Existing Techniques For Fundamental, Technical And Sentimental Analysis For Stock Investement
In today's complex financial world, where things like global economics, company strategies, and how people feel about things can all affect the stock market, buying and selling stocks has become really popular. People are attracted to it because they think they can make a lot of money quickly. But there's a big problem: while making money is exciting, there's also a big chance of losing money because the stock market can be very unpredictable. Trying to figure out what the stock market will do next is super important, but also really hard. There are lots of different things that can affect it, like what's happening in the world, how well companies are doing, and even how people are feeling about investing. So, our paper is all about using existing Techniques (like AI and machine learning) to try to predict what the stock market might do. This paper explores three main ways of doing this .i.e, Fundamental, Technical and Sentimental Analysis: looking at basic stuff about companies, studying the patterns of stock prices, and analyzing how people feel about investing. This paper will take in account what other researchers have already found out about these things, our main goal is to help other researchers to understand more about predicting the stock market. The aim of this paper is to initiate a comprehensive discourse on this subject, inviting widespread participation to equip individuals with the knowledge and confidence needed to make informed investment decisions, even among the unpredictable fluctuations of the stock market.
Authors
- Anuj Kumar (ORCID: https://orcid.org/0000-0002-8923-297X)
- Soumya J. Niranjan (ORCID: https://orcid.org/0000-0002-9477-9187)
- Atul Agrawal (ORCID: https://orcid.org/0009-0004-3601-3919)
- Pashupati Baniya (ORCID: https://orcid.org/0000-0003-1951-4469)
- Mohammad Shaazan Muzaffar
- Kadugula Joel
Institutions
- Sharda University (IN)
Publication Details
- Published
- 2024-08-29
- DOI
- https://doi.org/10.1109/iceect61758.2024.10739294
- Citations
- 2
- Primary Topic
- Stock Market Forecasting Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.98