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@article{198830,
        author = {Praveen S and Gnanasekar V and Dr.Maheswari S and Yogeshwaran S and Gokularangan S and Harevignesh J.M},
        title = {An Intelligent Multidimensional Framework for Stock Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11957-11964},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198830},
        abstract = {Stock evaluation involves piecing together several kinds of financial evidence, including market signals, company performance measures, and historical price movement. For many investors, especially those with limited experience, interpreting these elements manually can be slow and uncertain. To make the process simpler, this work presents IMDFSA (Intelligent Multidimensional Framework for Stock Analysis), a web-based platform built to support clearer and more systematic stock assessment. The system brings together fundamental analysis, technical indicators, and machine learning methods in one environment so that stock behavior can be examined from multiple angles.
The framework evaluates stocks by examining a combination of financial fundamentals and technical market indicators. Key financial measures such as the Price-to-Earnings (P/E) ratio, Earnings Per Share (EPS), Return on Equity (ROE), and the Debt-to- Equity ratio are analyzed to understand the financial condition of a company. At the same time, market- based indicators including the Simple Moving Average (SMA), Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD) are used to interpret price trends and trading momentum. To improve accessibility and user interaction, the platform includes a chatbot interface that enables users to ask questions about stocks and quickly obtain relevant insights.},
        keywords = {},
        month = {April},
        }