Abstract
We present a model for active trading based on reinforcement machine learning and apply this to five major cryptocurrencies in circulation. In relation to a buy-and-hold approach, we demonstrate how this model yields enhanced risk-adjusted returns and serves to reduce downside risk. These findings hold when accounting for actual transaction costs. We conclude that real-world portfolio management application of the model is viable, yet, performance can vary based on how it is calibrated in test samples.
Highlights
Is active trading viable in cryptocurrency markets and can it yield superior performance relative to a buy-and-hold approach? Using a direct reinforcement (DR) model we demonstrate that, yes, active trading is both viable and profitable in such markets and can yield superior risk-adjusted performance relative to a passive buy-and-hold approach
We show how reinforcement machine learning can make cryptocurrency trading decisions that optimize actively managed portfolios
Our results compare favorably to a naive buy-and-hold approach. These results provide some preliminary evidence that cryptocurrency prices may not follow a purely random walk process
Summary
Is active trading viable in cryptocurrency markets and can it yield superior performance relative to a buy-and-hold approach? Using a direct reinforcement (DR) model we demonstrate that, yes, active trading is both viable and profitable in such markets and can yield superior risk-adjusted performance relative to a passive buy-and-hold approach. The first is the growing interest in explaining or forecasting the price behavior of cryptocurrencies and the formation of trading strategies in such markets (Akcora et al 2018; Baur and Dimpfl 2018; Brauneis and Mestel 2018; Catania et al 2019; Phillip et al 2018) This stream of literature shows how these blockchain-based digital assets have distinct technological and network attributes that set them apart from traditional assets. Huck (2019) indicates that, first, machine learning approaches can yield mixed results depending on the number of predictor variables and, second, may not provide a competitive edge for investors given how readily available these models are to all investors in the market Notwithstanding their limitations, there is a budding strand of recent literature that tests the ability of machine learning methods to be used effectively in portfolio allocation decisions, exchange rate forecasting and even bankruptcy prediction (Jiang et al 2020; Kristóf and Virág 2020; Zhang and Hamori 2020).
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