Download or read book Portfolio Optimization Using Neuro Fuzzy System in Indian Stock Market written by M. Gunasekaran and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper describes a portfolio optimization system by using Neuro-Fuzzy framework in order to manage stock portfolio. It is great importance to stock investors and applied researchers. The proposed portfolio optimization approach Neuro-Fuzzy System reasoning in order to make a more yields from the stock portfolio, and hence maximize return and minimize risk of a stock portfolio through diversification and right investment allocation to the particular stock under uncertainty. To evaluate the performance of forecasting and optimization system, BSE Sensex index of India considered as benchmarks in this study to measure efficient forecasting models. The results show that the proposed Neuro-Fuzzy system produces much higher accuracy when compared to other portfolio models.

Download or read book Fuzzy Portfolio Optimization written by Pankaj Gupta and published by Springer. This book was released on 2014-03-17 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Neuro Fuzzy Based Stock Market Prediction System written by M. Gunasekaran and published by . This book was released on 2013 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have been used for forecasting purposes for some years now. Often arises the problem of a black-box approach, i.e. after having trained neural networks to a particular problem, it is almost impossible to analyze them for how they work. Fuzzy Neuronal Networks allow adding rules to neural networks. This avoids the black-box-problem. Additionally they are supposed to have a higher prediction precision in unlike situations. Applying artificial neural network, genetic algorithm and fuzzy logic for the stock market prediction has attracted much attention recently, which has better correlated the non-quantitative factors with the stock market performance. However these approaches perform less satisfactorily due to the memoryless nature of the stock market performance. In this paper, we propose a data compression-based portfolio prediction model hybridized with the fuzzy logic and genetic algorithm. In the model, the quantifiable microeconomic stock data are first optimized through the genetic algorithms to generate the most effective microeconomic data in relation to the stock market performance.

Download or read book Encyclopedia of Information Science and Technology Fourth Edition written by Khosrow-Pour, D.B.A., Mehdi and published by IGI Global. This book was released on 2017-06-20 with total page 8356 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, our world has experienced a profound shift and progression in available computing and knowledge sharing innovations. These emerging advancements have developed at a rapid pace, disseminating into and affecting numerous aspects of contemporary society. This has created a pivotal need for an innovative compendium encompassing the latest trends, concepts, and issues surrounding this relevant discipline area. During the past 15 years, the Encyclopedia of Information Science and Technology has become recognized as one of the landmark sources of the latest knowledge and discoveries in this discipline. The Encyclopedia of Information Science and Technology, Fourth Edition is a 10-volume set which includes 705 original and previously unpublished research articles covering a full range of perspectives, applications, and techniques contributed by thousands of experts and researchers from around the globe. This authoritative encyclopedia is an all-encompassing, well-established reference source that is ideally designed to disseminate the most forward-thinking and diverse research findings. With critical perspectives on the impact of information science management and new technologies in modern settings, including but not limited to computer science, education, healthcare, government, engineering, business, and natural and physical sciences, it is a pivotal and relevant source of knowledge that will benefit every professional within the field of information science and technology and is an invaluable addition to every academic and corporate library.

Download or read book First International Conference on Sustainable Technologies for Computational Intelligence written by Ashish Kumar Luhach and published by Springer Nature. This book was released on 2019-11-01 with total page 847 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers high-quality papers presented at the First International Conference on Sustainable Technologies for Computational Intelligence (ICTSCI 2019), which was organized by Sri Balaji College of Engineering and Technology, Jaipur, Rajasthan, India, on March 29–30, 2019. It covers emerging topics in computational intelligence and effective strategies for its implementation in engineering applications.

Download or read book Optimization of Financial Asset Neutrosophic Portfolios written by Marcel-Ioan Boloș and published by Infinite Study. This book was released on with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this paper was to model, with the help of neutrosophic fuzzy numbers, the optimal financial asset portfolios, offering additional information to those investing in the capital market. The optimal neutrosophic portfolios are those categories of portfolios consisting of two or more financial assets, modeled using neutrosophic triangular numbers, that allow for the determination of financial performance indicators, respectively the neutrosophic average, the neutrosophic risk, for each financial asset, and the neutrosophic covariance as well as the determination of the portfolio return, respectively of the portfolio risk.

Download or read book Advances in Data Science and Artificial Intelligence written by Rajiv Misra and published by Springer Nature. This book was released on 2023-05-13 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the intriguing development of technologies in several industries along with the advent of accrescent and ubiquitous computational resources, it creates an ample number of opportunities to develop innovative intelligence technologies in order to solve the wide range of uncertainties, imprecision, and vagueness issues in various real-life problems. Hybridizing modern computational intelligence with traditional computing methods has attracted researchers and academicians to focus on developing innovative AI techniques using data science. International Conference on Data Science and Artificial Intelligence (ICDSAI) 2022, organized on April 23-24, 2022 by the Indian Institute of Technology, Patna at NITIE Mumbai (India) in collaboration with the International Association of Academicians (IAASSE) USA collected scientific and technical contributions with respect to models, tools, technologies, and applications in the field of modern Artificial Intelligence and Data Science, covering the entire range of concepts from theory to practice, including case studies, works-in-progress, and conceptual explorations.

