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(searched for: doi:10.15415/jtmge.2020.111004)
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Pradeepta Kumar Sarangi, Ashok Kumar Sahoo, Gagandeep Kaur, Soumya Ranjan Nayak, Akash Kumar Bhoi
Published: 31 May 2022
The publisher has not yet granted permission to display this abstract.
Muskaan, Pradeepta Kumar Sarangi, Sunny Singh, Soumya Ranjan Nayak, Akash Kumar Bhoi
Published: 31 May 2022
The publisher has not yet granted permission to display this abstract.
Neha Titarmare, Mrinmayi Wazarkar, Parth Palkar, Ravi Alwaria, Abhishek Shetye, Ritesh Khodiyar
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE “PHYSICAL MESOMECHANICS. MATERIALS WITH MULTILEVEL HIERARCHICAL STRUCTURE AND INTELLIGENT MANUFACTURING TECHNOLOGY”, Volume 2424; https://doi.org/10.1063/5.0076964

Abstract:
In the world of finance, activities related to stock exchange are perhaps considered important. The demonstration of trying to gauge the prospective assessment of a stock or other money related tool traded on a financial exchange is called as the stock market prediction or forecast. Share Market is a messy spot for anticipating since there are not any critical guidelines to assess or foresee the estimation of offer inside the share market. Numerous techniques like specialized investigation, principal examination, and factual examination, and so forth arrange to anticipate the value inside the share market nevertheless none of these approaches are incontestable as a faithfully worthy expectation instrument. This paper discusses about how AI methods can be utilized to anticipate the yield of a stock. Most stockbrokers utilize methodological and fundamental analysis, along with time series analysis, while making stock forecasts. The programming language is utilized to deliver stock market estimates. In this research work, we propose an AI (ML) methodology that will be trained from the available stock information and gain understanding and subsequently uses the obtained data for a definite estimate. In this context, this investigation uses an AI methodology called Linear Regression to anticipate stock prices for the gigantic and slight capitalizations, for example, using costs with both day by day and on the time frequencies.
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