Ahmet Faruk Aysan
email: aaysan@hbku.edu.qa

Ali Yavuz Polat
email: aliyavuz.polat@agu.edu.tr

Hasan Tekin
email: hasantekin@karabuk.edu.tr

Ahmet Semih Tunali
email: tunali.ahmet@metu.edu.tr

Ahmet Semih

Ahmet Faruk

Aysan Hamad

Bin Khalifa

Ali Yavuz

Polat Abdullah

Bitcoin-specific fear sentiment and bitcoin returns in the COVID-19 outbreak

Keywords: Bitcoin's return, COVID-19, fear sentiment, transaction volume, TRMI, volatility JEL Codes: C22, G12, G18, G41

Introduction

Bitcoin's price experienced a sharp drop, from 9147 USD on 6 th March 2020 to 4959 USD on 12 th March 2020 at the beginning of the Covid-19 pandemic, which proves that Bitcoin is affected by global events significantly. Then, later it increased to 61288 USD on 13 th March 2021, within a year 1 . Considering this high volatility in Bitcoin prices, it is insightful to analyze the effects of market sentiments on cryptocurrencies. This is even more relevant for Bitcoin since the sentiment drives the Bitcoin market in a laxer regulatory framework. COVID-19 as a natural experiment provides an opportunity to explore the pure effects of market sentiment on return, transaction volume and volatility of Bitcoin considering its decentralized and lightly regulated feature. This study allows us to infer lessons from markets in the absence of strict regulatory responses by the governments. Hence, investigating dynamics in the Bitcoin markets reveal more on the potential responses of markets under lighter government regulations 2 .

The behavioral literature focuses on analyzing the effects of the sentiments in markets [START_REF] Tetlock | Giving content to investor sentiment: The role of media in the stock market[END_REF]. However, the literature mostly employs generic sentiment measures that are often too general to capture asset-specific sentiments. For example, [START_REF] Da | The sum of all FEARS investor sentiment and asset prices[END_REF] and [START_REF] Chen | Fear sentiment, uncertainty, and bitcoin price dynamics: The case of COVID-19[END_REF] use generic market sentiment data derived from Google Trends that are not specific to the Bitcoin market. Existing research resorts to the general sentiment measures due to the availability of the data. However, in this paper, we benefit from a novel data set of Bitcoin-1 The price data is obtained from coinmetrics.io 2 One may see some of the related works of the authors as follows in the references: [START_REF] Aysan | Blockchain Paths in Rebooting the Global Response to the Sustainable Development Goals after COVID-19[END_REF], Aysan, A. F., Bergigui, F. & Disli, M., (2021a), [START_REF] Aysan | Blockchain -Based Solutions in Achieving SDGs after COVID-19[END_REF], [START_REF] Aysan | The Ascent of Bitcoin: Bibliometric Analysis of Bitcoin Research[END_REF], [START_REF] Aysan | In search of safe haven assets during COVID-19 pandemic: An empirical analysis of different investor types[END_REF], Aysan, A. F., Khan, A. I. & Topuz, H., (2021), Aysan, A. F., Khan, A. I., Topuz, H., &[START_REF] Aysan | Survival of the fittest: A natural experiment from crypto exchanges[END_REF][START_REF] Aysan | Blockchain Futures In Cryptocurrencies, Trade and Finance: A Preliminary Assessment[END_REF][START_REF] Aysan | Blockchain Futures In Cryptocurrencies, Trade and Finance: A Preliminary Assessment[END_REF].

specific fear sentiment to capture the effects of market psychology on Bitcoin price, volume and price volatility.

