Accurately predicting the S\&P 500 index means knowing where the US economy is heading. If there was a model that could predict the S\&P 500 with even some accuracy, this would be extremely valuable. Machine learning techniques such as neural network and Random forest have become more popular in forecasting. This thesis compares the more traditional forecasting methods, ARIMA, Exponential smoothing, and Naïve, versus the Random forest regression model in predicting the S\&P 500 index. The models are compared using the scale measures MAE and RMSE. The Diebold-Mariano test is used to evaluate if the model's forecasts significantly have better accuracy than the last known observation (Naïve method). The result showed that the Random forest model did outperform the other models regarding the RMSE and MAE values, especially on a two-day forecast. Furthermore, the Random forest model was significantly better on all horizons on a five percent significance level, meaning that the model had a better forecast accuracy than the last known observation. However, further research on this subject is needed to ensure the effectiveness of the Random forest model when forecasting stock market indices.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-504578 |
| Date | January 2023 |
| Creators | Neikter, Axel, Sjöberg, Nils |
| Publisher | Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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