Download or read book Proceedings of the International Conference on Computational Intelligence and Sustainable Technologies written by Kedar Nath Das and published by Springer Nature. This book was released on 2022-02-12 with total page 758 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the collection of the accepted research papers presented in the 1st ‘International Conference on Computational Intelligence and Sustainable Technologies (ICoCIST-2021)’. This edited book contains the articles related to the themes on artificial intelligence in machine learning, big data analysis, soft computing techniques, pattern recognitions, sustainable infrastructural development, sustainable grid computing and innovative technology for societal development, renewable energy, and innovations in Internet of Things (IoT).

Download or read book Neural genetic hybrid system to portfolio building and management written by and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Esta dissertação apresenta o desenvolvimento de um sistema híbrido, baseado em Algoritmos Genéticos (AG) e Redes Neurais (RN), no processo de seleção de ações, na determinação do percentual a investir em cada ativo também denominado peso do ativo na carteira e gerenciamento de carteiras de investimento. O objetivo do trabalho é avaliar o desempenho de Algoritmos Genéticos e Redes neurais para a montagem e gerenciamento de carteiras de investimento. A construção e gerenciamento de carteiras de investimento é um problema de múltiplos objetivos (retorno e risco) onde deseja-se escolher um conjunto de ações de empresas com perspectivas de lucro para formar a carteira de investimento. Esta escolha é difícil devido ao grande número de possibilidades e parâmetros a serem considerados, como: retorno, risco, correlação, volatilidade, entre outros; razão pela que é considerado como problema do tipo NP-completo. O trabalho de pesquisa foi desenvolvido em 5 etapas principais: um estudo sobre a área de carteiras de investimento; um estudo sobre os modelos com técnicas de inteligência computacional empregados nesta área; a definição de um modelo híbrido Genético-Neural para a seleção e gerenciamento da carteira para o caso estacionário; a definição de um modelo híbrido Genético-Neural para a seleção e gerencia de carteira para o caso variante no tempo; e o estudo de casos. O estudo sobre a área de carteiras de investimento envolveu toda a teoria necessária para a construção e gerenciamento de carteiras de investimento. O estudo sobre as técnicas de inteligência computacional, define-se os conceitos principais de Algoritmos Genéticos e Redes Neurais empregados nesta dissertação.A modelagem híbrida Genético-Neural para o caso clássico ou estacionário, constituiu fundamentalmente mo emprego de um Algoritmo Genético para selecionar os ativos da carteira a partir de um subconjunto de ativos noticiados na Bolsa de Valores de São Paulo Brasil (BOVESPA). Uma Rede Neural auxilia na gerência da carteira, fazendo previsões dos retornos dos ativos para o próximo período de avaliação da carteira. Na seleção de ativos, dois algoritmos genéticos são modelados: o primeiro procura escolher 12 dentre 137 ativos negociados na BOVESPA, que apresentem maior expectativa de retorno, com menor risco e que apresentem baixa correlação com os demais ativos; e o segundo procura escolher os ativos empregando o modelo de Makowitz e o critério de Fronteira eficiente. A previsão de retornos da as ações é uma estratégia que visa melhorar o desempenho de carteiras de investimento que, tipicamente, consideram apenas o retorno médio do ativo. Diferentes modelos de redes neurais foram testados, como: Backpropagation, Redes Neurais Bayesianas, Sistema Neuro-Fuzzy Hierárquico e Redes Neurais com Filtros de Kalman; os melhores resultados de previsão foram obtidos com redes neurais com Filtros de Kalman. Para o caso estacionário foram usadas como entradas da rede neural os retornos semanais, tanto do ativo como do índice do mercado, empregando-se o método de janela deslizante para a previsão um passo a frente. A modelagem híbrida Genético-Neural para o caso variante no tempo, constituiu no emprego de 3 modelos: um AG para fazer a escolha dos ativos da carteira; o modelo GARCH para fazer as previsões da volatilidade dos ativos e o cálculo do risco de cada um deles dado pelo VAR (medida de risco que tenta quantificar a perda máxima que uma carteira (ou ativo) pode ter em um horizonte de tempo e com um intervalo de confiança); e uma RN para fazer as previsões dos retornos dos ativos para o próximo período de avaliação de carteira. Na montagem da carteira, empregou-se o Critério de Fronteira eficiente para a seleção dos ativos, também dentre os 137 negociados na BOVESPA. A previsão da volatilidade das ações é uma forma de indicar quanto pode variar o preço da ação, medida útil para determinar o risco de um ativo representado pelo VAR. Neste caso emprega-se o modelo GARCH para fazer estas previsões. Para a previsão dos retornos das ações foram usadas como entradas da Rede Neural Backpropagation os 10 últimos retornos semanais do ativo, empregando-se também o método de janela deslizante para fazer a previsão um passo a frente. O gerenciamento da carteira emprega um algoritmo genético que determina o peso de cada ativo, de modo a satisfazer às restrições do investidor quanto a risco e retorno. Neste trabalho foram construídos dois tipos de carteira: uma para minimizar o risco para um retorno dado; e o a outra para maximizar o retorno da carteira para um risco dado. O problema é basicamente encontrar os pesos dos ativos que minimizem o risco para um caso e maximizem o retorno para o outro. No estudo de casos, a carteira foi gerenciada durante um período de 49 semanas e avaliada comparando-a com o comportamento do mercado Índice BOVESPA. Os resultados obtidos com este trabalho demonstram a viabilidade da utilização de AG e RN na construção e gerenciamento de carteiras de investimento em mercados emergentes. Verificou-se que é possível obter lucro em carteiras formadas e gerenciadas por técnicas de inteligência computacional.

