CC BY
0
UDC 332; DOI 10.18551/rjoas.2022-12.02
STUDY OF CRYPTOCURRENCY'S MARKET EFFICIENCY POST-COVID-19
ANNOUNCEMENT
Putri Ida Ayu Widya Laksmi*, Candradewi Made Reina
Faculty of Economics and Business, University of Udayana, Indonesia *E-mail: ia.widyalaksmiputri@gmail.com
ABSTRACT
The announcement of the coronavirus disease (COVID-19) as a pandemic by the World Health Organization (WHO) on March 11, 2020, caused revenue decline in many companies due to the implementation of lockdown policies in various countries, which limited people's activities and mobility. The COVID-19 pandemic also caused panic that made many investors put their shares on the market, resulting in company stock prices dropping in various sectors. Therefore, many investors are interested in cryptocurrency, which has experienced a price surge since the announcement of COVID-19. This study tests the weak-form of the Efficient Market Hypothesis in 32 cryptocurrency markets categorized as the large and medium market capitalization within two years after the announcement of the COVID-19 pandemic. This study is quantitative research performed to test return predictability using run-test analysis techniques. The results of this study show that 20 out of 32 cryptocurrencies used in this study are efficient, including Terra, Cardano, and Dogecoin, which are categorized as large market caps. We also found inefficiencies in the cryptocurrencies within the large market caps, such as Bitcoin, Ethereum, Binance Coin, and XRP.
KEY WORDS
Efficient market hypothesis, weak-form, cryptocurrency, run test.
COVID-19, declared a pandemic by the World Health Organization (WHO) on March 11, 2020, led to the implementation of lockdown policies in many countries. The practice of a lockdown policy that limited community activities and mobility then hampered the company's operations and decreased its income. The decline in the company's income gave investors a negative perception of the company's ability to earn profits in the future. Investors' negative perceptions also arose since there was no certainty in the period of the implementation of the lockdown policy, resulting in many investors selling their stocks and leading to a decline in stock prices in numerous companies. The stock price plunges then made investors try to find an alternative investment that appeared to be promising, such as cryptocurrency, which had experienced a surge in price since the announcement of COVID-19.
Cryptocurrency is a digital asset obtained through a series of complex mathematical calculations, secured using cryptographic techniques, based on blockchain technology, and without any intermediary parties in transactions (Furneaux, 2018, pp. 3-8). Although originally designed as a payment method, many people presently consider cryptocurrency an alternative investment. Many studies also state that cryptocurrency can be used for portfolio diversification [Bouri, et al. (2017); Brauneis & Mestel (2019); Kajtazi & Moro (2019); Inci & Lagasse (2019); Ma, et al. (2020), hedging (Matkovskyy, et al., 2021), as well as a safe haven asset (Bouri, et al., 2017); Mariana et al. (2021); Melki & Nefzi (2022). In addition, the high level of volatility of cryptocurrency also attracts investors' attention to gain profits. The large number of participants in a market will make the market efficient. Therefore, many researchers are testing the cryptocurrency market's efficiency to prove whether the cryptocurrency market allows participants to make a profit.
Sarkodie, et al. (2022) state that the market prices of Bitcoin, Ethereum, Bitcoin Cash, and Litecoin increased as the number of confirmed cases and deaths due to COVID-19 increased. Based on data from the Coinmarketcap.com website, the price of Bitcoin reached USD 29,001.72 at the end of 2020 and USD 46,306.45 at the end of 2021. The data also
reports that the price of Ethereum reached USD 737.80 at the end of 2020 and USD 3,682.63 at the end of 2021. In addition, The Economic Times website stated that Bitcoin had experienced a rise in the price of up to 500% since COVID-19 to May 2021, as well as other cryptocurrencies such as Ethereum, Ripple, Dogecoin, and many more. Hou, et al. (2021) state that investors and the public's psychological state significantly influence the price of Bitcoin in the long run due to cashless transactions, lower risk of virus transmission, decentralization, and ease of payments.
The rapid change in cryptocurrency prices happens because there are many market participants involved. Based on a survey from the University of Chicago, around 13% of Americans bought or sold cryptocurrencies between July 2020 to July 2021 (lacurci, 2021). Based on the Buku Kajian Stabilitas Keuangan published by Bank Indonesia, the number of cryptocurrency investors in Indonesia is estimated to have reached 6.5 million in June 2021 (Bank Indonesia, 2021, pp. 34-35) and to reach 11 million in December 2021 based on data from the Ministry of Trade of the Republic Indonesia (Azka, 2022). In addition, based on the Crypto Market Sizing report published by Crypto.com, there was an increase in the global population of crypto ownership by 178% in 2021, with nearly 300 million users (Crypto, 2022).
