Prediction of Interest Rate Using Artificial Neural Network and Novel Meta-Heuristic Algorithms

Document Type : Original Article

Author

Department of Finance, Khatam University, Tehran, Iran

Abstract

One of the most parameters and variables in every economics is the interest rate. Government officials and lawmakers change interest rates for various purposes: controlling liquidity, inflation, and prices, Economic growth and development, lending, etc. So, it is important to set the interest rate correctly. If you can predict the interest rate correctly, you can earn and gain profit by investing in various sectors. Moreover, the interest rate can impact other sectors through parallel markets such as the stock market, automobile, housing, etc. Interest rates are related to parallel markets. Thus, if you can forecast the interest rate, you can predict the parallel markets too. The main goal of this article, as it is clear from the title, is the prediction of interest rate using ANN and improving the network using some novel heuristic algorithms such as Moth Flame Optimization algorithm (MFO), Chimp Optimization Algorithm (CHOA), Time-varying Correlation Particle Swarm Optimization algorithm (TVAC-PSO), etc. we used 17 variables such as oil price, gold coin price, house price, etc. as input variables. We used GA and a new algorithm called Grey Wolf Optimization, Particle Swarm Optimization (GWO-PSO) algorithm as a feature selection and choosing the best variables. We have used eight loss functions such as MSE, RMSE, MAE, etc. too. Finally, we have compared different algorithms due to their estimation errors. The main contribution of this paper is that, first, this is for the first time which these novel metaheuristic algorithms have been used for the prediction of interest rate. Second, it has tried to use different graphs and tables for better understanding and totally a comprehensive research paper. The results show that Whale Optimization Algorithm (WOA) performed better than other methods along with less error.

Keywords

References

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