There is a growing literature on Bitcoin market dynamics [START_REF] Sahoo | Is bitcoin a near stock? Linear and non-linear causal evidence from a price-volume relationship[END_REF][START_REF] Poyser | Exploring the dynamics of Bitcoin's price: a Bayesian structural time series approach[END_REF][START_REF] Dahir | Dynamic connectedness between Bitcoin and equity market information across BRICS countries: Evidence from TVP-VAR connectedness approach[END_REF][START_REF] Kristoufek | Is the Bitcoin price dynamics economically reasonable? Evidence from fundamental laws[END_REF][START_REF] Aloosh | The psychology of cryptocurrency prices[END_REF] and whether Bitcoin acts as a safe haven [START_REF] Aysan | Effects of the geopolitical risks on Bitcoin returns and volatility[END_REF][START_REF] Bouri | On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?[END_REF][START_REF] Guesmi | Portfolio diversification with virtual currency: Evidence from bitcoin[END_REF][START_REF] Shahzad | Is Bitcoin a better safe-haven investment than gold and commodities?[END_REF] or whether it is a bubble [START_REF] Cheah | Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin[END_REF][START_REF] Geuder | Cryptocurrencies as financial Bubbles: The Case of Bitcoin[END_REF][START_REF] Laurini | Is Bitcoin a bubble?[END_REF]. Previous research focused on the effects of sentiment on different market dynamics of Bitcoin (Karalevicius et al. 2017;[START_REF] Kalyvas | What drives Bitcoin's price crash risk?[END_REF]. [START_REF] Shen | Does twitter predict Bitcoin?[END_REF] analyze the relationship between Bitcoin return and investor attention using the number of twitters as a proxy for investor attention. [START_REF] Da | The sum of all FEARS investor sentiment and asset prices[END_REF] construct a daily Internet search-based fear index and find that the fear index can predict asset prices and volatility.

There is no agreement yet whether BTC and other cryptocurrencies satisfy the three main properties of a money/currency [START_REF] Bariviera | Some stylized facts of the Bitcoin market[END_REF][START_REF] Baur | Bitcoin: Medium of exchange or speculative assets?[END_REF]. Thus, investors mostly treat cryptocurrencies as an asset rather than a currency. [START_REF] Baur | Bitcoin: Medium of exchange or speculative assets?[END_REF] claim that: "Bitcoin is mainly used as a speculative investment" not "as an alternative currency". [START_REF] Corbet | Exploring the dynamic relationships between cryptocurrencies and other financial assets[END_REF] analyze the relationship between the three known cryptocurrencies and conventional assets.

They show that cryptocurrencies provide some diversification benefits in the short run. [START_REF] Zeng | Fancy Bitcoin and conventional financial assets: Measuring market integration based on connectedness networks[END_REF] investigate the connectedness between Bitcoin and other conventional assets and find that the relationship is limited implying a diversification opportunity. Regarding the regulatory difference between conventional assets and cryptocurrencies, Vandezande (2017) mentions that it is inevitable to understand crypto markets in relation to other conventional assets, in order to provide better insights for policy makers and regulators.

Earlier literature focuses on the effect of investor psychology and sentiment in the Bitcoin market [START_REF] Kaminski | Nowcasting the bitcoin market with twitter signals[END_REF][START_REF] Bukovina | Sentiment and bitcoin volatility[END_REF][START_REF] Oad Rajput | Bitcoin Sentiment Index, Bitcoin Performance and US Dollar Exchange Rate[END_REF]) and on the Covid-19 period [START_REF] Demir | The relationship between cryptocurrencies and COVID-19 pandemic[END_REF][START_REF] Conlon | Safe haven or risky hazard? Bitcoin during the COVID-19 bear market[END_REF][START_REF] Chen | Fear sentiment, uncertainty, and bitcoin price dynamics: The case of COVID-19[END_REF]), yet there are no studies investigating the impact of changing fear sentiment during Covid-19 outbreak on Bitcoin market dynamics. This study aims to analyze whether the Covid-19 pandemic, as an extreme shock, changed the dynamics in the Bitcoin market. More specifically, we check how fear sentiment affects Bitcoin variables before and after the pandemic and whether the effect of sentiment is more pronounced after the pandemic considering the unprecedented uncertainty during the Covid-19 period. We employ bivariate VAR models to investigate the relationship between the fear sentiment and Bitcoin's return, transaction volume and 30-day return volatility.

