Professor Achilleas Zapranis’ research lies at the intersection of financial engineering, machine learning, and computational finance, with a strong focus on the development and application of neural networks in asset pricing, risk forecasting, and financial model estimation. Since 1994, he has consistently combined advanced quantitative techniques with applied finance, implementing robust neural architectures in programming languages such as C/C++, Python, and Matlab. His work addresses a wide range of topics, including:
- Algorithmic technical analysis: Rule-based pattern recognition for trading signals (e.g., head and-shoulders, support/resistance), with formal testing of market inefficiencies.
- Behavioral finance: Examination of herding behavior, co-skewness, and systemic effects in response to financial shocks across developed markets.
- Dynamic time warping for financial time series: Algorithms for measuring similarity in time series of varying lengths and temporal structures.
- Forecasting with uncertainty quantification: Construction of confidence and prediction intervals for neural forecasts using bootstrap, likelihood, and analytical approaches.
- Interest rate modelling: Neural estimation of term structure parameters (e.g., Vasicek
model) for fixed-income applications.
- Neural model design and evaluation: Identification, adequacy testing, and residual diagnostics for neural network models in financial contexts.
- Nonlinear beta modelling: Estimation of time-varying beta coefficients for asset pricing
under changing market conditions. - Tactical asset allocation and stock ranking: Enhancement of factor-based investment
models (e.g., APT) through nonlinear learning systems capable of capturing complex interactions. - Weather and energy derivatives: Pricing of weather-dependent financial products
through wavelet analysis and hybrid neural systems.
In recent years, his research agenda has expanded to explore the transformational role of digital technology in finance, focusing on how emerging technologies such as blockchain, decentralized finance (DeFi), and algorithmic decision systems are reshaping traditional financial models, institutions, and regulatory frameworks. This line of inquiry investigates both the disruptive potential and systemic implications of financial technologies in capital markets, risk management, and financial intermediation.
He is currently authoring a monograph on this topic, which aims to provide a rigorous analytical and conceptual framework for understanding the convergence of technological innovation and financial theory-bridging artificial intelligence, distributed ledger technologies, and monetary economics.
Professor Zapranis’ work has been published in high-impact international journals, including IEEE Transactions on Neural Networks, Neural Networks, Neural Computing & Applications, Neurocomputing, Journal of Forecasting, and the International Journal of Forecasting, and has received over 1,300 citations on Google Scholar. His contributions are regularly referenced in both academic and practitioner circles, reinforcing his position as a leading researcher in the fields of computational and technological finance.
He is currently authoring a monograph on this topic, which aims to provide a rigorous analytical and conceptual framework for understanding the convergence of technological innovation and financial theory-bridging artificial intelligence, distributed ledger technologies, and monetary economics.
Professor Zapranis’ work has been published in high-impact international journals, including IEEE Transactions on Neural Networks, Neural Networks, Neural Computing & Applications, Neurocomputing, Journal of Forecasting, and the International Journal of Forecasting, and has received over 1,300 citations on Google Scholar. His contributions are regularly referenced in both academic and practitioner circles, reinforcing his position as a leading researcher in the fields of computational and technological finance.