Quantitative Forecasting of Moderna Stock Prices Using Linear Regression, Random Forest, and LSTM

Moderna, a trailblazer in mRNA-based biotechnology, has experienced substantial stock price volatility, particularly during the COVID-19 pandemic. Accurate prediction of such price movements is essential for effective decision-making and risk mitigation in financial markets. This study evaluates the predictive performance of three modeling approaches—Linear Regression, Random Forest, and Long Short-Term Memory (LSTM)—for forecasting Moderna’s next-day stock prices. The analysis incorporates key financial indicators, including short-and-long-term moving averages (MA10 and MA50), the Relative Strength Index (RSI), and the Moving Average Convergence Divergence (MACD), which capture both momentum-based signals and underlying trends. Among the tested models, LSTM demonstrated the highest predictive accuracy by effectively analyzing temporal dependencies and sequential patterns within stock price data. Random Forest exhibited strong adaptability to non-linear relationships, while Linear Regression offered an interpretable and computationally efficient baseline. The findings highlight the importance of adopting advanced machine learning models, such as LSTM, for analyzing volatile stock markets, particularly within industries characterized by rapid innovation and dynamic market conditions.