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Rough Sets in Economy and Finance

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Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

soft computing rough sets artificial intelligence risk management stock market prediction credit scoring 

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References

  1. 1.
    Ahn, B., Cho, S., Kim, C.: The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications 18(2), 65–74 (2000)Google Scholar
  2. 2.
    Al-Qaheri, H., Hassanien, A., Abraham, A.: Discovering stock price prediction rules using rough sets. Neural Network World Journal (2008)Google Scholar
  3. 3.
    Ang, K., Quek, C.: RSPOP: Rough set-based pseudo outer-product fuzzy rule identification algorithm. Neural Computation 17(1), 205–243 (2005)zbMATHGoogle Scholar
  4. 4.
    Ang, K., Quek, C.: Stock trading using RSPOP: A novel rough set-based neuro-fuzzy approach. IEEE Transactions on Neural Networks 17(5), 1301–1315 (2006)Google Scholar
  5. 5.
    Atsalakis, G., Valavanis, K.: Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications 36(3), 5932–5941 (2009)Google Scholar
  6. 6.
    Bahrammirzaee, A.: A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications 19(8), 1165–1195 (2010)Google Scholar
  7. 7.
    Baltzersen, J.: An attempt to predict stock market data: a rough sets approach. Ph.D. Dissertation (1996)Google Scholar
  8. 8.
    Bazan, J., Skowron, A., Synak, P.: Market data analysis: A rough set approach. ICS Research Reports 6, 94 (1994)Google Scholar
  9. 9.
    Beynon, M.: Reducts within the variable precision rough sets model: a further investigation. European Journal of Operational Research 134(3), 592–605 (2001)zbMATHGoogle Scholar
  10. 10.
    Beynon, M., Peel, M.: Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega 29(6), 561–576 (2001)Google Scholar
  11. 11.
    Beynon, M., Clatworthy, M., Jones, M.: The prediction of profitability using accounting narratives: a variable-precision rough set approach. Intelligent Systems in Accounting, Finance and Management 12(4), 227–242 (2004)Google Scholar
  12. 12.
    Bezdek, J.: Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers (1981)Google Scholar
  13. 13.
    Bhardwaj, G., Swanson, N.: An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series. Journal of Econometrics 131(1), 539–578 (2006)MathSciNetGoogle Scholar
  14. 14.
    Bioch, J., Popova, V.: Bankruptcy prediction with rough sets. ERIM Report Series Reference No. ERS-2001-11-LIS (2003)Google Scholar
  15. 15.
    Bose, I.: Deciding the financial health of dot-coms using rough sets. Information & Management 43(7), 835–846 (2006)Google Scholar
  16. 16.
    Boudreau-Trudel, B., Zaras, K.: Comparison of Analytic Hierarchy Process and Dominance-Based Rough Set Approach as Multi-Criteria Decision Aid Methods for the Selection of Investment Projects. American Journal of Industrial and Business Management 2(1), 7–12 (2012)Google Scholar
  17. 17.
    Capotorti, A., Barbanera, E.: Credit scoring analysis using a fuzzy probabilistic rough set model. Computational Statistics & Data Analysis 56(4), 981–994 (2012)zbMATHMathSciNetGoogle Scholar
  18. 18.
    Chen, S.-M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems 81(3), 311–319 (1996)Google Scholar
  19. 19.
    Chen, S.-H., Wang, P.: Computational intelligence in economics and finance. Springer (2004)Google Scholar
  20. 20.
    Chen, Y.-S., Cheng, C.-H.: Forecasting PGR of the financial industry using a rough sets classifier based on attribute-granularity. Knowledge and Information Systems 25(1), 57–79 (2010)Google Scholar
  21. 21.
    Chen, Y.-S., Cheng, C.-H., Chen, D.-R.: A fuzzy-based rough sets classifier for forecasting quarterly PGR in the stock market (Part I). International Journal of Innovative Computing, Information and Control 7(2), 555–569 (2011)Google Scholar
  22. 22.
