2016/12/05 by Zhengyao Jiang, Jinjun Liang, Jiang, Zhengyao +1 · 41 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Advanced Bandit Algorithms Research #Artificial intelligence #Computer science #Computer security #Cryptocurrency #Economics #Finance #Financial Markets and Investment Strategies #Financial market #Portfolio #Project portfolio management #Reinforcement learning #Stock Market Forecasting Methods #cs.LG
paper · pdf · doi:10.48550/arxiv.1612.01277
published in arXiv (Cornell University) (Cornell University) · accepted by Intelligent Systems Conference (IntelliSys) 2017
openalex publication_date 2016/12/05 · arxiv created 2017/05/11 · arxiv updated 2017/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. This paper presents a model-less convolutional neural network with historic prices of a set of financial assets as its input, outputting portfolio weights of the set. The network is trained with 0.7 years' price data from a cryptocurrency exchange. The training is done in a reinforcement manner, maximizing the accumulative return, which is regarded as the reward function of the network. Backtest trading experiments with trading period of 30 minutes is conducted in the same market, achieving 10-fold returns in 1.8 months' periods. Some recently published portfolio selection strategies are also used to perform the same back-tests, whose results are compared with the neural network. The network is not limited to cryptocurrency, but can be applied to any other financial markets.