we selected four methods to generate graphs from time series: the natural, the horizontal, the limited penetrable horizontal visibility graph, and the phase space reconstruction method. These methods claim that the distinction of chaos from randomness is possible by studying the degree distribution of the generated graphs. We evaluated these methods by computing the results for chaotic time series from the 2D Torus Automorphisms, the chaotic Lorenz system, and a random sequence derived from the normal distribution. Although the results confirm previous studies, we found that the distinction of chaos from randomness is not generally possible in the context of the above methodologies.

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“…For a time-series x = {x i | i=1, …, n}, the correlation integral C(ε) is calculated by the expression (Magafas, 2017;Hanias, 2020):…”

Section: ■ Detection Of Chaotic Structurementioning

confidence: 99%

“…Intuitively, the correlation dimension expresses the ways to which points can be close to each other along different dimensions, and this number is expected to rise faster when the space of embedding is of higher dimension. Therefore, the correlation vs. the embedding dimension diagram (v,m) can provide insight about the ways in which time-series points are close to each other, as the dimensionality of the space of embedding increases (Magafas, 2017;Hanias, 2020). Within this context, amongst the ESG and VGA network-based node-series , those with the (v,m) diagram being closer to the source time-series x are considered as more relevant to x in terms of chaotic structure.…”

Section: ■ Detection Of Chaotic Structurementioning

confidence: 99%

An electrostatics method for converting a time-series into a weighted complex network

Tsiotas

Magafas

Argyrakis

2021

Sci Rep

Self Cite

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This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be computed. This allows generating electrostatic-like graphs associated with time-series that, additionally to the existing transformations, can be also weighted and sometimes disconnected. Within this context, this paper examines the structural similarity between five different types of time-series and their associated graphs that are generated by the proposed algorithm and the visibility graph, which is currently the most popular algorithm in the literature. The analysis compares the source (original) time-series with the node-series generated by network measures (that are arranged into the node-ordering of the source time-series), in terms of a linear trend, chaotic behaviour, stationarity, periodicity, and cyclical structure. It is shown that the proposed electrostatic graph algorithm generates graphs with node-measures that are more representative of the structure of the source time-series than the visibility graph. This makes the proposed algorithm more natural rather than algebraic, in comparison with existing physics-defined methods. The overall approach also suggests a methodological framework for evaluating the structural relevance between the source time-series and their associated graphs produced by any possible transformation.

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Deterministic chaos and forecasting in Amazon?s share prices

Cited by 4 publications

References 33 publications

The Impact of Leverage on Financial Performance of Tunisian Quoted Firms

The Impact of Leverage on Financial Performance of Tunisian Quoted Firms

Distinction of Chaos from Randomness Is Not Possible from the Degree Distribution of the Visibility and Phase Space Reconstruction Graphs

An electrostatics method for converting a time-series into a weighted complex network

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