Time-varying nexus between investor sentiment and cryptocurrency market: New insights from a wavelet coherence framework
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Al-Nemer, Hashem Abdullah; Hkiri, Besma; Khan, Muhammed Asif Article Time-varying nexus between investor sentiment and cryptocurrency market: New insights from a wavelet coherence framework Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Al-Nemer, Hashem Abdullah; Hkiri, Besma; Khan, Muhammed Asif (2021) : Timevarying nexus between investor sentiment and cryptocurrency market: New insights from a wavelet coherence framework, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 14, Iss. 6, pp. 1-19, https://doi.org/10.3390/jrfm14060275 This Version is available at: https://hdl.handle.net/10419/239690 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/
Journal of Risk and Financial Management Article Time-Varying Nexus between Investor Sentiment and Cryptocurrency Market: New Insights from a Wavelet Coherence Framework Hashem A. AlNemer 1, Besma Hkiri 1,* and Muhammed Asif Khan 2 Citation: AlNemer, Hashem A., Besma Hkiri, and Muhammed Asif Khan. 2021. Time-Varying Nexus between Investor Sentiment and Cryptocurrency Market: New Insights from a Wavelet Coherence Framework. Journal of Risk and Financial Management 14: 275. https://doi.org/10.3390/ jrfm14060275 Academic Editors: Alessandra Canepa and Philip Hans Franses Received: 11 April 2021 Accepted: 10 June 2021 Published: 18 June 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Finance and Economics, College of Business, University of Jeddah, Asfan Road 2749, Jeddah 23890, Saudi Arabia; [email protected] or dr[email protected] 2Department of Management Sciences, University of Swabi, Khyber Pakhtunkhwa 23430, Pakistan; [email protected] *Correspondence: [email protected] or [email protected] Abstract: This study attempts to investigate the nexus between investor sentiment and cryptocurrencies prices. Our empirical investigation merges bivariate and multivariate wavelet tools to examine the investor sentiment nexus to inter-cryptocurrencies prices. The study outcomes show that the Sentix Investor Confidence index provides significant information in explaining long-term changes in Bitcoin and Litecoin prices. Moreover, the findings generated from the multiple wavelet coherence illustrate the simultaneous contribution of cryptocurrencies and the Sentix Investor Confidence index in explaining the Bitcoin index movement across frequencies and over horizons, especially during bubble burst periods. The study also suggests a time-dependent relationship of Bitcoin prices with alternative cryptocurrencies and the Sentix Investor Confidence index, mostly pronounced during the Bitcoin bubble. We discuss our results using GSV-based investor sentiment. Our findings remain robust and confirm the strong predictive power of investor sentiment in cryptocurrencies price movements over time and across scales. Keywords: cryptocurrency; investor sentiment; wavelet coherence; Google-search volume 1. Introduction and Literature Background Cryptocurrency (CC, hereafter) denotes a form of digital asset that utilizes blockchain technology to allow a secure transaction. It can be considered a fascinating idea which demonstrates tremendous growth within no time. We recognize different cryptocurrencies, each with their own set of regulations. However, some differences among the cryptocurrencies may involve, for example, the selection of the consensus mechanism as well as cryptographic hashing algorithms (Härdle et al. 2020). For instance, Litecoin is considered an alternative to Bitcoin. Additionally, like Bitcoin, Dogecoin was firstly introduced as “a joke currency” and was driven by an online community base. Nevertheless, Ethereum is different from Bitcoin because it has the potential to run a computer program on a network. The emergence of CC not only allows companies to raise money by trading without listing into the stock exchange or merging in venture capitalists, but also broadens the investor’s investment span beyond conventional securities. Primarily Nakamoto (2009) introduced CC in 2009, where soon after it gained attention among investors due to its cost efficiency, government-free policy and peer-to-peer online payment facility, which triggered a sharp upsurge in prices. Initially, both researchers and practitioners considered CC to be virtual currency, whereas later, the empirical literature deemed it to be a speculative asset due to the high turbulence in prices (Corbet et al. 2019). Lansky (2016) considered CC to be a form of digital currencies based on coding principles. According to this author, it is worth indicating that three combined characteristics can be attributed to CC; we recognize anonymity, independency of central authority and protection from double-spending assault. Likewise, Yuneline (2019) qualitatively investigated the nature of CC based on four characteristics J. Risk Financial Manag. 2021,14, 275. https://doi.org/10.3390/jrfm14060275 https://www.mdpi.com/journal/jrfm
