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Proceedings ArticleDOI

Stock market prediction using hybrid approach

TLDR
A model to predict stock value movement using the opinion mining and clustering method to predict National Stock Exchange (NSE) is constructed using stocks with maximum capitalization within all the important sectors.
Abstract
The objective of this paper is to construct a model to predict stock value movement using the opinion mining and clustering method to predict National Stock Exchange (NSE). We have used domain specific approach to predict the stocks from each domain we have taken some stock with maximum capitalization. Proposed Method is Not at all like past methodologies where the general states of mind or sentiments are considered, sentiments of the particular subjects of the organization or sector are fused into the stock prediction model. Topics and related opinion of shareholders are automatically extracted from the writings in a message board by utilizing our proposed strategy alongside isolating clusters of comparable sort of stocks from others using clustering algorithms. Proposed methodology will give us two output set i.e. one from sentiment analysis and another from clustering based prediction with respect to some specialized parameters of stock exchange. By examining both the results an efficient prediction is produced. In this paper stocks with maximum capitalization within all the important sectors are taken into consideration for empirical analysis.

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Citations
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Proceedings ArticleDOI

Sentiment-Driven Price Prediction of the Bitcoin based on Statistical and Deep Learning Approaches

TL;DR: This paper investigates the predictive power of network sentiments and explore statistical and deep-learning methods to predict Bitcoin future price and analyzes financial and sentiment features extracted from economic and crowd-sourced data, showing how the sentiment is the most significant factor in predicting Bitcoin market stocks.
Proceedings ArticleDOI

Stock Market Prediction using Machine Learning Algorithms: A Classification Study

TL;DR: Categorising various methods used for predictive analytics in different domains to date, their shortcomings and some improvements that could be incorporated to achieve better accuracy are suggested.
Proceedings ArticleDOI

Predicting Stock Prices Using Machine Learning Techniques

TL;DR: In this article, the authors proposed a strong technique to anticipate the offer rate utilizing Moving average based model and contrast how it vary and the genuine cost, which can be applied on throughout a wide range of time money related data to make models and further calculations.
Book ChapterDOI

Prediction of Gold Stock Market Using Hybrid Approach

TL;DR: In this article, the authors used ANNs for predicting the fluctuation in gold price, and the aim was to build a model which forecast price with maximum precision and also helps user to maximize their profit.
Journal ArticleDOI

Stock Prediction using Hybrid ARIMA and GRU Models

TL;DR: In this paper, a hybrid ARIMA-GRU model has been proposed which learns continuously from the history of stocks invested, sold and brought by the clients and identifies patterns, analyzes which kind of data is suitable for prediction and then predicts suitable value.
References
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Journal ArticleDOI

Giving Content to Investor Sentiment: The Role of Media in the Stock Market

Paul C. Tetlock
- 01 Jun 2007 - 
TL;DR: The authors quantitatively measure the interactions between the media and the stock market using daily content from a popular Wall Street Journal column and find that high media pessimism predicts downward pressure on market prices followed by a reversion to fundamentals.
Journal ArticleDOI

Giving Content to Investor Sentiment: The Role of Media in the Stock Market

TL;DR: The authors quantitatively measure the nature of the media's interactions with the stock market using daily content from a popular Wall Street Journal column and find that high media pessimism predicts downward pressure on market prices followed by a reversion to fundamentals.
Journal ArticleDOI

New Avenues in Opinion Mining and Sentiment Analysis

TL;DR: The history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools.
Journal ArticleDOI

Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums

TL;DR: Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.
Journal ArticleDOI

A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

TL;DR: Concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as a comparison, both from a theoretical and an empirical perspective are introduced.
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