The Efficient Market Hypothesis by Fama (1970) states that stock prices are always in a state of equilibrium because all information will be quickly absorbed in prices so that investors or traders do not have the opportunity to obtain abnormal returns from information circulating. Fama (1970) divided market efficiency into three forms based on the type of information circulating: weak, semi-strong, and strong. The test for weak-form market efficiency is often performed on Bitcoin or Ethereum, but only some studies test the weak-form market efficiency of the ever-growing Altcoins.
Previous studies on efficiency in the cryptocurrency market still give mixed results, namely that the cryptocurrency market is a weak-form of inefficient [Hu, et al. (2019); Palamalai, et al. (2021); Dowling (2022)], that the cryptocurrency market is weak-form efficient [Apopo & Phiri (2021); Aslan & Sensoy (2020)], and that the cryptocurrency market will become more efficient in the future [Wei (2018); Caporale, et al. (2018); Al-Yahyaee, et al. (2020); Nan & Kaizoji (2019); Tran & Leirvik (2020); Mnif, et al. (2020)]. In addition, studies are often performed on Bitcoin [Urquhart (2016); Vidal-Tomás et al. (2019); Jiang, et al. (2018); Zargar & Kumar (2019)] and are still limited to Altcoins. Therefore, testing the weak-form market efficiency of Bitcoin and Altcoin is deemed necessary to be carried out again, considering the mixed results of previous studies and the number of Altcoins that continues to grow. Therefore, this study examines whether the cryptocurrency market is weak-form efficient.
This study aims to test the weak-form market efficiency of the cryptocurrency market due to the phenomenon of significant price changes and an increase in the number of market participants in the cryptocurrency market after the emergence of the COVID-19 pandemic, which raises the question of whether investors can obtain abnormal returns from this significant price increase. Thus, it was deemed necessary to conduct tests on other types of cryptocurrency since previous studies still gave varying results and mainly focused on Bitcoin.
The data used in this study is cryptocurrency historical price (daily price) obtained from the Yahoo! Finance website. This study used 32 cryptocurrency markets categorized as large and medium market capitalization as of April 29, 2022. The data sample consisted of 730 daily observations for each cryptocurrency from March 11, 2020, to March 11, 2022. Table 1 is descriptive statistics for log return (rt) time series data, defined below:
METHODS OF RESEARCH
Table 1 - Descriptive statistics of the log return time series for 32 cryptocurrencies between March 11,
2020, to March 11, 2022
Cryptocurrency Name Code N Mean SD Max Min
Bitcoin BTC 730 0.0022 0.0418 0.1718 -0.4647
Ethereum ETH 730 0.0035 0.0543 0.2307 -0.5507
Binance Coin BNB 730 0.0043 0.0629 0.5292 -0.5431
XRP XRP 730 0.0018 0.0703 0.4448 -0.5505
Terra LUNA 730 0.0083 0.0842 0.6414 -0.4876
Cardano ADA 730 0.0041 0.0634 0.2794 -0.5036
Dogecoin DOGE 730 0.0054 0.1007 1.5163 -0.5151
Polygon MATIC 730 0.0059 0.0849 0.4578 -0.7140
Litecoin LTC 730 0.0011 0.0574 0.2484 -0.4491
TRON TRX 730 0.0019 0.0591 0.3342 -0.5231
Cosmos ATOM 730 0.0030 0.0762 0.2809 -0.5902
Bitcoin Cash BCH 730 0.0001 0.0612 0.4208 -0.5613
Stellar XLM 730 0.0018 0.0648 0.5592 -0.4100
Monero XMR 730 0.0016 0.0575 0.3450 -0.5342
Ethereum Classic ETC 730 0.0019 0.0654 0.3525 -0.5064
Filecoin FIL 730 0.0021 0.0981 0.7692 -0.4729
Hedera HBAR 730 0.0020 0.0706 0.4585 -0.5882
VeChain VET 730 0.0032 0.0752 0.2989 -0.6172
Theta Network THETA 730 0.0046 0.0765 0.2576 -0.6039
Tezos XTZ 730 0.0002 0.0704 0.3059 -0.6073
Fantom FTM 730 0.0073 0.1044 0.4150 -0.7059
EOS EOS 730 -0.0006 0.0658 0.4396 -0.5042
Zcash ZEC 730 0.0018 0.0669 0.2528 -0.5394
IOTA MIOTA 730 0.0018 0.0703 0.3192 -0.5436