    Chen, Y.-S.: Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach. Knowledge-Based Systems 26, 259–270 (2012)Google Scholar
  23. 23.
    Chen, Y.-S., Cheng, C.-H.: A soft-computing based rough sets classifier for classifying IPO returns in the financial markets. Applied Soft Computing 12(1), 462–475 (2012)Google Scholar
  24. 24.
    Cheng, C.-H., Chen, T.-L., Wei, L.-Y.: A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Information Sciences 180(9), 1610–1629 (2010)Google Scholar
  25. 25.
    Cheng, J.-H., Chen, H.-P., Lin, Y.-M.: A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4. 5. Expert systems with Applications 37(3), 1814–1820 (2010)Google Scholar
  26. 26.
    Cheng, C.-H., Chen, Y.-S.: A fuzzy-based rough sets classifier for forecasting quarterly PGR in the stock market (part II). International Journal of Innovative Computing, Information and Control 7(3), 1209–1228 (2011)Google Scholar
  27. 27.
    Cornelis, C., De Cock, M., Radzikowska, A.: Fuzzy rough sets: from theory into practice. Handbook of Granular Computing. Wiley, Chichester (2008)Google Scholar
  28. 28.
    Cornelis, C., Jensen, R., Hurtado, G.D.S.: Attribute selection with fuzzy decision reducts. Information Sciences 180(2), 209–224 (2010)zbMATHMathSciNetGoogle Scholar
  29. 29.
    Cox, E.: Fuzzy modeling and genetic algorithms for data mining and exploration. Morgan Kaufmann (2005)Google Scholar
  30. 30.
    d’Amato, M.: Appraising property with rough set theory. Journal of Property Investment & Finance 20(4), 406–418 (2002)Google Scholar
  31. 31.
    d’Amato, M.: A comparison between MRA and rough set theory for mass appraisal. A case in Bar. International Journal of Strategic Property Management 8(4), 205–217 (2004)Google Scholar
  32. 32.
    d’Amato, M.: Comparing rough set theory with multiple regression analysis as automated valuation methodologies. International Real Estate Review 10(2), 42–65 (2007)Google Scholar
  33. 33.
    Daubechies, I., et al.: Ten lectures on wavelets 61. SIAM (1992)Google Scholar
  34. 34.
    Lin, T., Tremba, J.: Attribute Transformations on Numerical Databases. Applications to Stock Market and Economic Data. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 181–192. Springer, Heidelberg (2000)Google Scholar
  35. 35.
    Dembczyński, K., Greco, S., Kotłowski, W., Słowiński, R.: Statistical model for rough set approach to multicriteria classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 164–175. Springer, Heidelberg (2007)Google Scholar
  36. 36.
    Demyanyk, Y., Hasan, I.: Financial crises and bank failures: a review of prediction methods. Omega 38(5), 315–324 (2010)Google Scholar
  37. 37.
    Deng, J.-L.: Introduction to grey system theory. The Journal of Grey System 1(1), 1–24 (1989)zbMATHMathSciNetGoogle Scholar
  38. 38.
    Dimitras, A., Zanakis, S., Zopounidis, C.: A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research 90(3), 487–513 (1996)zbMATHGoogle Scholar
  39. 39.
    Dimitras, A., Slowinski, R., Susmaga, R., Zopounidis, C.: Business failure prediction using rough sets. European Journal of Operational Research 114(2), 263–280 (1999)zbMATHGoogle Scholar
  40. 40.
    Doumpos, M., Zopounidis, C.: Multi–Criteria Classification Methods in Financial and Banking Decisions. International Transactions in Operational Research 9(5), 567–581 (2002)zbMATHGoogle Scholar
  41. 41.
    Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets*. International Journal of General System 17(2-3), 191–209 (1990)zbMATHGoogle Scholar
  42. 42.
