Time Series Modeling of the S&P 500 And Quantitative Trading Strategy Optimization
DOI:
https://doi.org/10.54097/1fq6f507Keywords:
S&P 500; ARIMA Model; Time Series Forecasting; Trading Strategy; Financial Modeling.Abstract
The stock market trend is an important indicator for evaluating the overall economy and the state of the financial market. The S&P 500, as one of the main stock indexes, is widely used by investors, fund managers, and policymakers. Using daily data of the S&P 500, this paper builds an ARIMA model and compares it with the simple Buy & Hold strategy to examine how useful it is for trading. Although the ARIMA model has a high mean absolute percentage error (MAPE), it performs better than Buy & Hold in total return and Sharpe ratio, suggesting higher risk-adjusted returns. However, it shows little progress in controlling volatility and maximum drawdown, which means the model has limits when the market is unstable. This study shows both the value and the limits of using a classic time series model like ARIMA for financial prediction. The results also provide a basis for later work to build stronger hybrid models.
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