Ekonomi, Politika & Finans Araştırmaları Dergisi, 202X, X(X): XXX-XXX
Journal of Research in Economics, Politics & Finance, 202X, X(X): XXX-XXX
Araştırma Makalesi / Research Article, https://doi.org/10.30784/epfad.xxxxx
KRİPTO PARA PİYASASINDA YATIRIM ALTERNATİFLERİNİN BULANIK
EDAS VE BULANIK CODAS YÖNTEMLERİYLE DEĞERLENDİRİLMESİ
Evaluation of Investment Alternatives in Crypto Currency Market by Fuzzy Edas
and Fuzzy Codas Method
Summary
Anahtar Kelimeler:
Kriptopara,
Kriptopara Market,
Çok Kriterli Karar
Verme,
Bulanık EDAS,
Bulanık Codas,
Cryptocurrency,
Cryptocurrency
Market,
Multicriteria Decision
Making,
Fuzzy EDAS,
Fuzzy CODAS
JEL Kodları:
A11, B11, C11, D11.
From the past to the present, people perform various commercial transactions
to meet their needs. Commercial transactions, which started with the exchange
of surplus goods produced, were carried out with various valuables and money
in various forms in the following periods. Today, with the development of
technology, commerce continues on the internet widely. Especially with the
development of the internet, the money called crypto money, which has no
physical existence and is used entirely on the internet, has become popular.
However, while this new financial concept contains many risks, the high
returns obtained from cryptocurrencies direct investors who are looking for
new investment products to crypto money. In addition to various commercial
transactions, the number of people who buy cryptocurrencies for investment
purposes is increasing due to the fact that cryptocurrencies have just
completed their development, are increasing in use, and will become even
more valuable in the future. In this study, the most suitable crypto money
alternatives for investing were evaluated with the Fuzzy EDAS and Fuzzy
CODAS (Combinative Distance-based ASsessment) methods, which are
among the Multi-Criteria Decision Making (MCDM) Methods. To be used in
the fuzzy CODAS approach, criteria such as Annual Average Return, Total
Market Value, Security Infrastructure, Transaction Speed, Supporting
Organizations, Change from the Highest Value, Number of Exchanges Traded,
and Price were used by crypto money experts and investors. As a result of the
study, the most suitable crypto money alternative for experts and investors was
determined.
Abstract (11 punto)
Yukarıda belirtilenlere ek olarak, İngilizce özetlerin İngilizce gramer ve yazım
kurallarına uygun olmasına dikkat edilmelidir.
Keywords:
Cryptocurrency 1,
Multicriteria Decision
Making 2,
Fuzzy EDAS
&CODAS 3,
JEL Codes:
A11, B11, C11, D11.
Dr., Marmara Üniversitesi Ekonometri/Yöneylem Araştırması
emrenike@hotmail.com https://orcid.org/: 0000-0002-4043-9750
Bilim
Dalı,
İstanbul,
Endüstri Mühendisi, Microsoft İş Geliştirme Uzmanı İstanbul, ozgee.gunel@gmail.com,
https://orcid.org/: 0000-0002-4192-1709
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1. Introduction
Money is defined as a paper or metal object with values written on it that has been used to
buy a good or service for centuries. Money, which is used in every field where mutual buying
and selling transactions take place, has been a medium of exchange that has survived until
today. However, coins began to be used as a means of exchange in ancient times, before paper
money, and these exchange transactions were carried out with precious assets such as gold and
silver. With the use of coins, paper money began to appear over time. Processes that have been
going on like this for a long time are about to leave their place to a brand new trend with the
emergence of the concept of technology. Contrary to the transactions that have classically been
carried out with coins and paper money for centuries, today, with the advancement of
technology, a concept defined as crypto money has emerged. Cryptocurrency is money that is
not issued and controlled by any central bank, as it has no physical reality. The concept of
crypto money, which has emerged in recent years, has become a new alternative exchange tool
for everyone. Unlike the classical understanding in which money continues to exist as a means
of exchange, this digital economy, which has emerged, has started to attract people's attention
rapidly. However, while this new financial concept contains many risks, the high returns earned
from cryptocurrencies direct investors looking for new investment products to crypto money.
The use of cryptocurrencies around the world is increasing day by day. The rapid spread
of the use of cryptocurrencies has increased the acceptance of money as an investment tool
other than its exchange function. Cryptocurrencies are coins that allow the supply of virtual
money, which are created using the encryption method, unlike paper money which is created to
represent precious metals. It is not possible to measure the value of these coins as commodities.
Cryptocurrencies exist in the virtual environment independently of coins that can be stored as
metal or paper and are priced according to supply and demand changes. (Gürsoy, S., Tunçel,
M.B. 2020. Kripto Paralar ve Finansal Piyasalar Arasındaki İlişkinin İncelenmesi: Bitcoin ve
Seçili Pay Piyasaları Arasında Yapılmış Nedensellik Analizi (2010-2020), Üçüncü Sektör
Sosyal Ekonomi Dergisi, 55(4), 2126-2142). Individuals always have a concern when making
decisions about the future. The source of this concern is the perception of risk. The perception
of risk has led to confident and careful steps being taken while planning the future. Commercial
transactions made by people to meet their needs have changed throughout history. The methods
used in commercial transactions have been differentiated and thanks to computer technology,
cryptocurrencies have started to make a name for themselves in the commercial market (Alkış,
2018, p. 70).
Alkış, A. (2018). İslam hukuku açısından Bitcoin ve kripto para. Kahramanmaraş Sütçü
İmam Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(2), 69-90.
Cryptocurrencies, which are increasingly used today, are used in many areas from
commercial transactions to personal expenditures, and their popularity is increasing day by day
as an investment tool. As an investment alternative, it is seen as a safe haven for all kinds of
investors, small or large, experienced or inexperienced, as it has the ability to compete with safe
and popular stock markets (Polat and Akbıyık, 2019, p. 444).
Polat, M. ve Akbıyık, A. (2019). Sosyal medya ve yatırım araçlarının değeri arasındaki
ilişkinin incelenmesi: Bitcoin örneği. Akademik İncelemeler Dergisi, 14(1), 443-462.
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The banking crises in Southern Cyprus and Europe in November 2010 mobilized the
people affected by the 2008 crisis and directed them to Bitcoin. In other words, Bitcoin is any
decentralized currency, born out of a lack of trust in central banks, banks, brokerage houses, and
governments. In the article titled “Bitcoin: A Peer-to-Peer Electronic Cash System” published
by Satoshi Nakamoto in 2008, it was defined as “Bitcoin is a system that allows transactions to
be made without the need for the intermediation of any third party”.
In the following years, although various cryptocurrencies were bought and sold in the
markets, the first and the most traded cryptocurrency was Bitcoin. Cryptocurrencies, whose
security is provided by encrypting mathematical methods, are produced and used by individual
users anywhere in the world without the support of any government and/or institution, unlike
the money used in the classical sense. Currently, around 1650 cryptocurrencies are traded in the
market. Prominent among them; They are Ripple, Ethereum, Bitcoin, Cardano, NEM, Litecoin,
Stellar, Bitcoin Cash, IOTA and TRON.Kesebir, Murat & Günceler, Bülent. “Kripto Para
Birimlerinin Parlak Geleceği.” Iğdır Üniversitesi Sosyal Bilimler Dergisi 17 (2019), 605-625
The increase in the transaction volumes of cryptocurrencies in the world is an
unavoidable fact. The necessity of including this reality in an academic article has emerged and
it is aimed to contribute to the literature by analyzing investment alternatives in the crypto
money market.
In this study, crypto money alternatives that emerged with the development of the internet
were evaluated and it was aimed to determine the most suitable alternative for investors in an
integrated way with fuzzy and classical methods. In the study, Fuzzy EDAS and Fuzzy CODAS
methods, which are rarely used in the literature, were used. In particular, no study has been
found in the Turkish literature that applies the Fuzzy EDAS and Fuzzy CODAS methods to this
subject. In addition, trapezoidal fuzzy numbers were used in studies dealing with Fuzzy EDAS
and Fuzzy CODAS methods. In this study, triangular fuzzy numbers were preferred for ease of
calculation. In addition to these, the study is unique in terms of evaluating cryptocurrencies with
one of the fuzzy MCDM methods. This study consists of four parts. In the first part, the concept
of crypto money, which is increasingly used, is defined and the characteristics of crypto money,
crypto money markets, and crypto money examined in the study are mentioned.
In the second part, the concept of fuzzy logic, which uses approximate values instead of
exact values, fuzzy set theory and fuzzy numbers are mentioned. In the third chapter, Fuzzy
EDAS and Fuzzy CODAS methods are explained and the steps of the method are given. In the
fourth part of the study, investment alternative cryptocurrencies were evaluated with Fuzzy
EDAS and Fuzzy CODAS methods and suggestions were made to investors. In the conclusion
part, the results obtained in the application are interpreted and various suggestions are given.
2. Literature Research
There are various studies in recent years in the literature on the subject of
cryptocurrencies. These are mentioned below.
Kristoufek (2013) examined the relationship between Bitcoin, Google Trends and
Wikipedia and revealed the differences.
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Güngördü and Zengin (2013) examined the effects of bitcoin, one of the electronic
payment systems, on monetary policies, marketing management, cash management and risk
management. As a result of the study, it was stated that consumers should adopt these new
payment tools (wallet, e-money, bitcoin).
Briere, Oosterlink, and Szafarz (2015) stated that bitcoin investment exhibits high returns
with high volatility, and it is concluded that high risk for a well-diversified portfolio owner
should be compensated by low correlation with other assets.
Atik et al. (2015) tested the interaction between Bitcoin exchange rates between 2009 and
2015 and the most widely used cross currency prices in the world, using the Grander causality
analysis.
Georgoula et al. (2015) examined the relationship between Bitcoin prices and the
S&P500 index. As a result of the study, investment advice was made that the decrease in the
S&P500 index would be an opportunity to buy Bitcoin.
Baek and Elbeck (2015) examined whether Bitcoin is an investment tool or a speculative
tool. Regression analysis was used and Bitcoin prices and S&P500 index data between July
2010 and February 2014 were taken into account. As a result of the analysis, it has been
determined that the Bitcoin price has no effect on the S&P500 index.
Ram et al. (2016) evaluated Bitcoin accounting with accountability, neoliberalism and
conformity analysis.
Gültekin and Bulut (2016), the emergence of bitcoin and the formation of new sectors
related to it were examined, in which; They found that sub-systems such as financial service
providers, e-wallet service providers, mining firms, money markets and payment processors are
formed by bitcoin.
Dirican and Canoz, (2017) examined the relationship between some indices in the stock
market and Bitcoin by using the ARDL bounds test method. The indices used in the study are
BIST100, ChinaA50, Dow30, Ftse100, Nasdaq100, Nikkei225 and S&P500 indices. As a result
of the study, it has been determined that there is a long-term relationship between Bitcoin and
the US and China Stock Market indices.
Lim and Masih (2017) examined the relationship between the stock index created with an
Islamic perspective and Bitcoin by using MGARCH-DCC, CWT and MODWT methods.
Chuen et al. (2018) examined the relationship between traditional investment instruments
and cryptocurrencies.
Şahin and Özkan (2018) used ARCH, GARCH, ARCHM, EGARCH and TARCH
models to determine asymmetric volatility and calculated the returns on Bitcoin from the
closing prices of Bitcoin/US Dollar exchange rates.
Aslan et al. (2018) estimated the price values of Bitcoin in the Turkish Lira using various
degree curves and polynomial curve fitting and ANFIS methods, which are artificial intelligence
techniques.
