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Abstract

Abstract

The exponential growth of financial news coming from different sources makes getting effective benefit from them very hard. Business decision makers who reply to these news, are unable to follow them accurately in real time. They need always to be alerted immediately for any potential financial events that may affect their businesses whenever they occur. Many important news can simply be embedded into thousands of lines of financial data and cannot be detected easily. Such kind of news may have sometimes a major impact on businesses and the key decision makers should be alerted about them. In this work, we propose an alert system that screens structured financial news and trigger alerts based on the users profiles. These alerts have different priority levels: low, medium and high. Whenever the alert priority level is high, a quick intervention should be taken to avoid potential risks on businesses. Such events are considered as tasks and should be treated immediately. Matching users profiles with news events can sometimes be straightforward. It can also be challenging especially whenever the keywords in the users profiles are just synonyms of the events keywords. In addition, alerts can sometimes be dependable on the combination of correlated news events coming from different sources of information. This makes their detection a computationally challenging problem. Our system allows the user to define their profiles in three different ways: (1) selecting from a list of keywords that are related to events properties; (2) providing free keywords; and (3) entering simple short sentences. The system triggers alerts immediately whenever some news events related to the users profiles occur. It takes into consideration the correlated events and the concordance of the events keywords with the synonymous of the users profiles keywords. The system uses the vector space model to match keywords with the news events words. As consequence, the rate of false-positive alerts is low whereas the rate of false-negative alerts is relatively high. However, enriching the dictionary of synonym words would reduce the false-negative alerts rate.

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/content/papers/10.5339/qfarf.2011.CSPS1
2011-11-20
2020-09-27
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2011.CSPS1
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