Purchasing Mangers' Index (PMI) is an index published by the Institute for Supply Management. The index is based on a monthly survey answered by hundreds of purchasing managers in the manufacturing business all over the United States. The questions capture the activities of companies when it comes to orders, production, employment, supplier deliveries and inventories. PMI is an important and reliable macro economic factor and has shown a high correlation with the GDP which means that it may be of interest for a bank as Nordea to predict it. The purpose of this thesis is to evaluate whether it is possible to use neural networks, more precisely sequence-to-sequence models and recurrent neural networks, to forecast PMI with a multivariate data set. Simpler methods and models are also evaluated and compared to the neural networks. In total, there was two naive models, one Autoregressive Moving Average (ARMA) model, two linear regression models, and three neural network models that was implemented and compared to each other. All models were evaluated by their mean squared error (MSE) and mean absolute error (MAE). By analyzing MSE and MAE for the different models it is shown that the ARMA-model predicted only slightly better than the persistence algorithm which assumes that PMI next month equals PMI current month. Only one neural network model performed better compared to the linear regression models, and the same model also gave a better prediction compared to the persistence algorithm.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-172416 |
| Date | January 2020 |
| Creators | Karlsson, Julia, Sjöström, Moa |
| Publisher | Umeå universitet, Institutionen för matematik och matematisk statistik, Umeå universitet, Institutionen för matematik och matematisk statistik |
| 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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