PREDICTING INTRADAY STOCK RETURNS BY INTEGRATING MARKET DATA AND FINANCIAL NEWS REPORTS

Forecasting in the financial domain is undoubtedly a challenging undertaking in data mining. While the majority of previous studies in this field utilize historical market data to predict future stock returns, we explore whether there is benefit in augmenting the prediction model with supplementary domain knowledge obtained from financial news reports. To this end, we empirically evaluate how the integration of these data sources helps to predict intraday stocks returns. We consider several types of integration methods: variable-based as well as bundling methods. To discern whether the integration methods are sensitive to the type of forecasting algorithm, we have implemented each integration method using three different data mining algorithms. The results show several scenarios in which appending market-based data with textual news-based data helps to improve forecasting performance. The successful integration strongly depends on which forecasting algorithm and variable representation method is utilized. The findings are promising enough to warrant further studies in this direction

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Analysis and Forecasting of Trending Topics in Online Media Streams

Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201

Methodological Triangulation at the Bank of England:An Investigation

This paper investigates the extent to which triangulation takes place within the Monetary Policy Committee (MPC) process at the Bank of England. Triangulation is at its most basic, the mixing of two or more methods, investigators, theories, methodologies or data in a single investigation. More specifically, we argue for triangulation as a commitment in research design to the mixing of methods in the act of inference. The paper argues that there are many motivations for triangulation as well as types of triangulation. It is argued that there is evidence of extensive triangulation of different types within the MPC process. However, there is very little theoretical triangulation present; raising concerns about pluralism. Also, it is argued that the triangulation which occurs is mainly undertaken for pragmatic reasons and does not reflect other, coherent ontological and epistemological positions.