    Dymowa, L.: Soft computing in economics and finance, vol. 6. Springer (2011)Google Scholar
  43. 43.
    Fayyad, U., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning (1993)Google Scholar
  44. 44.
    Flesch, R.: A new readability yardstick. The Journal of Applied Psychology 32(3), 221 (1948)Google Scholar
  45. 45.
    Golan, R., Edwards, D.: Temporal rules discovery using datalogic/R+ with stock market data. In: Rough Sets, Fuzzy Sets and Knowledge Discovery, pp. 74–81. Springer (1994)Google Scholar
  46. 46.
    Golan, R., Ziarko, W.: A methodology for stock market analysis utilizing rough set theory. In: Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering, pp. 32–40 (1995)Google Scholar
  47. 47.
    Greco, S., Cascio, S., Matarazzo, B.: Rough set approach to stock selection: An application to the Italian Market. In: Modelling Techniques for Financial Markets and Bank Management, pp. 192–211. Springer (1996)Google Scholar
  48. 48.
    Greco, S., Matarazzo, B., Slowinski, R.: Rough set approach to multi-attribute choice and ranking problems. In: Multiple Criteria Decision Making, pp. 318–329. Springer (1997)Google Scholar
  49. 49.
    Greco, S., Matarazzo, B., Slowinski, R.: A new rough set approach to evaluation of bankruptcy risk. In: Operational Tools in the Management of Financial Risks, pp. 121–136. Springer (1998)Google Scholar
  50. 50.
    Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation of a preference relation by dominance relations. European Journal of Operational Research 117(1), 63–83 (1999)zbMATHMathSciNetGoogle Scholar
  51. 51.
    Greco, S., Matarazzo, B., Słowiński, R., Stefanowski, J.: Variable consistency model of dominance-based rough sets approach. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 170–181. Springer, Heidelberg (2001)Google Scholar
  52. 52.
    Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation by dominance relations. International Journal of Intelligent Systems 17(2), 153–171 (2002)zbMATHMathSciNetGoogle Scholar
  53. 53.
    Greco, S., Matarazzo, B., Slowinski, R., Zanakis, S.: Global investing risk: a case study of knowledge assessment via rough sets. Annals of Operations Research 185(1), 105–138 (2011)MathSciNetGoogle Scholar
  54. 54.
    Griffiths, B., Beynon, M.: Expositing stages of VPRS analysis in an expert system: Application with bank credit ratings. Expert Systems with Applications 29(4), 879–888 (2005)Google Scholar
  55. 55.
    Grzymala-Busse, J.: LERS-a system for learning from examples based on rough sets. In: Intelligent Decision Support, pp. 3–18. Springer (1992)Google Scholar
  56. 56.
    Grzymala-Busse, J.: A new version of the rule induction system LERS. Fundamenta Informaticae 31(1), 27–39 (1997)zbMATHGoogle Scholar
  57. 57.
    Grzymala-Busse, J., Ziarko, W.: Data mining and rough set theory. Communications of the ACM 43(4), 108–109 (2000)Google Scholar
  58. 58.
    Hashemi, R., Le Blanc, L., Rucks, C., Rajaratnam, A.: A hybrid intelligent system for predicting bank holding structures. European Journal of Operational Research 109(2), 390–402 (1998)zbMATHGoogle Scholar
  59. 59.
    Herbert, J., Yao, J.: Time-series data analysis with rough sets. CIEF 4, 908–911 (2005)Google Scholar
  60. 60.
    Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection. Information Sciences 178(18), 3577–3594 (2008)zbMATHMathSciNetGoogle Scholar
  61. 61.
    Huang, K.: Application of VPRS model with enhanced threshold parameter selection mechanism to automatic stock market forecasting and portfolio selection. Expert Systems with Applications 36(9), 11652–11661 (2009)Google Scholar
  62. 62.
