Proceedings ArticleDOI
Stock market prediction using hybrid approach
Vivek Rajput,Sarika Bobde +1 more
- pp 82-86
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.read more
Citations
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Sentiment-Driven Price Prediction of the Bitcoin based on Statistical and Deep Learning Approaches
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Stock Market Prediction using Machine Learning Algorithms: A Classification Study
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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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Adil Fahad,Najlaa Alshatri,Zahir Tari,Abdullah Alamri,Ibrahim Khalil,Albert Y. Zomaya,Sebti Foufou,Abdelaziz Bouras +7 more
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.