A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price

【Author】 Nagula, Pavan Kumar; Alexakis, Christos

【Source】JOURNAL OF BEHAVIORAL AND EXPERIMENTAL FINANCE

【影响因子】8.222

【Abstract】Several machine learning techniques and hybrid architectures for predicting bitcoin price movement have been presented in the past. Our paper proposes a hybrid model encompassing classification and regression models for predicting bitcoin prices. Our analysis found that the automated feature interactions learner (deep cross networks) error performance using a plethora of technical indicators, including crypto-specific technical indicator difficulty ribbon compression and control variables such as Metcalfe's value of bitcoin, number of unique active addresses, bitcoin network hash rate, and S & P 500 log returns, in a hybrid architecture is better than the single-stage architecture. The hybrid model predicted a 100% directional hit rate and maintained steady volatility in returns for the out-of-sample period. Our paper concludes that in terms of risk (Sharpe ratio 1.03) and profitability (260% and 82%), the hybrid model's bitcoin futures strategy performed better than the deep cross network regression and buy-and-hold benchmark strategies. (c) 2022 Elsevier B.V. All rights reserved.

【Keywords】Efficient market hypothesis; Hybrid architecture; Machine learning; Technical indicators interactions; Deep cross networks; Bitcoin

【发表时间】2022 DEC

【收录时间】2022-09-28

【文献类型】实验仿真

【主题类别】

区块链治理-市场治理-市场分析