Fear sentiment data provided by TRMI is a novel sentiment measure since it is unique to the Bitcoin market, not a generic sentiment measure.

The results reveal that due to the excessive uncertainty led by the COVID-19 outbreak, an increase in fear sentiment negatively affects the Bitcoin returns more persistently and significantly. For the post-COVID-19 period, an increase in fear also results in more fluctuations in transaction volume while its initial and cumulative effects are both negative. Due to extreme uncertainty caused by the COVID-19 pandemic, investors may trade more aggressively in the initial phases of the shock.

The paper has several main contributions. First, [START_REF] Chen | Fear sentiment, uncertainty, and bitcoin price dynamics: The case of COVID-19[END_REF] focus on the effects of the COVID-19 pandemic and report only VAR estimation results. Their analysis is not very informative since they cannot capture the dynamic relationship between variables due to not analyzing impulse response functions (IRFs). However, we analyze IRFs to understand the dynamic relationship between fear and our Bitcoin variables. Second, we use Bitcoin-specific fear sentiment data set of Thomson Reuters MarketPsych instead of more general market sentiment measures used in the literature and show that fear merges to have more permanent and volatile effects after the outbreak. This enables us to investigate the impact of investor psychology on Bitcoin market dynamics more accurately, since generic sentiment measures, as employed by the earlier literature, cannot capture market specific psychology. Next, this is the first study to examine the association between fear and Bitcoin before and after COVID-19, considering the changing dynamics with the outbreak. Thus, we can investigate the effect of the pandemic on the Bitcoin market, through investor psychology, since the pandemic created an unprecedented shock on financial markets.

The paper is organized as follows: Section 2 describes the data and the methodology. Section 3 presents and discusses the results. Section 4 concludes and provides policy implications.

Data and methodology

We retrieve fear sentiment data from Thomson Reuters MarketPsych Indices (TRMI).

Bitcoin price and total transaction volume3 in its Blockchain in terms of US dollars, and daily volatility4 of Bitcoin return are obtained from coinmetrics.io for the period between January 1 st , 2019 and January 31 th , 2021. We denote the subperiods as pre-and post-COVID19 considering January 13th, 2020, when first Covid-19 confirmed case was reported outside China. With the pandemic, fear sentiment increased sharply to 0.012 by mid-March, which is unprecedentedly high (see Fig. 1) considering its mean and standard deviation (see Table 1).

[Figure 1 is about here] [Table 1 is about here] TRMI data is unique in capturing market sentiment in multiple ways. First, it is based on advanced linguistic machine learning techniques accounting for variation in sources and correlations among words. Contrary to the most of the methods used in the sentiment literature TRMI is sensitive to grammatical structures. Second, the sentiment data is highly dimensional including more than fifty sentiments and topics. Third, it has a very broad range of coverage compared to other sentiment data used in the literature. Fourth, TRMI is updated minutely and includes tens of thousands of social media and news sources [START_REF] Peterson | Thomson Reuters MarketPsych Indices (TRMI) white paper, inside the mind of the market[END_REF][START_REF] Audrino | Sentiment spillover effects for US and European companies[END_REF][START_REF] Griffith | Emotions in the stock market[END_REF]. Fifth, TRMI releases different types of sentiment data specific to each cryptocurrency. [START_REF] Peterson | Trading on sentiment: The power of minds over markets[END_REF] explains how TRMIs are constructed as follows. A TRMI is formed by a combination of variables (Vars). Initially, the absolute values of all TRMI-contributing Vars are determined by the algorithm, for all asset components, over the past 24 hours. Next, "Buzz" is calculated which is the summation of these absolute values for all components. "Buzz" index is also published along with TRMIs of each asset. Namely, let 𝑉 be the set of all Vars underlying any TRMI of the asset class, where 𝑎 denotes an asset, and 𝐶(𝑎) is the set of all components of 𝑎. Then, Buzz is defined as follows:

𝐵𝑢𝑧𝑧(𝑎) = + |𝑉𝑎𝑟 !,# | !$%('),#$)
Having the Buzz, a TRMI is computed as the ratio of the sum of all Vars related to a specific asset to the Buzz. 𝑉(𝑡) is defined as the set of all Vars relevant to a particular TRMIt. After that, a binary function is introduced to determine whether a Var 𝑣 ∈ 𝑉(𝑡) is additive or subtractive:

𝐼(𝑡, 𝑣) = 3 +1 𝑖𝑓 𝑎𝑑𝑑𝑖𝑡𝑖𝑣𝑒 -1 𝑖𝑓 𝑠𝑢𝑏𝑡𝑟𝑎𝑐𝑡𝑖𝑣𝑒
Finally, TRMIt of an asset 𝑎 is computed as follows:

𝑇𝑅𝑀𝐼 + (𝑎) = ∑ (𝐼(𝑡, 𝑣) × 𝑃𝑦𝑠𝑐ℎ𝑉𝑎𝑟 # (𝑐)) !∈%('),#∈)(+)

𝐵𝑢𝑧𝑧(𝐴𝑠𝑠𝑒𝑡)

In this study, we employ Bitcoin specific "fear" index as the 𝑃𝑦𝑠𝑐ℎ𝑉𝑎𝑟 which measures the fear and anxiety and takes values between 0 and 1 (Peterson 2016).

We employ bivariate vector autoregressive (VAR) models given below with lag-length k, to investigate the dynamics between Bitcoin variables and fear sentiment:

𝑀𝑜𝑑𝑒𝑙 1: 𝑌 + = 𝑐 + + 𝛽 - . -/0 𝑌 +1-+ 𝜖 +
where 𝑌 + = [𝑅𝑒𝑡𝑢𝑟𝑛, 𝐹𝑒𝑎𝑟 ]′ is a 2𝑥1 vector of endogenous variables, c is a vector of constants and 𝜖 + is a vector of error terms. Model 1 captures the relationship between Bitcoin return and fear sentiment. where 𝑍 + = [𝑉𝑜𝑙𝑎𝑡𝑖𝑙𝑖𝑡𝑦, 𝐹𝑒𝑎𝑟 ]′ is a 2𝑥1 vector of endogenous variables, c is a vector of constants and 𝜖 + is a vector of error terms. Model 3 captures the relationship between Bitcoin volatility and fear sentiment.

We determine the lag-length 5 using HQ Information Criteria (HQIC) criteria and check the stationarity of the variables by performing augmented Dickey-Fuller (ADF) and Phillips-Perron unit root tests with and without trends. Diagnostic tests suggest that fear, return, 30-day volatility and Log-transaction volume are stationary at their levels (Table 2).

[Table 2 is about here]

Empirical results

In order to capture dynamic relationship between variables we plot impulse response functions (IRF). 6 Fig. 2, 3 and 4 display the orthogonalized impulse response functions (OIRF)

of Bitcoin return, transaction volume and Bitcoin volatility respectively to one-unit standard 5 The empirical results and IRFs are generated also with different lag-lengths based on other information criteria such as AIC and BIC. We find qualitatively similar results.

6 See Appendix for the VAR estimation results of both subsamples.

deviation shock on fear, based on the estimated bivariate VAR models. The confidence intervals are for 1 standard error confidence intervals.

Figure 2 shows the IRF for Bitcoin return (Model 1). For the pre-COVID period, the response of return is negative to an increase in fear sentiment and dies out quickly in 6 periods (days).

However, after COVID-19, even the response is again negative in the initial periods, later it fluctuates, and the shock persists much longer (more than 15 periods) compared to pre-COVID.

