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NET ASSET VALUE PER UNIT IN VARIABLE UNIVERSAL LIFE INSURANCE PRODUCTS PREDICTION USING DEEP LEARNING
DOI:
https://doi.org/10.17654/0972361723069Abstract
The demand for variable universal life (VUL) insurance products in the Philippines has grown during the past few years. Globally, the performance of funds has been of interest to many researchers and investors. To evaluate some of the VUL funds in the Philippines, this study employed the Sharpe ratio. Twenty-seven funds with a 10-year daily net asset value per unit (NAVPU) were collected from 2013-2022. There were 2,444 daily datasets gathered for each fund. The funds with the best Sharpe ratio were modeled using the single-layer neural network functional link artificial neural network (FLANN) and the recurrent neural network long short-term memory (LSTM). Mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to compare the accuracy of the deep learning models. Overall, the simulations showed that LSTM has better accuracy results in forecasting the NAVPUs of the VUL funds than FLANN.
Received: September 19, 2023
Accepted: October 26, 2023
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