| تعداد نشریات | 60 |
| تعداد شمارهها | 2,034 |
| تعداد مقالات | 16,385 |
| تعداد مشاهده مقاله | 42,939,716 |
| تعداد دریافت فایل اصل مقاله | 24,584,525 |
Assessing machine learning performance in cryptocurrency market price prediction | ||
| Journal of Mathematics and Modeling in Finance | ||
| مقاله 1، دوره 2، شماره 1، مهر 2022، صفحه 1-32 اصل مقاله (790.73 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2022.14563 | ||
| نویسندگان | ||
| Kamran Pakizeh1؛ Arman Malek1؛ Mahya Karimzadeh khosroshahi* 1؛ Hasan Hamidi Razi2 | ||
| 1Faculty of Financial Sciences, Kharazmi University, Tehran, Iran | ||
| 2Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran. | ||
| چکیده | ||
| Cryptocurrencies, which are digitally encrypted and decentralized, continue to attract attention of nancial market players across the world. Because of high volatility in cryptocurrency market, predicting price of cryptocurrencies has become one of the most complicated elds in nancial markets. In this paper, we use Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to predict price of four well-known cryptocurrencies of Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), and Ripple (XRP). These models are subdivisions of Articial Intelligence, machine learning and data science. The main aim of this paper is to compare the accuracy of above-mentioned models in forecasting time series data, to nd out which model can better predict price in these four cryptocurrencies. 43 variables consisting of 28 technical indicators and t+10 lags were calculated and appended to the Open, High, Low, Close and Volume (OHLCV) data for selected cryptocurrencies. Applying random forest as feature selection, 25 variables were chosen, 24 of them selected as feature (independent variables) and one as a dependent variable. Each attribute value was converted into a relative standard score, followed by Min-max scaling; we compare models and results of Dieblod Mariano test that is used to examine whether the differences in predictive accuracy with these two models are signi cant, reveal that LSTM reaches better accuracy than GRU for BTC and ETH, but both models convey the same accuracy for LTC and XRP. | ||
| کلیدواژهها | ||
| Cryptocurrency؛ Long short-term memory؛ Gated recurrent unit؛ Random forest classifi er | ||
| مراجع | ||
|
[1] Patel, M.M., Tanwar, S., Grupta, R., Kumar, N., 2020, A deep learning-based cryptocurrency price prediction scheme for nancial institutions,Journal of Information Security and Applications, 55, 102-583. [2] Technical Indicators and Overlays (ChartSchool), November 2018, [3] Kritzer, A., Service, S.O., 2012, Forex for beginners [electronic resource]: a comprehensive guide to profting from the global currency markets/by Adam Kritzer. [4] Archer, M.D., 2010, Getting started in currency trading: winning in todays Forex market. Wiley, London, p 333 Bahrammirzaee A (2010) A comparative survey of artifcial intelligence applications in fnance: artifcial neural networks, expert system and hybrid intelligent systems,Neural Comput Appl, 19, 11651195. [5] Murphy, J.J., 1999, Technical analysis of the fnancial markets: TA - Book, 239. [6] Ozorhan, M.O., Toroslu, I.H., Sehitoglu, O.T., 2017, A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms, Soft Comput,21, 66536671. [7] Huang, J., Chai, J., Cho, S., 2020,Deep learning in nance and banking: A literaturereview and classi cation, Frontiers of Business Research in China,14, 1-24. [8] Sethia, A., Raut. P., 2019, Application of LSTM, GRU and ICA for stock price prediction, Information and Communication Technology for Intelligent Systems, 479-487. [9] Fischer, T., Krauss, Ch., 2018, Deep learning with long short-term memory networks fornancial market predictions European Journal of Operational Research, 2(270), 654-669. [10] Ghosh, A., Bose, S., Maji, G., Debnath, N., Sen, S., 2019, Stock price prediction using LSTM on Indian Share MarketProceedings of 32nd international conference on, 40, 101-110. [11] Gite, Sh., Khatavkar, H., Srivastava, Sh., Maheshwari, P., Pandey, N., 2021, Stock Prices Prediction from Financial News