Chuen, Guo, and Wang (2018) examined the static correlation between cryptocurrencies
and traditional investments and concluded that a cryptocurrency is a good option for
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diversifying portfolio risk. They also found that the average daily return on traditional
investments is less than the return on cryptocurrencies.
Ceylan et al. (2018) detected the existence of speculative bubbles in the cryptocurrencies
Bitcoin and Etherium and estimated when they formed.
Genç et al. (2018) evaluated the top ten cryptocurrencies in the market according to
various criteria using the Analytical Hierarchy Process method.
2.1. Cryptocurrencies
Cryptocurrency, which consists of the combination of the words crypto and currency,
which means crypto money in Turkish, does not have a clear definition yet, due to the
immaturity of crypto money exchanges and the lack of necessary legal regulations (Durdu,
2018, p. 11). Durdu, E. (2018). Kripto para birimi olarak Bitcoin ve ceza hukuku
(Yayımlanmamış yüksek lisans tezi). Galatasaray Üniversitesi, İstanbul.
Cryptocurrency; They are digital assets based on a cryptographic basis that enable
investors to make secure transactions. According to another definition, crypto money; is a
medium of exchange that is electronically created and stored in a blockchain network using
cryptographic techniques to control the creation of monetary units and verify the transfer of
funds. Kripto Para Nedir? (2017). https://koinbulteni.com/kripto-para-nedir (Erişim Tarihi:
04.03.2018).
Due to the 2007-2008 global financial crisis and the subsequent debt crisis, central
currencies came under pressure and people around the world began to lose faith in centralized
financial institutions. Therefore, there has been a call for decentralization and alternative
currencies. Call cryptocurrencies have been taken into account by decentralized digital
currencies based on peer-to-peer networks and cryptographic tools. Cryptographic users can
transfer virtual money to other users and thus sell or buy goods and services. Cryptocurrency,
on the one hand, is praised for its low transaction fees; It is promoted as a viable alternative to
banks and credit cards. On the other hand, it is warned that cryptocurrencies are an easy tool to
use for money laundering and trade in illegal transactions such as drugs (Morisse, 2015: 2).
Morisse, M. (2015). Cryptocurrencies and bitcoin: Charting the research landscape.
Twenty-first Americas Conference on Information Systems, Puerto Rico. 1-16.
Cryptocurrencies are digital assets that are used as virtual currency and do not exist in any
physical form. Unlike currencies in the classical sense, cryptocurrencies are money that can be
put on the market and used without the need for the support of any central bank, government, or
official institution. This feature is perhaps the most attractive aspect of cryptocurrencies for
investors. Because in this way, the majority of cryptocurrencies remain exempt from
government regulation or manipulation. In addition, cryptocurrencies are secured by
cryptography, that is, encryption. In this way, for example; The act of “double spending”, which
means counterfeiting or making multiple transactions with the same cryptocurrency, has become
almost impossible. Cryptocurrency is “digital assets that enable encrypted transactions and
additional supply of virtual money”. Cryptocurrency is a brand-new alternative currency. They
are also digital and virtual.
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Cryptocurrencies, unlike the money in the banking system, are decentralized. Therefore,
transactions are carried out by a system called a blockchain. Cryptocurrencies cannot be
produced by governments or companies, so they should be considered completely separate from
coins printed by governments' central banks. (Çarkacıoğlu, 2016:8-9). Çarkacıoğlu, A. (2016).
Kripto-Para Bitcoin, Sermaye Piyasası Kurulu Araştırma Dairesi Araştırma Raporu.
http://www.spk.gov.tr/SiteApps/Yayin/YayinGoster/1130. (Erişim Tarihi: 08.05.2019).
Countries have not yet made effective regulations regarding these digital currencies.
However, in some Asian countries (South Korea, Thailand, Vietnam, and the Philippines), four
large stock exchange companies were opened. Interest in cryptocurrencies is increasing not only
in these countries but also around the world. Cryptocurrencies are one of the innovations that
the 21st century has brought to human life. The first used cryptocurrency was Bitcoin. The use
of this money requires the existence of blockchain technology.
The basis of the rapid spread of crypto money called Bitcoin in the world is blockchain
technology. Satoshi Nakamoto is the creative father of the technology that allows this crypto
money to become widespread and used money safely. This name is not actually a person's
name, but a nickname. Nakamoto developed a theory on multiple spending in his 2008 article.
The concept of Bitcoin, the first cryptocurrency, entered human life with the article named
"Peer-to-peer electronic cash payment system" (Gültekin ve Bulut, 2016: 84). Gültekin, Y., &
Bulut, Y. (2016). Bitcoin ekonomisi: Bitcoin eko-sisteminden doğan yeni sektörler ve analizi.
Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(3), 82-92
There are around 1650 cryptocurrencies currently used and traded today. These continue
to increase day by day. When it comes to crypto money, the first thing that comes to mind is
Bitcoin (BTC). The world's first cryptocurrency was Bitcoin, created in 2008. Bitcoin was
followed by other types of cryptocurrencies, which today number in the thousands. Some of
them are: Bitcoin, Ethereum, Ripple, Bitcoin Cash, EOS, Cardano, Litecoin, Stellar, IOTA,
TRON, NEO, Dash, NEM, Monero, VeChain, Tether, Ethereum Classic, Qtum, OmiseGO,
ICON, Binance Coin, Lisk, Bitcoin Gold, Nano, Bytecoin, Verge, Zcash, Ontology, Aeternity,
Steem, Wanchain, Zilliqa, Siacoin, BitShares, Bytom, Bitcoin, Diamond, 0x, Populous, Waves,
Bitcoin Private, Stratis, Maker, RChain, Decred, Golem, Dogecoin, Status, Hshare, IOStoken,
Waltonchain, DigixDAO, Loopring, DigiByte, Aion, Basic Attenti, Augur, Komodo, Mixin,
Ardor, aelf, Mithril, DNotes, Ark, Nebulas, Loom Network, KuCoin Shares, Kyber Network,
Gas, PIVX, Substratum, Cortex, WaykiChain, ReddCoin, MonaCoin, QASH, Syscoin,
Centrality, Cryptonex, Pundi X, Dragonchain, Bancor, Ethos, Storm, GXChain, Dentacoin,
Veritaseum, FunFair, Fusion, WAX, Factom, Nxt, SALT , Elastos, Electroneum, Gifto, Enigma,
Matrix AI Net, ZCoin, Power Ledger, ChainLin k, Polymath, Neblio, Nucleus Vision,
BnkToTheFuture, Decentraland, Request Network, Revain, DeepBrain Chain, Monaco, Achain,
Kin, TenX, MaidSafeCoin, CyberMiles, Byteball Bytes, Cindicator, Storj, BitcoinDark, Bottos,
Time New Bank, Civic, Iconomi, Nuls, Particl, Emercoin, Skycoin, Paypex, POA Network,
ZenCash, Genaro Network Quantstamp, Nexus, SIRIN LABS Token, Huobi Token, Arcblock,
Ruff, SmartCash, iExec RLC, Po.et, Ignis, Gnosis, Apex, Enjin Coin, Scry.info,
SingularityNET, Metal, SmartMesh, Dent, Aragon, Ambrosus, Vertcoin, BOScoin, Bluzelle,
Bitcore, GameCredits, Santiment Net, Raiden Networ, TomoChain, High Performa, Dropil, Red
Pulse, Game.com, Theta Token , TokenPay, ETHLend, IHT Real Esta, Litecoin Cash,
BridgeCoin, All Sports, DigitalNote, PayPie, SophiaTX, Dynamic Tradi, Pillar, MediBloc,
Groestlcoin, DEW, SONM, DATA, Ink, Credits, Blocknet, Ubiq etc. cryptocurrencies such as .
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Keskin Köylü, M. (2018). “Kripto Paralar ve Uluslararasi Finansal Piyasalarda Yeri”,
International Journal of Academic Value Studies, Vol:4, Issue:21; pp:814-821
The cryptocurrencies currently in use above can be named according to their intended use
or to the liking of the development team, which ensures that the money is processed.The
cryptocurrencies currently in use above can be named according to their intended use or to the
liking of the development team, which ensures that the money is processed.
The concept of crypto money has actually been in our lives for many years. For example,
we used cryptocurrencies instead of physical banknotes in every transaction we made with debit
cards, virtual cards, or over the internet. Transactions were made on a digital basis, without
physical money transfers between banks. So from a point of view, cryptocurrencies were also
used in these transactions. Because as a result of these transactions, there were only numerical
changes in the financial systems. The new generation of cryptocurrencies, on the other hand, is
distinguished from their ancestors by the fact that they are primarily used in digital transactions
and do not exist physically. Cryptocurrencies are created through a process called mining.
Individuals with special hardware (hardware) are rewarded by a network with tokens or
cryptocurrencies such as Bitcoin in return for their services. In this decentralized competitive
process, if too many people try to mine a coin, it will become increasingly difficult to make a
profit with each new addition to the network. This is one of the main reasons why Bitcoin,
which can be produced on a limited basis, has increased in value over time with its increasing
popularity. Kripto Para Birimi Ne Demektir, Özellikleri Nelerdir? (halkbank.com.tr) 1.1.
2.2 Features of Cryptocurrency
Cryptocurrency systems are distributed systems and do not depend on a central authority.
The logic of all cryptocurrency systems is that they use cryptography to control transactions,
increase supply and prevent fraud. After the transactions are approved in the virtual
environment, the transactions are stored digitally. Unlike paper money, they are created using
the encryption method and enable the supply of virtual money. Cryptocurrencies are secured by
cryptography, that is, encryption. Cryptocurrency is an alternative currency, digital and virtual
money for secure transactions. (https://coinmarketcap.com).
2.3 Advantages and Disadvantages of Cryptocurrencies
Cryptocurrencies have many advantages and disadvantages. The advantages of
cryptocurrencies are as follows (Tüfek, 2017, s. 78): Yeni Başlayanlar İçin 12 Maddelik
Başlangıç Rehberi. (2016). Bitcoin Kullanmanın Avantajları Nelerdir?.https://cointurk.com/yeni-baslayanlar-icin-13- maddelik-bitcoin-rehberi (Erişim Tarihi: 08.11.2017).
• It is free from legal sanctions such as tax, declaration, or registration.
• States cannot impose any tax deductions on the cryptocurrencies in question.
• Cryptocurrencies are cryptocurrencies
confidentiality is important in these coins.
using cryptographic
foundations
• No one's money and transfer can be known by third parties unless they want it.
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• It is not possible to physically move it because it does not actually exist physically.
• No commission is applied for workplaces.
• Transfer fees are very low compared to other instruments.
• It does not cause any inflation as it is limited in number.
Cryptocurrency has advantages as well as disadvantages. These disadvantages are as
follows (Tüfek, 2017, s. 79) Tüfek, B. Ü. (2017). Elektronik ödeme araçları ve geleceğin
yaklaşımı kripto para (Yayımlanmamış yüksek lisans tezi). Bahçeşehir Üniversitesi, İstanbul.
• It does not stand behind any institution legally.
• Since it is not in the real world, it is used by a few people and cannot be used officially
for various payments.
• Since cryptocurrencies are virtual currencies, it is not possible to reverse the
transactions made.
2.4 Cryptocurrency Exchanges
Cryptocurrency exchanges are online platforms where one cryptocurrency can be
exchanged for another. Cryptocurrency exchanges with the highest trading volume worldwide
can be counted as Binance, Bithumb, BitMEX, Bitfinex, OKEx. In our country, the most active
cryptocurrency exchanges; Koineks, BTCTurk, VeBitcoin, Paribu, Koinim, Borsabit, Digilira,
Bitturk, Ovis. Turkish investors have an important role in the cryptocurrency market. On a
country-by-country basis, Turkey ranks in the top five in registered cryptocurrency transactions.
