• Prathik E P
• Suhas S K
• Swasthik
• Shivshankar P
• Daya Naik, Nithya B P

Stock market prediction remains one of the most researched and challenging problems in the field of financial data analytics. The nonlinear and dynamic nature of financial markets makes it difficult to model using traditional statistical approaches. This paper proposes a deep learning-based approach using Long Short-Term Memory (LSTM) networks to forecast stock prices based on historical data. The model is trained on daily stock price data from Yahoo Finance and captures long-term dependencies within time-series data. With high accuracy and low error rates, this system can assist traders and financial analysts in making informed decisions, potentially reducing investment risks and increasing returns.

STOCK MARKET PRICE PREDICTOR

"STOCK MARKET PRICE PREDICTOR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.609-613, July-2025, Available :http://www.jetir.org/papers/JETIRGX06117.pdf

"STOCK MARKET PRICE PREDICTOR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp609-613, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06117.pdf