Articles Using LSTM and XAI, Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, 153-161. [12] Qiu, J., Wang, B., Zhou, Ch., 2020, Forecasting stock prices with long-short term memory neural network based on attention mechanism, Public Library of Science San Francisco, CA USA, 15(1), 222-227. [13] Fadda, S., 2020, Pricing options with dual volatility input to modular neural networks, Borsa Istanbul Review, 20(3) 269-278. [14] Aggarwal, A., Gupta I., Garg, N., Goel A., 2019, Deep learning approach to determine the impact of socio economic factors on bitcoin price prediction, Twelfth International Conference on Contemporary Computing (IC3)IEEE, 15. [15] Nakamoto, S., 2008, Bitcoin: A peer-to-peer electronic cash system, Decentralized Business Review. [16] Sovbetov, Y., 2018 Factors in uencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litcoin, and monero, Journal of Economics and Financial Analysis, 2(2) 1-27. [17] Teker, D., Teker, S., Ozyesil, M, 2019, Determinants of cryptocurrency price movements, 14th Paris international conference on marketing, economics, education and inter-disciplinary studies, MEEIS-19, 2(2) 12-14. [18] Li, G., Zhao, Q., Song, M., Du, D., Yuan, J., Chen, X., Liang, H., 2019, Predicting global computing power of blockchain using cryptocurrency prices, 2019 International Conference on Machine Learning and Cybernetics (ICMLC), 2(2) 1-6. [19] Peng, Y., Albuquerque, P.H.M., Sa, J.M.C, Padula, A.J.A, Montenegro, M.R., 2018, The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression, Expert Systems with Applications, 97,177-192. [20] Poongodi, M., Sharma, A., Vijayakumar, V., Bhardwaj, V., Sharma, A.P., Iqbal, R., Kumar, R., 2020, Prediction of the price of Ethereum blockchain cryptocurrency in an industrial nance system, Computers & Electrical Engineering, 81, 106-527. [21] Sin, E., Wang, L., 2017, Bitcoin price prediction using ensembles of neural networks, 2017 13th International conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD), 666-671. [22] Jain, A., Tripathi, Sh., Dwivedi, H.D., Saxena, P., 2018, Forecasting price of cryptocurrencies using tweets sentiment analysis, 2018 eleventh international conference on contemporary computing (IC3), 1-7. [23] Madanayake, A., Wimalagunaratne, R., Dansereau, D., Bruton, L.T., 2011, Design and FPGA-implementation of 1 st order 4D IIR frequency-hyperplanar digital lters, 2011 IEEE 54th international midwest symposium on circuits and systems (MWSCAS), 1-4. [24] Smuts, N., 2019, What drives cryptocurrency prices? An investigation of google trends and telegram sentiment, ACM SIGMETRICS Performance Evaluation Review, 46(3), 131-134. [25] Cheuque, C., German, L., Reutter, J., 2019, Bitcoin price prediction through opinion mining, Companion Proceedings of The 2019 World Wide Web Conference, 755{762. [26] Mittal, A., Dhiman, V., Singh, A., Prakash, Ch., 2019, Short-term bitcoin price uctuation prediction using social media and web search data, 2019 Twelfth International Conference on Contemporary Computing (IC3), 1-6. [27] Mohanty, P., Patel, D., Patel, P., Roy, S., 2018, Predicting uctuations in cryptocurrencies' price using users' comments and real-time prices, 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 477-482. [28] Selvamuthu, D., Kumar, V., Mishra, A., 2019, Indian stock market prediction using articial neural networks on tick data, Financial Innovation,5(1), 1-12. [29] Mallqui, D., Fernandes, R., 2019, Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques, Applied Soft Computing, 75, 596-606. [30] Adebiyi, A., Ayo, Ch., Adebiyi, M., Otokiti, S., 2012, Stock price prediction using neural network with hybridized market indicators, Journal of Emerging Trends in Computing and Information Sciences, 3(1). [31] Lippmann, R., 1987, An introduction to computing with neural nets, IEEE Assp magazine, 4(2), 4-22. [32] Dutta, S., Shekhar, Sh., 1988, Bond rating: a non-conservative application of neural networks, IEEE Int Conf on Neural Networks, 443-450. [33] Poria, S., Cambria, E., Gelbukh, A., 2016, Aspect extraction for opinion mining with a deep convolutional