%0 Journal Article %T A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price %+ ESC Rennes School of Business %A Nagula, Pavan Kumar %A Alexakis, Christos %< avec comité de lecture %J Journal of Behavioral and Experimental Finance %V 36 %P 100741 %8 2022-12 %D 2022 %R 10.1016/j.jbef.2022.100741 %Z Statistics [stat]/Machine Learning [stat.ML] %Z Quantitative Finance [q-fin]Journal articles %X 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. %G English %L hal-03877093 %U https://rennes-sb.hal.science/hal-03877093 %~ ESC-RENNES %~ CER %~ CTIM %~ RSCOM