Download or read book Forecasting of Stock Market Indices Using Artificial Neural Network written by Dr.Jay Desai and published by . This book was released on 2013 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a computational approach for predicting the S&P CNX Nifty 50 Index. A neural network based model has been used in predicting the direction of the movement of the closing value for the next day of trading. The model presented in the paper also confirms that it can be used to predict price trend of the stock market. After studying the various features of the network model, a suitable model for stocks forecast is proposed. The model has used the preprocessed data set of closing value of S&P CNX Nifty 50 Index. The training data set encompasses the trading days from 1st January, 2010 to 30th November, 2011. The test data set encompasses the trading days from 1st January, 2011 to 31st December, 2011. Accuracy of the performance of the neural network is compared with buy and hold return of the index. The model generated returns of 59.84% against buy and hold return of -26.08%. The average accuracy of target forecasting is found to be 82%.

Download or read book Prediction of Stock Market Index Movements with Machine Learning written by Nazif AYYILDIZ and published by Özgür Publications. This book was released on 2023-12-16 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book titled "Prediction of Stock Market Index Movements with Machine Learning" focuses on the performance of machine learning methods in forecasting the future movements of stock market indexes and identifying the most advantageous methods that can be used across different stock exchanges. In this context, applications have been conducted on both developed and emerging market stock exchanges. The stock market indexes of developed countries such as NYSE 100, NIKKEI 225, FTSE 100, CAC 40, DAX 30, FTSE MIB, TSX; and the stock market indexes of emerging countries such as SSE, BOVESPA, RTS, NIFTY 50, IDX, IPC, and BIST 100 were selected. The movement directions of these stock market indexes were predicted using decision trees, random forests, k-nearest neighbors, naive Bayes, logistic regression, support vector machines, and artificial neural networks methods. Daily dataset from 01.01.2012 to 31.12.2021, along with technical indicators, were used as input data for analysis. According to the results obtained, it was determined that artificial neural networks were the most effective method during the examined period. Alongside artificial neural networks, logistic regression and support vector machines methods were found to predict the movement direction of all indexes with an accuracy of over 70%. Additionally, it was noted that while artificial neural networks were identified as the best method, they did not necessarily achieve the highest accuracy for all indexes. In this context, it was established that the performance of the examined methods varied among countries and indexes but did not differ based on the development levels of the countries. As a conclusion, artificial neural networks, logistic regression, and support vector machines methods are recommended as the most advantageous approaches for predicting stock market index movements.

Download or read book Credit Rating Modelling by Neural Networks written by Petr Hájek and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the modelling possibilities of neural networks on a complex real-world problem, i.e. credit rating process modelling. Current approaches in credit rating modelling are introduced, as well as the incorporation of previous findings on corporate and municipal credit rating modelling. Based on this analysis, the model is designed to classify US companies and municipalities into credit rating classes. The model includes data pre-processing, the selection process of input variables, and the design of various neural networks' structures for classification.

Download or read book Swarm Evolutionary and Memetic Computing written by Bijaya Ketan Panigrahi and published by Springer Science & Business Media. This book was released on 2012-12-16 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2012, held in Bhubaneswar, India, in December 2012. The 96 revised full papers presented were carefully reviewed and selected from 310 initial submissions. The papers cover a wide range of topics in swarm, evolutionary, memetic and other intelligent computing algorithms and their real world applications in problems selected from diverse domains of science and engineering.

Download or read book International Conference on Intelligent Computing and Applications written by Subhransu Sekhar Dash and published by Springer. This book was released on 2017-12-28 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a collection of best papers presented in International Conference on Intelligent Computing and Applications (ICICA 2016) organized by Department of Computer Engineering, D.Y. Patil College of Engineering, Pune, India during 20-22 December 2016. The book presents original work, information, techniques and applications in the field of computational intelligence, power and computing technology. This volume also talks about image language processing, computer vision and pattern recognition, machine learning, data mining and computational life sciences, management of data including Big Data and analytics, distributed and mobile systems including grid and cloud infrastructure.

Download or read book Forecasting Financial Markets Using Neural Networks written by Jason E. Kutsurelis and published by . This book was released on 1998 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research examines andanalyzes the use of neural networks as a forecasting tool. Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a traditional forecasting method, multiple linear regression analysis. Finally, the probability of the model's forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of usingneural networks as a forecasting tool for the individual investor. This study builds upon the work done byEdward Gately in his book Neural Networks for Financial Forecasting. This research validates the work of Gately and describes the development of a neural network that achieved a 93.3 percent probability of predicting a market rise, and an 88.07 percent probability of predicting a market drop in the S&P500. It was concluded that neural networks do have the capability to forecast financial markets and, if properly trained, the individual investor could benefit from the use of this forecasting tool.

Download or read book Stock Market Index Prediction Using Artificial Neural Network written by Amin Hedayati and published by . This book was released on 2017 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term historical stock prices as well as the day of week as inputs. Daily stock exchange rates of NASDAQ from January 28, 2015 to 18 June, 2015 are used to develop a robust model. First 70 days (January 28 to March 7) are selected as training dataset and the last 29 days are used for testing the model prediction ability. Networks for NASDAQ index prediction for two type of input dataset (four prior days and nine prior days) were developed and validated.

Download or read book Forecasting Financial Markets Using Neural Networks written by Jason Kutsurelis and published by . This book was released on 1998-09-01 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research examines and analyzes the use of neural networks as a forecasting tool. Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a traditional forecasting method, multiple linear regression analysis. Finally, the probability of the model's forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of using neural networks as a forecasting tool for the individual investor. This study builds upon the work done by Edward Gately in his book Neural Networks for Financial Forecasting. This research validates the work of Gately and describes the development of a neural network that achieved a 93.3 percent probability of predicting a market rise, and an 88.07 percent probability of predicting a market drop in the S&P500. It was concluded that neural networks do have the capability to forecast financial markets and, if properly trained, the individual investor could benefit from the use of this forecasting tool.

