Time-Series Analysis and Forecasting of S&P500 Index Based on ARIMA and ETS Model


  • Hairong Zhang




Stock Index, S&P500, Time-Series Forecasting Analysis, ARIMA Model, ETS Model


The study embarks on an insightful journey into the world of stock indices through the deeper understanding of time series analysis application, especially during the special period of oil price volatility. The goal of this paper is to utilize sophisticated statistical models, including Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS), to extract meaningful information from historical stock index data of S&P500 by Yahoo Finance, enhancing predictive accuracy and operating forecasts to inform strategic decision-making. In addition to the prediction results, the study also compares the forecasts of the two models through some values, and concludes that the prediction effect of the ARIMA model is better than that of the ETS model. The reason behind this result has a lot to do with the processing of the data itself and the fit of the models. For S&P500 during the period of the study, the ARIMA model’s prediction result is better. The insights derived from this analysis are expected to empower investors, researchers, and market analysts with a deeper understanding of the stock index’s past behavior and its implications for future performance.


Download data is not yet available.


Raddant M, Kenett D Y. Interconnectedness in the global financial market. Journal of International Money and Finance, 2021, 110: 102280.

Hayo B, Kutan A M. The impact of news, oil prices, and global market developments on Russian financial markets. Economics of Transition, 2005, 13(2): 373-393.

Wójcik D, Ioannou S. COVID‐19 and finance: market developments so far and potential impacts on the financial sector and centres. Tijdschrift voor economische en sociale geografie, 2020, 111(3): 387-400.

Sun M, Zhang C. Comprehensive analysis of global stock market reactions to the Russia-Ukraine war. Applied economics letters, 2022: 1-8.

Hamid S A, Iqbal Z. Using neural networks for forecasting volatility of S&P 500 Index futures prices. Journal of Business Research, 2004, 57(10): 1116-1125.

Shang H L. Forecasting intraday S&P 500 index returns: A functional time series approach. Journal of forecasting, 2017, 36(7): 741-755.

Chan E G. Forecasting the S&P 500 index using time series analysis and simulation methods. Massachusetts Institute of Technology, 2009.

Nguyen H, Nguyen H, Pham A. Oil price declines could hurt US financial markets: the role of oil price level. The energy journal, 2020, 41(5).

Yahoo Finance, URL: https://finance.yahoo.com/quote/%5EGSPC?p=%5EGSPC, last accessed 2023/8/7.

Sun Z. Comparison of trend forecast using ARIMA and ETS Models for S&P500 close price. Proceedings of the 2020 4th International Conference on E-Business and Internet. 2020: 57-60.




How to Cite

Zhang, H. (2024). Time-Series Analysis and Forecasting of S&P500 Index Based on ARIMA and ETS Model. Highlights in Business, Economics and Management, 24, 306-316. https://doi.org/10.54097/anhgak96