In this study, we apply a bibliometric analysis paired with a subsequent snowball sampling
procedure. Moreover, we display a full citation network analysis, outlining the most relevant …

Lyapunov exponents provide quantitative evidence for determining the stability and
classifying the limit set of dynamical systems. There are several well-established techniques …

In this study, we analyse the advantageous effects of neural networks in combination with
wavelet functions on the performance of financial market predictions. We implement different …

A very important area where COVID-19 has seriously disrupted is the global financial
markets, where stock markets have experienced great turmoil. To shed light on the nature of …

In this study, we conduct a rolling window approach to wavelet-filtered (denoised) S&P500
returns (2000− 2020) to obtain time-varying Hurst exponents. We discuss implications of …

We explore the synergic interplay between entropy (disorder), predictability, and
informational efficiency of the daily closing price time series of 13 sectoral economics …

We utilize Lyapunov exponents to quantitatively assess the hyperchaos and categorize the
limit sets of complex dynamical systems. While there are numerous methods for computing …

In this study, we conduct a comparative analysis of the nonlinear dynamics of the time series
data for green and conventional bond indices spanning the period from 2014 to 2023. Our …

Dynamic pricing depends on the understanding of uncertain demand. We ask the question
whether a stochastic system is sufficient to model this uncertainty. We propose a novel …

To answer the question if chaos changes over time, we apply rolling windows to wavelet-
denoised logarithmic S&P500 returns (2000–2020) and calculate consecutive chaos …