This article uses the S&P 500 index as an example to analyze the impact of macroeconomic factors on stock returns. By using the S&P 500 index data from 1968 to 2020 as the dependent variable, and the monthly data of 221 macroeconomic variables such as the consumer price index and the US mid-term election as the independent variable, this paper finds that: (1) a wavelet denoising method helps to capture the low-frequency and long-term fluctuations in monthly returns of the index, which can effectively remove the short-term fluctuations in returns, better reflect the macroeconomic trend, and improve the power of out-of-sample forecasting. (2) the Granger causality test may be used to pick the top 30 most significant variables, which can be incorporated into several prediction models. Among all the prediction models, the combined prediction algorithm has the best out-of-sample prediction effect. (3) investors need to consider investment practices under timing strategies. Elastic network, scaling principal component analysis, combination prediction, and other algorithms are used to select the time, and the best results are obtained based on the scaling principal component analysis algorithm and the combination prediction algorithm when the transaction fee is set to 5‰. The returns based on these two algorithms have reached 14.00% and 12.59%, their Sharpe ratios are the highest among all algorithms, reaching 0.69 and 0.62, respectively, and this result is significantly better than the historical mean model used as a measurement benchmark (with the average return of 8.14%, and aii Sharpe ratio of 0.34). (4) explore investment practice under a stock-picking strategy. We use methods such as sector rotation strategy and mean-variance of sectors for stock selection, and find that the strategy returns achieved by investing in stocks using the sector rotation strategy are the best, reaching 14.30%, and the Sharpe ratio is the highest at 0.79, significantly better than the benchmark S&P 500 index (with the average return of 8.8% and a Sharpe ratio of 0.57).