Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily,...

Semantic frames are a rich linguistic resource. There has been much work on semantic frame parsers, but less that applies them to general NLP problems. We address a task to predict change in stock price from financial news. Semantic frames help to generalize from specific sentences to scenarios,...

This paper explores the power of news sentiment to predict financial returns, in particular the returns of a set of European stocks. Building on past decision support work going back to the Delphi method this paper describes a text analysis expert weighting algorithm that aggregates the...

This paper develops a global simulation-based solution method to solve large states space macro-finance models using machine learning. We use an artificial neural network (ANN) to approximate the expectations in the optimality conditions in the spirit of the parameterized expectations algorithm...

Forecasts of stock market volatility is an important input for market participants in measuring and managing investment risks. Thus, understanding the most appropriate methods to generate accurate is key. This paper examines the ability of Machine Learning methods, and specifically Artificial...

In this paper we apply the multivariate construction for Lévy processes introduced by Ballotta and Bonfiglioli (2014) to propose an integrated model for the joint dynamics of FX exchange rates and asset prices. We show that the proposed construction is consistent in terms of symmetries with...