Authors - Aarya Pendharkar, Tanmay Pampatwar, Mrunal Zombade, Ashwini Bankar Abstract - This study offers an effective approach for forecasting changes in stock prices using a binary classification model that makes use of sentiment analysis, technical indicators, and historical stock data. The model forecasts whether a stock will gain or lose the following day, rather than predicting actual stock prices. Technical indicators including moving averages, the Relative Strength Index (RSI), and Bollinger Bands are among the input elements, along with historical price data (open, close, high, low, and volume). Market news and social media data are subjected to sentiment analysis, which produces sentiment ratings (positive, neutral, or negative) in order to identify general patterns in market sentiment. When combined with technical indicators, these mood scores provide additional context for stock movements. The model uses machine learning techniques like XGBoost, SVC, Logistic Regression, and Random Forest, and it outputs a confidence score and a binary forecast. Performance indicators like accuracy, precision, recall, and F1 score are used to assess the model's efficacy. Back testing is also done to evaluate the robustness and performance of the past. The suggested model offers a comprehensive perspective of stock movements by integrating technical and sentimental aspects, producing better prediction skills than conventional models that only use past price data.
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