    Huang, K., Jane, C.-J.: A hybrid model for stock market forecasting and portfolio selection based on ARX, grey system and RS theories. Expert Systems with Applications 36(3), 5387–5392 (2009)Google Scholar
  63. 63.
    Jankowski, A., Skowron, A.: Practical Issues of Complex Systems Engineering: Wisdom Technology Approach. Springer, Heidelberg (in preparation, 2014)Google Scholar
  64. 64.
    Jensen, R., Shen, Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of the 2003 UK Workshop on Computational Intelligence, vol. 1 (2003)Google Scholar
  65. 65.
    Jie, Z., Yan, L., Xin, L.: Research on financial crisis prediction model based on Rough Sets and Neural Network. In: 2011 International Conference on E-Business and E-Government (ICEE), pp. 1–4 (2011)Google Scholar
  66. 66.
    Jorion, P.: Value at risk: the new benchmark for managing financial risk 2. McGraw-Hill, New York (2007)Google Scholar
  67. 67.
    Kasabov, N.: Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 31(6), 902–918 (2001)Google Scholar
  68. 68.
    Kasabov, N., Song, Q.: DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Transactions on Fuzzy Systems 10(2), 144–154 (2002)Google Scholar
  69. 69.
    Kawasaki, S., Binh, N., Bao, T.: Hierarchical document clustering based on tolerance rough set model. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 458–463. Springer, Heidelberg (2000)Google Scholar
  70. 70.
    Khoza, M., Marwala, T.: A rough set theory based predictive model for stock prices. In: 2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 57–62 (2011)Google Scholar
  71. 71.
    Kim, K.-J., Han, I.: The extraction of trading rules from stock market data using rough sets. Expert Systems 18(4), 194–202 (2001)zbMATHMathSciNetGoogle Scholar
  72. 72.
    Koczkodaj, W., Orlowski, M., Marek, V.: Myths about rough set theory. Communications of the ACM 41(11), 102–103 (1998)Google Scholar
  73. 73.
    Kohonen, T.: Self-organizing maps. Springer Series in Information Sciences, vol. 30. Springer, Berlin (2001)zbMATHGoogle Scholar
  74. 74.
    Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: A tutorial. Rough fuzzy hybridization: A new trend in decision-making, 3–98 (1999)Google Scholar
  75. 75.
    Kot, D., Greco, S.S.: Stochastic dominance-based rough set model for ordinal classification. Information Sciences 178(21), 4019–4037 (2008)MathSciNetGoogle Scholar
  76. 76.
    Kovalerchuk, B., Vityaev, E.: Data mining in finance: advances in relational and hybrid methods. Springer (2000)Google Scholar
  77. 77.
    Kretowski, M., Stepaniuk, J.: Selection of objects and attributes a tolerance rough set approach. In: Proceedings of the Ninth International Symposium on Methodologies for Intelligent Systems, Zakopane, Poland (1996)Google Scholar
  78. 78.
    Kryszkiewicz, M.: Maintenance of reducts in the variable precision rough set model. In: Rough Sets and Data Mining, pp. 355–372. Springer (1996)Google Scholar
  79. 79.
    Kryszkiewicz, M.: Rough set approach to incomplete information systems. Information Sciences 112(1), 39–49 (1998)zbMATHMathSciNetGoogle Scholar
  80. 80.
    Kumar, A.: New techniques for data reduction in a database system for knowledge discovery applications. Journal of Intelligent Information Systems 10(1), 31–48 (1998)Google Scholar
  81. 81.
    Kumar, A., Agrawal, D., Joshi, S.: Multiscale rough set data analysis with application to stock performance modeling. Intelligent Data Analysis 8(2), 197–209 (2004)Google Scholar
  82. 82.
    Lee, S., Ann, J., Oh, K., Kim, T., Lee, H., Song, C.: Using Rough Set to Support Investment Strategies of Rule-Based Trading with Real-Time Data in Futures Market. In: 42nd Hawaii International Conference on System Sciences, HICSS 2009, pp. 1–10 (2009)Google Scholar
  83. 83.
