TY - RPRT A1 - Vogl, Markus A1 - Rötzel, Peter T1 - Chaoticity Versus Stochasticity in Financial Markets: Are Daily S&P 500 Return Dynamics Chaotic? N2 - In this study, we present a combinatory chaos analysis of daily wavelet-filtered (denoised) S&P 500 returns (2000–2020) compared with respective surrogate datasets, Brownian motion returns and a Lorenz system realisation. We show that the dynamics of the S&P 500 return series consist of an almost equally divided combination of stochastic and deterministic chaos. The strange attractor of the S&P 500 return system is graphically displayed via Takens’ embedding and by spectral embedding in combination with Laplacian Eigenmaps. For the field of nonlinear and financial chaos research, we present a bibliometric analysis paired with citation network analysis. We critically discuss implications and future prospects. KW - nonlinear dynamics KW - chaos KW - recurrence analysis KW - finance KW - financial market KW - Kreditmarkt KW - Chaostheorie Y1 - 2021 UR - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3802753 ER - TY - JOUR A1 - Vogl, Markus A1 - Rötzel, Peter T1 - Chaoticity Versus Stochasticity in Financial Markets: Are Daily S&P 500 Return Dynamics Chaotic? JF - Communications in Nonlinear Science and Numerical Simulation N2 - In this study, we empirically show the dynamics of daily wavelet-filtered (denoised) S&P 500 returns (2000–2020) to consist of an almost equally divided combination of stochastic and deterministic chaos, rendering the series unpredictable after expiration of the Lyapunov time, resulting in futile forecasting attempts. We achieve a clear distinction of the true nature of the underlying time series dynamics by applying a novel and combinatory chaos analysis framework comparing the wavelet-filtered S&P 500 returns with respective surrogate datasets, Brownian motion returns and a Lorenz system realisation. Furthermore, we are the first to show the strange attractor of especially the daily-frequented S&P 500 return system graphically via Takens´ embedding and by spectral embedding in combination with Laplacian Eigenmaps. Finally, we critically discuss implications and future prospects in terms of financial forecasting. KW - nonlinear dynamics KW - chaos KW - recurrence analysis KW - finance KW - prediction KW - Aktienrendite KW - Kreditmarkt Y1 - 2022 VL - 2022 IS - Forthcoming SP - 1 EP - 51 ER -