A Dynamic Linguistic Decision Making Approach for a Cryptocurrency Investment Scenario

Cryptocurrencies have been receiving the sustained attention of investors since 2009. These new investment vehicles are digitally native, meaning that they are traded exclusively on 24/7 digital platforms. Consequently, they offer an excellent scenario to test the Efficient Market Hypothesis, by dev...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.228514-228524
Main Authors: Torres, Romina, Solis, Miguel A., Salas, Rodrigo, Bariviera, Aurelio F.
Format: Article
Language:English
Subjects:
Algorithms
Biological system modeling
Bitcoin
Cryptocurrency
Cryptography
Decision making
Decision support systems
Digital currencies
Econometrics
Efficient markets
Investment
linguistic decision models
Linguistics
Mathematical model
multi-period multi-attribute decision making
Volatility
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Summary:Cryptocurrencies have been receiving the sustained attention of investors since 2009. These new investment vehicles are digitally native, meaning that they are traded exclusively on 24/7 digital platforms. Consequently, they offer an excellent scenario to test the Efficient Market Hypothesis, by developing algorithm-based trading strategies. Such strategies aim to beat the market. It has been previously reported that daily returns do not exhibit long range dependence. However, daily volatility in major cryptocurrencies is highly persistent. Therefore, buy/hold/sell decision support systems could be able to capture such market inefficiency. This is especially important for investors interested in periodically trading a set of cryptocurrencies, in order to maximize their wealth. This paper presents a dynamic linguistic decision making approach for building decision models to support cryptocurrency investors in buy/hold/sell decisions. This approach exhibits a good computational performance for obtaining recommendations based on quantitative data. Moreover, this procedure is able to identify some inefficient cryptocurrency behaviors which are not captured by traditional econometric techniques. Our results uncover arbitrage opportunities that outperform buy-and-hold or random strategies.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3045923