J. Risk Financial Manag. 2021,14, 275 2 of 19 of legal, money, Sharia, and economic perspectives. Their study found that from a money perspective, CC shows currency features, unlike the case for legal, Sharia and economic perspectives. Moreover, Yermack (2013) claimed that CC is more like speculative investment rather than virtual currency due to its higher market capitalizations than economic transactions. Supportively, Bouoiyour et al. (2015) examined the dynamic nature of CC prices and found a sharp upsurge in Bitcoin price from USD 130 to USD 1000 and back down to USD 200 at the end of 2013. Moreover, on 16 December 2016, the Bitcoin price was USD 778, which jumped to USD 19,650 in the following 12 months, showing 2394% market growth (Rognone et al. 2020. The coinmarketcap website provides meaningful information about more than 9800 cryptocurrencies. This website gives an overview of the current prices as well as daily transactions of these cryptocurrencies. During the first term of 2021, Bitcoin becomes an extremely volatile cryptocurrency with a road record of “boom and bust” cycles. The Bitcoin price escalated but it has been moving in a descending path (Bitcoin was worth over USD 60,000 in February as well as April 2021, then prices fell to USD 45,000 and it continues to drop dramatically). Considering the speculative nature of CC, it is categorized as a speculative investment asset and referred to as a financial asset of an “extremely speculative nature”. In the same vein, studies of Corbet et al. (2018) revealed visible date-stamp bubble behavior in certain periods, particularly during 2017, and found that there is a short time fundamental effect which will disseminate very soon. Moreover, numerous studies underlined the effect of various risk factors on CC returns and price volatility. For instance, Nguyen et al. (2019) attempted to examine the asymmetry effect of monetary policy (tightening and easy) regimes. The findings exhibit that the asymmetry effect is significant for Chinese monetary policy regimes, while it remains insignificant for the U.S. Using the quantile-on-quantile model, Aysan et al. (2019) studied the effect of global geopolitical risk on Bitcoin price volatility and returns. The authors illustrated a positive relationship for returns and volatility at higher quantile. Consistently, Demir et al. (2018) investigated the economic policy uncertainty effect on Bitcoin returns predictability. The results of their study reveal positive and significant effects at both higher and lower quantiles. Moreover, Fang et al. (2019) examined the correlations among different financial assets and global policy uncertainty. Global economic policy uncertainty demonstrates negative correlations with Bitcoin-bond, while the correlation remains positive for both Bitcoin-commodities and Bitcoin-equities. Recently, empirical studies evidenced the substantial influence of investor sentiment on the CC market. For instance, using intra-day data, Chen et al. (2020) examined the effect of fear sentiment on Bitcoin returns and the trading volume. They observed that fear sentiment has a negative effect on Bitcoin returns and a positive effect on the trading volume. Likewise, using 15 min intraday data, Rognone et al. (2020) illustrated a positive relationship between investor sentiment and Bitcoin. Kraajeveld and De Smed (2020) evidenced higher predictability of twitter-based investor sentiment for various crypto coins. Gurdgiev and OLoughlin (2020) depicted that public sentiment can reveal the direct effect of anchoring and herding biases. Eom et al. (2019) studied the statistical properties of investors sentiment and Bitcoin returns and volatility. The outcomes of their study demonstrated a higher explanatory power for predicting Bitcoin’s volatility. Similarly, Baig et al. (2019) examined investor sentiment and Bitcoin price clustering and reported a strong positive relationship between them. Using the social media sentiment index Karalevicius et al. (2018) analyzed investor sentiment nexuses with the Bitcoin price. They revealed a strong short-term relationship by concluding that investors overreact toward short-term news. Furthermore, using Twitter messages, Li et al. (2019) investigated the investor sentiment effect on CC price fluctuation for which they reported that social sentiment signals as a powerful indicator that can better predict CC price movements. Drobetz et al. (2019) examined the influence of crypto-related sentiment on initial coins offering, and they suggested that negative sentiment predicts negative returns in the short run, whereas Chu et al. (2019) examined the news-based
J. Risk Financial Manag. 2021,14, 275 3 of 19 sentiment and events effect on CC market efficiency. The authors revealed that news-based sentiment may not be a significant predictor to predict CC market efficiency. Investigating the potential change in the behavior of investors over the pre- and post- Bitcoin bubble, Guègan and Renault (2020) showed a positive and statistically significant relationship between investor sentiment and Bitcoin during the Bitcoin bubble, unlike in the post-Bitcoin period (2018 to December 2019). This indicates that the CC market is more exposed to investor sentiment during high-sentiment investment horizons. Supportively, Lopez-Cabarcos et al. (2019) stated that Bitcoin investor sentiment influences the Bitcoin volatility across different periods (speculative and stable periods). Likewise, Bariviera (2017) examined information efficiency in the Bitcoin market, where he found long memory price behavior at both high and low prices. In this perspective, it is interesting to investigate whether investor sentiment can influence the cryptocurrency dynamics over time and across scale bands. Our paper contributes to the literature by answering the following questions: how does investor sentiment affect CC co-movements, and how do investor sentiment impacts on CC vary over time and across frequencies? The main objectives of this study can be categorized into two significant aspects. In the theoretical view, we attempt to participate in the actual academic debate in the behavioral finance and finance technology (Fintech) inherent to the role of investor sentiment in clearing up the asset pricing, particularly in the CC market dynamic. We also pay attention to the investor sentiment proxies that can directly reflect investor emotions (optimism and/or pessimism, bearish/bullish). These emotions ebb and flow between panic and