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Generating Investment Strategies Using Multiobjective Genetic Programming And Internet Term Popularity Data

-477. Bohdalová, M., and Greguš, M., 2012. Portfolio optimization and Sharpe ratio based on copula approach. Research Journal of Economics, Business and ICT, 6 , 6-10. Bradshaw, N. A., Walshaw, C., Ierotheou, C., and Parrott, A. K., 2009. A Multi-Objective Evolutionary Algorithm for Portfolio Optimisation. Proceedings of the Adaptive and Emergent Behaviour and Complex Systems Convention , 27–32. Chen, S. H., and Navet, N., 2007. Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms

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Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency

, 1993), when others, such as the Hill Climbing or evolutionary methods, are based on heuristic approach ( Juels and Wattenbergy, 1994 ). The commonly used methods and algorithms with application in scientific problems are discussed by Hastie et al . (2013) and Hastie et al . (2001) . The algorithmic strategies are widely used in the financial markets, but most of them are not discussed in papers, due to exclusive character. Nevertheless, some types of the quantitative strategies are widely known, and therefore, discussed in books and papers. The strategy based

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Bankruptcy Prediction: A Survey on Evolution, Critiques, and Solutions

References Adnan, M.; Dar, A. H. (2006). Predicting corporate bankruptcy: where we stand? Corporate Governance 6(1): 18–33. Agarwal, V.; Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance 32: 1541–1555. Alam, P.; Booth, D.; Lee, K.; Thordarson, T. (2000). The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study. Expert Systems with Applications 18: 185

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Fundamental Analysis – Possiblity of Application on the Real Estate Market

u E., 2016, The Roles of Past Return and Firm Fundamentals in Driving US Stock Price Movements , International Review of Financial Analysis, Vol. 43, pp.: 62-75. H ott C., M onnin P., 2008, Fundamental Real Estate Prices: An Empirical Estimation with International Data , Journal of Real Estate Finance and Economics, Vol. 36, No. 4, pp.: 427-450. http://bossa.pl/edukacja/AF/ , available at 21.07.2015. H u Y., L iu K., Z hang X., S u L., N gai E. W. T., L iu M., 2015, Application of Evolutionary Computation for Rule Discovery in Stock

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Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

References Abdallah A., Maarof M.A., Zainal A. (2016) Fraud detection system: A survey, Journal of Network and Computer Applications, 68, 90-113. Balakrishnan P., Kumar S., Han P. (2011) Dual objective segmentation to improve targetability: An evolutionary algorithm approach, Decision Sciences, 42(4), 831-857. Bermúdez L., Pérez J.M., Ayuso M., Gómez E., Vázquez F.J. (2008) A Bayesian dichotomous model with asymmetric link for fraud in insurance, Insurance: Mathematics and Economics, 42(2), 779-786. Bodon F., (2010) Adatbányászati

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Forensic Accounting in the Fraud Auditing Case

REFERENCES Alden, M.E., Bryan, D.M., Lessley, B.J., & Tripathy, A. (2012). Detection of Financial Statement Fraud Using Evolutionary Algorithms. Journal of Emerging Technologies in Accounting , 9(1), 71-94. doi:10.2308/jeta-50390 Arežina, N., Knežević, G., Simeunović, N., & Vukićević, S. (2014). Forensic Accountant: Innate Trait or Acquired Skill ? Singidunum University International Scientific Conference Financial Reporting Function of the Corporate Governance, Belgrade, December 5 (pp. 131-134). Belgrade: Singidunum University. doi:10.15308/finiz

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The Proportions and Rates of Economic Activities as a Factor of Gross Value Added Maximization in Transition Economy

References Akbari, R., and Ziarati, K., 2011. A multi level evolutionary algorithm for optimizing numerical functions. International Journal of Industrial Engineering Computations, 2, 419-430. doi: http://dx.doi.org/10.5267/j.ijiec.2010.03.002 Atencia, M., Joya, G., and Sandoval, F., 2005. Hopfield Neural Networks for Parametric Identification of Dynamical Systems. Neural Processing Letters, 21(2), 143-152. doi: http://dx.doi.org/10.1007/s11063-004-3424-3 Balakrishnan, S., Kannan, P. S., Aravindan, C., and

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