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A study on forecasting SOFR with a recurrent neural network using long short-term memory cells

Cornelissen, J. (2021) A study on forecasting SOFR with a recurrent neural network using long short-term memory cells.

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Abstract:In this research the forecast performance of a neural network on the Secured Overnight Financing Rate (SOFR) is evaluated. The financial market underlying SOFR is studied and suitable exogenous variables are picked to help the neural network forecast SOFR. Through a literature research, a neural network model is chosen which is suitable for forecasting SOFR according to the consulted studies. The recurrent neural network model using long short-term memory cells is chosen and applied to historical data of SOFR and its exogenous variables. The neural network model outperforms the autoregressive integrated moving average model with exogenous variables (ARIMAX) on all experiments and achieves an average of 53% reduction in root mean squared error.
Item Type:Essay (Master)
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:54 computer science, 83 economics
Programme:Industrial Engineering and Management MSc (60029)
Link to this item:http://purl.utwente.nl/essays/86418
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