In the present paper we examine whether financial markets could have helped predict exchange rates in three selected Central and Eastern European (CEE) economies of the EU, namely the Czech Republic, Hungary and Poland, during the current financial crisis. To this end, we derive risk-neutral...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

This paper analyzes the predictability of emerging market currency crises by comparing the often used probit model to a new method, namely a multi-layer perceptron artificial neural network (ANN) model. According to the results, both models were able to signal currency crises reasonably well...

In this paper, we study the effectiveness of carry trade strategies during and after the financial crisis using a flexible approach to modeling currency returns. We decompose the currency returns into multiplicative sign and absolute return components, which exhibit much greater predictability...

This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are...

Predicting stock returns has been a never ending endeavour of both, practitioners and academics. Accurate forecasts are crucial for investment decisions and performances as well as for analysing market microstructures. This paper offers an innovative approach towards forecasting based on Neural...

This paper shows that CEO tweets contain informational content on the U.S. stock markets and provide investors with value-relevant information on predicting the stock price movement. We create a large, unique sample of CEO users on Twitter, extract hashtags and sentiments that can be used as...

This paper examines the performance of several state-of-the-art deep learning techniques for exchange rate forecasting (deep feedforward network, convolutional network and a long short-term memory). On the one hand, the configuration of the different architectures is clearly detailed, as well as...

The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the...