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Loukeris, N. and Eleftheriadis, I. (2010) Default Prediction and Bankruptcy Hazard Analysis into Recurent Neuro-Genetic Hybrid Networks to AdaBoost M1 Regression and Logistic Regression models in Finance, Included in ISI/SCI Web of Science and Web of Knowledge,14th International Conference on Computers CSCC, Corfu, 22-25 July 2010, 35-41.

has been cited by the following article:

• TITLE: Support Vector Machines Networks to Hybrid Neuro-Genetic SVMs in Portfolio Selection

  AUTHORS: N. Loukeris, I. Eleftheriadis

  KEYWORDS: Support Vector Machines, Genetic Algorithms, Corporate Finance, Financial Markets

  JOURNAL NAME: Intelligent Information Management, Vol.7 No.3, May 8, 2015

  ABSTRACT: Corporate net value is efficiently described on its stock price, offering investors a chance to include a potentially surplus value to the net worth of the overall investment portfolio. Financial analysis of corporations extracted from the accounting statements is constantly demanded to support decisions making of portfolio managers. Econometrics and Artificial Intelligence methods aim to extract hidden information from complex accounting and financial data. Support Vector Machines hybrids optimized in their components by Genetic Algorithms provide effective results in corporate financial analysis.