본문 바로가기
  • Home

A Deep Learning Based Predictive Modeling of Bitcoin Prices: Analysis with a Focus on Investor Rationality and Risk Management

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(9), pp.11~19
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : August 13, 2025
  • Accepted : September 15, 2025
  • Published : September 30, 2025

Tae-Wook Kim 1 Jung-woo Sohn 2

1한국과학기술원
2연세대학교

Accredited

ABSTRACT

A number of studies have been conducted using diverse data sources to forecast cryptocurrency prices. However, unlike the rational behavior of human investors who manage risk through asset diversification between risky and safe assets, most deep learning models for cryptocurrency price prediction have overlooked features related to safe assets that may not appear directly connected to Bitcoin as risky assets. In response, this study demonstrates that a deep learning price-forecasting model designed by incorporating safe assets—specifically Korean bond indices—alongside Bitcoin significantly improves prediction performance. In our experiment, features derived from the Korean Bond Indexes were selected through the Granger Causality Test to assess their relevance for Bitcoin price prediction. To ensure reliability in forecasting, the dataset was preprocessed to achieve stationarity across all features. The results indicate that a model trained on both Bitcoin-related variables (representing risky assets) and Korean Bond features (representing safe assets) delivers enhanced predictive performance.

Citation status

* References for papers published after 2024 are currently being built.