Predicting the Top and Bottom Prices of Bitcoin Using Ensemble Machine Learning
الموضوعات :
Emad Koosha
1
(Department of Financial Management, Islamic Azad University, Qazvin Branch, Qazvin, Iran)
Mohsen Seighaly
2
(Department of Financial Management, Islamic Azad University, Qazvin Branch, Qazvin, Iran)
Ebrahim Abbasi
3
(Department of Management, Faculty of Social Sciences and Economics, ALzahra University, Tehran, Iran)
الکلمات المفتاحية: ensemble machine learning, XGBoost, top and bottom price prediction, LightGBM, Algorithmic Trading,
ملخص المقالة :
The purpose of the present study is to use the ensemble learning model to combine the predictions of Random Forest (RF), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) models for the Top and Bottom Prices of Bitcoin. To this aim, in the first stage, Bitcoin's top and bottom prices are predicted using three machine learning models. In the second stage, the outputs of the models are presented as feature variables to the Extreme Gradient Boosting (Xgboost) and Light Gradient Boosting Machine (LightGBM) models to predict the price tops and bottoms. Then, in the third stage, the outputs of the second stage are combined through the voting ensemble classifier pattern to predict the next top and bottom prices. The data of top and bottom Bitcoin prices in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 are used as target variables and 31 technical analysis indicators as feature variables for the three models in the first stage. 70% of the data is regarded as learning data, and the remaining 30% is considered for the second and third stages. In the second phase, 50% of the data is considered for learning the output of the previous stage and 50% for the test data. Finally, the prediction values are evaluated with real data for the three models and the proposed ensemble learning model. The results reveal the improvement of the performance, precision, and accuracy of the ensemble model compared to weak learning models.
[1] Ampomah, E.K., Qin, Z., Nyame, G., Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement, Information, 2020, 11(6), 332. Doi:10.3390/info11060332.
[1] Ampomah, E., Qin, Z., Nyame, G., Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement, Information, 2020, 11. 332. 10.3390/info11060332.
[2] Bashiri, Paryab, Bitcoin price prediction using machine learning algorithms, Applied Economics Quarterly, 2019, 10 (34,35), 1-13.
[3] Basak, S., Kar, S., Saha, S., Khaidem, L., Dey, S., Predicting the direction of stock market prices using tree-based classifiers The North American Journal of Economics and Finance, 2018, 47. 10.1016/j.najef.2018.06.013.
[4] Dennys C.A. Mallqui, Ricardo A.S. Fernandes, Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques, Applied Soft Computing, 2019, Volume 75, Pages 596-606, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2018.11.038.
[5] Divya A., Shabana C., Balamurugan A., A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices, Journal of Behavioral and Experimental Finance, 2020,Volume 27, 100335, ISSN 2214-6350, https://doi.org/10.1016/j.jbef.2020.100335.
[6] Faghihi Nezhad, M., Minaei Bidgoli, B. Development of an ensemble learning-based intelligent model for stock market forecasting, Scientia Iranica, 2021, 28(1), 395-411. DOI: 10.24200/sci.2019.50353.1654.
[7] Gupta, A., Nain, H., Bitcoin Price Prediction Using Time Series Analysis and Machine Learning Techniques, 2020, 10.1007/978-981-15-7106-0_54.
[8] Jiang X., Bitcoin Price Prediction Based on Deep Learning Methods, Journal of Mathematical Finance, 2020, Volume 10, PP 132-139.
[9] Kyung K. Y., Sang W.Y., Daehan W., Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process, Expert Systems with Applications, 2021, Volume 186, 2021,115716, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2021.115716.
[10] Li, Y., Pan, Y. A novel ensemble deep learning model for stock prediction based on stock prices and news. Int J Data Sci Anal, 2022,13, 139–149, https://doi.org/10.1007/s41060-021-00279-9
[11] Minqi J., Jiapeng L., Lu Z., Chunyu L., An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms, Physica A: Statistical Mechanics and its Applications, 2020, Volume 541.
[12] Moshari M., Didekhani H., Khalili Damghani, K. Abbasi, E, Designing a hybrid intelligent model to predict the golden points of stock prices, Investment Science Quarterly, 2018, 8(29), 45-65.
[13] Nakamoto, S, Bitcoin: A peer-to-peer electronic cash system. 2008, Decentralized Business Review, 21260
[14] Nti, I. K., Adekoya, A. F., Weyori, B. A., Random Forest Base Feature Selection of Macroeconomic Variables for Stock Market Prediction, American Journal of Applied Sciences, 2019, 16(7), 200–212. https://doi.org/10.3844/ajassp.2019.200.212.
[15] Nti, I.K., Adekoya, A.F. Weyori, B.A. A comprehensive evaluation of ensemble learning for stock-market prediction, J Big Data, 2020, 7, 20. https://doi.org/10.1186/s40537-020-00299-5.
