Publizierte Paper

• Chaoticity Versus Stochasticity in Financial Markets: Are Daily S&P 500 Return Dynamics Chaotic? Vogl & Rötzel (2022). In: Communications in Nonlinear Science and Numerical Simulation, 108, 106218.
  
• Forecasting performance of wavelet neural networks and other neural network topologies: A comparative study based on financial market data sets. Vogl et al. (2022). In: Machine Learning with Applications, 8, 100302.
• Why do mega-projects fail? Knowledge management as a successful basis for effective flood protection measures – Critical success factors as a guarantee for successful realisation. Vogl & Scheffler (2022). IFKAD conference 2022.
• Controversy in financial chaos research and nonlinear dynamics: A short literature review. Vogl (2022). In: Chaos, Solitons & Fractals, 162, 112444.
• Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framework. Vogl (2023). In: Chaos, Solitons & Fractals, 166, 112884.
• Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008-2019). Vogl (2022). In: SN Business & Economics, 2, 183.
• Trend Momentum II: Driving Forces of Low Volatility and Momentum. Berghorn et al. (2021). In: International Journal of Financial Research, 12(3), Special Issue, 300-319.

Working Paper

• Chaos Measure Dynamics and a Multifactor Model for Financial Markets. Under Review.

Andere Arbeiten und Artikel

• „The Mandelbrot Market Model – Theoretical and Empirical Valuation“ (Vogl, 2016).
• „Scheinunterschiede: Low Volatility und Momentum“ in Institutional Money (Berghorn, Schulz & Vogl, 2018).