Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …

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 …

… and Creamer, G., Semantic frames to predict stock price movement. In Proceedings of the
51st Annual Meeting of the Association for Computational Linguistics, 2013 (Association for …

This research provides new perspectives on carbon risk factors by using raw carbon footprint
data, applying more accurate measurement of carbon footprint data, analyzing the global …

This paper evaluates the question of whether sentiment extracted from social media and
options volume anticipates future asset return. The research utilized both textual based data …

Monitoring and forecasting epidemic diseases are of prime importance to public health
organizations and policymakers in taking proper measures and adjusting prevention tactics. …

The objective of this paper is to demonstrate how the boosting approach can be used to
quantify the corporate governance risk in the case of Latin American markets. We compare our …

This paper explores the power of news sentiment to predict financial returns, particularly the
returns of a set of European stocks. Building on past decision support work going back to the …

This paper analyzes the relationship between asset return, volatility and the centrality
indicators of a corporate news network conducting a longitudinal network analysis. We build a …

We examine whether a put-call ratio, derived from a unique set of market data, can be used
to predict directional moves in asset prices during various market conditions between March …