An Empirical Study of U.S. Stock Market Forecasts and Trend Trading Strategies Based on ARIMA Model

Financial forecasting is an important practical guidance for discovering objective trends in financial development and guiding financial investments. According to domestic and foreign research, many scholars have made forecasts for the stock market with various methods. This paper applies the ARIMA model to forecast the future U.S. stock market returns by studying the S&P S&P500 index. It also explores the feasibility of trend-based trading strategies that are commonly used. The original data is collected from the wind database, and the data is the closing price of the S&P 500 index, ranging from 2000-1-3 to 2023-4-27. The data is divided into two parts: modeling data and testing data. The results show that the prediction model is in the form of ARIMA (3, 1, 4), and the average accuracy of the model is 1.8%, which indicates that the model is real and effective. At the same time, this paper verifies that the trading strategy of chasing up and killing down is feasible. The research results can provide theoretical empirical reference for financial investment. However, it is also found that the ARIMA model used in this paper should be combined with other models for more refinement in the prediction effect.