Cryptocurrencies that attract the most attention from Turkish investors; Ethereum, Ripple, and
Bitcoin (Küçük, 2018). Küçük, E. (2018). “Türkiye, kripto para borsalarında ilk 5'te”. Dünya
Finans. https://www.dunya.com/finans/haberler/turkiye-kripto-para-borsalarinda-ilk -5te-haberi
414500 adresinden 07.05.2018 tarihinde erişildi.
Bitcoin, which is the ancestor of cryptocurrencies and has been traded in the markets for
about nine years, ranks first in the world in terms of transaction volumes. Although there are
over 200 crypto exchanges operating in the world, it is possible to talk about four different
crypto exchanges in general.
2.4.1. Traditional Cryptocurrency Exchanges
It is a type of exchange similar to the traditional exchange, where buyers and sellers buy
and sell cryptocurrencies at current market prices. Such exchanges generally charge a certain
amount of fees for transactions.
2.4.2 Cryptocurrency Brokers
Web-based exchanges that allow clients to buy and sell cryptocurrencies at slightly above
market price through brokers. Due to its ease of use, it is a suitable method for those who are
new to buying and selling cryptocurrencies.
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2.4.3. Direct Trading Platforms
Such platforms offer buyers and sellers the opportunity to trade directly, but do not trade
at a fixed market price. Instead, sellers reach buyers by determining the prices of the crypto
money they will trade.
2.4.4 Cryptocurrency Funds
Funds are pools of assets that are professionally managed by the public sector, enabling
them to buy cryptocurrencies through the fund and hold the cryptocurrencies they receive. Such
exchanges allow you to invest in cryptocurrency without having to buy directly or store it.
Akcan, M. M. (2018). Kripto para piyasalarında sürü davranışının ölçülmesi (Yayımlanmamış
yüksek lisans tezi). Manisa Celal Bayar Üniversitesi, Manisa.
Considering the applications of the study, the crypto money investment alternatives and
criteria are explained in detail.
2.5 Cryptocurrency Investment Alternatives
2.5.1 Bitcoin
Bitcoin is an electrified form of payment that was started in 2008 and later. It has been a
pioneer of fighting in the market. Examples of this have attracted the attention of the stops with
the features of Bitcoin technology and helped the Crypto Money Market. After the emergence
of Bitcoin, it managed to appear in other coins. https://www.bitlo.com/
2.5.2 Ethereum
From blockchains, Ethereum is a cryptocurrency that was introduced towards the end of
2013, emerging after the Cryptocurrencies Bitcoin. The most important feature of Ethereum is
businesses, starting a financial stream cheaply, fast and decentralized. https://www.bitlo.com/
2.5.3 Ripple(XRP)
Ripple is the only crypto money run through the Ripple company, compared to other
cryptocurrencies. Many banks and installations use Ripple because of its open-source network.
More bank service movements; remittance, payment, etc.; are used in the fields.
https://www.bitlo.com/
2.5.4. Litecoin (LTC)
Litecoin, which has similar features to Bitcoin, emerged in 2011 with the elimination of
errors in Bitcoin. It is open-source and not affiliated with any center. It is also the first altcoin. It
has a larger storage space compared to other cryptocurrencies. https://www.bitlo.com/
2.5.5 Cardano(ADA)
Cardano is a type of cryptocurrency in the ADA system. It is a blockchain platform that
operates with a decentralized third share. Ada coin is the local currency of the network. Its most
prominent features are cheap transaction fees, transaction speed, and enabling transactions via
smart contracts. https://www.bitlo.com/
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2.5.6 Biancecoin(BNB)
BNB money is a type of cryptocurrency in the BNB Chain system. With reference to
other cryptocurrencies in the market, Binance is the main power both as a network and a project
on the world's highest-volume money exchange. It has grown much faster than its competitors
in the blockchain market. https://www.bitlo.com/
2.5.7 Polkadot(DOT)
Polkadot (DOT), which is home to multiple cryptocurrencies, has an internet chain
system that does not have a centralized system and thus publicly connects networks. Running
this entire network over the internet has benefited its growth. https://www.bitlo.com/
2.5.8 Dogecoin(DOGE)
The most important feature of Dogecoin, which was revealed as an alternative to Bitcoin
in 2013, is its abundance. Dogecoin supply has no limit. Its value increases with supply and
demand. It is a cryptocurrency today that provides a fun and transaction fee-free payment
system. https://www.bitlo.com/
2.5.9 Chainlink(LINK)
Chainlink was created in 2017 by a technology company. It is a type of crypto money that
aims to provide fast web and computer-based data flow and provides the connection. As the
name suggests, Chainlink develops on the LINK network. It serves API-based companies.
https://www.bitlo.com/
2.5.10 Tether(USDT)
Tether, known as the most stable coin in the crypto money market, emerged in 2014.
Tether, which presents the price of the US dollar as a coin, keeps promissory notes and treasury
bills. It facilitated user access by supporting many different network structures.
https://www.bitlo.com/
2.6 Decision Criteria Used in Evaluation of Cryptocurrency Investment Alternatives
2.6.1 Annual Average Return
Cryptocurrency investors will especially prefer the one with the highest return before
investing their money in this virtual system. Yilmaz, N. K., & Hazar, H. B. (2018)
2.6.2 Total Market Value
The market value of cryptocurrencies is a criterion in which values such as the situation
in the market, the current situation, and the size of their competitors in the market are taken into
account. This criterion should be considered when buying or selling cryptocurrencies. Yilmaz,
N. K., & Hazar, H. B. (2018)
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2.6.3 Security Infrastructure
With the developing technology, the security of virtual money, that is, crypto money,
especially in the digital environment, has increased considerably. Hiding data, keeping payment
information, providing safe shopping, and having a strong infrastructure against any cyber
security attack is one of the most important criteria evaluated by crypto money investors/users.
Yilmaz, N. K., & Hazar, H. B. (2018)
2.6.4 Transaction Speed
The transaction speed of cryptocurrencies in the market is an important criterion for
investors. Transaction speed is related to how fast transactions are made in cryptocurrencies.
The high transaction speed is related to how useful the cryptocurrency is. Yilmaz, N. K., &
Hazar, H. B. (2018)
2.6.5 Supporting Organizations
Organizations that support cryptocurrencies in trading virtual currency are important.
These organizations increase the reliability of coins. In addition, the technological infrastructure
of the organizations is also a criterion that facilitates the investment of the users. Yilmaz, N. K.,
& Hazar, H. B. (2018)
2.6.6 Change from Peak Value
Cryptocurrencies increase and decrease every second in the exchanges where they are
traded. For virtual currency investors, the higher this exchange is, the higher the value of the
currency, so the higher a cryptocurrency is exchanged, the more valuable it will be. Yilmaz, N.
K., & Hazar, H. B. (2018)
2.6.7 Number of Exchanges Traded in
Cryptocurrency trading is done through virtual exchanges. The trading volume of
cryptocurrencies is affected by the number of exchanges on which it is traded. The number of
exchanges where these virtual currencies are traded is important for investors, as
cryptocurrencies are increasingly diversified and digitized. Yilmaz, N. K., & Hazar, H. B.
(2018)
2.6.8 Price
The price value of cryptocurrencies is determined by the supply-demand relationship. It is
obtained by multiplying the price by the current supply in the market and increases and
decreases accordingly. Price is one of the most important criteria because investors want to
invest more and buy more cryptocurrencies as the price drops, or make a profit by selling their
existing crypto as the price rises. Yilmaz, N. K., & Hazar, H. B. (2018)
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2.7
Fuzzy Logic
Events in the real world are complex, and since these complex events cannot be
represented by mathematical expressions, they can be approximated. These approximate
expressions that show the uncertainty state express the fuzziness. The logic developed to make
these uncertainties more specific by using verbal expressions is called "Fuzzy Logic". Fuzzy
logic was first introduced by Lotfi Zadeh (1965) to model uncertainty in natural language.
Fuzzy logic is a generalization of classical two-valued logic and refers to all theories that use
fuzzy sets. The view that forms the basis of fuzzy logic is the acceptance of the truth of a
proposition as a function that relates the values in a set containing an infinite number of truth
values between propositions and definite false and definite true or numerically to the range of 0
and 1 real numbers (Baykal and Beyan, 2004, p. 39). ).Baykal N. ve Beyan T. (2004), Bulanık
mantık ilke ve temelleri. Ankara: Bıçaklar Kitabevi.
We say that there is uncertainty in the presence of a situation whose outcome is not clear
and which contains subjectivity, that is, it has the feature of changing from person to person. In
every moment of life, human beings are faced with uncertainty. For example; at work, at home,
at school, etc. We are faced with uncertainty. It is not possible to solve uncertainties with
classical logic. The fuzzy expression corresponds to the uncertainty. The concept that emerges
to express uncertainties is fuzzy logic. In cases where classical logic is valid, an event can be
described as either true or false, while in fuzzy logic there are many different axioms between
true and false. Today, where uncertainty and complex situations are very high, computers have
been developed in order to solve them. But computers could not solve this situation either.
Azeri-origin scientist Zadeh brought fuzzy sets to the literature by expressing uncertainty as a
result of his studies. Fuzzy logic has developed since then and has been applied by researchers
in many different fields. (Birgili vd., 2013). Birgili, E., Sekmen, F. & Esen, S. (2013). Bulanık
Mantık Yaklaşımıyla Finansal Yönetim Uygulamaları: Bir Literatür Taraması. Uluslararası
Yönetim İktisat ve İşletme Dergisi, 9 (19), 121-136.
Fuzzy logic shows that not only black and white exist, but also gray tones between these
colors and are taken into account. In non-fuzzy logic, that is, in classical logic, the definition of
a set is a collection of well-defined objects. What is meant by the concept of well-defined object
is certain situations. Numerical inputs are blurred and based on the values, rules are run in the
rule database according to the inputs. (Baral. & Aslan, 2018). Baral, G. & Aslan, S. T. (2018).
Bulanık Maliyet Tahminlemesi. Muhasebe ve Denetime Bakış, 17 (53), 199-214.
If we touch on the differences between fuzzy logic and classical logic; While classical
logic includes certain situations, fuzzy logic includes uncertainty and uncertain situations. In
classical logic, states take the value of 0 or 1 numerically, while in fuzzy logic, states take
values in the range of 0 to 1. While there are binary units in classical logic, there are fuzzy units
in fuzzy logic.
2.7.1 Advantages and disadvantages of fuzzy logic
The main advantage of fuzzy logic is that it includes the complexity and uncertainty of
human nature and living conditions in the model and expresses it mathematically. If we look at
the other advantages it provides;
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These are situations such as being suitable for people's ideas and thinking, solving in a
simple and easy-to-understand way, reaching a solution cheaply, and not needing too many
mathematical operations and models.
The disadvantages of fuzzy logic are; The rules of the method are based on experience,
stability analysis is not performed and the result is difficult to predict beforehand. (Sanca vd.,
2022). Sanca, M., Artun, H. & Okur, M. (2022). Fen Eğitiminde Bulanık Mantık Uygulamaları
Neden Kullanılmalıdır? Ulusal Eğitim Akademisi Dergisi, 6 (1), 130-144.
