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@article{175747,
        author = {Shafrin Jeba C and Dr Aravind Swaminathan G},
        title = {DATA DRIVEN ON THE STOCK TREND PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3687-3695},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175747},
        abstract = {Forecasting the price of stocks effectively has become progressively significant in the stock market, where returns and concerns can vary greatly both financial institutions and regulatory authorities are highly focused on this area stocks have consistently been a popular investment due to their high returns and research into forecasting stock prices has been ongoing for many years initially the efforts to forecast stock prices were largely driven by economists they relied on traditional financial theories and models which were based on the assumption that markets are efficient and that price movements are primarily driven by rational factors however these early models often fell short of accurately forecasting stock prices particularly in the face of unexpected market events and irrational behaviors of investors support vector machines svms are one of the machine learning methods that have shown promise in stock trend forecasting},
        keywords = {stock price, SVM, investment, machine learning methods.},
        month = {April},
        }