Stock Market Forecasting Using Fuzzy Logic
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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.
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.
Forecasting Ibovespa Index With Fuzzy Logic
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Author : Cesar Duarte Souto-Maior
language : en
Publisher:
Release Date : 2006
Forecasting Ibovespa Index With Fuzzy Logic written by Cesar Duarte Souto-Maior and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
Much research has been done aiming the forecasting of stock market index values. However, very few researches focus on the predictability of the direction of stock market movements. This paper fills this gap through the estimation of a model, using fuzzy logic to forecast the direction of the movements of the São Paulo Stock Exchange index (IBOVESPA). To establish the rules of the model it was used an estimation period based on 1,000 daily data sets, corresponding to the period of January 8, 1997 to January 22, 2001. The test period was from January 23, 2001 to February 2, 2005. A software called FuzzyTECH® was used. Despite the estimated model produces an inexact answer, with a probabilistic output, it was possible to implement an investment strategy, using the IBOVESPA index as a proxy for an investment fund, which outperformed a buy-and-hold strategy.
Applying Fuzzy Logic To Stock Price Prediction
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Author : Ali Ghodsi Boushehri
language : en
Publisher:
Release Date : 2000
Applying Fuzzy Logic To Stock Price Prediction written by Ali Ghodsi Boushehri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Fuzzy logic categories.
The major concern of this study is to develop a system that can predict future prices in the stock markets by taking samples of past prices. Stock markets are complex. Their dramatic movements, and unexpected booms and crashes, dull all traditional tools. This study attempts to resolve such complexity using the subtractive clustering based fuzzy system identification method, the Sugeno type reasoning mechanism, and candlestick chart analysis. Candlestick chart analysis shows that if a certain pattern of prices occurs in the market, then the stock price will increase or decrease. Inspired by the key information that candlestick analysis uses, this study assumes that everything impacting a market, from economic factors to politics, is distilled into market price. The model presented in this study elicits, from historical data price, some of the rules which govern the market, and shows that rules which are drawn from a particular stock are to some extent independent of that stock, and can be generalized and applied to other stocks regardless of specific time or industrial field. The experimental results of this study in the duration of 3 months reveals that the model can correctly predict the direction of the market with an average hit ratio of 87%. In addition to daily prediction, this model is also capable of predicting the open, high, low, and close prices of desired stock, weekly and monthly.
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.
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.
Ordinary Shares Exotic Methods
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Author : Francis E. H. Tay
language : en
Publisher: World Scientific
Release Date : 2003
Ordinary Shares Exotic Methods written by Francis E. H. Tay and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Business & Economics categories.
Exotic methods refer to specific functions within general soft computing methods such as genetic algorithms, neural networks and rough sets theory. They are applied to ordinary shares for a variety of financial purposes, such as portfolio selection and optimization, classification of market states, forecasting of market states and data mining. This is in contrast to the wide spectrum of work done on exotic financial instruments, wherein advanced mathematics is used to construct financial instruments for hedging risks and for investment.In this book, particular aspects of the general method are used to create interesting applications. For instance, genetic niching produces a family of portfolios for the trader to choose from. Support vector machines, a special form of neural networks, forecast the financial markets; such a forecast is on market states, of which there are three OCo uptrending, mean reverting and downtrending. A self-organizing map displays in a vivid manner the states of the market. Rough sets with a new discretization method extract information from stock prices."
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.
Fuzzy Logic For Business Finance And Management
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Author : George Bojadziev
language : en
Publisher: World Scientific
Release Date : 1997
Fuzzy Logic For Business Finance And Management written by George Bojadziev and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Business & Economics categories.
This is an interdisciplinary book for knowledge workers in business, finance, management, and socio-economic sciences. It provides a guide to and techniques for forecasting, decision making, conclusions, and evaluations in an environment involving uncertainty, vagueness, and impression. Traditional modeling techniques do not capture the nature of complex systems especially when humans are involved. Fuzzy logic provides effective tools for dealing with such systems. Emphasis is on applications presented in case studies including Time Forecasting for Project Management, New Product Pricing, Client Financial Risk Tolerance Policy, Deviation and Potential Problem Analysis, Inventory Control Model, Stock Market Strategy.