S&P 500 Stock Price Financial Risk Prediction Using ARIMA, SARIMA and LSTM Models

Author(s)	Ms. Sumedha Arya
Country	India
Abstract	This study presents a comprehensive analysis of the S&P 500 stock dataset, to explore market trends, sector volatility, and short-term financial risk through price forecasting. Along with Exploratory data analysis (EDA), a short-term (30-day ahead) price forecasting was performed specifically for NVIDIA (NVDA) stock us. Three distinct time series models were used: ARIMA (5,1,0), SARIMA with weekly seasonality, and a deep learning-based LSTM enhanced with a Linear Attention mechanism. The model predictions were compared with each other and evaluated against the actual NVDA price at the end of December 2025. Results indicate that SARIMA provided the most balanced and directionally reasonable forecast, while ARIMA was overly conservative, and LSTM tended to over-extrapolate trends. The analysis highlights challenges in accurate stock price prediction with strong momentum-driven markets.
Keywords	Stock Price Forecasting, Financial Risk Prediction, S&P 500, ARIMA, SARIMA, LSTM
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-11
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.65922

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