Table 9.
Research on cryptocurrency portfolio construction (1)
| Models | Data | Results | Innovation | References |
|---|---|---|---|---|
| CNN, DRL | 12 most traded cryptocurrency assets | The performance of the model strategy is compared to three benchmarks and three other portfolio management algorithms with positive results. | In this paper, we propose a model-free convolutional neural network that takes the historical prices of a group of financial assets as input and outputs the weights of this portfolio. | Jiang and Liang15 |
| DRL, MDP | 10 Cryptocurrencies with Transaction Costs data from 2011/10/01 to 2011/10/20 | The BTC buy-and-hold strategy has a cumulative yield of 93%. | A state-of-the-art DRL algorithm implementation framework called FinRL has been created enabling users to train trading agents in the pipeline. An automatic backtesting module is also offered to assess trading performance. | Liu et al.153 |
| DRL, CVaR | Data on the cryptocurrency market from 2015 to 2021 was used | When the economic structure collapses, it captures the nonlinear compound effect of many risk shocks on the risk distribution and directs investment in the financial market with hightail risk. | Based on CVaR risk measurement and a deep reinforcement learning optimization framework, a new bitcoin portfolio model framework is created. | Cui et al.154 |
| DRN, MultiObjective Evolutionary Algo rithms(MOEA) |
BTC, ETH, LTC, XRP, DSH, XLM, HMR | The proposed framework utilizes a multi-layer deep recurrent neural network regression model, which can provide more accurate prediction estimates. | The allocation of a bitcoin portfolio using a multi-objective evolutionary algorithm and deep learning model. Additionally, its capacity for forecasting can produce precise ex ante assessments of portfolio returns and dangers. | Estalayo et al.155 |
| LSTM, ARIMA, CMA, ANN |
10 cryptocurrencies including bitcoin from January 1, 2018 to September 1, 2019 | Bitcoin shows a fantastic investment opportunity with a buy-and-hold Sharpe ratio of 2.85, a return of 78.52%, and no volatility. | Paired trading using cryptocurrencies adds an edge to traders. | Osifo and Bhattacharyya156 |
| RNN, LSTM, GRU | Ethereum | Deep learning is more effective than traditional methods in predicting transaction value. | It is proved that the deep learning-based method is suitable for forecasting large-scale and long-term data scenarios | Gu et al.157 |