2.7.2. Fuzzy Sets
Fuzzy logic was introduced to the literature by Lotfi Zadeh in 1965. Fuzzy logic has a
superior feature in the explanation of uncertainties. With fuzzy logic theory, mathematical
operations and programming are applied in fuzzy field. The fuzzy set is represented by crossing
out the symbol. The triangular fuzzy number is denoted as (l/m, m/u) or (l, m, u). l, m, u
respectively; it shows the minimum probability, net numerical value and maximum probabilities
for the fuzzy situation . Güner, H. (2005). Bulanık AHP Ve Bir İşletme İçin Tedarikçi Seçimi
Problemine Uygulanması. Pamukkale Üniversitesi. Fen Bilimleri Enstitüsü, Yayınlanmış
Yüksek Lisans Tezi.
According to the classical set logic specified in mathematics, there is a case of an element
belonging to the set or not. For example; Let's look at the set A = {2,4,7}. While 2 is a member
of A, 5 is not a member of this set. The degree of membership indicates belonging to a cluster,
not belonging, and the degree of belonging to the cluster. In classical sets, the membership
degree is specified as 1 for the element in the set and 0 for the element not in the set.
The fuzzy set is the process of assigning a membership degree to each of the elements in
the universal set. The degree of membership is the degree of compliance of the assigned element
with the feature of the fuzzy set. There is no question whether this element definitely belongs to
the set or not. (Erdin, 2007). Erdin, C. (2007). Bulanık Hedef Programlama ve İşletme
Yönetiminde Bir Uygulama. İstanbul Üniversitesi Sosyal Bilimler Enstitüsü. Doktora Tezi. 2729.
2.7.3. Fuzzy Numbers
Fuzzy numbers represent a fuzzy subset of real numbers. According to another definition,
fuzzy numbers are functions that match each of the real numbers with a closed interval of 0.1.
Non-fuzzy numbers are defined for a single point. For non-fuzzy numbers, the membership
degree is 0 or 1. Fuzzy numbers are defined within at least one interval. The degree of
membership corresponds to a value in the range of 0.1 closed. As a result, the fuzzy number
does not have an exact value. However, its values and membership degrees can be known. Since
fuzzy numbers are defined for a range, they have different names in terms of the values they
will take in the related range. For example; (3,10) fuzzy number, (3,7,10) triangular fuzzy
number, (3,6,8,10) trapezoidal fuzzy number are defined as. Since triangular fuzzy numbers are
used in practice, triangular fuzzy numbers are mentioned. (Ecer, 2007). Ecer, F. (2007). Üyelik
Fonksiyonu Olarak Üçgen Bulanık Sayılar Mı Yamuk Bulanık Sayılar Mı?. Gazi Üniversitesi
İktisadi ve İdari Bilimler Fakültesi Dergisi, 9 (2), 161-180.
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2.7.4. Triangular fuzzy numbers
Triangular fuzzy numbers are ordered triples defined in the set of real numbers. Fuzzy
numbers are used to facilitate operations in fuzzy sets. Generally, triangular fuzzy numbers are
used, and triangular fuzzy numbers correspond to the kind of fuzzy numbers. The fuzzy number
represented by three definite numbers is a triangular fuzzy number.
However, the distinguishing feature of fuzzy numbers from ordered triples in algebra is
that the elements are arranged from smallest to largest. Each number consists of three
components. The first of these three components shows the minimum value, the second shows
the middle value and the third shows the maximum value (Işık, 2011). Işık A. (2011).
Bütünleşik Üretim Planlamasında Bulanık Mantık Yaklaşımı Ve Bir Uygulama. Adnan
Menderes Üniversitesi Sosyal Bilimler Enstitüsü. Yayınlanmış Yüksek Lisans Tezi. 77-79.
3. Introduction
3.1 Data Set
Fuzzy EDAS and Fuzzy CODAS methods, which are among the Multi-Criteria Decision
Making techniques, were applied to the problem handled in the application.
Table 1: Alternative-Criteria Set Used in Practice
Alternative
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
Bitcoin(BTC)
Ethereum(ETH)
Ripple(XRP)
Litecoin(LTC)
Cardano(ADA)
Biancecoin(BNB)
Polkadot(DOT)
Dogecoin(DOGE)
Chainlink(LINK)
Tether(USDT)
Criteria
K1
K2
K3
K4
K5
K6
K7
K8
Annual Average Return(K1)
Total Market Value (K2)
Security Infrastructure(K3)
Processing Speed(K4)
Supporting Organizations(K5)
Change from the Highest
Value(K6)
Number of Exchanges Traded (K7)
Price(K8)
Optimization
Max
Max
Max
Max
Max
Max
Max
Max
3.2 Fuzzy EDAS Method
Decision analysis is one of the most suitable methods in the application of fuzzy set
theory. Fuzzy set theory is suitable for use in complex or non-verbal MCDM problems. (Gültaş,
2007, s. 31). This situation has recently caused the field expansion of MCDM and thus fuzzy
MCDM methods have emerged. (Karakaşoğlu, 2008, s. 94). Karakaşoğlu, N. (2008). Bulanık
çok kriterli karar verme yöntemleri ve uygulama (Yayımlanmamış yüksek lisans tezi).
Pamukkale Üniversitesi, Denizli
The EDAS method is one of the MCDM methods in which absolute values are used when
determining criterion weights and alternatives. When this method is combined with a fuzzy set,
it emerges as a fuzzy EDAS method.
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The method was first described by Keshavarz Ghrobaee et al. It was put forward by the
problem of supplier selection in 2016. (Kas Bayrakdaroğlu & Kundakcı, 2019). According to
this;
K={𝐾1 , 𝐾2 , … , 𝐾𝑚 } (i=1,..,m) set of criteria, A={𝐴1 , 𝐴2 , . . , 𝐴𝑛 } (j=1,..,n) set of alternatives and
KV= {𝐾𝑉1 , 𝐾𝑉2 , . . , 𝐾𝑉𝑘 } (p=1,..,k) the decision maker set fuzzy EDAS method is the set of
equations below: (Ghorabaee, Zavadskas, Amiri, & Turskis, 2016).
Step 1: A combined decision matrix is created from the decision matrices containing the
performance values assigned by the decision makers to the alternatives. This matrix is shown in
Equation 1.
X=[(𝑋_𝑖𝑗 )]𝑚𝑥𝑛
𝑋̃𝑖𝑗 =
1 𝑘
̃𝑝
∑
𝑋
𝑘 𝑝=1 𝑖𝑗
(1)
(2)
𝑝
𝑋̃𝑖𝑗 value 𝐴𝑗 = (1 ≤ 𝑗 ≤ 𝑛 ) in this alternatives p.(1 ≤ p ≤ k) appointed with the decision maker
𝐾𝑗 (1 ≤ 𝑖 ≤ 𝑚) It shows the performance value in line with the criteria. The performance
values assigned by the decision maker are summed, divided by the number of decision makers,
and the combined decision matrix is found as in Equation 3.
Step 2: The criterion weights matrix is formed by summing the performance values and
dividing by the number of decision makers. This matrix is shown in Equation 3.
𝑊 = [𝑤
̃]
𝑖 𝑚𝑥1
1̃
̃𝑝
𝑘
∑
𝑤 =
𝑤
𝑖
𝑝
𝑤𝑖 ,
𝑘
𝑝=1
𝑖
(3)
(4)
̃
𝐾𝑖 (1
≤ 𝑖 ≤ 𝑚) criteria p.(1 ≤ p ≤ k) is the weight value assigned by the decision maker.
Step 3: The mean solution matrix (AV) is found as follows.
𝐴𝑉 = [𝑎𝑣
̃𝑖 ]𝑚𝑥1
1 𝑛
̃
𝑎𝑣𝑖 = ∑𝑗=1 𝑋̃𝑖𝑗
𝑛
(5)
(6)
𝑎𝑣𝑖 The elements of the matrix show the average solution value of the alternatives of each
criterion separately. Therefore, the matrix size of the criterion weights is equal to the size of this
matrix.
Step 4: The positive distance matrix (PDA) from the mean and the negative distance matrix
(NDA) from the mean, with N set cost criteria and B set benefit criteria, are calculated as in the
equations below.
PDA=[𝑝𝑑𝑎̃
(7)
𝑖𝑗 ]𝑚𝑥𝑛
̃
NDA=[𝑛𝑑𝑎𝑖𝑗 ]𝑚𝑥𝑛
(8)
̃𝑖
𝜑(𝑥̃𝑖𝑗 −𝑎𝑣
̃ 𝑖)
̃𝑖𝑗 = { 𝑘(𝑎𝑣
𝑝𝑑𝑎
̃ −𝑥̃
𝜑(𝑎𝑣
𝑖
𝑖𝑗 )
̃ 𝑖)
𝑘(𝑎𝑣
, 𝑖𝜖𝐵
(9)
,
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̃ 𝑖 −𝑥̃𝑖𝑗 )
𝜑(𝑎𝑣
̃ 𝑖)
̃ 𝑖𝑗 = { 𝑘(𝑎𝑣
𝑛𝑑𝑎
̃ )
𝜑(𝑥̃ −𝑎𝑣
𝑖𝑗
𝑖
̃ 𝑖)
𝑘(𝑎𝑣
, 𝑖 𝜖 𝐵
(10)
, 𝑖 𝜖 𝑁
Step 5: The weighted negative and positive distances for all alternatives are found by
multiplying the positive and negative distance values of the criteria weights obtained.
̃ 𝑖𝑗 )
𝑠𝑝
̃𝑗 = ∑𝑚
̃ 𝑖 ∗ 𝑝𝑑𝑎
𝑖=1(𝑤
𝑚
̃ 𝑖𝑗 )
𝑛𝑝
̃ 𝑗 = ∑𝑖=1(𝑤
̃ 𝑖 ∗ 𝑛𝑑𝑎
(11)
(12)
Step 6: For all alternatives 𝑠𝑝
̃𝑖 𝑣𝑒 𝑛𝑝
̃ 𝑖 the normalized state of the values is shown in Equations
13 and 14.
𝑛𝑠𝑝
̃𝑗=
̃𝑗
𝑠𝑝
̃ 𝑗 ))
𝑚𝑎𝑥𝑗 (𝑘(𝑠𝑝
̃𝑗
𝑠𝑛
𝑛𝑠𝑛
̃𝑗 =1−
̃ 𝑗 ))
𝑚𝑎𝑥𝑗 (𝑘(𝑠𝑛
(13)
(14)
Step 7: Evaluation score for all alternatives (𝑎𝑠
̃𝑗 ) found with the Equation 15.
̃𝑗 ) =
(𝑎𝑠
1
(𝑛𝑠𝑝
̃𝑗
2
∗ 𝑛𝑠𝑛
̃ 𝑗)
(15)
Step 8: All of the alternatives are ranked from largest to smallest based on their evaluation
scores. The alternative with the largest evaluation score is selected as the best alternative.
3.3 CODAS Method
CODAS method CODAS (Combinative Distance – Based Assessment) is a combined
distance based assessment method. This method was developed in 1996 and is a fairly new
MCDM method. The difference of this method from other MCDM methods is that it uses two
different distance approaches, Euclidean and Taxicab distances.
Since the CODAS method is a recently developed method, it has been determined that
studies on CODAS are limited in the literature. In addition, when the literature is examined, the
CODAS method is mostly used in selection and performance evaluation problems. Alioğulları,
E. & Tüysüz, F. (2020). Investigation of Foreign Trade Capacity of Istanbul Province by Edas
and Codas Method. European Journal of Science and Technology, (Special Issue), 240-248.
With the CODAS method, performance measurement is provided with certain interconnected
steps. These steps are described below.
