Since 1994 Zapranis has been working on neural networks and financial theory and developed numerous applications. Fluent in C/C++, Python and Matlab, has written thousands of lines of programming code and implemented advanced neural network models in C and Python. His main research themes cover the following:

  • Machine learning and non-parametric model estimation: development of learning procedures and neural models.

  • Neural model selection and adequacy testing: development of identification procedures for neural network models and diagnostics/residual analysis for model (mis-) specification.

  • Input variable significance testing of neural models: model sensitivity of input variables to sampling variance and parameter perturbations.

  • Confidence and prediction intervals of neural models: bootstrap, maximum likelihood and analytical approaches constructing confidence and prediction intervals.

  • Stock ranking with neural networks: neural networks as a replacement to classical statistical techniques for forecasting within the framework of the APT (Arbitrage Pricing Theory).

  • Tactical asset allocation with neural networks: expanding modern investment management models, that relying on the assumption that asset returns can be explained in terms of a set of factors, with neural networks which have the ability to infer complex non-linear relationships between an asset price and its determinants.

  • Modelling the term structure of the short rate with neural networks: non-parametric non-linear neural estimation of the reversion speed α, in the context of the Vasicek model, for deriving the term structure of the short rate.

  • Wavelet neural networks: mathematical aspects of wavelet neural network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification.

  • Technical analysis for algorithmic pattern recognition: developing novel rule-based technical analysis pattern recognizers and implementing statistical tests for assessing their performance. Based on the algorithmic and thus unbiased pattern recognition.

  • Weather derivatives pricing: pricing and modeling of weather derivatives written on various underlying weather variables and strategies for effectively hedging against weather-related risk.  

  • Dynamic time warping in financial time series: development of a dynamic time warping algorithm that can provide a similarity measure for different financial time series that might differ in length.

  • Asset pricing with time-varying betas: capturing time variation in a beta coefficient which is treated as a function of market return.

  • Herding behavior in the capital markets: investigation of the existence of herding towards market, co-skewness and co-kurtosis in developed markets and the effect of unexpected shocks in the emergence of herding.

This website or its third-party tools process personal data (e.g. browsing data or IP addresses) and use cookies or other identifiers, which are necessary for its functioning. You accept the use of cookies or other identifiers by closing or dismissing this notice, by clicking a link or button or by continuing to browse otherwise.