Download or read book Emerging Technologies in Computer Engineering Microservices in Big Data Analytics written by Arun K. Somani and published by Springer. This book was released on 2019-05-17 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Conference on Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics, ICETCE 2019, held in Jaipur, India, in February 2019. The 28 revised full papers along with 1 short paper presented were carefully reviewed and selected from 253 submissions. ICETCE conference aims to showcase advanced technologies, techniques, innovations and equipments in computer engineering. It provides a platform for researchers, scholars, experts, technicians, government officials and industry personnel from all over the world to discuss and share their valuable ideas and experiences.

Download or read book A Fusion Model Integrating ANFIS and Artificial Immune Algorithm for Forecasting Indian Stock Market written by M. Gunasekaran and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market forecasting provides challenging and interesting task to both investors and academic researchers because trading decision at an appropriate time makes more profit for investors. In present study, a new approach has been proposed to integrate Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Immune Algorithm (AIA) for predicting the future index value of National Stock Exchange (NSE) of India. In order to make an efficient forecasting model, ANFIS is employed to optimize decision-making process and an efficient artificial immune algorithm is adopted to adjust membership function parameters of Fuzzy Inference System (FIS). The proposed system was simulated using daily closing value of NSE Nifty data and well-known technical indicators as input data values and output is the predicted future index value of NSE Nifty. Simulation results of our fusion model have been compared with other soft computing models and actual NSE Nifty data as benchmark. The experimental results showed that the proposed forecasting model yielded significantly higher forecasting accuracy values than other forecasting models.

Download or read book Portfolio Risk Optimization by Fuzzy Approaches written by Thanh Thi Nguyen and published by . This book was released on 2013 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the complexity and uncertainty in real world portfolio management, investors might be reluctant and sometimes unable to provide precise judgements regarding stock performance. In this context, analysts have long advocated use of fuzzy mathematics so that uncertainties and lack of precision can be acknowledged. This research therefore explores the applications of fuzzy sets in particular, or fuzzy logic in general for representing vague and imprecise financial data for portfolio risk optimization. Asset returns are uncertain and changeable over time so we model asset returns as fuzzy random variables and propose portfolio optimization models. Using fuzzy random variables, we introduce a new concept of financial risk, and the fuzzy Sharpe ratio contributing an important advancement in portfolio selection in the fuzzy environment. Two solution methods using a fuzzy approach and a genetic algorithm are applied to the proposed models. The proposed approach exhibits advantages over the so-called standard mean-variance optimization (MVO), throughout experimental results. The non-Gaussian distribution of asset returns has long been recognized, and the conventional MVO has been criticized as inadequate. Hence utilizing higher moments than variance, i.e. skewness, kurtosis soon emerged in portfolio selection. This research investigates the importance of higher moments in portfolio optimization through deploying fuzzy approaches. Marginal impacts of stocks on portfolio return and higher moment risks, are modelled by fuzzy numbers. The fuzzy models are constructed to optimize not only portfolio return and normal variance risk but also the portfolio higher moment risks. From the stock marginal impact modelling, two fuzzy approaches are used to derive optimal portfolio allocations. The first approach applies the constrained fuzzy analytic hierarchy process, whereas the second approach uses the fuzzy linear programming method. The efficiency of both approaches shows advantages of the proposed fuzzy models in portfolio selection. Going beyond the normal variance and higher moment risks, investors also should take into account downside risk measures. The downside risks are inspired by the principle of safety first in portfolio selection. The principle states that an investor would prefer the investment with the smallest probability of going below the target return. A fuzzy integrated framework is proposed accounting for portfolio return and six risk criteria including normal risk (volatility), asymmetric risk (skewness), "fat-tail" risk (kurtosis) and downside risks, i.e. semi-variance, modified Value-at-Risk, and modified Expected Shortfall. Fuzzy goals of portfolio's return and risks are constructed by bootstrapping, and kernel smoothing density estimate. A preselection process dealing with large datasets is also adopted to eliminate low diversification potential stocks before running the optimization model. Various investors' risk preference schemes are implemented with both national and international experimental datasets. Results reported demonstrate the advantages of the proposed fuzzy framework compared to a conventional higher moment portfolio optimization model. The conclusion is that fuzzy modelling is efficient and competent in various portfolio selection formulations when uncertainty and vagueness are deemed present. When appropriately utilized, fuzzy approaches can bring superior investment outcomes compared to conventional non-fuzzy models prevalent in the literature.

