We study investor sentiment on a non-classical asset, cryptocurrencies using a “cryptospecificlexicon” recently proposed in Chen et al. (2018) and statistical learning methods.We account for context-specific information and word similarity by learning word embeddingsvia neural network-based...

Deep learning has substantially advanced the state-of-the-art in computer vision, natural language processing and other elds. The paper examines the potential of contemporary recurrent deep learning architectures for nancial time series forecasting. Considering the foreign exchange market as...

The paper examines the potential of deep learning to produce decision support models from structured, tabular data. Considering the context of financial risk management, we develop a deep learning model for predicting whether individual spread traders are likely to secure profits from future...

The shift of human communication to online platforms brings many benefits to society due to the ease of publication of opinions, sharing experience, getting immediate feedback and the opportunity to discuss the hottest topics. Besides that, it builds up a space for antisocial behavior such as...

Deep learning has substantially advanced the state of the art in computer vision, natural language processing, and other fields. The paper examines the potential of deep learning for exchange rate forecasting. We systematically compare long short- term memory networks and gated recurrent units...

Financial statement fraud is an area of significant consternation for potential investors, auditing companies, and state regulators. Intelligent systems facilitate detecting financial statement fraud and assist the decision-making of relevant stakeholders. Previous research detected instances in...