Waves WAVES 730 0.0040 0.0729 0.4478 -0.4871
Bitcoin SV BSV 730 -0.0012 0.0630 0.4552 -0.5603
Stacks STX 730 0.0031 0.0816 0.7994 -0.7124
Kusama KSM 730 0.0055 0.0830 0.4642 -0.5387
Neo NEO 730 0.0010 0.0648 0.2531 -0.4656
Harmony ONE 730 0.0048 0.0976 0.6437 -0.7285
Zilliqa ZIL 730 0.0027 0.0755 0.3237 -0.5659
Dash DASH 730 0.0004 0.0651 0.4513 -0.4655
If a market is weak-form efficient, market participantss cannot predict future prices because prices move randomly (random walk). To analyze whether the cryptocurrency market is weak-form efficient, we perform the run test of Wald & Wolfowitz (1940), which could capture the randomness of the data. We use the run test (Wald & Wolfowitz , 1940) since it is a non-parametric statistical test, where the analyzed data does not have to meet certain assumptions or parameters, such as the assumption of normality of the data. The null hypothesis of the run test is that the order in the sample data is randomly generated.
RESULTS AND DISCUSSION
Table 2 is the result of the run test, where the null hypothesis is rejected when the p-value is less than the significance level of 0.05 or 5%, and vice versa. Based on the results of the run test, random walk patterns were not found in as many as 12 types of cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), XRP (XRP), Bitcoin Cash (BCH), Ethereum Classic (ETC), Hedera (HBAR), VeChain (VET), EOS (EOS, IOTA (MIOTA), Bitcoin SV (BSV), and Zilliqa (ZIL).
No random walk pattern was found, indicating that those 12 cryptocurrencies are weak-form inefficient, making their price predictable. Therefore market participants have the opportunity to earn abnormal returns. In addition, predictable prices also enable market participants to use technical analysis to develop strategies in order to gain abnormal returns. Jalali & Heidari (2020) states that the price of Bitcoin can be predicted accurately and investors can acquire maximum profit by choosing the right time frame.
Jogiyanto (2017) states that there are several causes for a market to be inefficient, namely because there are a small number of market participants, the price of obtaining information is high, unequal access among market participants, the information can be predicted by partly of market participants, as well as the investors involved are naive and unsophisticated investors who have limited ability to interpret the information received and consequently make wrong decisions.
Referring to Jogiyanto (2017), the weak form inefficiency in the cryptocurrency above might occur due to unequal access to information, and the market consists of many naive investors that often make decisions based on sentiment or follow other investors. Those refer to the result of Lewis (2018), which states that changes in cryptocurrency prices occur due to encouragement from sellers and buyers who make decisions based on factors such as sentiment, technical success or failure, support from community leaders, and many more; result of Haryanto et al., (2020) which states that herding increases in periods when there is an increase or decrease in the price of Bitcoin; result of Gurdgiev & O'Loughlin (2020) which states that investor sentiment can predict the direction of cryptocurrency prices and indicates a direct effect on herding and anchoring bias; as well as a result of Rubbaniy et al. (2021) which states that there is evidence of herding in investing in the cryptocurrency market during COVID-19. In addition, Zargar & Kumar (2019) states that inefficiency in cryptocurrency occurs due to endogenous factors from developing and immature markets, as well as the absence of fundamental traders in the cryptocurrency market with the intention that prices are determined purely based on speculation. Those statements explain the inefficiency in the cryptocurrency market despite the increasing number of market participants.
