References of "Minev, Mihail 40021274"
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See detailFeature Detection and Classification in Financial News
Minev, Mihail UL

Doctoral thesis (2014)

The thesis concerns the detection of composite features in news articles as well as their significance for the classification's performance. The considered documents are related to the monetary policy ... [more ▼]

The thesis concerns the detection of composite features in news articles as well as their significance for the classification's performance. The considered documents are related to the monetary policy conducted by the Federal Reserve. One principal goal of this work is to quantify embedded information in financial texts by using phrase structure grammar trees in combination with statistical measures and domain knowledge. Thereby, each document is represented as a combination of linguistic features and feature-values. Furthermore, the work examines the correlations between the determined features and an equity market index by modelling the index volatilities as functions of key announcements. A design is targeted, which should enable the temporal tracking of information alterations. Essential aspects of the thesis are the identification, the extraction, and the representation of domain-specific features and their conditional feature-values. [less ▲]

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See detailQuantification of Financial News for Economic Surveys
Minev, Mihail UL

in International Conference on Data Mining Workshops (2013, December)

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See detailNews Representation with Multi-Word Features
Minev, Mihail UL; Schommer, Christoph UL

in Proceedings ECDA (2013, July)

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See detailDomain-driven news representation using conditional attribute-value pairs
Minev, Mihail UL; Schommer, Christoph UL

in Ferro, Nicola (Ed.) PROMISE Winter School 2013: Bridging between Information Retrieval and Databases (2013)

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See detailNews and stock markets: A survey on abnormal returns and prediction models
Minev, Mihail UL; Schommer, Christoph UL; Grammatikos, Theoharry UL

Report (2012)

Vast amount of news articles are published daily reflecting global topics. The stories represent information about events and expert opinions, which may trigger positive or negative expectations on the ... [more ▼]

Vast amount of news articles are published daily reflecting global topics. The stories represent information about events and expert opinions, which may trigger positive or negative expectations on the stock markets. The literature describes various methods for analyzing such correlations. In this paper we consider related approaches for tracking the impact of news on abnormal stock returns. In the first part we introduce studies with back- ground in Finance. Primarily by applying statistical functions the works examine unusual price volatilities and explore possible sources and market conditions, e.g. biased investors, limited attention, macro-economic variables, country development state, et cetera. In the second part we present studies with background in Computer Science, which take advan- tage of historic news and the equivalent market values. By following the common learning paradigm the projects elaborate prototypes for trend and stock price prediction. In the current survey we evaluate leading approaches regarding the objectives, assumptions, in- put, techniques, and performance. Moreover we provide a comparison framework of the recent prototypes and identify gaps for future research. [less ▲]

Detailed reference viewed: 544 (19 UL)