TY - RPRT A1 - Vogl, Markus A1 - Rötzel, Peter T1 - Insights, Trends and Frontiers: A Literature Review on Financial and Risk Modelling in the Information Age (2008-2019) N2 - This study provides an overview of the model evolution and research trends in the field of financial and risk modelling by applying a bibliometric approach from 2008–2019 and an overall citation network analysis. We present a content analysis of contributing authors, countries, journals, main topics, agreements, disagreements and frontiers within the research community and highlight quantitative features such as implemented models, aggregated model-family combinations and algorithms. Moreover, we describe the data sets employed by researchers. Finally, we discuss insights, such as the main statement, namely the non-existence of a “single-best”-approach as well as the future prospects of our findings. KW - Financial Modelling KW - Literature Review KW - risk modelling KW - Quantitative Finance KW - Citation Network KW - Risikomanagment KW - Finanzwirtschaft KW - Modellierung Y1 - 2021 UR - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3764570 ER - TY - JOUR A1 - Vogl, Markus A1 - Rötzel, Peter A1 - Homes, Stefan T1 - Forecasting performance of wavelet neural networks and other neural network topologies: A comparative study based on financial market data sets JF - Machine Learning with Applications N2 - 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 approaches in multiple experiments and test their predictive abilities with different financial time series. We demonstrate experimentally that both wavelet neural networks and neural networks with data pre-processed by wavelets outperform classical network topologies. However, the precision of conducted forecasts implementing neural network algorithms still propose potential for further refinement and enhancement. Hence, we discuss our findings, comparisons with “buy-and-hold” strategies and ethical considerations critically and elaborate on future prospects. KW - Wavelet neural networks KW - Financial forecasting KW - Neural network topology KW - Intelligent systems KW - Finance KW - Wavelet KW - Neuronales Netz KW - Kreditmarkt Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S2666827022000287 U6 - https://doi.org/10.1016/j.mlwa.2022.100302 VL - 8 IS - 6 SP - 100302 EP - 100302 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 - TY - CHAP A1 - Vogl, Markus A1 - Rötzel, Peter T1 - Chaoticity Versus Stochasticity in Financial Markets T2 - International Symposium on Forecasting KW - Chaos KW - Financial Market KW - S&P500 KW - Kapitalmarkt Y1 - 2021 VL - 2021 ER -