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Rough Sets in Economy and Finance

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Book cover Transactions on Rough Sets XVII

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 8375))

Abstract

The Rough Set Theory makes it possible to represent and infer knowledge from incomplete or noisy data, and has attracted much focus of the research community and applications have been found in a wide range of disciplines where knowledge discovery and data mining are indispensable. This paper provides a detailed review of the currently available literature covering applications of rough sets in the economy and finance. The classical rough set model and its important extensions applied to the economic and financial problems in crucial areas of risk management (business failure, credit scoring), financial market prediction, valuation and portfolio management are described, showing that the rough set theory is an interesting and increasingly popular method employed alongside traditional statistical methods, neural networks and genetic algorithms to support resolution of the most difficult problems in economy and finance.

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Authors and Affiliations

  1. Misys plc, UK

    Mariusz Podsiadło

  2. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Henryk Rybiński

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  1. Mariusz Podsiadło
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  1. Computational Intelligence Laboratory, ECE Department, University of Manitoba, MB R3T 5V6, Winnipeg, Canada

    James F. Peters

  2. Institute of Mathematics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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Podsiadło, M., Rybiński, H. (2014). Rough Sets in Economy and Finance. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets XVII. Lecture Notes in Computer Science, vol 8375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54756-0_6

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