An extreme firm-specific news sentiment asymmetry based trading strategy

News sentiment has been empirically observed to have impact on financial market returns. In this study, we investigate firm-specific news from the Thomson Reuters News Analytics data from 2003 to 2014 and propose an optimal trading strategy based on a sentiment shock score and a sentiment trend score which measure extreme positive and negative sentiment levels for individual stocks. The intuition behind this approach is that the impact of events that generate extreme investor sentiment changes tends to have long and lasting effects to market movement and hence provides better prediction to market returns. We document that there exists an optimal signal region for both indicators. And we also show extreme positive sentiment provides better a signal than extreme negative sentiment, which presents an asymmetric market behavior in terms of news sentiment impact. The back test results show that extreme positive sentiment generates robust and superior trading signals in all market conditions, and its risk-adjusted returns significantly outperform the S&P 500 index over the same time period

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

There is a general consensus of the good sensing and novelty character- istics of Twitter as an information media for the complex fi nancial market. This paper investigates the permeability of Twitter sphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to fi nancial-specifi c events and establishes Twitter as a relevant feeder for taking decisions regarding the fi nancial market and event fraudulent activities in that market. However, the provenance of contributions, their diferent levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specifi c financial action on the 27th January 2017: the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange (LSE), to create the UK's Leading Food Business. The experiment attempts to answer two research questions which aim to characterize the features of Twitter permeability to the fi nancial market. The experimental results con rm that a far-impacting financial event, such as the merger considered, caused apparent disturbances in all the features considered, that is, information volume, content and sentiment as well as geographical provenance. Analysis shows that despite, Twitter not being a specifi c fi nancial forum, it is permeable to financial events

Using frame-based resources for sentiment analysis within the financial domain

User-generated data in blogs and social networks have recently become a valuable resource for sentiment analysis in the financial domain, since they have been shown to be extremely significant to marketing research companies and public opinion organizations. In order to identify bullish and bearish sentiments associated with companies and stocks, we propose a fine-grained approach that returns a continuous score in the [-1,+1] range. Our supervised approach leverages a frame-based ontological resource which produces feature sets such as lexical features, semantic features and their combination. One of the outcome of our analysis suggests that the frame-based ontological resource we have used might be successfully applied for sentiment analysis within the financial domain achieving better results than traditional sentiment analysis methods that do not embody semantics. We also show the higher performance of a fine-grained approach based solely on the evaluation of specific substrings of the message, rather than on features extracted from the whole text of a financial microblog message through the frame-based ontological resource. We have also compared our system with semi-supervised and unsupervised approaches and results indicate that our approach outperforms the others. Last but not the least, our approach is general and can be applied on top of any existing supervised method of polarity detection

Forex exchange rate forecasting using deep recurrent neural networks

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 to traditional recurrent network architectures as well as feedforward networks in terms of their directional forecasting accuracy and the profitability of trading model predictions. Empirical results indicate the suitability of deep networks for exchange rate forecasting in general but also evidence the difficulty of implementing and tuning corresponding architectures. Especially with regard to trading profit, a simpler neural network may perform as well as if not better than a more complex deep neural network.Peer Reviewe