3.3.1
Fuzzy CODAS
The CODAS method, developed by Keshavarz Ghorabaee in 2016, is based on
determining the Euclidean and Taksicab distances between the alternatives in cases where there
are more than one alternative, and preferring the alternatives to each other. A fuzzy extension of
the CODAS method was recently developed by Keshavarz Ghorabaee et al. The basis of this
method is based on choosing the alternative that is farthest from the negative ideal solution
(Keshavarz Ghorabaee et al., 2016).
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In this method, the Euclidean distance is used first. If there are two equal alternatives in
Euclidean distance, then if they have equal Euclidean distances, in such cases the method is
solved by using the Taksicab distance. The normal indifference field is related to the Taxicap
distance. The distance of the alternatives to the negative ideal is related to the Euclidean
distance. The CODAS method offers a better solution than the other distance-based VIKOR and
TOPSIS methods according to the solution similarity of the MCDM methods because the
evaluation scores of the alternatives are compared and solved using Euclidean and Hamming
distances (Keshavarz Ghorabaee et al., 2016, p. 28).
In the Fuzzy CODAS method, the most desired alternative is the alternative with the
furthest distance from the negative-ideal solution. In this method, when the similarity of the two
alternatives is higher at Euclidean distance, the Hamming distance, that is, the second criterion,
is used as the basis of comparison (Kershavarz Ghorabaee, Zavadskas, Turskis, &
Antucheviciene, 2016).
Step 1: Creating the fuzzy decision matrix 𝑥̃𝑙
𝑥̃11𝑙
𝑥̃21𝑙
𝑥̃𝑙 = [𝑥𝑖𝑗𝑙 ]
𝑛𝑥𝑚 ⋮
𝑥̃𝑛1𝑙
𝑥̃11
𝑥̃21
𝑥̃𝑙 = [𝑥𝑖𝑗 ]𝑛𝑥𝑚 =
⋮
𝑥̃𝑛1
𝑥̃12
𝑥̃22
⋮
𝑥̃𝑛2
𝑥̃12𝑙
𝑥̃22𝑙
⋮
𝑥̃𝑛2𝑙
… 𝑥̃1𝑚𝑙
… 𝑥̃2𝑚𝑙
⋮
⋮
… 𝑥̃𝑛𝑚𝑙
… 𝑥̃1𝑚
… 𝑥̃2𝑚
;
⋮
⋮
… 𝑥̃𝑛𝑚
(16)
(17)
𝑞
𝑞
𝑥̃𝑖𝑗= ⊕𝑙=1 𝑥̃𝑖𝑗𝑙 ,
(18)
𝑙=1
Where 𝑥̃𝑖𝑗𝑙 denotes the fuzzy performance value of ith (ⅈn ∈ 1,2,...𝑛 } ) alternative with respect
to jth criterion ( 𝑗 ∈{1,2,…. , 𝑚 } ) and 𝑙th (𝑙 ∈ 1,2,… 𝑞 } ) decision-maker, and ⅈ𝑗 x shows
the average fuzzy performance value of ⅈth alternative with respect to 𝑗th criterion.
Step 2: Taking the fuzzy weight of each criterion from each decision maker and finding the
average fuzzy weights:
17
𝑤
̃ 𝑙 = [𝑤
̃𝑗𝑙 ]𝑙×𝑚 ;
(19)
𝑤
̃ = [𝑤
̃𝑗 ]𝑙×𝑚 ;
(20)
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q;
𝑞
𝑤
̃𝑗 = ⊕𝑙=1 𝑤
̃𝑗𝑙 ,
(21)
𝑙 = 1;
where 𝑤
̃𝑗𝑙 denotes the fuzzy weight of 𝑗th criterion ( j ∈{1,2,…,𝑚 } ) with respect to 𝑙 th ( 𝑙
∈ {1,2, …., 𝑞 } ) decision-maker, and 𝑤
̃𝑗 shows the average fuzzy weight of 𝑗th criterion.
Step 3: Determining the fuzzy normalized decision matrix according to the type of each
criterion determined using these equations:
̃ = [𝑛̃𝑖𝑗 ]
𝑁
𝑛×𝑚
;
(22)
𝑥̃𝑖𝑗 , max 𝔇 (𝑥̃𝑖𝑗 ) 𝑖𝑓 𝑗 ∈ 𝐵𝑒𝑛𝑒𝑓𝑖𝑡
ⅈ
𝑛̃𝑖𝑗 = {
1 − (𝑥̃𝑖𝑗 /𝑚𝑎𝑥 𝔇(𝑥̃𝑖𝑗 )) 𝑖𝑓 𝑗 ∈ 𝐶𝑜𝑠𝑡
(23)
ⅈ
where B and C represent the sets of benefit and cost criteria, respectively, and 𝑛̃𝑖𝑗 denotes the
normalized fuzzy performance values.
Step 4: Calculate fuzzy weighted normalized decision matrix. The fuzzy weighted normalized
performance values follows :𝑟̃𝑖𝑗 are calculated as follows:
𝑅̃ = [𝑟̃𝑖𝑗 ]𝑛×𝑚 ;
𝑟̃𝑖𝑗 = 𝑤
̃𝑗 ⊗ 𝑛̃𝑖𝑗 ,
(24)
Step 5: Determine fuzzy negative-ideal solution as follows:
̃ = [𝑛𝑠
𝑁𝑆
̃𝑗 ]1×𝑚 ;
(25)
𝑛𝑠
̃𝑗 = mⅈn 𝑟̃,
𝑖𝑗̇
(26)
ⅈ
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Fuzzy Codas Method
”
Step 6: Calculate the fuzzy weighted Euclidean (𝐸𝐷𝑖) and fuzzy weighted Hamming (𝐻𝐷𝑖)
distances of alternatives from the fuzzy negative-ideal solution, shown as follows:
𝑚
𝐸𝐷𝑖 = ∑
𝑗=1
ⅆ𝐸 (𝑟̃𝑖𝑗 , 𝑛𝑠
̃𝑗 ) ;
(27)
𝑑𝐻 (𝑟̃𝑖𝑗 , 𝑛𝑠
̃𝑗 ).;
(28)
𝑚
𝐻𝐷𝑖 = ∑
𝑗=1
Step 7: Determine relative assessment matrix (𝑅𝐴), shown as follows:
𝑅𝐴 = [𝑝𝑖𝑘 ] 𝑚𝑥𝑚 ;
𝑝𝑖𝑘 = (𝐸𝐷𝑖 − 𝐸𝐷𝑘) + (𝑡(𝐸𝐷𝑖 − 𝐸𝐷𝑘) × (𝐻𝐷𝑖 − 𝐻𝐷𝑘)),
(29)
( 30)
where 𝑘 ∈ {1,2, … 𝑛} 𝑎𝑛𝑑 𝑡 is a threshold function that is defined as follows;
𝑡(𝑥) = {
1 𝑖𝑓 |𝑥| ≥ 𝜃
0 𝑖𝑓 |𝑥| < 𝜃
(31)
Step 8: Calculate the assessment score (𝐴𝑆𝑖 ) of each alternative, shown as follows:
𝐴𝑆𝑖= ∑𝑛𝑘=1 𝑝𝑖𝑘 .
(32)
Step 9: According to the decreasing values of assessment scores, we can rank the alternatives.
The alternative with the highest assessment score is the most desirable alternative.
4. APPLICATION
4.1 Decision Makers
The opinions of 3 different decision makers were used in the evaluation of the crypto money
market. The 1st decision maker is the Cryptocurrency Expert. He has given both investments
and advice in the cryptocurrency market for many years. The 2nd decision maker is the
Cryptocurrency Expert. He is an expert who closely follows the state of the market and is a
consultancy company on this subject. The 3rd decision maker is the Cryptocurrency Investor.
What to pay attention to when investing, which alternative is important in the crypto market,
unlike the experts, the opinions of the investors were taken. all decision makers affected the
solution to the same degree is 0.33.
19
Ekonomi, Politika & Finans Araştırmaları Dergisi, 202X, X(X): XXX-XXX
Journal of Research in Economics, Politics & Finance, 202X, X(X): XXX-XXX
Table 2: Decision Makers
DM No.
1.
2.
3.
Position
Cryptocurrency Expert
Cryptocurrency Expert
Cryptocurrency Investor
Rate
%33
%33
%33
4.2 Application of Fuzzy EDAS
Table 3: IVN Decision Matrix Scales
CL
VL
L
BA
A
AA
H
VH
CH
IVN decision matrix scales
0.15
0.25
0.25
0.35
0.35
0.45
0.45
0.55
0.55
0.6
0.55
0.65
0.65
0.75
0.75
0.85
0.85
0.95
TL
TU
IL
Certainly Low
Very Low
Low
Below Average
Average
Above Average
High
Very High
Commonly High
0.15
0.35
0.45
0.55
0.65
0.55
0.45
0.25
0.15
0.25
0.45
0.55
0.65
0.75
0.65
0.55
0.35
0.25
IU
0.85
0.75
0.65
0.55
0.45
0.45
0.35
0.25
0.15
FL
0.95
0.85
0.75
0.65
0.55
0.55
0.45
0.35
0.25
FU
Table 4: The Rating of Each Criterion and Alternatives by Each Decision Maker
Cost
Benefit
Benefit
Benefit
Benefit
Cost
Benefit
Benefit
Criteria
C1
C2
C3
C4
C5
C6
C7
C8
0.33
DM1
CHI
CLI
CLI
HI
CHI
CHI
CHI
CHI
0.33
DM2
CHI
HI
HI
AHI
CHI
CHI
HI
HI
0.33
DM3
CHI
AI
AI
CHI
HI
HI
CHI
HI
Step 1: The rates given by the decision makers to the criteria are given in the table. Based on
the answers given by the decision makers, the weighting criteria abbreviations in the solution of
the Fuzzy Edas method are given in the table.