Download or read book Rough Sets Selected Methods and Applications in Management and Engineering written by Georg Peters and published by Springer Science & Business Media. This book was released on 2012-02-22 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rough Set Theory, introduced by Pawlak in the early 1980s, has become an important part of soft computing within the last 25 years. However, much of the focus has been on the theoretical understanding of Rough Sets, with a survey of Rough Sets and their applications within business and industry much desired. Rough Sets: Selected Methods and Applications in Management and Engineering provides context to Rough Set theory, with each chapter exploring a real-world application of Rough Sets. Rough Sets is relevant to managers striving to improve their businesses, industry researchers looking to improve the efficiency of their solutions, and university researchers wanting to apply Rough Sets to real-world problems.

Download or read book Portfolio Optimization Using Data Envelopment Analysis Sharpe s Method written by Harendra Singh and published by . This book was released on 2016 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the advancement of Information Technology it has been easier for investors to invest valuable money in portfolios. There has been availability of tools & data all the time to predict the market in decision-making, for which some models have been developed. With the rigorous research in this attractive topic some mathematical models have been developed & some models from other industries have been considered. Models like Sharpe, Genetic Algorithm, and Monte-Carlo are very helpful in investment decision making. DEA model, originated from production industry, helps in selecting securities for portfolio. In this paper we have exquisitely tried to find out the best method for selection of efficient securities using historical data of BSE-30 industries & compared DEA, Sharpe's model with Market, which gives some exciting results for future investment.

Download or read book Innovative Data Communication Technologies and Application written by Jennifer S. Raj and published by Springer Nature. This book was released on 2020-01-30 with total page 852 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents emerging concepts in data mining, big data analysis, communication, and networking technologies, and discusses the state-of-the-art in data engineering practices to tackle massive data distributions in smart networked environments. It also provides insights into potential data distribution challenges in ubiquitous data-driven networks, highlighting research on the theoretical and systematic framework for analyzing, testing and designing intelligent data analysis models for evolving communication frameworks. Further, the book showcases the latest developments in wireless sensor networks, cloud computing, mobile network, autonomous systems, cryptography, automation, and other communication and networking technologies. In addition, it addresses data security, privacy and trust, wireless networks, data classification, data prediction, performance analysis, data validation and verification models, machine learning, sentiment analysis, and various data analysis techniques.

Download or read book A Framework for Utilizing Stock Trend Prediction Outputs in Stock Selection and Portfolio Optimization written by Varun Ramamohan and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we develop a framework for using stock trend prediction outputs, which we generate using long short-term memory (LSTM) deep neural networks, in both stock selection and portfolio optimization. We use LSTM networks to predict the direction of stock movement and a numerical measure of the strength of the stock trend prediction, and use these in stock selection and within the Markowitz mean-variance portfolio optimization framework. Four types of LSTM models are constructed using the Indian SENSEX stock data - individual and ensemble models, each trained using both batch and incremental learning methods. We utilize the accuracy of classification of stock movement direction in shortlisting stocks for the portfolio optimization stage. Diversified and short-selling enabled Markowitz formulations in addition to the standard Markowitz formulation are constructed in the portfolio optimization stage. We also explore the use of a function of the LSTM classification accuracies as a risk measure both in lieu of and in addition to the covariance matrix within the Markowitz framework. Results from each of the above combinations of LSTM construction and portfolio optimization formulation type are benchmarked against the SENSEX and the standard optimal Markowitz portfolios without stock selection. We also analytically derive the conditions under which Markowitz formulations with stock price predictors more accurate than the mean stock price outperform the standard Markowitz formulations. Our work presents a framework that investment analysts can use to incorporate stock trend prediction outputs generated by machine learning techniques in informing their stock selection and optimal portfolio allocation decisions.

Download or read book Research Anthology on Artificial Neural Network Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2021-07-16 with total page 1575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.