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http://dx.doi.org/10.3745/KTSDE.2022.11.1.19

Understanding the Association Between Cryptocurrency Price Predictive Performance and Input Features  

Park, Jaehyun (영남대학교 컴퓨터공학과)
Seo, Yeong-Seok (영남대학교 컴퓨터공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.1, 2022 , pp. 19-28 More about this Journal
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.
Keywords
LSTM; Deep Learning; Input Feature; Cryptocurrency; Price Prediction; Data Analysis;
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1 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.   DOI
2 R. Hadi and F. Hamidreza, "Stock price prediction using deep learning and frequency decomposition," Expert Systems with Applications, Vol.169, pp.1-29, 2021.
3 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.   DOI
4 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.
5 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.
6 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.
7 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.
8 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.
9 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.   DOI
10 S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol.9, No.8, pp.1735-1780, 1997.   DOI
11 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.   DOI
12 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.
13 Top Cryptocurrency Spot Exchanges [Internet], https://coinmarketcap.com/rankings/exchanges, July 15, 2021.
14 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.   DOI
15 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.
16 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.
17 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.
18 V. Buterin, Ethereum Whitepaper [Internet], https://http://kryptosvet.eu/wp-content/uploads/2021/05/ethereum-whitepaper-kryptosvet.eu_.pdf, Jul. 12, 2021.
19 M. Watorek, S. Drozdz, and J. Kwaipien, "Multiscale characteristics of the emerging global cryptocurrency market," in Physics Reports, 2020.
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.   DOI
21 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.   DOI
22 B. Jacob, C. Jingdong, and H, Yiteng, "Pearson correlation coefficient," in Noise Reduction in Speech Processing, pp.1-4, 2009.
23 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.   DOI
24 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.
25 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.