Table 5: The rates of criteria
C1
DM
weıght
0.33
0.33
0.33
WEIGHTS
C1.2
TL
TU
IL
IU
FL
FU
0.85
0.85
0.85
0.53
0.53
0.53
0.15
0.85
1
1
1
0.00
0.00
0.00
0.00
1.00
0.69
0.69
0.69
0.68
0.68
0.68
0.31
0.69
0.8
0.8
0.8
0.59
0.59
0.59
0.20
0.80
0.08
0.08
0.08
0.97
0.97
0.97
0.92
0.08
0.24
0.24
0.24
0.91
0.91
0.91
0.76
0.24
0.33
0.33
0.33
20
TL
TU
IL
IU
FL
FU
0.08
0.63
0.52
0.97
0.72
0.78
0.55
0.45
0.24
0.74
0.58
0.91
0.64
0.75
0.44
0.56
0.69
0.46
0.13
0.68
0.82
0.96
0.53
0.47
0.8
0.58
0.24
0.59
0.75
0.91
0.40
0.60
0.85
0.35
0.46
0.53
0.87
0.82
0.38
0.62
1
0.46
0.52
0.00
0.82
0.78
0.00
1.00
“Evaluation
of Investment Alternatives in Crypto Currency Market by Fuzzy Edas and
Fuzzy Codas Method
”
0.33
0.33
0.33
0.33
0.33
0.33
0.33
0.33
0.33
TL
0.08
0.63
0.52
0.97
0.72
0.78
0.55
0.45
TU
0.24
0.74
0.58
0.91
0.64
0.75
0.44
0.56
C1.3
IL
IU
0.69 0.8
0.46 0.58
0.13 0.24
0.68 0.59
0.82 0.75
0.96 0.91
0.53 0.40
0.47 0.60
FL
0.85
0.35
0.46
0.53
0.87
0.82
0.38
0.62
FU
1
0.46
0.52
0.00
0.82
0.78
0.00
1.00
0.33
0.33
0.33
TL
0.85
0.85
0.63
0.53
0.53
0.72
TU
1
1
0.74
0.00
0.00
0.64
C5
IL
IU
0.69
0.8
0.69
0.8
0.46 0.58
0.68 0.59
0.68 0.59
0.82 0.75
FL
0.08
0.08
0.35
0.97
0.97
0.87
FU
0.24
0.24
0.46
0.91
0.91
0.82
0.21
0.79
0.00
1.00
0.38
0.62
0.82
0.18
0.68
0.32
0.26
0.74
TL
0.85
0.63
0.85
0.53
0.72
0.53
TU
1
0.74
1
0.00
0.64
0.00
C1.7
IL
IU
0.69 0.8
0.46 0.58
0.69 0.8
0.68 0.59
0.82 0.75
0.68 0.59
FL
0.08
0.35
0.08
0.97
0.87
0.97
FU
0.24
0.46
0.24
0.91
0.82
0.91
0.21
0.79
0.00
1.00
0.38
0.62
0.82
0.18
0.68
0.32
0.26
0.74
TL
0.63
0.85
0.85
0.72
0.53
0.53
0.21
0.79
0.33
0.33
0.33
0.33
0.33
0.33
C1.4
IL
IU
0.46 0.58
0.69 0.8
0.69 0.8
0.82 0.75
0.68 0.59
0.68 0.59
0.38 0.26
0.62 0.74
TU
0.74
1
1
0.64
0.00
0.00
0.00
1.00
FL
0.35
0.08
0.08
0.87
0.97
0.97
0.82
0.18
TL
0.85
0.85
0.63
0.53
0.53
0.72
TU
1
1
0.74
0.00
0.00
0.64
C6
IL
IU
0.69 0.8
0.69 0.8
0.46 0.58
0.68 0.59
0.68 0.59
0.82 0.75
FL
0.08
0.08
0.35
0.97
0.97
0.87
FU
0.24
0.24
0.46
0.91
0.91
0.82
0.21
0.79
0.00
1.00
0.38
0.62
0.82
0.18
0.68
0.32
0.26
0.74
TL
0.85
0.63
0.63
0.53
0.72
0.72
TU
1
0.74
0.74
0.00
0.64
0.64
C1.8
IL
IU
0.69 0.8
0.46 0.58
0.46 0.58
0.68 0.59
0.82 0.75
0.82 0.75
FL
0.08
0.35
0.35
0.97
0.87
0.87
FU
0.24
0.46
0.46
0.91
0.82
0.82
0.28
0.72
0.00
1.00
0.45
0.55
0.73
0.27
0.61
0.39
0.33
0.67
Step 2: The mathematical values against the abbreviations given with the criteria according to
the formula in Equation (3) and (4) are given in the table according to the answer of each
decision maker. An average value was obtained as a result of multiplying the rate of influence
of the decision makers on the results of the survey (0.33) and the score for which they gave the
criteria.

For the Criteria 1
DM1
C1
H
C2
CH
C3
CH
C4
A
C5
CH
C6
CH
C7
CH
C8
AA
DM2
H
H
H
H
CH
A
CH
CH
DM3
A
AA
AA
A
H
A
CH
H
21
FU
0.46
0.24
0.24
0.82
0.91
0.91
0.68
0.32
Ekonomi, Politika & Finans Araştırmaları Dergisi, 202X, X(X): XXX-XXX
Journal of Research in Economics, Politics & Finance, 202X, X(X): XXX-XXX
Table 6: Weight Criteria Matrix
0.33
0.33
0.33
0.33
0.33
0.33
TL
0.65
0.65
0.55
0.71
0.71
0.77
TU
0.75
0.75
0.6
0.63
0.63
0.74
C1
IL
IU
0.45 0.55
0.45 0.55
0.65 0.75
0.82 0.77
0.82 0.77
0.71 0.63
FL
0.35
0.35
0.45
0.87
0.87
0.82
FU
0.45
0.45
0.55
0.82
0.82
0.77
0.38
0.62
0.30
0.70
0.48
0.52
0.62
0.38
0.52
0.48
0.37
0.63
0.33
0.33
0.33
TL
0.85
0.65
0.55
0.53
0.71
0.77
TU
0.95
0.75
0.65
0.37
0.63
0.71
C3
IL
IU
0.15 0.25
0.45 0.55
0.55 0.65
0.95 0.91
0.82 0.77
0.77 0.71
FL
0.15
0.35
0.45
0.95
0.87
0.82
FU
0.25
0.45
0.55
0.91
0.82
0.77
0.29
0.51
0.17
0.33
0.60
0.43
0.67
1.00
0.57
0.71
0.49
1.00
TL
0.85
0.65
0.55
0.53
0.71
0.77
TU
0.95
0.75
0.65
0.37
0.63
0.71
C2
IL
IU
0.15 0.25
0.45 0.55
0.55 0.65
0.95 0.91
0.82 0.77
0.77 0.71
FL
0.15
0.35
0.45
0.95
0.87
0.82
FU
0.25
0.45
0.55
0.91
0.82
0.77
0.29
0.71
0.17
0.83
0.60
0.40
0.67
0.33
0.57
0.43
0.33
0.33
0.33
TL
0.55
0.65
0.55
0.77
0.71
0.77
TU
0.6
0.75
0.6
0.74
0.63
0.74
C4
IL
IU
0.65 0.75
0.45 0.55
0.65 0.75
0.71 0.63
0.82 0.77
0.71 0.63
FL
0.45
0.35
0.45
0.82
0.87
0.82
FU
0.55
0.45
0.55
0.77
0.82
0.77
0.42
0.58
0.35
0.65
0.41
0.59
0.58
0.42
0.48
0.52
C5
0.33
0.33
0.33
0.33
0.33
0.33
0.49
0.51
0.31
0.69
C6
TL
0.85
0.85
0.65
0.53
0.53
0.71
TU
0.95
0.95
0.75
0.37
0.37
0.63
IL
0.15
0.15
0.45
0.95
0.95
0.82
IU
0.25
0.25
0.55
0.91
0.91
0.77
FL
0.15
0.15
0.35
0.95
0.95
0.87
FU
0.25
0.25
0.45
0.91
0.91
0.82
0.20
0.80
0.09
0.91
0.74
0.26
0.64
0.36
0.78
0.22
0.68
0.32
TL
0.85
0.85
0.85
0.53
0.53
0.53
TU
0.95
0.95
0.95
0.37
0.37
0.37
C7
IL
IU
0.15 0.25
0.15 0.25
0.15 0.25
0.95 0.91
0.95 0.91
0.95 0.91
FL
0.15
0.15
0.15
0.95
0.95
0.95
FU
0.25
0.25
0.25
0.91
0.91
0.91
0.15
0.85
0.05
0.95
0.85
0.15
0.85
0.15
0.75
0.25
0.75
0.25
0.33
0.33
0.33
22
0.33
0.33
0.33
TL
0.85
0.55
0.55
0.53
0.77
0.77
TU
0.95
0.6
0.6
0.37
0.74
0.74
IL
0.15
0.65
0.65
0.95
0.71
0.71
IU
0.25
0.75
0.75
0.91
0.63
0.63
FL
0.15
0.45
0.45
0.95
0.82
0.82
FU
0.25
0.55
0.55
0.91
0.77
0.77
0.32
0.68
0.20
0.80
0.47
0.53
0.36
0.64
0.64
0.36
0.54
0.46
TL
0.55
0.85
0.65
0.77
0.53
0.71
TU
0.65
0.95
0.75
0.71
0.37
0.63
C8
IL
IU
0.55 0.65
0.15 0.25
0.45 0.55
0.77 0.71
0.95 0.91
0.82 0.77
FL
0.45
0.15
0.35
0.82
0.95
0.87
FU
0.55
0.25
0.45
0.77
0.91
0.82
0.29
0.71
0.17
0.83
0.60
0.40
0.67
0.33
0.57
0.43
0.49
0.51
“Evaluation
of Investment Alternatives in Crypto Currency Market by Fuzzy Edas and
Fuzzy Codas Method
”
Step 3: According to the formula in Equations 6 and 7, the values corresponding to the response
of each decision maker for each alternative were collected. As a result of this calculation, for
each criterion, weighting was done by multiplying each alternative value with the decision
maker ratio.
Table 7: Benefit of Alternatives
Benefit
Bitcoin(BTC)
Ethereum(ETH)
Ripple(XRP)
Litecoin(LTC)
Cardano(ADA)
Biancecoin(BNB)
Polkadot(DOT)
Dogecoin(DOGE)
Chainlink(LINK)
Tether(USDT)
TL
0.71
0.58
0.58
0.55
0.71
0.80
0.64
0.68
0.62
0.38
TU
0.83
0.67
0.65
0.63
0.83
0.91
0.78
0.81
0.70
0.30
C1.1
IL
0.40
0.55
0.59
0.58
0.45
0.26
0.40
0.48
0.52
0.48
IU
0.51
0.66
0.69
0.68
0.56
0.36
0.51
0.59
0.63
0.37
FL
0.33
0.42
0.42
0.45
0.33
0.22
0.45
0.36
0.38
0.62
FU
0.43
0.52
0.52
0.55
0.43
0.32
0.56
0.46
0.48
0.52
Mean Value
0.63
0.71
0.47
0.56
0.40
0.48
0.30
k(a)
Step 4: Each criterion determined in the survey has been evaluated separately as it can provide
benefit or harm to the Crypto currency market. As a result of the increase of each criterion, if it
will provide a benefit in the Crypto currency market, then the transactions are made in the table
below.