neural network, Knowledge-Based Systems, 108, 42-49. [34] LeCun, Y., Bengio, Y., Hinton, G., 2015, Deep learning, Nature Publishing Group, 521,436-444. [35] Schmidhuber, L., 2015, Deep learning in neural networks: An overview, Neural networks, 61, 85-115. [36] Kucharcikova, A., Konusikova, L., Tokarcikova, E., 2016, Approaches to the quantication of the human capital efficiency in enterprises, Communications-Scienti c letters of the University of Zilina, 18(1A), 49-54. [37] Pimentel, H., Bray, N., Puente, S., Melsted, S., Pachter, L., 2017, Differential analysis of RNA-seq incorporating quanti cation uncertainty, Nature methods, 14(7), 687-690. [38] Arora, N., et. al., 2019, Financial analysis: stock market prediction using deep learning algorithms, Proceedings of International Conference on Sustainable Computing in Science,mTechnology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India. [39] Naik, N., Mohan, B.R., 2019, Stock price movements classi cation using machine and deep learning techniques-the case study of indian stock market, International Conference on Engineering Applications of Neural Networks, 445-452. [40] Sak, H., Senior, A., Beaufays, F., 2014, Long short-term memory recurrent neural network architectures for large scale acoustic modeling. [41] Wu, Ch., Lu, Ch., Ma, Yu., Lu, Ru., 2018, A new forecasting framework for bitcoin price with LSTM, 2018 IEEE International Conference on Data Mining Workshops (ICDMW),168-175. [42] Yang, Sh., Yu, Xu., Zhou, Y., 2020, LSTM and GRU neural network performance comparison study: Taking Yelp review dataset as an example, 2020 International workshop on electronic communication and arti cial intelligence (IWECAI), 98-101. [43] Tandon, S., Tripathi, Sh., Saraswat, P., Dabas, Ch., 2019, Bitcoin price forecasting using lstm and 10-fold cross validation, 2019 International Conference on Signal Processing and Communication (ICSC), 323-328. [44] Hashish, I.A., Forni, F., Andreotti, G., Facchinetti, T., Darjani, Sh., 2019, A hybrid model for bitcoin prices prediction using hidden Markov models and optimized LSTM networks, 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 721-728. [45] Yiying, W., Yeze, Z., 2019, Cryptocurrency price analysis with arti cial intelligence, 2019 5th International Conference on Information Management (ICIM), 97-101. [46] Python releases for windows, url = https://www.python.org/downloads/windows [47] McKinney, W., and and others, 2010, Data structures for statistical computing in python, Proceedings of the 9th Python in Science Conference, 445, 51-56. [48] van der Walt, S., Colbert, G., 2011, The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science and Engineering, 13, 22, Doi: 10.1109/M-CSE.2011.37. [49] Hunter, J.D., Colbert, G., 2007, Matplotlib: A 2D graphics environment, Computing in science & engineering, 9(3), 90-95, Doi: 10.1109/MCSE. [50] Lib: Technical analysis library, url = www.ta-lib.org. [51] Chollet, F., 2016, Building powerful image classi cation models using very little data, Keras Blog, 5, 90-95, Doi: 10.1109/MCSE. [52] Abadi, M. and Coauthors, 2016, Tensor ow: Large-scale machine learning on heteroge-neous distributed systems, arXiv preprint arXiv:1603.04467. [53] Pedregosa, F. and Coauthors, 2011, Scikit-learn: Machine learning in Python, the Journal of machine Learning research, 12, 25-30. [54] Ho, T.K., 1995, Scikit-learn: Machine learning in Python, Proceedings of 3rd international conference on document analysis and recognition, 1, 278-282. [55] Sethia, A., Raut, P., 2019, Application of lstm, gru and ica for stock price prediction, Information and Communication Technology for Intelligent Systems, 479-487. [56] Chandrashekar, G., Sahin, F., 2014, A survey on feature selection methods, Computers & Electrical Engineering, 40(1), 16-28. [57] Yamak, P.T., Yujian, L., Gadosey, P.K., 2019, A comparison between arima, lstm, and gru for time series forecasting, Proceedings of the 2019 2nd International Conference on Algorithms Computing and Arti cial Intelligence, 4955. | ||
|
آمار تعداد مشاهده مقاله: 1,150 تعداد دریافت فایل اصل مقاله: 969 |
||