A Dynamic Linguistic Decision Making Approach for a Cryptocurrency Investment Scenario

Romina Torres, Miguel A. Solis, Rodrigo Salas, Aurelio F. Bariviera

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish
Article number9300216
Pages (from-to)228514-228524
Number of pages11
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020
Externally publishedYes

Keywords

  • Cryptocurrency
  • linguistic decision models
  • multi-period multi-attribute decision making

Fingerprint

Dive into the research topics of 'A Dynamic Linguistic Decision Making Approach for a Cryptocurrency Investment Scenario'. Together they form a unique fingerprint.

Cite this