Besides, the initial response of return to a fear shock is bigger in magnitude for post-COVID compared to pre-COVID. These results imply that due to the excessive uncertainty caused by the COVID-19 outbreak, an increase in fear negatively affects the Bitcoin returns more persistently. With an increasing fear, investors become more pessimistic which increases the selling pressure [START_REF] Baker | Investor sentiment and the cross-section of stock returns[END_REF][START_REF] Chen | Fear sentiment, uncertainty, and bitcoin price dynamics: The case of COVID-19[END_REF]. The reaction of the Bitcoin fluctuates as fear sentiment leads to return reversals after COVID-19 [START_REF] Tetlock | Giving content to investor sentiment: The role of media in the stock market[END_REF][START_REF] Da | The sum of all FEARS investor sentiment and asset prices[END_REF]. This result also implies that Bitcoin prices become exceptionally volatile with coronavirus outbreak. Investors became more sensitive to news after the outbreak. The granger causality test results further support our findings. Fear granger causes Bitcoin return in the post-COVID period more significantly (Table 3).

[Figure 2 is about here]

[Table 3 is about here] Fig. 4 shows the response of volatility based on Model 3. During the pre-COVID period, an increase in fear decreases the volatility. Conversely, increasing fear has a sharp increasing effect on volatility after COVID-19. The effect is larger in magnitude and is more permanent for the post-COVID period. The increasing severity of the pandemic presumably drives investors to become more uncertain. This may lead to an increase in bid-ask spread and volatility [START_REF] Lerner | Fear, anger, and risk[END_REF].

[Figure 4 is about here]

Conclusions

We investigate the relationship between Bitcoin-specific fear sentiment and Bitcoin's return, volatility and transaction volume considering the COVID-19 pandemic driven crisis. The results

show that the relationship between fear and our bitcoin variables become more volatile while the shocks have more persistent effects after the COVID-19 outbreak. For conventional currencies, governments, regulators and central banks intervene in the market to stabilize their economies when faced with a crisis like the COVID-19 outbreak. However, the Bitcoin market in nature came out as a reaction to these policies. In this sense, we are convinced that our results in this paper have more far-reaching implications for other markets regulated by the states.

Bitcoin provides a natural benchmark to understand how fear sentiment drives and impacts the markets isolated from any interventions. Focusing on Bitcoin-as a decentralized and unregulated market-gives us clues about how the market would have behaved in the absence of policy responses and trust provided by any central authority. Hence, our results show that in the absence of regulatory frameworks, market dynamics are likely to be more volatile, and the fear sentiment has more persistent impacts. This implies that smart regulation and timely interventions in the markets, such as monetary and fiscal policy responses conducted during the early stage of the outbreak, are relevant and may mitigate the sentiment driven extreme volatility in the markets. Also, considering the expectation management role of central banks, the timely response may help investors to gain confidence in markets. We also highlight the importance of using micro, asset-specific sentiment measures to capture market dynamics better. 1 5 . 0 1 . 2 0 2 0 1 5 . 0 2 . 2 0 2 0 1 5 . 0 3 . 2 0 2 0 1 5 . 0 4 . 2 0 2 0 1 5 . 0 5 . 2 0 2 0 1 5 . 0 6 . 2 0 2 0 1 5 . 0 7 . 2 0 2 0 1 5 . 0 8 . 2 0 2 0 1 5 . 0 9 . 2 0 2 0 1 5 . 1 0 . 2 0 2 0 1 5 . 1 1 . 2 0 2 0 1 5 . 1 2 . 2 0 2 0 1 5 . 0 1 . 2 0 2 1

Post-Covid

Price Fear

𝑋

  +1-+ 𝜖 + where 𝑋 + = [𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛, 𝐹𝑒𝑎𝑟 ]′ is a 2𝑥1 vector of endogenous variables, c is a vector of constants and 𝜖 + is a vector of error terms. Model 2 captures the relationship between Bitcoin transaction value and fear sentiment.𝑀𝑜𝑑𝑒𝑙 3: 𝑍 + = 𝑐 + + 𝛽 - . -/0 𝑍 +1-+ 𝜖 +

Fig. 3

 3 Fig. 3 displays the response of transaction volume based on Model 2. During the pre-COVID

Fig. 1 .