Table 8: Mean Weight of Alternatives
C1.1
TL
Bitcoin(BTC)
TU
IL
IU
FL
FU
0.00
0.21
0.47
0.56
-0.15
0.03
Ethereum(ETH)
-0.13
0.04
0.55
0.66
-0.06
0.12
Ripple(XRP)
Litecoin(LTC)
-0.13
0.03
0.59
0.69
-0.06
0.12
-0.17
0.00
0.58
0.68
-0.03
0.15
Cardano(ADA)
0.00
0.20
0.47
0.56
-0.15
0.03
Biancecoin(BNB)
0.09
0.29
0.47
0.56
-0.26
-0.07
Polkadot(DOT)
-0.07
0.16
0.47
0.56
-0.03
0.16
Dogecoin(DOGE)
-0.03
0.18
0.48
0.59
-0.12
0.07
Chainlink(LINK)
-0.10
0.08
0.52
0.63
-0.09
0.09
Tether(USDT)
-0.33
-0.33
0.48
0.56
0.14
0.12
23
Ekonomi, Politika & Finans Araştırmaları Dergisi, 202X, X(X): XXX-XXX
Journal of Research in Economics, Politics & Finance, 202X, X(X): XXX-XXX
Table 9: Positive Distance from Average of Alternatives
C1.1
PDA NDA
TL
TU
IL
IU
FL
FU
Bitcoin(BTC)
-0.01
0.69
1.58
1.86
-0.51
0.10
Ethereum(ETH)
-0.43
0.15
1.85
2.19
-0.21
0.40
Ripple(XRP)
Litecoin(LTC)
-0.43
0.10
1.97
2.31
-0.21
0.40
-0.55
0.02
1.95
2.28
-0.10
0.50
Cardano(ADA)
-0.01
0.67
1.58
1.86
-0.51
0.10
Biancecoin(BNB)
0.29
0.96
1.58
1.86
-0.86
-0.25
Polkadot(DOT)
-0.23
0.53
1.58
1.86
-0.09
0.54
Dogecoin(DOGE)
-0.09
0.60
1.62
1.98
-0.39
0.22
Chainlink(LINK)
-0.32
0.26
1.75
2.09
-0.32
0.29
Tether(USDT)
-1.09
-1.10
1.59
1.86
0.47
0.41
Weight (Kriter)
0.85
1.00
0.69
0.80
0.08
0.24
IL
IU
FL
FU
Table 10: Cost of Alternatives
C1.1
COST
TL
TU
Bitcoin(BTC)
-0.01
0.69
1.08
1.48
-0.04
0.02
Ethereum(ETH)
-0.37
0.15
1.27
1.75
-0.02
0.09
Ripple(XRP)
Litecoin(LTC)
-0.37
0.10
1.35
1.84
-0.02
0.09
-0.47
0.02
1.34
1.82
-0.01
0.12
Cardano(ADA)
-0.01
0.67
1.08
1.49
-0.04
0.02
Biancecoin(BNB)
0.24
0.96
1.08
1.48
-0.07
-0.06
Polkadot(DOT)
-0.19
0.53
1.08
1.48
-0.01
0.13
Dogecoin(DOGE)
-0.08
0.60
1.11
1.58
-0.03
0.05
Chainlink(LINK)
-0.27
0.26
1.20
1.67
-0.03
0.07
Tether(USDT)
-0.93
-1.10
1.09
1.48
0.04
0.10
24
“Evaluation
of Investment Alternatives in Crypto Currency Market by Fuzzy Edas and
Fuzzy Codas Method
”
Table 11: Mean Weight of Alternatives
C1.1
TL
Bitcoin(BTC)
Ethereum(ETH)
Ripple(XRP)
Litecoin(LTC)
Cardano(ADA)
Biancecoin(BNB)
Polkadot(DOT)
Dogecoin(DOGE)
Chainlink(LINK)
Tether(USDT)
Ortalama
TU
IL
IU
FL
FU
0.71
0.58
0.58
0.55
0.71
0.80
0.64
0.7
0.62
0.384278
0.83
0.67
0.65
0.63
0.83
0.91
0.78
0.8
0.70
0.296019
0.40
0.55
0.59
0.58
0.45
0.26
0.40
0.5
0.52
0.47663
0.51
0.66
0.69
0.68
0.56
0.36
0.51
0.6
0.63
0.373629
0.33
0.42
0.42
0.45
0.33
0.22
0.45
0.4
0.38
0.617792
0.43
0.52
0.52
0.55
0.43
0.32
0.56
0.5
0.48
0.517845
0.63
0.71
0.47
0.56
0.40
0.48
0.30
k(a)
Table 12: PDA and NDA Results of Alternatives
C1.1
TL
Bitcoin(BTC)
Ethereum(ETH)
Ripple(XRP)
Litecoin(LTC)
Cardano(ADA)
Biancecoin(BNB)
Polkadot(DOT)
Dogecoin(DOGE)
Chainlink(LINK)
Tether(USDT)
TU
0.00
0.13
0.13
0.17
0.00
-0.09
0.07
0.03
0.10
0.33
IL
-0.21
-0.04
-0.03
0.00
-0.20
-0.29
-0.16
-0.18
-0.08
0.33
IU
0.47
0.55
0.59
0.58
0.47
0.47
0.47
0.48
0.52
0.48
FL
0.56
0.66
0.69
0.68
0.56
0.56
0.56
0.59
0.63
0.56
FU
0.15
0.06
0.06
0.03
0.15
0.26
0.03
0.12
0.09
-0.14
-0.03
-0.12
-0.12
-0.15
-0.03
0.07
-0.16
-0.07
-0.09
-0.12
C1.1
TL
TU
IL
IU
FL
FU
0.01
0.43
0.43
0.55
0.01
-0.29
0.23
0.09
0.32
1.09
-0.69
-0.15
-0.10
-0.02
-0.67
-0.96
-0.53
-0.60
-0.26
1.10
1.58
1.85
1.97
1.95
1.58
1.58
1.58
1.62
1.75
1.59
1.86
2.19
2.31
2.28
1.86
1.86
1.86
1.98
2.09
1.86
0.51
0.21
0.21
0.10
0.51
0.86
0.09
0.39
0.32
-0.47
-0.10
-0.40
-0.40
-0.50
-0.10
0.25
-0.54
-0.22
-0.29
-0.41
0.85
1.00
0.69
0.80
0.08
0.24
25
Ekonomi, Politika & Finans Araştırmaları Dergisi, 202X, X(X): XXX-XXX
Journal of Research in Economics, Politics & Finance, 202X, X(X): XXX-XXX
Table 13: Average Values of Alternatives
C1.1
TL
Bitcoin(BTC)
Ethereum(ETH)
Ripple(XRP)
Litecoin(LTC)
Cardano(ADA)
Biancecoin(BNB)
Polkadot(DOT)
Dogecoin(DOGE)
Chainlink(LINK)
Tether(USDT)
TU
0.01
0.37
0.37
0.47
0.01
-0.24
0.19
0.08
0.27
0.93
IL
-0.69
-0.15
-0.10
-0.02
-0.67
-0.96
-0.53
-0.60
-0.26
1.10
IU
1.08
1.27
1.35
1.34
1.08
1.08
1.08
1.11
1.20
1.09
FL
1.48
1.75
1.84
1.82
1.49
1.48
1.48
1.58
1.67
1.48
FU
0.04
0.02
0.02
0.01
0.04
0.07
0.01
0.03
0.03
-0.04
-0.02
-0.09
-0.09
-0.12
-0.02
0.06
-0.13
-0.05
-0.07
-0.10
Step 5: According to the determined benefit and cost values , two separate tables are created as
positive and negative .This table thanks to the benefit criteria to be calculated as plus and the
cost saving values as minus. Based on the positive and negative values calculated in these two
tables, the average values are calculated.
Table 16: Results of NSP
NSP
TL
Bitcoin(BTC)
Ethereum(ETH)
TU
IL
IU
FL
FU
-0.159
1.130
38.689
65.527
1.513
-7.459
0.957
0.645
36.640
63.066
6.685
-7.692
Ripple(XRP)
Litecoin(LTC)
1.076
0.831
37.031
63.566
6.590
-9.089
-1.928
-5.039
42.518
67.810
8.851
-6.489
Cardano(ADA)
-1.595
-3.968
39.357
64.778
8.353
-6.083
Biancecoin(BNB)
Polkadot(DOT)
0.965
1.553
36.503
62.851
5.631
-6.923
-4.099
0.657
42.513
71.268
9.872
-14.832
Dogecoin(DOGE)
Chainlink(LINK)
0.556
-1.344
36.616
62.173
7.219
-10.382
0.041
0.492
36.592
62.987
6.534
-9.102
Tether(USDT)
1.358
2.958
34.999
60.905
8.474
-4.831
Step 6: For all alternatives, cost and benefit values are calculated according to each criterion
and indicated in the table.
26
“Evaluation
of Investment Alternatives in Crypto Currency Market by Fuzzy Edas and
Fuzzy Codas Method
”
¶
Table 17: Results of NSN
NSN
TL
TU
0.997
1.019
1.022
0.961
0.968
1.019
0.990
1.011
1.001
1.027
IL
IU
1.023
1.013
1.017
0.899
0.920
1.031
1.087
0.973
1.010
1.059
0.223
0.265
0.257
0.147
0.210
0.267
0.212
0.265
0.265
0.297
Table 18: Final Values of Alternatives
TL
TU
Bitcoin(BTC)
0.50
0.50
Ethereum(ETH)
0.50
0.50
Ripple(XRP)
0.50
0.50
Litecoin(LTC)
0.44
0.19
Cardano(ADA)
0.46
0.30
Biancecoin(BNB)
0.50
0.49
Polkadot(DOT)
0.47
0.51
Dogecoin(DOGE)
0.50
0.47
Chainlink(LINK)
0.50
0.50
Tether(USDT)
0.50
0.44
FL
-0.315
-0.266
-0.276
-0.361
-0.300
-0.262
-0.284
-0.248
-0.264
-0.223
IL
IU
4.32
4.85
4.75
3.11
4.13
4.88
4.50
4.85
4.86
5.21
FU
1.030
1.134
1.132
1.178
1.168
1.113
0.826
1.145
1.131
1.170
FL
-10.33
-8.39
-8.77
-12.25
-9.73
-8.22
-10.11
-7.71
-8.33
-6.78
0.850
0.846
0.818
0.870
0.878
0.861
0.690
0.792
0.817
0.903
FU
0.78
3.79
3.73
5.21
4.88
3.13
4.08
4.13
3.70
4.96
-3.17
-3.25
-3.72
-2.82
-2.67
-2.98
-5.12
-4.11
-3.72
-2.18
43.17
13.65
17.97
2.76
4.21
15.17
30.69
14.33
16.55
0.57
Step 7: According to all calculated values, the result of each alternative according to each
criterion was calculated. Based on this calculation, it was concluded that the best alternative in
the crypto money market is Bitcoin.
1
2
3
4
5
6
7
8
9
10
RANK
43.171 Bitcoin(BTC)
30.690
Polkadot(DOT)
17.970
16.553
Ripple(XRP)
Chainlink(LINK)
15.172
Biancecoin(BNB)
14.325
Dogecoin(DOGE)
13.654
Ethereum(ETH)
Cardano(ADA)
4.209
2.761
Litecoin(LTC)
0.574
Tether(USDT)
27
Ekonomi, Politika & Finans Araştırmaları Dergisi, 202X, X(X): XXX-XXX
Journal of Research in Economics, Politics & Finance, 202X, X(X): XXX-XXX
4.3
Application of Fuzzy Codas
Table 19: Linguistic variables&fuzzy numbers
Usage
Linguistic variable
For weighting criteria
Very low (VL)
(0,0, 0.1, 0.2)
Low (L)
(0.1, 0.2, 0.2, 0.3)
Medium low (ML)
(0.2, 0.3, 0.4, 0.5)
Medium (M)
(0.4, 0.5, 0.5, 0.6)
Medium high (MH)
(0.5, 0.6, 0.7, 0.8)
High (H)
(0.7, 0.8, 0.8, 0.9)
Very high (VH)
(0.8, 0.9, 1,1)
Very poor (VP)
(0, 0, 1, 2)
Poor (P)
(1, 2, 2, 3)
Medium poor (MP)
For rating alternatives
Trapezoidal fuzzy number
Fair (F)
(2, 3, 4, 5)
(4, 5, 5, 6)
Medium good (MG)
(5, 6, 7, 8)
Good (G)
(7, 8, 8, 9)
Very good (VG)
(8, 9, 10, 10)
Step 1: Each criterion and each alternative was filled by the decision makers according to the
linguistic variables. The answers given by each decision maker are given in Table 19. The
average fuzzy matrix is calculated according to (16) and (18) equations specified in the fuzzy
codas method in Table 19.
