It is widely proved the existence of non-linear deterministic structures in the exchange rates dynamic. In this work we intend to exploit these non-linear structures using forecasting methods such as Genetic Algorithm and Neural Networks in the specific case of the Yen/$ and British Pound/$...

The mixture of two already known soft computing technics, like Genetic Algorithms and Neural Networks (NN) in Financial modeling, takes a new approach in the search for the best variables involving an Econometric model using a Neural Network. This new approach helps to recognice the importance...

This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

In the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specific estimator and model when aiming for the...

Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep neural networks as an opportunity to improve upon...

In asset pricing, most studies focus on finding new factors such as macroeconomic factors or firm characteristics to explain risk premium. Investigating whether these factors are useful in forecasting stock returns remains active research in the field of finance and computer science. This paper...

We propose an automatic machine-learning system to forecast realized volatility for S&P 100 stocks using 118 features and five machine learning algorithms. A simple average ensemble model combining all learning algorithms delivers extraordinary performance across forecast horizons, and the...

This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic...

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual and non-linear relationships,...

In this work we use Recurrent Neural Networks and Multilayer Perceptrons, to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market...