voracity. In addition, +the exponential increase in CC prices hides behind an important number of retail investors’ speculations. Thus, analyzing the relationship between CC and investor sentiment can provide helpful insights into the global market perception and awareness. As well, from a methodological point of view, we propose new insights into the relationship between investor sentiment and the CC market by using different variants of bivariate and multivariate wavelet methods. By doing that, we provide operational implications for decisions in portfolio management and investment allocation between CC when taking into consideration the investor sentiment effect. Thus, we hypothesized that investor sentiment has coherence with CC prices at multiple time and frequencies. The wavelet approach inspects the behavior of time series jointly, in frequency and time spaces. When we create feedback on the wavelet analysis origin, we see that the root of this tool instigates from a need to localize a signal in the time–frequency domain. As well, the wavelet analysis is brought forth because the Fourier transform falls short when analyzing non-stationary signals. Used by a growing number of researchers, wavelet analysis has demonstrated its ability to explicitly expose and follow the time-scale varying outlines of time series. The wavelet approach stretches to isolate slow and persistent movements. As well, wavelet analysis is proving to be a powerful tool for analyzing market operators’ behavior, especially self-similar behavior, over different time scales. Indeed, some participants have an investment horizon of several minutes or hours to several days (e.g., when considering short-term movements of stock markets) while others may have an investment horizon of several weeks or months (e.g., with medium-term movements of the stock markets) or an investment horizon of several years (e.g., with long-term movements of the stock markets). Generally, for the wavelet approach, we distinguish between two main parts of wavelet transformation, which are the discrete wavelet transform (hereafter, DWT) and the continuous wavelet transform (hereafter, CWT). The main contributions of the paper are the following. First, in the investor sentiment and CC literature, most of the studies focused on either association or their future explanatory ability of returns, volatility, and efficiency. However, there are few events that affect both sentiment and the CC price co-movement. We deem that our study fills the gap in the related literature. This study is a challenge to bridge the findings linked to investors’ sentiment and the new tendency in research which view CC as new assets for investment. As a direct investment, CC carries on moving at the calling of speculators
J. Risk Financial Manag. 2021,14, 275 4 of 19 and retail investors. Second, we investigate the leader–follower relationships between CC prices and investor sentiment simultaneously over time and across multiple frequencies. We opt for two wavelet tools: the bivariate and multivariate wavelet coherence analysis (Ng and Chan 2012) . Moreover, the leader–follower relationship will also be examined for the time-varying purpose to know about the long- and short-term relationships. Methodologically, we follow three main steps. First, we refer to the bivariate wavelet method to analyze the co-variation between the CC prices. This tool is specifically able to assess the link between two variables across scales and over the sample period. It allows us to identify the cross-correlation between these variables at different investment horizons. In the second step, we resort to the multivariate wavelet consisting of two approaches: partial and the multiple wavelet coherence (PWC and MWC, respectively) to understand the co-movements of three variables (jointly, CC and investor sentiment proxy) over time scales and frequencies bands. The main idea in the tri-variate wavelet analysis is to provide meaningful insights into the influence of multiple independent variables on a dependent one. To the best of our knowledge, no prior studies have employed the multivariate wavelet to investigate the interactions between investor sentiment and CC. The use of MWC and PWC permits us, contrary to the bivariate wavelet analysis, to include a third variable as a conditioning factor. The inclusion of the third factor supposes a mutual interaction between the two first variables on the target one. As supported by many recent studies (e.g., Kristoufek 2013;Stavroyiannis et al. 2019) the relationship between CC prices and investor sentiment is not linear. This makes wavelet analysis a relevant approach to study the lead-lag interactions between these variables across various time horizons. From this study, interesting conclusions emerged. First, in the bivariate case, the co-movement across CC indexes is revealed across frequencies and over time and is especially localized at short and long horizons. The Bitcoin prices show a long-term positive relationship with the Sentix Investor Confidence (hereafter, SIC) index. Our findings supported the significant information effect of the investor sentiment index in explaining changes in Bitcoin prices. Second, in the multivariate case, when looking to the PWC plots by which we study the co-movement between Bitcoin and the SIC index after cancelling out the effect of the remaining CC indexes, our findings exhibit interesting areas of coherency between these indexes. The crucial finding is that medium term connections with the investor sentiment strengthen during CC prices bubbles. As well, the MWC used for analyzing the joined effect of the cryptocurrency investor sentiment index and the remaining CC indexes on the Bitcoin index demonstrate, generally, that when combining these variables, they disclose short- and long-term effects on Bitcoin prices, indicating that altcoins and investor sentiment together provide some explanations of the variation in Bitcoin prices, particularly during Bitcoin bubble. The rest of the paper is structured as follows. Section 2concisely exposes the data and methodology design. Findings and their implications are conveyed in Section 3. Section 4 presents the robustness check, while Section 5gives some concluding remarks. 