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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63758
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Application
Bapuji Institute of Engineering and Technology
Master of Computer Application
Bapuji Institute of Engineering and Technology
Because of the digitization phenomenon, a massive volume of financial data is being generated. As a result, traditional methods of financial data analysis have become inefficient and are unable to deliver an effective market analysis platform. This project explores how financial data analysis can be performed efficiently through the project make use of Hadoop and Apache Spark which help to deal with big data. Various types of analysis like time series analysis and machine learning analysis among others are used to uncover valuable patterns. Real-time data are also used to interpret the real-time actions in the market. In addition to this the project utilizes sentiment analysis to interpret the effect of public sentiments on market pattern The project establishes that analytics is possible and feasible for decision-making in uncertain environments for financial markets. The models for data-driven finance and serves as a background for research at the interface between financial analytics and data science and new technology trends.
KEYWORDS
The project named "Big Data Analysis for Financial Market Trends" has been able to prove its importance as it is able to analyze complex market data which is quite hard to interpret manually. In the course of its development the project has been able to incorporate many modules including an overall analytical solutionThis project apart from emphasizing the value of big data for the future also represents This is considering that the visualization aspect of the project helps to create an effective means of depicting the trends related to the financial world. In this case the project seeks to prevent errors created by financial decision-making through the use of big data.
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Publication Date
5/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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