Portfolio Optimization Using Fundamental Indicators Based On Multi Objective Ea

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

This book constitutes the refereed conference proceedings of the 14th International Conference on Bio-inspired Information and Communications Technologies, held in Okinawa, Japan, during April 11-12, 2023. The 17 full papers were carefully reviewed and selected from 33 submissions. The papers focus on the latest research that leverages the understanding of key principles, processes, and mechanisms in biological systems for development of novel information and communications technologies (bio-inspired ICT). BICT 2023 will also highlight innovative research and technologies being developed for biomedicine that are inspired by ICT (ICT-inspired biomedicine).

This book constitutes the refereed proceedings of the 10th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2014, held in Rhodes, Greece, in September 2014. The 33 revised full papers and 29 short papers presented were carefully reviewed and selected from numerous submissions. They are organized in the following topical sections: learning-ensemble learning; social media and mobile applications of AI; hybrid-changing environments; agent (AGE); classification pattern recognition; genetic algorithms; image and video processing; feature extraction; environmental AI; simulations and fuzzy modeling; and data mining forecasting.

This book explores the risk-return paradox in portfolio selection by incorporating multi-objective criteria. Empirical research is presented on the development of alternate portfolio models and their relative performance in the risk/return framework to provide solutions to multi-objective optimization. Next to outlining techniques for undertaking individual investor’s profiling and portfolio programming, it also offers a new and practical approach for multi-objective portfolio optimization. This book will be of interest to Foreign Institutional Investors (FIIs), Mutual Funds, investors, and researchers and students in the field.

In answer to the intense development of new financial products and the increasing complexity of portfolio management theory, Portfolio Optimization and Performance Analysis offers a solid grounding in modern portfolio theory. The book presents both standard and novel results on the axiomatics of the individual choice in an uncertain framework, cont

Targeted towards institutional asset managers in general and chief investment officers, portfolio managers and risk managers in particular, this practical book serves as a comprehensive guide to quantitative portfolio optimization, asset allocation and risk management. Providing an accessible yet rigorous approach to investment management, it gradually introduces ever more advanced quantitative tools for these areas. Using extensive examples, this book guides the reader from basic return and risk analysis, all the way through to portfolio optimization and risk characterization, and finally on to fully fledged quantitative asset allocation and risk management. It employs such tools as enhanced modern portfolio theory using Monte Carlo simulation and advanced return distribution analysis, analysis of marginal contributions to absolute and active portfolio risk, Value-at-Risk and Extreme Value Theory. All this is performed within the same conceptual, theoretical and empirical framework, providing a self-contained, comprehensive reading experience with a strongly practical aim.

This monograph presents a comprehensive study of portfolio optimization, an important area of quantitative finance. Considering that the information available in financial markets is incomplete and that the markets are affected by vagueness and ambiguity, the monograph deals with fuzzy portfolio optimization models. At first, the book makes the reader familiar with basic concepts, including the classical mean–variance portfolio analysis. Then, it introduces advanced optimization techniques and applies them for the development of various multi-criteria portfolio optimization models in an uncertain environment. The models are developed considering both the financial and non-financial criteria of investment decision making, and the inputs from the investment experts. The utility of these models in practice is then demonstrated using numerical illustrations based on real-world data, which were collected from one of the premier stock exchanges in India. The book addresses both academics and professionals pursuing advanced research and/or engaged in practical issues in the rapidly evolving field of portfolio optimization.

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.