INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Obtaining accurate prediction of stock index and Stock prices significantly helps decision maker to take correct actions to develop a better economy. For many Trading houses and retail traders the inability to predict fluctuation of the stock market might cause serious profit loss. The main challenge is that we always deal with the dynamic market which is influenced by many factors in live market. They include political, financial and reserve occasions and unplanned events. Thus, stable, robust and adaptive approaches which can provide models have the capability to accurately predict stock index and trend of market are urgently needed. In this paper, we explore the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) along with Multivariate LSTM to build prediction models for the Nifty50 and Bank Nifty stock index as well as listed equity shares (stocks). Here we will do web scrapping for identifying market sentiments from different financial magazines and newspapers. The model will be a hybrid model where we will be using a combination of three different algorithms viz LSTM, Google Prophet and linear Regression. We will also show how traditional models such as multiple linear regressions (MLR) behave in this case. The developed models will be evaluated and compared based on a number of evaluation criteria for NSE as well as BSE. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. Our motivation is based on the notion that financial planning guided by pattern discovery and prediction of stock index prices maybe more realistic and effective than traditional approaches, such as Autoregressive Integrated Moving Average (ARIMA) model
Keywords:
Artificial Intelligence, Computer Programming, Machine language
Cite Article:
"Share Market price prediction using AI and Sentiments", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 1, page no.a407-a419, January-2023, Available :http://www.ijnrd.org/papers/IJNRD2301049.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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