Predictability of interest rates using data mining tools: A comparative analysis of Korea and the US

Abstract The prediction of economic and financial variables is a critical task for many decision makers. One of the most important variables is found in the interest rate, which strongly affects other economic and financial parameters. The literature is rife with negative results in forecasting time series in financial markets: The predictive techniques have been unable to outperform the random walk model at a statistically significant level. However, knowledge-based methods can reverse this phenomenon. This paper presents a comparative investigation of the predictability of interest rates through neural networks, case-based reasoning and their integration. In the integrated model, case-based reasoning serves as a filter by providing estimates which are then used as input into the neural network model. Predictions from these models are compared against each other and also with the random walk model. Overall, the prediction performance of the forecasting models for the US interest rate was superior to the random walk model. On the other hand, none of the models outperformed the random walk model at a statistically significant level in forecasting the Korean interest rate.

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