Machine Learning-Based Two-Stage Prediction Model for Fund Risk Rating Prediction 


Vol. 48,  No. 11, pp. 1447-1456, Nov.  2023
10.7840/kics.2023.48.11.1447


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  Abstract

This study proposes a two-stage prediction model for fund risk ratings using machine learning. In step 1, the GARCH model, the LSTM model and the GARCH-LSTM model, which combines the two models, are used to predict 1-month volatility. In step 2, the volatility and other important risk indicators are entered into the four kernel-specific SVM classification algorithms to predict the fund's risk rating. As a result, the GARCH-LSTM combination model showed the highest volatility prediction performance, and the multivariate SVM using the RBF kernel showed the highest risk rating prediction accuracy.

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[IEEE Style]

A. Kim and M. Park, "Machine Learning-Based Two-Stage Prediction Model for Fund Risk Rating Prediction," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 11, pp. 1447-1456, 2023. DOI: 10.7840/kics.2023.48.11.1447.

[ACM Style]

A-Ram Kim and Min-Ho Park. 2023. Machine Learning-Based Two-Stage Prediction Model for Fund Risk Rating Prediction. The Journal of Korean Institute of Communications and Information Sciences, 48, 11, (2023), 1447-1456. DOI: 10.7840/kics.2023.48.11.1447.

[KICS Style]

A-Ram Kim and Min-Ho Park, "Machine Learning-Based Two-Stage Prediction Model for Fund Risk Rating Prediction," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 11, pp. 1447-1456, 11. 2023. (https://doi.org/10.7840/kics.2023.48.11.1447)
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