Using ARIMA Model Predict Stock Market

The dynamic nature of the stock market, shaped by a complex interplay of economic and non-economic variables, presents both opportunities and challenges for investors seeking to optimize their returns. Prudent participation necessitates careful consideration of risk mitigation strategies, often involving the construction of diversified investment portfolios. In the pursuit of enhanced predictive capabilities, investors frequently turn to mathematical models to discern underlying patterns and anticipate future stock movements. However, the inherent complexity of financial markets means that various analytical approaches can yield divergent and potentially biased forecasts. Among the array of available methodologies, the Autoregressive Integrated Moving Average (ARIMA) model stands out as a widely utilized statistical tool for time series analysis and forecasting. Its appeal lies in its relative simplicity, relying exclusively on the historical behavior of the target variable itself, thereby obviating the need for external explanatory factors. Furthermore, under appropriate conditions and parameter selection, the ARIMA model has demonstrated the potential for achieving a considerable degree of predictive accuracy. This research endeavors to develop and implement an ARIMA model specifically tailored to forecasting the future stock trends of JPMorgan Chase & Co., a prominent player in the financial sector.