2. Materials and Methods 2.1. The Wavelet Coherence Analysis According to Torrence and Compo (1998), the wavelet coherence (hereafter, WC) of two signals, which are denoted as x={xn} and y={yn} , gives the localized correlation coefficient between these two signals over time and across frequencies. Evidently, the WC can faithfully detect the co-movements between signals over different investment horizons. Accurately, for each time series, the WC is calculated as the scared absolute value
J. Risk Financial Manag. 2021,14, 275 5 of 19 of the smoothed cross-wavelet spectra, which is standardized by the multiplication of the smoothed individual WPS of each signal: R2(u,s)= Ss−1Wxy(u,s) 2 Ss−1|Wx(u,s)|2Ss−1 Wy(u,s) 2(1) S gives the smoothing parameter. The R2(u,s) is like the correlation coefficient, which meets the ensuing dissimilarity 0 ≤R2(u,s)≤ 1. When the squared wavelet coherence value is close to zero, this indicates that the correlation between the two-time signals is weak. As well, a correlation coefficient value close to the unit shows the existence of a strong correlation. Furthermore, from the WC plots, the leader–follower relationship between two time-series can be detected through the arrow directions. Based on this, the two signals can be in an in-phase (the arrows are directed to the right direction), or anti-phase (the arrows take a left direction) relationship. 2.2. The Multivariate Wavelet The multivariate wavelet analysis consists of two main approaches: the partial wavelet coherence (hereafter, PWC) and the multiple wavelet coherence (hereafter, MWC). The PWC is analogous to a simple correlation. The principle of this tool consists of identifying the wavelet coherence between two signals y and x1 after eliminating the power of a third signal x2 . According to Mihanovi´c et al. (2009), similarly to the partial correlation squared, the PWC squared is expressed as follows: RP2(y,x1,x2)=|R(y,x1)−R(y,x2)·R(y,x1)|2 [1−R(y,x2)]2[1−R(x2,x1)]2(2) The interconnections between variables are determined according to the level of PWC, which is ranged from 0 to 1. A high level of PWC squared signifies that x1 has a significant influence on the y signal at the frequency-time band. The MWC is analogous to multiple correlations (Ng and Chan 2012). This correlation allows us to examine the ensuing WC of multiple independent variables on a dependent one. More precisely, the MWC computes the proportion of the wavelet power of the dependent time series y that is explainable by the two independents x1and x2at a given timefrequency space. The MWC can be revealed by the succeeding equation: RM2(y,x2,x1)=R2(y,x1)+R2(y,x2)−2Re[R(y,x1)∗R(y,x2)∗R(x2,x1] 1−R2(x2,x1)(3) 2.3. The Phase and Antiphase Relationship As well, the phase difference provides ideas about the lateness of the oscillations between two variables as a function of frequency. The interpretation of the phase difference refers to the arrows’ direction. Arrows pointed to the right (left) indicate that variables are in phase (out of phase), or in another word, they reveal a positive (negative) relationship between these variables. If the arrows move to the right and up (down), the first variable x is the driver (follower). By contrast, if the arrows move to the left and up (down), the variable x is lagging (leading). 2.4. Data Description The present study covers two different sets of monthly sample data from 15 January 2013 to 15 November 2020 (except for Ethereum, which started from 2015). The first set consists of CC prices available on www.coinmarketcap.com, which is a free site carrying over 9800 different CC categories based on exchange markets and market capitalization. The list of CCs is based on market capitalization that is adjusted in real time based on their market capitalization. We retrieved the list and respective data dated on 15 November 2020.
J. Risk Financial Manag. 2021,14, 275 6 of 19 The second set of data includes the Sentix Investor Confidence for CC, which is retrieved from DataStream. The sample consists of 84 monthly observations for the Sentix index and CC prices, respectively. Note that the SIC index is based on a monthly online survey. Generally, 1600 financial analysts and institutional investors contribute to this survey by enunciating their own opinion regarding the current and expected economic circumstances over the next months. The SIC index is elaborated by using investors one-month expectations for the cryptocurrency market to reveal the emotions of market participants. These emotions vary between greediness and anxiety. When the sentiment is negative, this usually indicates an interesting rising of prices. Furthermore, the sign of the index provides a good perception about the investors’ emotions, which alter between bullish and bearish. A strongly worsening index (negative extreme) indicates a price recovery, whereas a very high improved index (positive extreme) is a sign of imminent price weakness. Table 1reports the elementary statistics of SIC and CC indexes, respectively. The summary statistics englobe mainly the minimum, maximum, mean, and standard deviation and show that Bitcoin reveal the most important risk compared to other cryptocurrencies, whereas Dogecoin exhibits less standard deviation among all CCs. Note that the kurtosis value is habitually