Table 2 - Run test results of the log return time series for 32 cryptocurrencies between March 11,
2020, to March 11, 2022
Cryptocurrency Name Code N N1 N2 Runs Z-statistics P-value
Bitcoin BTC 730 392 338 399 2.6067 0.0091
Ethereum ETH 730 401 329 393 2.2853 0.0223
Binance Coin BNB 730 400 330 404 3.0919 0.0020
XRP XRP 730 379 351 400 2.5621 0.0104
Terra LUNA 730 378 352 387 1.5919 0.1114
Cardano ADA 730 380 350 389 1.7523 0.0797
Dogecoin DOGE 730 359 366 381 1.3034 0.1924
Polygon MATIC 730 377 353 389 1.7348 0.0828
Litecoin LTC 730 385 345 382 1.2702 0.2040
TRON TRX 730 398 331 382 1.4637 0.1433
Cosmos ATOM 730 374 356 387 1.5730 0.1157
Bitcoin Cash BCH 730 380 350 415 3.6815 0.0002
Stellar XLM 730 374 356 390 1.7953 0.0726
Monero XMR 730 407 323 385 1.7891 0.0736
Ethereum Classic ETC 730 381 349 399 2.5012 0.0124
Filecoin FIL 730 349 381 376 0.7942 0.4271
Hedera HBAR 730 384 345 405 3.0139 0.0026
VeChain VET 730 386 344 397 2.3937 0.0167
Theta Network THETA 730 384 346 387 1.6333 0.1024
Tezos XTZ 730 377 353 373 0.5483 0.5835
Fantom FTM 730 382 348 385 1.4693 0.1418
EOS EOS 730 370 360 399 2.4500 0.0143
Zcash ZEC 730 391 339 367 0.2123 0.8318
IOTA MIOTA 730 383 347 403 2.8133 0.0049
Waves WAVES 730 403 327 347 -1.1266 0.2599
Bitcoin SV BSV 730 350 380 395 2.1975 0.0280
Stacks STX 730 377 353 373 0.5483 0.5835
Kusama KSM 730 369 361 379 0.9663 0.3339
Neo NEO 730 382 348 371 0.4300 0.6672
Harmony ONE 730 369 360 387 1.5980 0.1100
Zilliqa ZIL 730 381 349 395 2.2043 0.0275
Dash DASH 730 388 342 387 1.6696 0.0950
Meanwhile, based on the results of the run test, random walk patterns were found in as many as 20 types of cryptocurrencies consisting of three types of cryptocurrencies categorized as large market capitalizations, namely Terra, Cardano, and Dogecoin, as well as 17 other types of cryptocurrencies categorized as medium market capitalization, namely Terra Classic (LUNA), Cardano (ADA), Dogecoin (DOGE), Polygon (MATIC), Litecoin (LTC), TRON (TRX), Cosmos (ATOM), Stellar (XLM), Monero (XMR), Filecoin ( FIL), Theta Network (THETA), Tezos (XTZ), Fantom (FTM), Zcash (ZEC), Waves (WAVES), Stacks (STX), Kusama (KSM), Neo (NEO), Harmony (ONE), and Dash (DASH). The finding of a random walk pattern indicates that the cryptocurrency market is weak-form efficient. Therefore, market participants cannot use technical analysis to obtain abnormal returns.
CONCLUSION
Most types of the cryptocurrency market are weak-form efficient, such as Terra, Cardano, Dogecoin, Polygon, Litecoin, TRON, Cosmos, Stellar, Monero, Filecoin, Theta Network, Tezos, Fantom, Zacash, Waves, Stack, Kusama, Neo, Harmony, and Dash. Therefore, market participants do not have the opportunity to gain abnormal returns from changes in cryptocurrency prices. In addition, market participants cannot use technical analysis to develop strategies to obtain abnormal returns.
The types of cryptocurrencies that do not follow a random walk and hence are inefficient in weak forms are Bitcoin, Ethereum, Binance Coin, XRP, Bitcoin Cash, Ethereum Classic, Hedera, VeChain, EOS, IOTA, Bitcoin SV, and Zilliqa. No random walk pattern is found, making the price of related cryptocurrencies predictable, and thus market participants have the opportunity to gain abnormal returns. In addition, predictable prices also allow market participants to use technical analysis to develop strategies in order to obtain abnormal returns.
Individuals and institutions considering investing in cryptocurrency should develop strategies based on deep technical analysis of the cryptocurrency market of various time frames, particularly those considered weak-form inefficient. Investors should also consider global events affecting cryptocurrencies' price movement. Regulators should consider all cryptocurrency aspects thoroughly in making cryptocurrency trading regulations to boost investors' confidence in the cryptocurrency market.
Future studies may involve cryptocurrencies categorized as small market capitalization, conducting tests on market efficiency in semi-strong forms or strong forms on the cryptocurrency market, or comparing the market efficiency level of the period during the implementation of the lockdown policy and the period after it was revoked.
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