STOCK MARKET TREND PREDICTION USING DATA SCIENCE
Keywords:
.Abstract
The stock market is a complex and highly dynamic financial environment influenced by numerous factors such as historical price movements, economic indicators, geopolitical events, market sentiment, and investor behavior. Due to its volatile and nonlinear characteristics, predicting future stock market trends has always been a challenging task for investors, analysts, and researchers. Traditional statistical models and manual forecasting approaches often fail to capture the hidden patterns and temporal dependencies present in large-scale financial datasets. To address these challenges, this project proposes a Stock Market Trend Prediction System that utilizes advanced Data Science and Deep Learning techniques to forecast future stock prices with improved accuracy and reliability. The proposed system employs a Long Short-Term Memory (LSTM) network, which is a specialized architecture of Recurrent Neural Networks (RNNs) designed specifically for time-series data analysis. Unlike conventional regression models that treat each data point independently, LSTM networks are capable of remembering previous information over long sequences, allowing them to learn complex patterns and long-term dependencies in stock price data. The system uses historical stock market data including Open, High, Low, Close, and Volume values collected from financial data sources such as Yahoo Finance APIs. During the preprocessing phase, the dataset undergoes cleaning, normalization using MinMax scaling, and the creation of sequential time-window datasets, where past observations (for example, the previous 60 days of stock prices) are used to predict future trends. The LSTM model is then trained on this processed data to minimize prediction errors, which are evaluated using performance metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Visualization techniques are also used to compare actual stock prices with predicted values to assess the accuracy of trend forecasting. Overall, the system demonstrates how modern deep learning models can effectively analyze financial time-series data and provide a scalable, automated, and efficient framework for stock market trend prediction and investment decision support.
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Published
2026-03-15
How to Cite
Dr.M. NANCHARAIAH,PITTALA SIRI CHANDANA,ANDALURI VARSHITHA,MANEPALLI GEETHA SRI,KROVI SAI CHAITANYA, & DOI: 10.48047/IJCNIS.18.3.40. (2026). STOCK MARKET TREND PREDICTION USING DATA SCIENCE. International Journal of Communication Networks and Information Security (IJCNIS), 18(3), 31–40. Retrieved from http://www.ijcnis.org/index.php/ijcnis/article/view/8867
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Research Articles