    Lee, S., Ahn, J., Oh, K., Kim, T.: Using rough set to support investment strategies of real-time trading in futures market. Applied Intelligence 32(3), 364–377 (2010)Google Scholar
  84. 84.
    Liu, G., Zhu, Y.: Credit assessment of contractors: a rough set method. Tsinghua Science & Technology 11(3), 357–362 (2006)Google Scholar
  85. 85.
    Liu, J.-W., Cheng, C.-H., Chen, Y.-H., Chen, T.-L.: OWA rough set model for forecasting the revenues growth rate of the electronic industry. Expert Systems with Applications 37(1), 610–617 (2010)Google Scholar
  86. 86.
    McKee, T.: Predicting bankruptcy via induction. Journal of Information Technology 10(1), 26–36 (1995)MathSciNetGoogle Scholar
  87. 87.
    McKee, T.: Developing a bankruptcy prediction model via rough sets theory. International Journal of Intelligent Systems in Accounting, Finance & Management 9(3), 159–173 (2000)Google Scholar
  88. 88.
    McKee, T., Lensberg, T.: Genetic programming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research 138(2), 436–451 (2002)zbMATHMathSciNetGoogle Scholar
  89. 89.
    McKee, T.: Rough sets bankruptcy prediction models versus auditor signalling rates. Journal of Forecasting 22(8), 569–586 (2003)Google Scholar
  90. 90.
    Mienko, R., Slowinski, R., Stefanowski, J.: Rule Classifier Based on Valued Closeness Relation: ROUGHCLASS version 2.0. Poznan University of Technology Research Report RA-95/002, Pozan, Poland (1995)Google Scholar
  91. 91.
    Mrózek, A., Skabek, K.: Rough sets in economic applications. In: Rough Sets in Knowledge Discovery 2, pp. 238–271. Springer (1998)Google Scholar
  92. 92.
    Murphy, J.: Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance (1999)Google Scholar
  93. 93.
    Nair, B., Mohandas, V., Sakthivel, N.: A Decision tree- Rough set Hybrid System for Stock Market Trend Prediction. International Journal of Computer Applications 6(9) (2010)Google Scholar
  94. 94.
    Øhrn, A.: Rosetta technical reference manual. Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, 1-66 (2000)Google Scholar
  95. 95.
    Pai, P.-F., Chen, S.-Y., Huang, C.-W., Chang, Y.-H.: Analyzing foreign exchange rates by rough set theory and directed acyclic graph support vector machines. Expert Systems with Applications 37(8), 5993–5998 (2010)Google Scholar
  96. 96.
    Pawlak, Z.: Information systems theoretical foundations. Information Systems 6(3), 205–218 (1981)zbMATHMathSciNetGoogle Scholar
  97. 97.
    Pawlak, Z.: Rough sets. International Journal of Computer & Information Sciences 11(5), 341–356 (1982)zbMATHMathSciNetGoogle Scholar
  98. 98.
    Pawlak, Z.: Rough Sets Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, London (1991)zbMATHGoogle Scholar
  99. 99.
    Pawlak, Z., Skowron, A.: Rough sets: some extensions. Information Sciences 177(1), 28–40 (2007)zbMATHMathSciNetGoogle Scholar
  100. 100.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007)zbMATHMathSciNetGoogle Scholar
  101. 101.
    Qizhong, Z.: An approach to rough set decomposition of incomplete information systems. In: 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, pp. 2455–2460 (2007)Google Scholar
  102. 102.
    Radzikowska, A., Kerre, E.: A comparative study of fuzzy rough sets. Fuzzy Sets and Systems 126(2), 137–155 (2002)zbMATHMathSciNetGoogle Scholar
  103. 103.
    Ravi Kumar, P., Ravi, V.: Bankruptcy prediction in banks and firms via statistical and intelligent techniques–A review. European Journal of Operational Research 180(1), 1–28 (2007)zbMATHGoogle Scholar
  104. 104.
