Authors - K P N S Dayarathne, U Thayasivam Abstract - Deep learning has achieved amazing success in multiple areas, such as Image Classification, speech recognition, Object Detection & Segmentation, natural language processing, audio-visual recognition, adaptive testing, etc., and is gaining major interest from the research community. The application for deep learning is growing day by day. Predicting interest rate as univariate analysis is important given that total spectrum of the interest rates is not available to apply yield curve analysis. This paper investigates the applicability of deep learning models such as RNN, LSTM, CNN and TCN to interest rates in Asina frontier countries such as Sri Lanka, Pakistan and Bangladesh. The deep learning approach for interest rate perdition is still under the radar, and this is the first attempt on the Asian Fronter market. Interest rates associates with Government securities were considered to have uniqueness for all three countries, where data range from 2010-2022 for Sri Lanka and Pakistan, whereas Bangladesh analysis was based on the same from 2015-2022. The results revealed CNN was the best model for Sri Lanka and Bangladesh, while LSTM was the best model for Pakistan based on the lowest RMSE. The study further investigates the applicability of different activation function for output layers and hidden layers, but found ReLU is the most viable activation function along with Max pooling. Further, it was found that CNN works better for countries with stable term structures of interest rates, and the immediate dynamics of the interest rate influence the near future interest rate.
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