compared to the value 3, going to the kurtosis value for a Normal Distribution. Table 1shows that CC indexes have high probabilities of exceptional positive monthly indexes, as they show high kurtosis. While the Tether index presents the highest kurtosis (13.98), Bitcoin shows the lowest value (4.31). While positive values of kurtosis are a sign of the presence of extreme values, it is, however, unclear where these values are exactly localized (on the left/right or on both side) in the distribution. Furthermore, all CC indexes are positively skewed, indicating that, for these indexes, the extremity on the right side is lengthier than the left side. The same inference is also perceived for CC indexes. Table 1. Elementary statistics of CC prices and Sentix Investor Confidence index. The sample covers two different sets of monthly data from 15 January 2013 to 15 November 2020. The CC prices are listed on the “Coinmarketcap” site whereas the Sentix CC is provided by Datastream. Bitcoin Dogecoin Ethereum Litecoin SENTIX CC Tether Mean 2601.647 0.001381 217.8344 33.36953 −42.88281 0.173776 Median 684.35 0.000316 180.015 7.105 −46.25 0.009113 Maximum 13,850.4 0.008972 1118.31 232.1 −21.5 1.877742 Minimum 218.5 0.000103 0.738644 1.44 −61.5 0.004111 Std. Dev. 3298.403 0.001873 225.1042 49.98785 11.38285 0.322237 Skewness 1.480963 1.953783 1.640499 2.205586 0.497164 2.948231 Kurtosis 4.313236 6.748212 6.399608 7.739697 2.107605 13.98628 Jarque–Bera 27.9936 78.1818 59.52608 111.7951 4.760148 414.5779 Probability 0.000001 *** 0.000 *** 0.000 *** 0.000 *** 0.092544 *** 0.000 *** *** report significance at 10% level. 3. Findings and Discussions 3.1. Wavelet Coherency Analysis of CC Prices We use the bivariate wavelet coherence analysis to investigate the co-movements between CC prices in the frequency–time domain. The wavelet plots are reported in Figure 1a–j . From this figure, we reveal interesting findings on the connection between the five CC prices over time and across frequency bands. Generally, a visual look at these graphs permits to recognize a significant co-movement between the CC prices at different scales, mainly localized, for most couples, at the end of the sample period. Nevertheless, only a few couples show strong relationships. Essentially, the Bitcoin–Litecoin pairwise (Figure 1a) exhibits a robust and persevered connection at the 8–16 months frequency band over the period 2015–2019, revealing that Bitcoin and Litecoin indexes acquire a substantial relationship in the long-run horizon. This finding is not surprising, given that Litecoin is technically very similar to Bitcoin. From the economic perspective, CCs are investable assets in nature, moving in the same direction. More explicitly, Litecoin is principally Bitcoin
J. Risk Financial Manag. 2021,14, 275 7 of 19 with more swift block confirmations. Furthermore, this couple is in a phase relationship where the arrows are overall right and up, meaning that Litecoin is the follower; in other words, Bitcoin affects positively the Litecoin price index variation. However, in the short and middle horizons corresponding to the 2–4 months and 4–8 months scales, no positive correlation between this couple is perceived, especially during the sub-period starting from 2013 to the end of 2018. The finding corroborates that of Philips and Gorse (2018) in which the authors explained the lack of positive correlation between the two currencies by the price movements decoupled, which is attributed to the history between the Securities and Exchange Commission of the United States (SEC) and Bitcoin exchange-traded funds (EFTs) during March 2017 and March 2019. Back in March 2017, a Bitcoin EFT was rejected by SEC, claiming that Bitcoin is still volatile and resilient to surveillance. In March 2019, the investment vehicle was not yet approved by the SCE. This disagreement impacted Litecoin and made it a hedge against subsequent Bitcoin price fluctuations. As well, the adoption of Segregate Witness (SegWit) reduced the likeness between Bitcoin and Litecoin’s technology. Meanwhile, this co-movement spread over the whole sub-period. J.RiskFinancialManag.2021,14,xFORPEERREVIEW8of20 Aswell,theCOVIDoutbreakhascastashadowoverthecryptocurrenciesmarket. TheCOVID‐19epidemicstartedattheendof2019inWuhanandwasdeclaredapandemicbytheWorldHealthOrganization(WHO)on11March2020.Itisworthnotingthat thisunexpectedoutbreakofthenovelcoronavirushasdramaticallyamplifiedtheuncertaintythatbroughtmassiveeffectsontherealeconomyaswellasinthefinancialsphere. Theimpactoftheepidemiconthecryptocurrenciesmarkethasbeenwidelyinvestigated (amongothers,Demiretal.2020;Corbetetal.2020;ConlonandMcGee2020).ThesestudiesrevealedthatthereisacausalrelationshipbetweentheCOVID‐19outbreakandcryptocurrencypricefluctuationsandstipulatedthatastheinfluenceofCOVID‐19expands, cryptocurrenciesplayaninterestinghedgingroleagainstuncertaintyandanxietyraised bythisrecentpandemic. TheWCplotsdisclosethat,duringtherecentpandemic,DogecoinandLitecoinexhibitedapositiveandsignificantrelationshipwithBitcoinintheshort‐termhorizon, whereBitcoinledthevariabilityofthesecryptocurrencies.Throughoutthesameperiod (endof2019tomiddleof2020),LitecoindrovetheDogecoinmovementintheshortand mediumterms.Forallothercouples,noremarkableinteractionswereseenacrosscryptocurrencies.OurfindingscorroboratethoseofDemiretal.(2020).Intheirpaper,usinga waveletcoherenceanalysis,theauthorsindicatedthatduringthestartingperiodofthe spreadofCOVID‐19,anegativerelationshipbetweenthenumberofconfirmedcasesand deathsandBitcoinwasreported;thisrelationshipbecamepositiveinthelaterperiod.Furthermore,thefindingsforothercryptocurrenciesunveiledthattheinteractionswere weakercomparedtoBitcoin. (a).Bitcoinvs.Litecoin (b).Bitcoinvs.Dogecoin (c).Bitcoinvs.Ethereum (d).Bitcoinvs.Tether Figure 1. Cont.