 1 Fig. 1. Fear and BTC price (USD).

Fig. 2 .

 2 Fig. 2. Orthogonalized and cumulative IRFs for pre-and post-COVID periods of Bitcoin return (Model 1). Notes: This figure show the response of BTC return to a shock in fear having 1-standard deviation confidence intervals.

Fig. 3 .Fig. 4 .

 34 Fig. 3. Orthogonalized and cumulative IRFs for pre-and post-COVID periods of transaction volume to a shock in fear.

Table A

 A

	.3. VAR estimation results for Model 3
		Pre-COVID			Post-COVID
		Volatilityt	Feart	Volatilityt	Feart
	Vt-1	1.0048***	-0.0084	1.0254***	0.0314514***
	Vt-2	0.1051	0.0195	-0.0591	-0.0232347
	Vt-3	-0.1340***	-0.0085	-0.0527	0.024437
	Vt-4			0.0445	-0.0306693***
	Ft-1	0.1488	0.4349***	0.1639	0.4908896***
	Ft-2	-0.0757	0.0338	0.080	0.1413264**
	Ft-3	-0.2457	0.0140	1.000***	0.095465*
	Ft-4			-0.380	0.0101118
	Notes: pre-and post-Covid periods cover the dates from 1 January, 2019 to 14 March, 2020 and from 15 March, 2020 to 31
	January, 2021, respectively.

Table 1

 1

	.a. Summary statistics for pre-Covid period
		Mean	SD	Min	Max	Skewness	Kurtosis
	Fear	0.0042	0.0008	0.0025	0.0075	0.90931	4.2465
	Return	0.0022	0.0365	-0.1465	0.1697	0.3302	6.9914
	Log-Transaction Volume	23.4472	0.5116	22.1874	24.5322	-0.6643	2.8457
	30-day Return Volatility	0.0353	0.0116	0.0119	0.0696	0.8123	3.4139
	Notes: pre-Covid covers the period from 1 January, 2019 to 14 March, 2020.
	Table 1.b. Summary statistics for post-Covid period
		Mean	SD	Min	Max	Skewness	Kurtosis
	Fear	0.0041	0.0013	0.0019	0.0121	2.2333	10.850
	Return	0.0007	0.0442	-0.4705	0.1401	-4.411	51.889
	Log-Transaction Volume	24.1253	0.3812	23.229	25.0294	-0.3255	2.3615
	30-day Return Volatility	0.0376	0 .0242	0.0117	0.1055	2.0116	5.7668
	Notes: post-Covid covers the period from 15 March, 2019 to 31 January, 2021.

Table 2 .

 2 Unit root test results

	Pre-Covid	Post-Covid

Table 3 .

 3 Granger causality Wald test results. Indicates significance at 1%. * Indicates significance at 10%. Notes: pre-and post-Covid periods cover the dates from 1 January, 2019 to 14March, 2020 and from 15 March, 2020 to 31 January, 2021, respectively.

	Pre-COVID		Post-COVID
	Fear does not granger cause Return	2.7952*	Fear does not granger cause Return	16.899***
	Fear does not granger cause transaction	24.383*** Fear does not granger cause transaction	20.624 ***
	volume		volume
	Fear does not granger cause volatility	3.3468	Fear does not granger cause volatility	33.03***
	***

The sum USD value of all native units transferred (i.e., the aggregate size in USD of all transfers) that day.

Computed as the standard deviation of the daily natural log returns over 30 days

APPENDIX