Neuro Fuzzy Based Stock Market Prediction System
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Neuro Fuzzy Based Stock Market Prediction System
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Author : M. Gunasekaran
language : en
Publisher:
Release Date : 2013
Neuro Fuzzy Based Stock Market Prediction System written by M. Gunasekaran and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
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.
A Neuro Fuzzy Logic Forecasting System In Stock Investment Decision Making Processes
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Author : Xu Wang
language : en
Publisher:
Release Date : 2007
A Neuro Fuzzy Logic Forecasting System In Stock Investment Decision Making Processes written by Xu Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Artificial intelligence categories.
"The sophisticated financial investment world is characterized by highly random variations in stock prices, financial indexes and trading volumes so that it is quite difficult to get fundamental understanding of the financial investment process and to predict the stock market. This research attempts to develop a new and innovative approach to predict the stock time series with artificial intelligence techniques. Specifically, a fuzzy logic analysis has been made to predict the stock time series with different characteristic variables and different investments horizons, respectively. A neural network is designed to fine-tune the parameters involved and thus a neuron-fuzzy logic time series forecasting model has been developed" - abstract.
Explainable Artificial Intelligence Based On Neuro Fuzzy Modeling With Applications In Finance
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Author : Tom Rutkowski
language : en
Publisher: Springer Nature
Release Date : 2021-06-07
Explainable Artificial Intelligence Based On Neuro Fuzzy Modeling With Applications In Finance written by Tom Rutkowski and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-07 with Technology & Engineering categories.
The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.
Computational Science Iccs 2001
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Author : Vassil N. Alexandrov
language : en
Publisher: Springer
Release Date : 2003-05-15
Computational Science Iccs 2001 written by Vassil N. Alexandrov and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-05-15 with Computers categories.
LNCS volumes 2073 and 2074 contain the proceedings of the International Conference on Computational Science, ICCS 2001, held in San Francisco, California, May 27-31, 2001. The two volumes consist of more than 230 contributed and invited papers that reflect the aims of the conference to bring together researchers and scientists from mathematics and computer science as basic computing disciplines, researchers from various application areas who are pioneering advanced application of computational methods to sciences such as physics, chemistry, life sciences, and engineering, arts and humanitarian fields, along with software developers and vendors, to discuss problems and solutions in the area, to identify new issues, and to shape future directions for research, as well as to help industrial users apply various advanced computational techniques.
An Improved Intelligent Model For Stock Market Time Series Data Prediction Using Fuzzy Logic And Deep Neural Networks
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Author : Parniyan Mousaie
language : en
Publisher:
Release Date : 2023
An Improved Intelligent Model For Stock Market Time Series Data Prediction Using Fuzzy Logic And Deep Neural Networks written by Parniyan Mousaie and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
It is vitally crucial to establish a method that can accurately forecast prices on the stock exchange market because of the influence the stock market has on the country's ability to raise capital and advance its economic growth. On the stock market, a great number of sensitivity factors are connected to price movement, which is why the progressions associated with such a phenomenon are routinely evaluated. Several neural network models have recently been used to forecast stock prices. In this research, the data related to active companies in the stock market was used to evaluate research questions. Also, the neural network technique was used to look at all data from the market index, fuzzy neural network model, and long short-term memory (LSTM) model from 2020 to 2021. Accordingly, this study aims to forecast the stock price and give a dynamic model with fewer errors using integrated factors, the technical, cardinal, and economic assessment of the market index using the neural network technique. This will be accomplished by utilizing the neural network method. The findings demonstrated that if the combined data of basic analytical factors was used further, we would not only have better training and receive better results, but we would also be able to decrease the prediction error.
Stock Market Trend Prediction Using Neural Networks And Fuzzy Logic
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Author : Maha Abdelrasoul
language : en
Publisher:
Release Date : 2016-11-22
Stock Market Trend Prediction Using Neural Networks And Fuzzy Logic written by Maha Abdelrasoul and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-22 with categories.
Forecast Of Financial Markets Stock Prices Using Neural Networks And Anfis
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Author : Luis Alberto Valencia Vega
language : en
Publisher:
Release Date : 2011
Forecast Of Financial Markets Stock Prices Using Neural Networks And Anfis written by Luis Alberto Valencia Vega and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Finance categories.
The financial market is a very complex nonlinear series of time. There have been a lot of opinions in the topic of the predictability of it. The need to predict a next day, week, or month has always existed for the final purpose of making money. The most common way of forecasting this time series is with statistic methods and linear regression models. However, the use of artificial intelligence algorithms may have a better outcome, due to the capability of them to handle nonlinear data. The present thesis will be focused on evaluating the use of artificial intelligence algorithms as forecasters for financial markets stock prices. Two algorithms will be used, Feed-Forward Neural networks and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). All forecasts are made with the purpose of a short term trading strategy. Three stocks will be used as an example of the consistency of the method; Google, Apple and the Mexican stock ALFA. These three stocks have different distributed data and different behavior from the neural networks and ANFIS ¡s expected.
Type 3 Fuzzy Logic In Time Series Prediction
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Author : Oscar Castillo
language : en
Publisher: Springer Nature
Release Date :
Type 3 Fuzzy Logic In Time Series Prediction written by Oscar Castillo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Stock Price Prediction A Referential Approach On How To Predict The Stock Price Using Simple Time Series
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Author : Dr.N.Srinivasan
language : en
Publisher: Clever Fox Publishing
Release Date :
Stock Price Prediction A Referential Approach On How To Predict The Stock Price Using Simple Time Series written by Dr.N.Srinivasan and has been published by Clever Fox Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on with Business & Economics categories.
This book is about the various techniques involved in the stock price prediction. Even the people who are new to this book, after completion they can do stock trading individually with more profit.
Stock Market Forecasting Using Fuzzy Logic
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Author :
language : en
Publisher:
Release Date : 2016
Stock Market Forecasting Using Fuzzy Logic written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Electronic books categories.
Forecasting is a very tedious task and many factors should be taken into consideration for proper predictions. The chaotic nature and randomness of stock market index values, makes forecasting stock market values a very challenging task. Financial forecasting can be done in many areas such as currencies, commodities, bonds and stocks. This project is restricted to stocks; and in particular the SENSEX, National Stock Exchange of India. Prediction of the stock market can be of interest to investors, traders and researchers. To take appropriate buy and sell decision for a stock knowing the momentum of the stock market can be of great help. Forecasting becomes difficult considering highly unpredictable attributes such as historical prices, company orders, company earnings, company revenue, etc. The proposed fuzzy model identifies the momentum of the stock index for next 5 days by considering the 14-day historic data as the base. The fuzzy model is applied to the close and open values and a system is designed which takes input as 14-day data and outputs the future moment as Up(bearish), Neural and Down(Bullish). The results found closely match with the expected real-world values when compared with already known data.