Table 20 : The rating of each criterion and alternatives by each decision maker
DM1
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
Criteria
1
OI
O
O
O
I
I
I
O
O
Z
Criteria
2
CI
I
I
O
OI
I
O
I
I
CI
Criteria
3
CI
I
OI
O
I
CI
O
OZ
O
I
Criteria
4
O
I
I
Z
I
CI
OI
OI
OI
I
28
Criteria
5
CI
CI
CI
I
I
OI
I
CI
I
CZ
Criteria
6
CI
I
OI
I
I
OI
OI
CI
O
CZ
Criteria
7
CI
CI
I
I
CI
I
CI
CI
CI
CI
Criteria
8
OI
OZ
O
O
OZ
OI
O
OI
OI
O
“Evaluation
of Investment Alternatives in Crypto Currency Market by Fuzzy Edas and
Fuzzy Codas Method
”
Table 21 : Average fuzzy decision matrix
m1
Criteria
1
m2
0.8
0.9
1
1
DM2
0.8
0.9
1
1
DM3
0.8
0.9
1
1
0.8
0.9
1
1
m2
0.8
0.8
0.9
DM2
0
0
0.1
0.2
DM3
0.5
0.6
0.7
0.8
0.4
0.466667
0.533333
0.633333
m2
m4
0.5
0.6
0.7
0.8
DM2
DM3
0
0.4
0
0.5
0.1
0.5
0.2
0.6
0.3
0.366667
0.433333
0.533333
m2
m3
m4
DM1
0.7
0.8
0.8
0.9
DM2
0.7
0.8
0.8
0.9
DM3
0.8
0.9
1
1
0.733333
0.833333
0.866667
0.933333
m2
m3
m4
DM1
0.4
0.5
DM2
0.8
0.9
1
1
DM3
0.7
0.8
0.8
0.9
0.633333
0.733333
0.766667
0.833333
m1
Criteria
6
m3
DM1
m1
Criteria
5
m4
0.7
m1
Criteria
4
m3
DM1
m1
Criteria
3
m4
DM1
m1
Criteria
2
m3
m2
0.5
m3
0.6
m4
DM1
0.5
0.6
0.7
0.8
DM2
0.8
0.9
1
1
DM3
0.7
0.8
0.8
0.9
0.666667
0.766667
0.833333
0.9
29
Ekonomi, Politika & Finans Araştırmaları Dergisi, 202X, X(X): XXX-XXX
Journal of Research in Economics, Politics & Finance, 202X, X(X): XXX-XXX
m1
Criteria7
m2
m4
DM1
0.8
0.9
1
1
DM2
0.8
0.9
1
1
DM3
0.8
0.9
1
1
0.8
0.9
1
1
m1
Criteria
8
m3
m2
m3
m4
DM1
0.7
0.8
0.8
0.9
DM2
0.8
0.9
1
1
DM3
0.7
0.8
0.8
0.9
0.733333
0.833333
0.866667
0.933333
Table 22 : Importance of the criteria and average fuzzy weights
A
1
K1
0.16,0.16,
0.20,0.15
K2
0.00,0.00,
0.00,0.00
K3
0.00,0.00,
0.00,0.00
K4
0.14,0.14,
0.17,0.13
K5
0.63,0.73,
0.77,0.83
K6
0.46,0.56,
0.59,0.68
K7
0.80,0.90,
1.00,1.00
K8
0.73,0.83,
0.87,0.93
A
2
0.15,0.15,
0.19,0.14
0.04,0.04,
0.09,0.07
0.01,0.01,
0.03,0.02
0.21,0.2,0.
23,0.19
0.47,0.56,
0.61,0.69
0.53,0.62,
0.64,0.75
0.7,0.8,0.9
,0.93
0.59,0.69,
0.74,0.82
A
3
A
4
A
5
A
6
0.04,0.04,
0.09,0.07
0.2,0.21,0.
23,0.23
0.13,0.14,
0.18,0.16
-0.04,0.03,0.01,0.01
0.1,0.11,0.
11,0.1
0.06,0.06,
0.08,0.08
0.11,0.12,
0.16,0.17
0.04,0.04,
0.04,0.37
0.06,0.07,
0.09,0.1
0.15,0.14,
0.12,0.08
0.33,0.33,
0.36,0.33
0,0,0,0
0.55,0.65,
0.69,0.78
0.33,0.33,
0.36,0.33
0.47,0.56,
0.6,0.71
0.56,0.66,
0.72,0.78
0.52,0.62,
0.72,0.81
0.44,0.54,
0.55,0.66
0.53,0.62,
0.63,0.75
0.44,0.53,
0.63,0.74
0.73,0.83,
0.87,0.93
0.47,0.57,
0.61,0.72
0.7,0.8,0.9
,0.93
0.63,0.73,
0.76,0.86
0.61,0.72,
0.75,0.82
0.6,0.7,0.7
,0.8
0.39,0.5,0.
56,0.65
0.66,0.76,
0.83,0.89
0.58,0.68,
0.75,0.84
0.67,0.77,
0.83,0.9
0.58,0.68,
0.69,0.78
0.1,0.1,0.1
5,0.14
-0.02,0.02,0.02,0.006
0.14,0.15,
0.18,0.19
0.56,0.66,
0.72,0.78
A
8
0.2,0.2,0.2
9,0.23
0.21,0.21,
0.22,0.17
0.05,0.05,
0.1,0.08
-0.16,0.15,0.14,-0.08
-0.16,0.15,0.14,-0.08
0.11,0.11,
0.12,0.11
0.61,0.71,
0.72,0.8
0.64,0.74,
0.77,0.87
0.7,0.8,0.9
,0.93
0.64,0.74,
0.78,0.87
A
9
0.04,0.05,
0.14,0.1
0.02,0.02,
0.04,0.02
0.07,0.07,
0.11,0.12
0.47,0.56,
0.61,0.72
0.56,0.66,
0.71,0.78
0.73,0.83,
0.87,0.93
0.73,0.87,
0.88,0.94
A
1
0
ns
j
0.07,0.08,
0.11,0.14
-0.07,0.07,0.77,-0.05
-0.07,0.07,0.07,-0.05
-0.04,0.04,0.03,-0.02
-0.04,0.04,0.04,-0.02
0.45,0.52,
0.6,0.64
0.44,0.51,
0.58,0.66
0.8,0.9,1,1
0.68,0.78,
0.78,0.86
0.44,0.52,
0.56,0.64
0.44,0.52,
0.58,0.66
0.6,0.7,0.7
,0.8
0.39,0.5,0.
56,0.66
A
7
-0.16,0.15,0.15,-0.08
-0.28,0.26,0.36,-0.24
-0.08,0.07,0.08,-0.08
-0.08,0.06,0.12,-0.11
-0.11,0.1,-0.14,0.12
-0.24,0.22,0.29,-0.21
-0.28,0.26,0.36,-0.24
30
“Evaluation
of Investment Alternatives in Crypto Currency Market by Fuzzy Edas and
Fuzzy Codas Method
”
Steps 3 to 6: According to the results of the equations in step 1, the fuzzy normalized
decision matrix is determined with (19) and (20). Then the mean fuzzy weight value of each
criterion (Table 22) and the equations of the steps (19) and (20) are used to calculate the fuzzy
weighted normalized decision matrix.
According to equations (21) and (22), fuzzy ideal negative solution is calculated. The
fuzzy ideal negative solution is calculated with the fuzzy weight-normalized decision matrix and
the fuzzy weighted Hamming and Euclidean distances for each alternative with equations (24)
and (25). These processes are shown in Table 23.
Table 23: Relative assessment matrix, appraisal scores and rank of the alternatives
RA
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A1
4.89
4.89
4.89
4.88
4.89
4.63
4.74
4.67
4.58
4.64
A2
3.16
2.98
3.17
3.35
2.98
2.72
2.82
2.76
2.66
2.73
A3
4.74
4.74
4.75
4.75
4.74
4.48
4.58
4.51
4.42
4.48
A4
4.81
4.81
4.82
4.81
4.81
4.55
4.66
4.59
4.50
4.56
A5
-0.12
-0.66
-0.11
0.43
-0.66
-0.92
-0.82
-0.89
-0.98
-0.92
A6
-5.99
-7.22
-5.98
-4.76
-7.22
-7.48
-7.37
-7.44
-7.53
-7.47
A7
-2.38
-3.20
-2.38
-1.57
-3.20
-3.46
-3.35
-3.42
-3.52
-3.45
A8
2.60
2.33
2.61
2.87
2.33
2.07
2.17
2.11
2.01
2.07
A9
-0.96
-1.64
-0.95
-0.28
-1.64
-1.90
-1.79
-1.86
-1.95
-1.89
Asi
RANK
47.70
1.00
29.33
4.00
46.19
3.00
46.92
2.00
-5.65
6.00
68.46
10.00
29.93
8.00
23.17
5.00
14.87
7.00
A10
-4.74
-5.83
-4.73
-3.65
-5.83
-6.09
-5.99
-6.05
-6.15
-6.08
-55.15
9.00
Steps 7 to 9: Compute the evaluation matrix RA, which is shown in table 22 and
compared with equation (29) and (31). Threshold value q = 0.02 was determined. According to
equation (32), the evaluation matrix and the score of the alternatives are calculated. According
to the result, the RANK table is made by sorting the alternatives according to the decreasing
score as shown in Table 23. According to the results of Table 23, the 1st alternative (Bitcoin) is
more preferable than the other alternatives.
5. KAYNAKÇA
(https://www.bitlo.com/, tarih yok; https://www.bitlo.com/, 2022)
(Karaoğlan S. T., 2018)
(https://www.coindesk.com/price/bitcoin/, https://www.coindesk.com/price/bitcoin/, 2022)
(https://coinmarketcap.com/, 2022)
(Yilmaz, DETERMINING THE FACTORS AFFECTING INVESTORS’DECISION MAKING
PROCESS IN CRYPTOCURRENCY INVESTMENTS. PressAcademia Procedia, 8(1), 5-8., 2018)
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Ekonomi, Politika & Finans Araştırmaları Dergisi, 202X, X(X): XXX-XXX
Journal of Research in Economics, Politics & Finance, 202X, X(X): XXX-XXX
6. RESULT&DISCUSSION
EVALUATION OF INVESTMENT ALTERNATİVES IN CRYPTO
CURRENCY MARKET BY FUZZY EDAS AND FUZZY CODAS METHOD
Contrary to the money produced and distributed based on the central system,
cryptocurrencies using infrastructure technologies that facilitate the reliability, portability, and
transfer of money with a broad-based validator system have become widespread rapidly. The
market values of cryptocurrencies, which are currently under discussion as commodities or
currencies, continue to increase and attract the attention of more investors in the world.
Cryptocurrency also continues to divide financial experts. Some see it as the money of the
future, while others continue to claim that this development is a bubble.
There is a team behind the cryptocurrencies, which continue to attract more and more
investors, with increasing market circulation amounts day by day, which makes algorithm
software and works to facilitate and improve its reliability and use. Of course, crypto money has
not been strengthened by the presence of the team behind it. Electronic payments made from
person to person and multiple confirmations of these payments, as well as the fact that the
personal data of the payer and the person receiving the payment, are not revealed, have
increased the prevalence of cryptocurrencies used.
Another reality is that the increase in the number of those multiplying their returns with
the rise of some cryptocurrencies in a few years has attracted the attention of other investors and
continues to attract them. Cryptocurrencies, which have a large transaction volume around the
world, continue to be attractive to Turkish investors. That is why Turkey is the country with the
fifth most transactions in the world. It is also worth remembering that great returns require high
levels of risk.
Based on the results of the application, the crypto money market Fuzzy Edas and Fuzzy
codas methods were evaluated. Annual Average Return (K1), Total Market Value (K2),
Security Infrastructure (K3), Transaction Speed (K4), Supporting Organizations (K5), Change
from the Highest Value (K6), Number of Stock Exchanges Traded (K7), Price (K8), Bitcoin
(BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC), Cardano (ADA), Biancecoin (BNB),
Polkadot (DOT), Dogecoin (DOGE), Chainlink (LINK) 10 different alternatives, including
Tether (USDT), are discussed. Based on the opinions of three different decision makers, two of
whom are experts and one investor, in the crypto money market making the evaluation, each
criterion and each alternative are scored mathematically according to Fuzzy methods. The data
obtained according to this scoring were solved by Fuzzy Edas and Fuzzy Codas method.
According to the results obtained according to the solution of both methods, the best alternative
in the crypto money market has been determined as Bitcoin.
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