Stock Market Analysis and Prediction


Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock’s future price could yield significant profit.

Stock Market Analysis and Prediction
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Stock market analysis is a widely studied problem as it offers practical applications for signal processing and predictive methods and a tangible financial reward. Creating a system that yields consistent returns is extremely challenging and is currently an open problem as stock market prices are extremely volatile and vary widely both within a given stock and comparatively amongst many stocks. Further, stock market data is influenced by a large number of factors including foreign and domestic economies, trade agreements, wars

Application of neural network to technical analysis of stock market prediction
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This paper presents a neural network model for technical analysis of stock market , and its application to a buying and selling timing prediction system for stock index. When the numbers of learning samples are uneven among categories, the neural network with normal

Text opinion mining to analyze news for stock market prediction
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This is a known fact that news and stock prices are closely related and news usually has a great influence on stock market investment. There have been many researches aimed at identifying that relationship or predicting stock market movements using news analysis

Prediction of stock market index movement by ten data mining techniques
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Ability to predict direction of stock /index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, in stead of a single

Momentum analysis based stock market prediction using adaptive neuro-fuzzy inference system (anfis)
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This paper presents an innovative approach for indicating stock market decisions that the investor should take for minimizing the risk involved in making investments. The system uses Adaptive Neuro-Fuzzy Inference System (ANFIS) for taking decisions based on the values of

A decision tree rough set hybrid system for stock market trend prediction
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Prediction of stock market trends has been an area of great interest both to those who wish to profit by trading stocks in the stock market and for researchers attempting to uncover the information hidden in the stock market data. Applications of data mining techniques for stock

Sentiment analysis of twitter feeds for the prediction of stock market movement
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In this paper, we investigate the relationship between Twitter feed content and stock market movement. Specifically, we wish to see if, and how well, sentiment information extracted from these feeds can be used to predict future shifts in prices. To answer this question, we

Accuracy driven artificial neural networks in stock market prediction
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Globalization has made the stock market prediction (SMP) accuracy more challenging and rewarding for the researchers and other participants in the stock market . Local and global economic situations along with the companys financial strength and prospects have to be

Parameters for stock market prediction
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In recent years researchers have developed a lot of interest in stock market prediction because of its dynamic unpredictable nature. Although there were lots of methods of prediction none of them is prove to produce satisfactory results. Machine learning

Stock market prediction based on fundamentalist analysis with fuzzy-neural networks
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In this article, we discuss the application of a combination of Neural Networks and Fuzzy Logic techniques to fundamentalist analysis of stock investment. Several researchers have used neural networks models in a variety of ways to predict short and long-term stock

Comparative study between FPA, BA, MCS, ABC, and PSO algorithms in training and optimizing of LS-SVM for stock market prediction
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In this Paper, five recent natural inspired algorithms are proposed to optimize and train Least Square-Support Vector Machine (LS-SVM). These algorithms are namely, Flower Pollination Algorithm (FPA), Bat algorithm (BA), Modified Cuckoo Search (MCS), Artificial Bee Colony

Translated Nigeria stock market prices using artificial neural network for effective prediction
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This paper used error back propagation algorithm and regression analysis to analyze and predict untranslated and translated Nigeria Stock Market Price (NSMP). Nigeria stock market prices were collected for the periods of seven hundred and twenty days and grouped into

Margin variations in support vector regression for the stock market prediction
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Abstract Support Vector Regression (SVR) has been applied successfully to financial time series prediction recently. In SVR, the ε-insensitive loss function is usually used to measure the empirical risk. The margin in this loss function is fixed and symmetrical. Typically

Data Mining and Neural Network techniques in Stock market prediction : A methodological review
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Prediction in any field is a complicated, challenging and daunting process. Employing traditional methods not ensure the reliability of the prediction . In this paper, we are reviewing the possibility of applying two well-known techniques neural network and data

A genetic algorithm optimized decision tree-SVM based stock market trend prediction system
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Prediction of stock market trends has been an area of great interest both to researchers attempting to uncover the information hidden in the stock market data and for those who wish to profit by trading stocks. The extremely nonlinear nature of the stock market data

A linear regression approach to prediction of stock market trading volume: a case study
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Predicting daily behavior of stock market is a serious challenge for investors and corporate stockholders and it can help them to invest with more confident by taking risks and fluctuations into consideration. In this paper, by applying linear regression for predicting

Indian stock market prediction using differential evolutionary neural network model
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This paper presents a scheme using Differential Evolution based Functional Link Artificial Neural Network (FLANN) to predict the Indian Stock Market Indices. The Model uses Back- Propagation (BP) algorithm and Differential Evolution (DE) algorithm respectively for

Prediction of the Bombay Stock Exchange (BSE) market returns using artificial neural network and genetic algorithm
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Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which

Stock market prediction using artificial neural networks. Case Study of TAL1T, Nasdaq OMX Baltic Stock
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Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention in last two decades. The combined prediction model, based on artificial neural networks (ANNs) with principal component analysis (PCA) for financial time

Identifying relative contribution of selected technical indicators in stock market prediction
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This paper investigates a sensitivity analysis of key technical indicators used in an artificial neural network (ANN) for predicting stock market trends. This process of forecasting future price movements in the stock market involves technical analysis. The data to be collected

Application of neural network in analysis of stock market prediction
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Predicting the stock market is very difficult since it depends on several known and unknown factors. So many methods like Technical analysis, Fundamental analysis, Time series analysis and statistical analysis etc. are all used to attempt to predict the price in the share



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