Empirical Analysis of Two Methods for Forecasting S&P 500 Index Returns
DOI:
https://doi.org/10.54097/vngfta58Keywords:
Yield forecasting; Principal Component Analysis (PCA); Economic variable forecasting; ARMA model forecasting.Abstract
The S&P 500 index is one of the important barometers of the global financial market trends, and therefore predicting its direction is of significant importance to the financial market. This article aims to improve the predictive ability for the S&P 500 index returns (excess returns) using various methods. Economic variable forecasting is introduced, including 15 new economic variables such as inflation rates and treasury bond yields. For this purpose, this article uses data from May 1937 to April 1972, a total of 35 years, as in-sample data to estimate the forecasting model, and data from May 1972 to December 2023 as out-of-sample data to test the model's effectiveness. In the process, we rely on Principal Component Analysis (PCA) to extract a common factor from the predictive variables. Empirical results indicate that the PCA regression model corresponding to these 15 economic variables cannot successfully predict the S&P 500 index returns within the sample and out of sample. At the same time, Python was used to analyze each economic variable and provided a certain explanation for the inability to predict. We also conducted ARMA model forecasting for the S&P 500 index returns.
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[1] Baker, M., & Wurgler, J. (2006). Investor sentiment and the crosssection of stock returns. The Journal of Finance, 61, 1645–1680. DOI: https://doi.org/10.1111/j.1540-6261.2006.00885.x
[2] Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21, 208–230. DOI: https://doi.org/10.3386/w13189
[3] David,A., M. Belén.,& Antonio P.(2024) Hybrid ARMA-GARCH-Neural Networks for intraday strategy exploration in high-frequency trading.Pattern Recognition,148,110-139 DOI: https://doi.org/10.1016/j.patcog.2023.110139
[4] Huang, D., Jiang, F., Tu, J., & Zhou, G. (2015). Investor sentiment aligned: A powerful predictor of stock returns. The Review of Financial Studies, 28, 791–837. DOI: https://doi.org/10.1093/rfs/hhu080
[5] Liang, C., Wei, Y., Lei, L., & Ma, F. (2022). Global equity market volatility forecasting: New evidence. International Journal of Finance and Economics, 27, 594–609. DOI: https://doi.org/10.1002/ijfe.2170
[6] Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014). Forecasting the equity risk premium: The role of technical indicators. Management Science, 60, 1772–1791. DOI: https://doi.org/10.1287/mnsc.2013.1838
[7] Wei, Y., Bai, L., Yang, K., & Wei, G. (2021). Are industry-level indicators more helpful to forecast industrial stock volatility? Evidence from Chinese manufacturing purchasing managers index. Journal of Forecasting, 40, 17–39. DOI: https://doi.org/10.1002/for.2696
[8] Rapach, D., Strauss, J. K., & Zhou, G. (2010). Out-of-sample equity premium prediction: Combination forecasts and links to the real economy. Review of Financial Studies, 23, 821–862. DOI: https://doi.org/10.1093/rfs/hhp063
[9] Wen, D., He, M., Zhang, Y., & Wang, Y. (2022). Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach. Journal of Forecasting, 41, 230–251. DOI: https://doi.org/10.1002/for.2807
[10] Zhang, Y., He, M., Liao, C., & Wang, Y. (2023). Climate risk exposure and the cross-section of Chinese stock returns. Finance Research Letters, 55, 103987. DOI: https://doi.org/10.1016/j.frl.2023.103987
[11] Zhang, Y., Ma, F., & Zhu, B. (2019). Intraday momentum and stock return predictability: Evidence from China. Economic Modelling, 76, 319–329. DOI: https://doi.org/10.1016/j.econmod.2018.08.009
[12] Zhang, Y., Wei, Y., Ma, F., & Yi, Y. (2019). Economic constraints and stock return predictability: A new approach. International Review of Financial Analysis, 63, 1–9. DOI: https://doi.org/10.1016/j.irfa.2019.02.007
[13] Zhang, Y., Zeng, Q., Ma, F., & Shi, B. (2019). Forecasting stock returns: Do less powerful predictors help? Economic Modelling, 78, 32–39. DOI: https://doi.org/10.1016/j.econmod.2018.09.014
[14] Zhang,Y.,Han,Q.&He,M.(2024).Forecasting stock market returns with a lottery index: Evidence from China.Journal of Forecasting,43,1595-1606 DOI: https://doi.org/10.1002/for.3100
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