J. Risk Financial Manag. 2021,14, 275 8 of 19 J.RiskFinancialManag.2021,14,xFORPEERREVIEW9of20 (e).Litecoinvs.Dogecoin (f).Litecoinvs.Ethereum (g).Litecoinvs.Tether (h).Dogecoinvs.Ethereum (i).Dogecoinvs.Tether(j).Ethereumvs.Tether Figure1.WaveletcoherenciesbetweenBitcoinandaltcoins.Theblackcontourrecognizestheregionsinwhichthespectrumissignificantatthe5%levelagainstrednoise.Theconeofinfluence(COI)isdesignatedbythelightershade,which delimitshighpowerregions,anditdelimitstheautocorrelationofthewaveletpowerateachscale.Thehorizontaland verticalaxesdenotetimeandscalebands,respectively. Figure 1. Wavelet coherencies between Bitcoin and altcoins. The black contour recognizes the regions in which the spectrum is significant at the 5% level against red noise. The cone of influence (COI) is designated by the lighter shade, which delimits high power regions, and it delimits the autocorrelation of the wavelet power at each scale. The horizontal and vertical axes denote time and scale bands, respectively.
J. Risk Financial Manag. 2021,14, 275 15 of 19 4. Robustness Check and Discussion To validate the main findings of our study, we employed a robustness test to analyze the co-movement between the Google search volume (hereafter GSV) and the study’s sample cryptocurrencies. The intensive use of the Google search engine and other social media to track news and financial events may transmit a real time signal to the investor sentiment. Indeed, it does not mirror only the attitude of market operators and accumulated data related to the volume of search queries, but rather also provides an idea on the households’ future predictions and decisions (see Gao et al. 2020). Primarily, Da et al. (2011) and Joseph et al. (2011), among others, used company’s names and tickers as a measure of investor sentiment/attention to predict stock returns. In contrast, recently studies tilted their focus to examine the predictability of GSV information transmission in the cryptocurrency market (Aalborg et al. 2019; Baig et al. 2019; Nasir et al. 2019 , among others). For instance, Baig et al. (2019) demonstrated the positive impact of investor sentiment (proxied by GSV) on Bitcoin prices. Likewise, Nasir et al. (2019) found GSV to have a significant, short-term positive effect on cryptocurrency returns. Following the studies of Joseph et al. (2011) and Baig et al. (2019), we employed GSV of respective cryptocurrencies as a measure of investor sentiment to illustrate their time–frequency coherence with cryptocurrency returns. Monthly GSV series of “Bitcoin”, “Litecoin”, “Ethereum”, “Dogecoin” and “Tether” were retrieved using the link (https://www.google.com/trends/ accessed on 25 November 2020). We deemed that our designated investor sentiment measure unfolds three main benefits. First, it is a direct sentiment measure determined by inspecting objective variables implicitly evocative of investor sentiment. Second, GSV is a free, publicly available source, providing household search intensity at multiple frequencies, region-wise, from January 2004 and onward (Asif Khan et al. 2019) . Third, GSV represents the investors sentiment, which might help traders to make more informed investment decisions. Figure 5displays the wavelet coherence of the GSV-based investor sentiment correlation with cryptocurrencies at multiple times and frequencies. Plot 5a reveals that GSV-based investor sentiment exhibits significant coherence with Bitcoin’s price in the short-term at the end of 2013, start of 2014, and during 2014, 2016 and 2018 in the medium-term (Bitcoin’s bubble period) and a moderate coherence in the long-term. The coherence between Bitcoin’s price and the GSV-based investor sentiment suggests that investor sentiment is more effective for explaining Bitcoin’s price movement during high-sentiment periods. Figure 5b illustrates a strong, significant coherence of GSV-based investor sentiment with Dogecoin’s prices in the long-term, whereas a moderate coherence is perceived in the short and medium-term. It is worth noting that in the long horizons phase, the pattern reveals that GSV-based investor sentiment derives positive significant variations in the prices of Dogecoin. Plot 5c highlights the presence of strong correlation between GSV-based investor sentiment and Litecoin prices at the beginning of the sample period in the short-term, whereas a moderate correlation is revealed in the long-term (50–63 months) and in the short-term (75–90 months), showing that GSV-based investor sentiment drives Litecoin prices over time and across scales. Likewise, Plot 5d presents strong coherence of GSV-based investor sentiment with Tether in the long term, from 20 to 50 months. For this scale, the GSV-based investor sentiment leads the Tether’s prices change. As well, in the short term, smaller episodes of coherence are perceived, showing that GSV-based investor sentiment exhibited a moderate significant correlation with Tether’s prices from 8 to 10 months and 31 to 39 months of investment horizons. However, GSV-based investor sentiment demonstrated a stronger correlation with Ethereum across all investment horizons, which suggests some convincing fluctuations in Ethereum prices. Our findings, shown in Figure 5, are consistent with those of Nasir et al. (2019) and Aalborg et al. (2019), which reveal significant nexuses of GSV with cryptocurrency returns. Our findings reveal the expected insights by showing GSV as a stronger predictive investor
