TY - BOOK ID - 8285772 TI - Portfolio Optimization Using Fundamental Indicators Based on Multi-Objective EA AU - Silva, Antonio Daniel. AU - Neves, Rui Ferreira. AU - Horta, Nuno. PY - 2016 SN - 3319293907 3319293923 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Computer Science KW - Engineering & Applied Sciences KW - Investments KW - Portfolio management. KW - Mathematics. KW - Mathematical models. KW - Mathematics of investment KW - Investment management KW - Engineering. KW - Finance. KW - Algorithms. KW - Economics, Mathematical. KW - Computational intelligence. KW - Computational Intelligence. KW - Algorithm Analysis and Problem Complexity. KW - Quantitative Finance. KW - Finance, general. KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Economics KW - Mathematical economics KW - Econometrics KW - Mathematics KW - Algorism KW - Algebra KW - Arithmetic KW - Funding KW - Funds KW - Currency question KW - Construction KW - Industrial arts KW - Technology KW - Methodology KW - Foundations KW - Investment analysis KW - Securities KW - Business mathematics KW - Computer software. KW - Software, Computer KW - Computer systems KW - Economics, Mathematical . UR - https://www.unicat.be/uniCat?func=search&query=sysid:8285772 AB - This work presents a new approach to portfolio composition in the stock market. It incorporates a fundamental approach using financial ratios and technical indicators with a Multi-Objective Evolutionary Algorithms to choose the portfolio composition with two objectives the return and the risk. Two different chromosomes are used for representing different investment models with real constraints equivalents to the ones faced by managers of mutual funds, hedge funds, and pension funds. To validate the present solution two case studies are presented for the SP&500 for the period June 2010 until end of 2012. The simulations demonstrates that stock selection based on financial ratios is a combination that can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. In this case the optimizer found stocks with high return on investment in a conjunction with high rate of growth of the net income and a high profit margin. To obtain stocks with high valuation potential it is necessary to choose companies with a lower or average market capitalization, low PER, high rates of revenue growth and high operating leverage. ER -