LSTM-Based Dynamic Linguistic Decision-Making for Cryptocurrency Selection

Analyzing the overwhelming number of options available in the cryptocurrency market through technical analysis becomes unfeasible, whether expert investor or not. Similarly, challenging is the application of fundamental analysis in such markets. To address these complexities, this paper proposes a novel method to assist investors in navigating the overwhelming array of cryptocurrency options by employing a ‘buy-and-sell’ strategy. The approach incorporates a dynamic daily ranking system generated through LSTM neural network predictions and a dynamic linguistic decision-making model (DLDM). Simulated on a dataset of 68 cryptocurrencies observed from March to May 2018, the method surpasses state-of-the-art returns by over 300% when considering combinations of Day Profitability, Day Variability, and one of Open, Close, Low, or High attributes. Furthermore, by using a unitary constant as the third attribute, it achieves even higher returns, outperforming the state-of-the-art by more than 1700%. Comparatively, the proposed method easily outshines alternative strategies such as random selection, Bitcoin buy-and-hold, and equitable investment in all cryptocurrencies, which yielded returns of 3%, -34%, and 54%, respectively. The integration of LSTM predictions and DLDM showcases a potent tool for making informed decisions in the dynamic cryptocurrency market, especially crucial given the multitude of investment options and prevalence of non-expert investors. This paper presents a powerful approach to cryptocurrency investment, leveraging LSTM predictions and dynamic linguistic decision-making to provide investors with a competitive edge. The method's superior performance against published strategies showcases its potential for effectively tackling the complexities in cryptocurrency market, benefiting investors, despite experienced or not.