International Journal of Science, Engineering and TechnologyAuthors: Akshatha N S, Keerthi T S, Pallavi B, Associate Professor Dr. Venkatesh
Abstract: Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. Since stock trading is so important to the financial industry, investors are constantly looking for trustworthy strategies to predict market fluctuations. Predicting the future values of stocks or other financial assets that are traded on exchanges is known as stock market prediction. The use of machine learning (ML) in stock price forecasting is investigated in this paper. Techniques including time-series forecasting, technical analysis, and fundamental analysis are commonly used by traders and investors to inform their investment decisions. The main programming language used to create the machine learning models in this study is Python. The suggested method trains an ML model using historical stock data with the goal of finding trends and producing predictions based on insights gleaned from the data. The Support Vector Machine (SVM) method is specifically used in the study to forecast stock values under a range of market scenarios. Both large-cap and small-cap stocks are used to test the model's performance and daily and real-time data are used to examine price changes. The goal of this research is to improve stock price prediction accuracy and give investors useful decision-making assistance by incorporating machine learning techniques.
