KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Understanding the Association Between Cryptocurrency Price Predictive Performance and Input Features

암호화폐 종가 예측 성능과 입력 변수 간의 연관성 분석

  • Received : 2021.06.25
  • Accepted : 2021.08.11
  • Published : 2022.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, cryptocurrency has attracted much attention, and price prediction studies of cryptocurrency have been actively conducted. Especially, efforts to improve the prediction performance by applying the deep learning model are continuing. LSTM (Long Short-Term Memory) model, which shows high performance in time series data among deep learning models, is applied in various views. However, it shows low performance in cryptocurrency price data with high volatility. Although, to solve this problem, new input features were found and study was conducted using them, there is a lack of study on input features that drop predictive performance. Thus, in this paper, we collect the recent trends of six cryptocurrencies including Bitcoin and Ethereum and analyze effects of input features on the cryptocurrency price predictive performance through statistics and deep learning. The results of the experiment showed that cryptocurrency price predictive performance the best when open price, high price, low price, volume and price were combined except for rate of closing price fluctuation.

최근 암호화폐가 많은 주목을 받음에 따라 암호화폐의 종가 예측 연구들이 활발히 진행되고 있다. 특히 딥 러닝 모델을 적용시켜 예측 성능을 높이려는 연구들이 지속되고 있다. 딥 러닝 모델 중 시계열 데이터에서 높은 예측 성능을 보이는 LSTM (Long Short-Term Memory) 모델이 다각도로 응용되고 있으나 변동성이 큰 암호화폐 종가 데이터에서는 낮은 예측 성능을 보인다. 이를 해결하기 위해 새로운 입력 변수를 찾아내고, 이를 사용하는 종가 예측 연구가 수행되고 있다. 그러나 딥 러닝 기반의 암호화폐 종가 예측에 사용되는 데이터들의 각 입력 변수들이 예측 성능에 미치는 영향력이나 학습에 효율적인 입력 변수들의 조합에 관한 연구 사례가 부족한 실정이다. 따라서 본 논문에서는 Bitcoin과 Ethereum을 포함한 6가지 암호화폐의 최근 동향 자료를 수집하였고, 통계와 딥 러닝을 통해 입력 변수들이 암호화폐 종가 예측에 미치는 영향력을 분석한다. 실험 결과 모든 암호화폐의 종가 예측 성능 평가에서 종가 변동률을 제외한 개장가, 고가, 저가, 거래량, 종가를 조합했을 때 가장 우수한 성능을 보였다.

Keywords

Acknowledgement

이 논문은 2021년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No. NRF-2020R1I1A3073313).

References

  1. H. Zexin, Z. Yiqi, and K. Matloob, "A survey of forex and stock price prediction using deep learning," in Applied System Innovation, Vol.4, No.1, pp.1-9, 2021. https://doi.org/10.3390/asi4010001
  2. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol.9, No.8, pp.1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  3. E. Hoseinzade and S. Haratizadeh, "CNNpred: CNN-based stock market prediction using a diverse set of variables," Expert Systems with Applications, Vol.129, pp.273-285, 2019. https://doi.org/10.1016/j.eswa.2019.03.029
  4. Z. Jin, Y. Yang, and Y. Liu, "Stock closing price prediction based on sentiment analysis and LSTM," Neural Computing and Applications, Vol.32, No.13, pp.9713-9729, 2020. https://doi.org/10.1007/s00521-019-04504-2
  5. H. Yamamoto, H. Sakaji, and H. Matsushima, "Forecasting crypto-asset price using influencer tweets," in International Conference on Advanced Information Networking and Applications, pp.940-951, 2019.
  6. H. Maqsood, I. Mehmood, and M. Maqsood, "A local and global event sentiment based efficient stock exchange forecasting using deep learning," International Journal of Information Management, Vol.50, pp.432-451, 2020. https://doi.org/10.1016/j.ijinfomgt.2019.07.011
  7. Z. Hu, W. Liu, and J. Bian, "Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction," in Proceedings of the eleventh ACM International Conference on Web Search and Data Mining, pp.261-269, 2018.
  8. Y. Xuan, Y. Yu, and K. Wu, "Prediction of short-term stock prices based on EMD-LSTM-CSI neural network method," in 2020 5th IEEE International Conference on Big Data Analytics, pp.135-139, 2020.
  9. N. E. Huang, Z. Shen, and S. R. Long, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," in Proceedings of the Royal Society of London. Series A: Mathematical, physical and engineering sciences, Vol.454, No.1971, pp.903-995, 1998. https://doi.org/10.1098/rspa.1998.0193
  10. R. Hadi and F. Hamidreza, "Stock price prediction using deep learning and frequency decomposition," Expert Systems with Applications, Vol.169, pp.1-29, 2021.
  11. Y. Li, P. Ni, and V. Chang, "Application of deep reinforcement learning in stock trading strategies and stock forecasting," Computing, Vol.102, No.6, pp.1305-1322, 2020. https://doi.org/10.1007/s00607-019-00773-w
  12. B. S. Lin. W. T. Chu, and C. M. Wang, "Application of stock analysis using deep learning," in 2018 7th International Congress on Advanced Applied Informatics, pp.612-617, 2018.
  13. P. Oncharoen and P. Vateekul, "Deep learning using risk-reward function for stock market prediction," in Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, pp.556-561, 2018.
  14. Z. Li, D. Yang, and L. Zhao, "Individualized indicator for all: Stock-wise technical indicator optimization with stock embedding," in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.894-902, 2019.
  15. S. Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System [Internet], https://www.ussc.gov/sites/default/files/pdf/training/annual-national-training-seminar/2018/Emerging_Tech_Bitcoin_Crypto.pdf, Jul. 12, 2021.
  16. V. Buterin, Ethereum Whitepaper [Internet], https://http://kryptosvet.eu/wp-content/uploads/2021/05/ethereum-whitepaper-kryptosvet.eu_.pdf, Jul. 12, 2021.
  17. M. Watorek, S. Drozdz, and J. Kwaipien, "Multiscale characteristics of the emerging global cryptocurrency market," in Physics Reports, 2020.
  18. C. Worley and A. Skjellum, "Blockchain tradeoffs and challenges for current and emerging applications: Generalization, fragmentation, sidechains, and scalability," in 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp.1582- 1587, 2018.
  19. F. Colon, C. Kim, and W. Kim, "The effect of political and economic uncertainty on the cryptocurrency market," Finance Research Letters, Vol.39, pp.1-7, 2021.
  20. A. K. Tanwar, S. Kumar, and R. Patthak, "Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models," in Applied Economics, Vol.51, No.37, pp.4073-4082, 2019. https://doi.org/10.1080/00036846.2019.1588951
  21. Top Cryptocurrency Spot Exchanges [Internet], https://coinmarketcap.com/rankings/exchanges, July 15, 2021.
  22. M. Patel and S. Tanwar, "A deep learning-based cryptocurrency price prediction scheme for financial institutions," Journal of Information Security and Applications, Vol.55, pp.1-13, 2020.
  23. B. Jacob, C. Jingdong, and H, Yiteng, "Pearson correlation coefficient," in Noise Reduction in Speech Processing, pp.1-4, 2009.
  24. T. A. Craney, J. G. Surles, and SR. Long, "Model-dependent variance inflation factor cutoff values," in Quality Engineering, Vol.14, No.3, pp.391-403, 2002. https://doi.org/10.1081/QEN-120001878
  25. T. Phaladisailoed and T. Numnonda, "Machine learning models comparison for bitcoin price prediction," in 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), Bali, Indonesia, pp.506-511, 2018.