    Ruggiero, M.: How to build a system framework. Futures 23(12), 50–56 (1994)Google Scholar
  105. 105.
    Ruggiero, M.: Rules are made to be traded. AI in Finance Fall, 35-40 (1994)Google Scholar
  106. 106.
    Ruggiero, M.: Turning the key. Futures 23(14), 38–40 (1994)Google Scholar
  107. 107.
    Ruggiero, M.: Cybernetic Trading Strategies: developing a profitable trading system with state-of-the-art technologies 68. Wiley (1997)Google Scholar
  108. 108.
    Ruizhong, W.: Analyses the Financial Data of Stocks Based Rough Set Theory. In: 2012 Eighth International Conference on Computational Intelligence and Security (CIS), pp. 387–390 (2012)Google Scholar
  109. 109.
    Ruzgar, N.S., Unsal, F., Ruzgar, B.: Predicting business failures using the rough set theory approach: The case of the Turkish banks. International Journal of Mathematical models and Methods in Applied Sciences 2, 57–64 (2008)Google Scholar
  110. 110.
    Sanchis, A., Segovia, M., Gil, J., Heras, A., Vilar, J.: Rough sets and the role of the monetary policy in financial stability (macroeconomic problem) and the prediction of insolvency in insurance sector (microeconomic problem). European Journal of Operational Research 181(3), 1554–1573 (2007)zbMATHGoogle Scholar
  111. 111.
    Seiford, L., Zhu, J.: An acceptance system decision rule with data envelopment analysis. Computers & Operations Research 25(4), 329–332 (1998)zbMATHMathSciNetGoogle Scholar
  112. 112.
    Shen, L., Tay, F.E.H.: Classifying market states with WARS. In: Leung, K.-S., Chan, L., Meng, H. (eds.) IDEAL 2000. LNCS, vol. 1983, pp. 280–285. Springer, Heidelberg (2000)Google Scholar
  113. 113.
    Shen, L.: Data mining techniques based on rough sets theory. Ph.D. Dissertation (2003)Google Scholar
  114. 114.
    Shen, L., Loh, H.: Applying rough sets to market timing decisions. Decision Support Systems 37(4), 583–597 (2004)Google Scholar
  115. 115.
    Shen, Q., Jensen, R.: Rough sets, their extensions and applications. International Journal of Automation and Computing 4(3), 217–228 (2007)Google Scholar
  116. 116.
    Shuai, J.-J., Li, H.-L.: Using rough set and worst practice DEA in business failure prediction. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 503–510. Springer, Heidelberg (2005)Google Scholar
  117. 117.
    Shyng, J.-Y., Shieh, H.-M., Tzeng, G.-H.: An integration method combining Rough Set Theory with formal concept analysis for personal investment portfolios. Knowledge-Based Systems 23(6), 586–597 (2010)Google Scholar
  118. 118.
    Shyng, J.-Y., Shieh, H.-M., Tzeng, G.-H., Hsieh, S.-H.: Using FSBT technique with Rough Set Theory for personal investment portfolio analysis. European Journal of Operational Research 201(2), 601–607 (2010)Google Scholar
  119. 119.
    Skalko, C.: Rough sets help time the OEX. Journal of Computational Intelligence in Finance 4(6), 20–27 (1996)Google Scholar
  120. 120.
    Skowron, A., Stepaniuk, J., Jankowski, A., Bazan, J., Swiniarski, R.: Rough Set Based Reasoning About Changes. Fundamenta Informaticae 119(3-4), 421–437 (2012)MathSciNetGoogle Scholar
  121. 121.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Intelligent Decision Support, pp. 331–362. Springer (1992)Google Scholar
  122. 122.
    Skowron, A., Stepaniuk, J.: Generalized approximation spaces. In: Soft Computing, Simulation Councils, San Diego, pp. 18–21 (1995)Google Scholar
  123. 123.
    Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27(2), 245–253 (1996)zbMATHMathSciNetGoogle Scholar
  124. 124.
    Slowinski, R., Zapounidis, C.: Application of the rough set approach to evaluation of bankruptcy risk. International J. of Intelligent Systems in Accounting, Finance & Management 4, 27–41 (1995)Google Scholar
  125. 125.
    Slowinski, R., Zopounidis, C.: Rough-set sorting of firms according to bankruptcy risk. In: Applying Multiple Criteria Aid for Decision to Environmental Management, pp. 339–357. Springer (1994)Google Scholar
  126. 126.
    Slowinski, R., Zopounidis, C., Dimitras, A.: Prediction of company acquisition in Greece by means of the rough set approach. European Journal of Operational Research 100(1), 1–15 (1997)zbMATHGoogle Scholar
  127. 127.
    Stefanowski, J.: On rough set based approaches to induction of decision rules. Rough Sets in Knowledge Discovery 1(1), 500–529 (1998)Google Scholar
  128. 128.
    Stefanowski, J., Tsoukiàs, A.: Valued tolerance and decision rules. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 212–219. Springer, Heidelberg (2001)Google Scholar
  129. 129.
    Susmaga, R., Michalowski, W., Slowinski, R.: Identifying regularities in stock portfolio tilting. Tech. rep. (1997)Google Scholar
  130. 130.
    Triana, P.: VaR: The number that killed us. Futures Magazine (2010)Google Scholar
  131. 131.
    Tan, A., Quek, C., Yow, K.: Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system. Applied Intelligence 29(2), 116–128 (2008)Google Scholar
  132. 132.
    Tay, F., Shen, L.: Economic and financial prediction using rough sets model. European Journal of Operational Research 141(3), 641–659 (2002)zbMATHGoogle Scholar
  133. 133.
    Tay, F., Shen, L.: A modified Chi2 algorithm for discretization. IEEE Transactions on Knowledge and Data Engineering 14(3), 666–670 (2002)Google Scholar
  134. 134.
    Teoh, H., Cheng, C.-H., Chu, H.-H., Chen, J.-S.: Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets. Data & Knowledge Engineering 67(1), 103–117 (2008)Google Scholar
  135. 135.
    Tremba, J., Lin, T.: Attribute transformations for data mining II: Applications to economic and stock market data. International Journal of Intelligent Systems 17(2), 223–233 (2002)zbMATHGoogle Scholar
  136. 136.
    Wang, X.-Y., Wang, Z.-O.: Stock market time series data mining based on regularized neural network and rough set. In: Proceedings of 2002 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 315–318 (2002)Google Scholar
  137. 137.
    Wang, Y.-F.: Mining stock price using fuzzy rough set system. Expert Systems with Applications 24(1), 13–23 (2003)Google Scholar
  138. 138.
    Wroblewski, J.: Finding minimal reducts using genetic algorithms. In: Proceedings of Second International Joint Conference on Information Science, pp. 186–189 (1995)Google Scholar
  139. 139.
    Xiao, Z., Yang, X., Pang, Y., Dang, X.: The prediction for listed companies’ financial distress by using multiple prediction methods with rough set and Dempster–Shafer evidence theory. Knowledge-Based Systems 26, 196–206 (2012)Google Scholar
  140. 140.
    Yager, R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man and Cybernetics 18(1), 183–190 (1988)zbMATHMathSciNetGoogle Scholar
  141. 141.
    Yao, Y., Wong, S.: Generalization of rough sets using relationships between attribute values. In: Proceedings of the 2nd Annual Joint Conference on Information Sciences, pp. 30–33 (1995)Google Scholar
  142. 142.
    Yao, J., Teng, N., Poh, H.-L., Tan, C.: Forecasting and analysis of marketing data using neural networks. J. Inf. Sci. Eng. 14(4), 843–862 (1998)Google Scholar
  143. 143.