J. Risk Financial Manag. 2021,14, 275 16 of 19 sentiment in the cryptocurrency market at multiple time-frequencies, particularly during high sentiment horizons. J.RiskFinancialManag.2021,14,xFORPEERREVIEW16of20 correlationwithEthereumacrossallinvestmenthorizons,whichsuggestssomeconvincingfluctuationsinEthereumprices. Ourfindings,showninFigure5,areconsistentwiththoseofNasiretal.(2019)and Aalborgetal.(2019),whichrevealsignificantnexusesofGSVwithcryptocurrencyreturns.OurfindingsrevealtheexpectedinsightsbyshowingGSVasastrongerpredictive investorsentimentinthecryptocurrencymarketatmultipletime‐frequencies,particularly duringhighsentimenthorizons. (a).WTC:GSVandBitcoin MonthDates 102013m10 202014m08 302015m06 402016m04 502017m02 602017m12 702018m10 802019m08 902020m06 (b).WTC:GSVandDogecoin MonthDates 102014m9 202015m7 302016m5 402017m3 502018m1 602018m11 702019m9 802020m7 (c).WTC:GSVandLitecoin MonthDates 102014m1 202014m11 302015m9 402016m7 502017m5 602018m3 702019m1 802019m11 902020m9 Figure 5. Cont.
J. Risk Financial Manag. 2021,14, 275 17 of 19 J.RiskFinancialManag.2021,14,xFORPEERREVIEW17of20 (d).WTC:GSVandTether MonthDates 102015m11 202016m9 302017m7 402018m5 502019m3 602020m1 702020m11 (e).WTC:GSVandEthereum MonthDates 102016m5 202017m3 302018m1 402018m11 502019m9 602020m7 Figure5.WaveletcoherencybetweencryptocurrenciesandGooglesearchvolumeovertimeandacrossscalebands.The blackcontourrecognizestheregionsinwhichthespectrumissignificantatthe5%levelagainstrednoise.Theconeof influence(COI)isdesignatedbythelightershade,whichdelimitshigh‐powerregions.Thehorizontalandverticalaxes denotetimeandscalebands,respectively. 5.ConcludingRemarks Priorstudiesmadeanefforttoexaminetherelationshipbetweeninvestorsentiment andCCreturns.However,weattemptedinthispapertoinvestigatethetime‐frequency nexusbetweenCCpricesandinvestorsentiment.Moreover,thestudyanalysiswasextendedtothreevariables,usingmultiplewaveletcoherence.WeusedCCslistedon“Coinmarketcap”site,datedon“15November2020”,andtheSentixInvestorConfidenceindex collectedfromDataStream.Whenlookingtothecoherencybetweencryptocurrenciesand theSICindex,strongcoherenciesweremostlylocalizedatlowfrequencies,indicatingthat thereisalong‐term,positiverelationshipbetweenCCandtheSICindex.Ourfindings demonstratealsothatSentixdrivescryptocurrenciespricechangesacrossfrequenciesand investmenthorizons,particularlyinthelongrunandduringtheBitcoincrisisperiod.As well,aftercombiningtheeffectofCCandSentix,theMWCplotsdisplayajointlystrong influenceofCCpricesandtheSICindexonBitcoinpricemovementsacrossallfrequencies andoverthesampleperiodofthestudy. Figure 5. Wavelet coherency between cryptocurrencies and Google search volume over time and across scale bands. The black contour recognizes the regions in which the spectrum is significant at the 5% level against red noise. The cone of influence (COI) is designated by the lighter shade, which delimits high-power regions. The horizontal and vertical axes denote time and scale bands, respectively. 5. Concluding Remarks Prior studies made an effort to examine the relationship between investor sentiment and CC returns. However, we attempted in this paper to investigate the time-frequency nexus between CC prices and investor sentiment. Moreover, the study analysis was extended to three variables, using multiple wavelet coherence. We used CCs listed on “Coinmarketcap” site, dated on “15 November 2020”, and the Sentix Investor Confidence index collected from DataStream. When looking to the coherency between cryptocurrencies and the SIC index, strong coherencies were mostly localized at low frequencies, indicating that there is a long-term, positive relationship between CC and the SIC index. Our findings demonstrate also that Sentix drives cryptocurrencies price changes across frequencies and investment horizons, particularly in the long run and during the Bitcoin crisis period. As well, after combining the effect of CC and Sentix, the MWC plots display a jointly strong influence of CC prices and the SIC index on Bitcoin price movements across all frequencies and over the sample period of the study. Furthermore, for robustness purposes, we employed Google search volume as a proxy of investor sentiment to illustrate the coherency with the CC. The GSV-based investor sentiment exhibited a strong predictive power on the cryptocurrency market across all scales band. The overall findings of the study imply that investor sentiment plays a crucial