[16] Yue Y., Yang W., Peikun W., Xu J., Stock Price Prediction Based on XGBoost and LightGBM, E3S Web Conf., 275, 2021, 01040. https://doi.org/10.1051/e3sconf/202127501040.
[17] Poordavoodi, A., Reza, M., Haj, H., Rahmani, A. M., Izadikhah, M., Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs. CMES-Computer Modeling in Engineering & Sciences, 2020, 123(2), P. 525–570. Doi: 10.32604/cmes.2020.08854
[18] Azadi, M., Izadikhah, M., Ramezani, F., Hussain, F.K., A mixed ideal and anti-ideal DEA model: an application to evaluate cloud service providers, IMA Journal of Management Mathematics, 2000, 31(20), P. 233–256, Doi: 10.1093/imaman/dpz012
[18] Izadikhah, M. Financial Assessment of Banks and Financial Institutes in Stock Exchange by Means of an Enhanced Two stage DEA Model. Advances in Mathematical Finance and Applications, 2021, 6(2), P. 207-232. Doi: 10.22034/amfa.2020.1910507.1491
[19] Sun, X., Liu, M., Sima, Z., A novel cryptocurrency price trendforecasting model based on LightGBM, 2020, Finance Research Letters, 32, 101084.
[20] Ta, V. D., Liu, C. M., Tadesse, D. A. Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 2020, 10(2), 437.
[21] Livieris, I. E., Pintelas, E., Stavroyiannis, S., Pintelas, P., Ensemble deep learning models for forecasting cryptocurrency time-series. Algorithms, 2020, 13(5), 121.
[22] Chowdhury, R., Rahman, M. A., Rahman, M. S., Mahdy, M. R. C., An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning. Physica A: Statistical Mechanics and its Applications, 2020, 551, 124569.
[23] Manchanda, H., Aggarwal, S., Forecasting Cryptocurrency Time Series Using Adaboost-Based Ensemble Learning Techniques. In Innovations in Cyber Physical Systems, 2021, (pp. 207-219). Springer, Singapore.
[24] Ji, S., Kim, J., Im, H., A comparative study of bitcoin price prediction using deep learning. Mathematics, 2019, 7(10), 898.
[25] Kervan, C., Akay, F.,, Review on Bitcoin Price Prediction Using Machine Learning and Statistical Methods, Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, pp. 272-282, Dec. 2020, doi:10.35377/saucis.03.03.774276.
[26] Lahmiri, S., Bekiros, S., Cryptocurrency Forecasting with Deep Learning Chaotic Neural Networks, Chaos, Solitons and Fractals, 2019, vol 118, pp. 35-40.
[27] Ye Z, Wu Y, Chen H, Pan Y, Jiang Q. A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin, Mathematics. 2022, 10(8):1307. https://doi.org/10.3390/math10081307
[28] Jaquart, P., Dann, D., Weinhardt, C., Short-term bitcoin market prediction via machine learning, The Journal of Finance and Data Science, 2021, Volume 7, Pages 45-66, ISSN 2405-9188, https://doi.org/10.1016/j.jfds.2021.03.001.
[29] Almeida Borges, T.,, Ferreira Neves, R.,, Ensemble of machine learning algorithms for cryptocurrency investment with different data resampling methods, Applied Soft Computing, Volume 90, 2020, 106187, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2020.106187.
[30] Kumar Nagula, P., Alexakis, C., A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price, Journal of Behavioral and Experimental Finance, Volume 36, 2022, 100741, ISSN 2214-6350, https://doi.org/10.1016/j.jbef.2022.100741.
[31] Koosha, E., Seighaly, M., Abbasi, E., Measuring the Accuracy and Precision of Random Forest, Long Short-Term Memory, and Recurrent Neural Network Models in Predicting the Top and Bottom of Bitcoin price, Journal of Mathematics and Modeling in Finance (JMMF), 2022, 10.22054/jmmf.2022.69198.1068.
[32] Zamani, S., Zanjirdar, M., Lalbar, A. The effect of information disclosure on market reaction with meta-analysis approach. Advances in Mathematical Finance and Applications, 2022, 7(3), P. 629-644. Doi: 10.22034/amfa.2021.1937478.1625
[33] Zanjirdar, M., Overview of Portfolio Optimization Models. Advances in Mathematical Finance and Applications, 2020. 5(4), P.419-435. Doi: 10.22034/amfa.2020.674941.
[34] Zanjirdar, M., Kasbi, P., Madahi, Z., Investigating the effect of adjusted DuPont ratio and its components
on investor & quot; s decisions in short and long term, Management Science Letters, 2014, 4(3), P.591-596.
Doi: 10.5267/j.msl.2014.1.003
[35] Rahmani, A., Zanjirdar, M., Ghiabi, H. Effect of Peer Performance, Future Competitive Performance, and Factors of Correlation with Peer Companies on Manipulation of Abnormal Real Operations. Advances in Mathematical Finance and Applications, 2021, 6(1), P. 57-70. Doi: 10.22034/amfa.2020.1875478.1272