Download or read book Neural Networks in Business Forecasting written by G. Peter Zhang and published by IGI Global. This book was released on 2004-01-01 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. This book provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.

Download or read book Performance Analysis of Artificial Neural Networks in Forecasting Financial Time Series written by Assia Lasfer and published by . This book was released on 2013 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Forecasting stock prices is of critical importance for investors who wish to reduce investment risks. Forecasting is based on the idea that stock prices move in patterns. So far, it is understood that developed, emerging, and frontier markets have different general characteristics. Subsequently, this research uses design of experiments (DOE) to study the significance and behavior of artificial neural networks (ANN) design parameters and their effect on the performance of predicting movement of developed, emerging, and frontier markets. In this study, each classification is represented by two market indices. The data is based on Morgan Stanley Country Index (MSCI), and includes the indices of UAE, Jordan, Egypt, Turkey, Japan, and UK. Two designed experiments are conducted where 5 neural network design parameters are varied between two levels"--Abstract.

Download or read book Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network written by Joish Bosco and published by GRIN Verlag. This book was released on 2018-09-18 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.

Download or read book Applications and Innovations in Intelligent Systems XIII written by Ann Macintosh and published by Springer Science & Business Media. This book was released on 2007-10-27 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers in this volume are the refereed application papers presented at AI-2005, the Twenty-fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2005. The papers present new and innovative developments in the field, divided into sections on Synthesis and Prediction, Scheduling and Search, Diagnosis and Monitoring, Classification and Design, and Analysis and Evaluation. This is the thirteenth volume in the Applications and Innovations series. The series serves as a key reference on the use of AI Technology to enable organisations to solve complex problems and gain significant business benefits. The Technical Stream papers are published as a companion volume under the title Research and Development in Intelligent Systems XXII.

Download or read book Applied Soft Computing and Communication Networks written by Sabu M. Thampi and published by Springer Nature. This book was released on 2021-07-01 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes thoroughly refereed post-conference proceedings of the International Applied Soft Computing and Communication Networks (ACN 2020) held in VIT, Chennai, India, during October 14–17, 2020. The research papers presented were carefully reviewed and selected from several initial submissions. The book is directed to the researchers and scientists engaged in various fields of intelligent systems.

Download or read book Forecast Stock Index Using Neural Networks and Evolutionary Computing written by Hassan Abdelbary and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting price index is an important problem in financial markets. In the past decades the prediction of stock index has played a vital role in the financial situation of several companies which have stocks in the market. In the past this prediction process was simple and easy for several reasons: the behavior of the stocks was known and not complicated beside the existence of a number of experts in this field. Several techniques are used to predict and model the stock market behavior and try to increase the accuracy of prediction. Neural networks have several characteristics which make them good models to predict the complex behavior of stock index and increase the accuracy of the prediction. Combining neural networks with evolutionary computational methods like Genetic Algorithms and Simulated Annealing can give better results in learning neural networks specially for problem of forecasting stock index.

Download or read book Neural Network Time Series written by E. Michael Azoff and published by . This book was released on 1994-09-27 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software.

Download or read book Metaheuristics in Machine Learning Theory and Applications written by Diego Oliva and published by Springer Nature. This book was released on with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

Download or read book Fuzzy Sets in Management Economics and Marketing written by Panos M. Pardalos and published by World Scientific. This book was released on 2001 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid changes that have taken place globally on the economic, social and business fronts characterized the 20th century. The magnitude of these changes has formed an extremely complex and unpredictable decision-making framework, which is difficult to model through traditional approaches. The main purpose of this book is to present the most recent advances in the development of innovative techniques for managing the uncertainty that prevails in the global economic and management environments. These techniques originate mainly from fuzzy sets theory. However, the book also explores the integration of fuzzy sets with other decision support and modeling disciplines, such as multicriteria decision aid, neural networks, genetic algorithms, machine learning, chaos theory, etc. The presentation of the advances in these fields and their real world applications adds a new perspective to the broad fields of management science and economics. Contents: Decision Making, Management and Marketing: Algorithms for Orderly Structuring of Financial OC ObjectsOCO (J Gil-Aluja); A Fuzzy Goal Programming Model for Evaluating a Hospital Service Performance (M Arenas et al.); A Group Decision Making Method Using Fuzzy Triangular Numbers (J L Garc a-Lapresta et al.); Developing Sorting Models Using Preference Disaggregation Analysis: An Experimental Investigation (M Doumpos & C Zopounidis); Stock Markets and Portfolio Management: The Causality Between Interest Rate, Exchange Rate and Stock Price in Emerging Markets: The Case of the Jakarta Stock Exchange (J Gupta et al.); Fuzzy Cognitive Maps in Stock Market (D Koulouriotis et al.); Neural Network vs Linear Models of Stock Returns: An Application to the UK and German Stock Market Indices (A Kanas); Corporate Finance and Banking Management: Expertons and Behaviour of Companies with Regard to the Adequacy Between Business Decisions and Objectives (A Couturier & B Fioleau); Multiple Fuzzy IRR in the Financial Decision Environment (S F Gonzilez et al.); An Automated Knowledge Generation Approach for Managing Credit Scoring Problems (M Michalopoulos et al.); and other papers. Readership: Financial managers, economists, management scientists and computer scientists."