J. Risk Financial Manag. 2021,14, 275 18 of 19 role on the variation of CC price in the frequency–time space. The financial implications of our study lie in the following two aspects. First, investor sentiment is a timely indicator in the financial markets, whereby an investor can earn higher returns than market returns. The positive in the short run versus the negative in the long run asymmetry correlation between SIC and Bitcoin directs investors to opt for a stylized investment strategy in the CC market. Precisely, the findings support passive investors and day traders who intend to earn higher capital gain by trading against sentiment risk. Second, generally, investors believe Bitcoin movement to be a barometer for other CCs in the market. However, this study confirms the influence of CCs along with the SIC on Bitcoin. It indicates that investors must hedge against CC prices while scheming their financial decision making. In future, for optimal trading strategies, the study can be extended by including stock prices and exchange rates. As in the trader’s view, CC is an alternative to speculative assets and virtual money. The extended study will be able to provide evidence against CC as an alternative strategy to the stock or exchange markets when sentiments are high in the market. Author Contributions: For this research, all authors contributed to its elaboration. Mainly, conceptualization, B.H. and H.A.A.; methodology, B.H.; software, B.H.; validation, B.H., H.A.A., and M.A.K.; formal analysis, H.A.A.; investigation, B.H.; resources, B.H.; data curation, M.A.K.; writing—original draft preparation, B.H.; writing—review and editing, B.H.; visualization, H.A.A.; supervision, B.H.; project administration, H.A.A.; funding acquisition, H.A.A. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by a grant from the Deanship of Scientific Research, at the University of Jeddah through the research proposal No. (UJ-02-002-DR). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors of this research would like to present their great appreciation to the Deanship of Scientific Research for their supporting and funding this research. Without this support, we would not have succeeded. Conflicts of Interest: The authors declare no conflict of interest. References Aalborg, Halvor Aarhus, Molnár Peter, and Jon Erik de Vries. 2019. What can explain the price, volatility and trading volume of Bitcoin? Finance Research Letters 29: 255–65. [CrossRef] Aysan, Ahmet Faruk, Ender Demir, Giray Gozgor, and Chi Kenug Marco Lau. 2019. Effects of the geopolitical risks on Bitcoin returns and volatility. Research in International Business Finance 47: 511–18. [CrossRef] Baig, Ahmed, Benjamin M. Blau, and Nasim Sabah. 2019. Price Clustering and Sentiment in Bitcoin. Finance Research Letters 29: 111–16. [CrossRef] Bariviera, Aurelio F. 2017. The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters 161: 1–4. [CrossRef] Bouoiyour, Jamal, Refk Selmi, Aviral K. Tiwari, and Muhammad Shahbaz. 2015. The nexus between oil price and Russia’s real exchange rate: Better paths via unconditional vs. conditional analysis. Energy Economics 51: 54–66. [CrossRef] Burggraf, Tobias, Toan Luu Duc Huynh, Markus Rudolf, and Mei Wang. 2020. Do FEARS drive Bitcoin? Review of Behavioral Finance. [CrossRef] Chen, Conghui, Lanlan Liu, and Ningru Zhao. 2020. Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19. Emerging Markets Finance and Trade 56: 2298–309. [CrossRef] Chu, Jeffrey, Yuanyuan Zhang, and Stephen Chan. 2019. The adaptive market hypothesis in the high frequency cryptocurrency market. International Review of Financial Analysis 64: 221–31. [CrossRef] Ciaian, Pavel, Miroslava Rajcaniova, and DArtis Kancs. 2018. Virtual relationships: Short- and long-run evidence from BitCoin and altcoin markets. Journal of International Financial Markets, Institutions and Money 52: 173–95. [CrossRef] Conlon, Tomas, and Richard McGee. 2020. Safe haven or risky hazard? Bitcoin during the COVID-19 bear market. Finance Research Letters 35: 101607. [CrossRef] Corbet, Shaen, Andrew Meegan, Charles Larkin, Brian Lucey, and Larisa Yarovaya. 2018. Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economic Letters 165: 28–34. [CrossRef] Corbet, Shaen, Brian Lucey, Andrew Urquhart, and Larisa Yarovaya. 2019. Cryptocurrencies as a financial asset: A systematic analysis. International Review of Financial Analysis 62: 182–99. [CrossRef]
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