    Yao, J., Li, Y., Tan, C.: Option price forecasting using neural networks. Omega 28(4), 455–466 (2000)Google Scholar
  144. 144.
    Yao, J., Tan, C.: Time dependent directional profit model for financial time series forecasting. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, vol. 5, pp. 291–296 (2000)Google Scholar
  145. 145.
    Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Information Sciences 178(17), 3356–3373 (2008)zbMATHMathSciNetGoogle Scholar
  146. 146.
    Yao, J., Herbert, J.: Financial time-series analysis with rough sets. Applied Soft Computing 9(3), 1000–1007 (2009)Google Scholar
  147. 147.
    Yao, P.: Hybrid classifier using neighborhood rough set and SVM for credit scoring. In: International Conference on Business Intelligence and Financial Engineering, BIFE 2009, pp. 138–142 (2009)Google Scholar
  148. 148.
    Yeh, C.-C., Chi, D.-J., Hsu, M.-F.: A hybrid approach of DEA, rough set and support vector machines for business failure prediction. Expert Systems with Applications 37(2), 1535–1541 (2010)Google Scholar
  149. 149.
    Yeh, C.-C., Lin, F., Hsu, C.-Y.: A hybrid KMV model, random forests and rough set theory approach for credit rating. Knowledge-Based Systems (2012)Google Scholar
  150. 150.
    Yeung, D., Chen, D., Tsang, E., Lee, J., Xizhao, W.: On the generalization of fuzzy rough sets. IEEE Transactions on Fuzzy Systems 13(3), 343–361 (2005)Google Scholar
  151. 151.
    Yu, L., Wang, S., Lai, K.: A rough-set-refined text mining approach for crude oil market tendency forecasting. International Journal of Knowledge and Systems Sciences 2(1), 33–46 (2005)Google Scholar
  152. 152.
    Yu, H.-K.: Weighted fuzzy time series models for TAIEX forecasting. Physica A: Statistical Mechanics and its Applications 349(3), 609–624 (2005)Google Scholar
  153. 153.
    Zadeh, L.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)zbMATHMathSciNetGoogle Scholar
  154. 154.
    Zhang, Y.-Q., Wan, X.: Statistical fuzzy interval neural networks for currency exchange rate time series prediction. Applied Soft Computing 7(4), 1149–1156 (2007)Google Scholar
  155. 155.
    Zhang, Q.-F., Zhao, S.-Y., Bai, Y.-C.: On the application of rough sets to data mining in economic practice. In: 2009 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 272–276 (2009)Google Scholar
  156. 156.
    Zhao, G., Yan, W., Li, Y.: LS-SVM Financial Achievement Prediction Based on Targets Optimization of Neighborhood Rough Sets. In: Shen, G., Huang, X. (eds.) CSIE 2011, Part I. CCIS, vol. 152, pp. 171–178. Springer, Heidelberg (2011)Google Scholar
  157. 157.
    Zhou, J., Bai, T.: Credit risk assessment using rough set theory and GA-based SVM. In: The 3rd International Conference on Grid and Pervasive Computing Workshops, GPC Workshops 2008, pp. 320–325 (2008)Google Scholar
  158. 158.
    Ziarko, W., Golan, R., Edwards, D.: An application of datalogic/R knowledge discovery tool to identify strong predictive rules in stock market data. In: Proceedings of AAAI Workshop on Knowledge Discovery in Databases, Washington, DC, pp. 89–101 (1993)Google Scholar
  159. 159.
    Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46(1), 39–59 (1993)zbMATHMathSciNetGoogle Scholar
  160. 160.
    Zighed, D., Rabaseda, S., Rakotomalala, R.: FUSINTER: a method for discretization of continuous attributes. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6(03), 307–326 (1998)zbMATHGoogle Scholar
  161. 161.
    Zimmermann, H.: Fuzzy set theory-and its applications. Springer (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Misys plcUK
  2. 2.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland

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