In this paper we present, propose and examine additional membership functions as also we propose least squares with genetic algorithms optimization in order to find the optimum fuzzy membership functions parameters. More specifically, we present the tangent hyperbolic, Gaussian and Generalized...

In this paper we present the Radial Basis Neural Network Function. We examine some simple numerical examples of time-series in economics and finance. The forecasting performance is significant superior, especially in financial time-series, to traditional econometric modeling indicating that...

In this paper discrete choice models, Logit and Probit are examined in order to predict the economic recession or expansion periods in USA. Additionally we propose an adaptive neurofuzzy inference system with triangular and Gaussian membership functions and genetic algorithms training...

In this paper we present a very brief description of least mean square algorithm with applications in time-series analysis of economic and financial time series. We present some numerical applications; forecasts for the Gross Domestic Product growth rate of UK and Italy, forecasts for S&P 500...

In this paper we present, propose and examine additional membership functions. There is no reason why more functions cannot be proposed. More specifically, we present the tangent hyperbolic, Gaussian and Generalized bell functions. Because Smoothing Transition Autoregressive (STAR) models follow...

In this paper we examine feed-forward neural networks using genetic algorithms in the training process instead of error backpropagation algorithm. Additionally real encoding is preferred to binary encoding as it is more appropriate to find the optimum weights. We use learning and momentum rates...

In this paper we examine and present the methodology of feed-forward neural networks with error backpropagation algorithm and non-linear methods. We test some applications of time-series analysis in economics. The first part is consisted by applications following the traditional approach of...

This paper investigates various machine learning trading and portfolio optimisation models and techniques. The notebooks to this paper are Python based. By last count there are about 15 distinct trading varieties and around 100 trading strategies. Code and data are made available where...

We investigate the latent volatility structures of the fluctuations in the US business cycle and stock market valuations. The technical novelty of this work lies in the estimation of a Markov-switching stochastic-volatility model that allows for Bayesian sequential evaluation on both the...

Using an agent-based model (ABM) with fundamentalists and chartists, prone to develop bubbles and crashes, we demonstrate the usefulness of direct market intervention by a policy maker, documenting strong performance in preventing bubbles and drawdowns and augmenting significantly the welfare of...