Bitcoin: A life in crises.

In this study, we investigate the BTC price time-series (17 August 2010-27 June 2021) and show that the 2017 pricing episode is not unique. We describe at least ten new events, which occurred since 2010-2011 and span more than five orders of price magnitudes ($US 1 -$US 60k). We find that those events have a similar duration of approx. 50-100 days. Although we are not able to predict times of a price peak, we however succeed to approximate the BTC price evolution using a function that is similar to a Fibonacci sequence. Finally, we complete a comparison with other types of financial instruments (equities, currencies, gold) which suggests that BTC may be classified as an illiquid asset.

1. Introduction

As of today, the combined market value of the 5 most popular cryptocurrencies (CCs) is > $1500 bn (>$950 bn for BTC alone); a number similar to the market cap of Amazon, and larger than those of Tesla or Facebook. In the light of its high historical volatility (90d historical volatility ~ 100%), irregular market trading volumes, and because its main underlying is yet unknown, classifying BTC (and some of the other CCs) for risk assessment remains necessary. Although it was intensely scrutinized, it is yet unclear whether BTC should be treated as a commodity (volatile and liquid), a currency (stable and liquid), an equity (variably liquid and variably volatile), or whether it should be receive a singular definition for each investment context [1-7]. Our main objective is to contribute to this debate by focusing on the study of time-series.

With the increasing speed and improving reliability of financial apps-based services, and with a strong editorial presence in economic arenas, crypto currencies are poised to gain an ever-greater base of users and services [8, 9]. In the wider context of blockchain development, CC may help in trading goods and services across political boundaries, avoiding fees imposed by financial intermediaries, and in hedging uncertainties on financial markets [10]. Even though some progress has been made through the understanding of the bitcoin (BTC) price structure [2, 11], the business model remains largely opaque. The overall opacity surrounding CC trades quickly triggered informal criticisms and, soon after, many warnings issued by a wide range of actors, from intelligence services [12] to market regulators [13].

In November 2017, the price of BTC has unexpectedly risen for a month by an average 1.8% daily, leading to the biggest and most widely publicized exponential price rise in cryptocurrencies. Despite the fact that holding BTC carries some volatility risk (implied volatility > 100%; [14], large cohorts of market participants seek to invest in BTC and cryptocurrencies. This has left banks and other financial services firms scrambling to deploy financial services (custodianship, cold storage, BTC payment services) and investment products (like funds, derivatives, and complex structured notes) into the market to capitalize on this trend, which in turn exposed them to non-trivial challenges, both regulatory and economical. Most difficulties were due to the nature of the BTC price process, which did not lend itself to straightforward Black-Scholes-Merton modelling. This further encourages us to try and understand 1) the price dynamics of BTC and 2) under which assumption(s) the risk of holding BTC exposure should be modelled.

In this study, we use numerical methods such as time-series analysis, which proved their efficiency in many other scientific fields such as finance forensics or geophysics. Time-series analysis allows the uncovering of intrinsic parameters (and their dynamics) of a time evolving phenomenon, and we treat the BTC price time-series as we would any seismological or meteorological records, further comparing them against a model we deem appropriate. In order to get the most of this approach, we carried out our analysis is in both time and frequency domains. In the time domain, trends, amplitudes and scattering can be quantified, while in the frequency domain, hidden discontinuities and periodicities can be explored. By combining both approaches, our aim has been to detect market price discontinuities, irregularities of prices, large changes of market capital (cash influx), large buy/sales and crowd effects in the context of a market that is not immune to other financial information available to the greater public. Finally, we fit BTC prices with a so-called Hockey Stick Function (HSF) and suggest that one, same, recurring dynamic fuels all BTC price surges. We hope our findings will help to characterize the nature of BTC for risk mitigation purposes.

2. Data

In this study, we use daily (‘Open’) price data freely available on the Yahoo Finance® website, in order to determine long-term dynamics of the BTC market value. The dataset used in this study starts on 17 Aug 2010 and ends on 27 June 2021. We compared these prices with other BTC price feeds, such as those provided by Bloomberg® and Market Map® (Morningstar FOREX prices), and found some differences at given times as observed before [15]. For instance, inter-exchange Bitcoin price differences did not exceed 500 USD during fall 2017- winter 2018 when the price passed USD 10’000 for the first time (Fig 1). As we focus on large changes of prices over long time spans (>2 days), we have made sure that using the other sources for prices would not have led us to different conclusions.

Fig 1

Price of BTC between 2010-08-17 and 2021-07-31 (a) for various periods (b-d). Many periods show the episodes of price increase (and decrease) of similar shape. For reference the same events are highlighted in Fig 2 using same labels.

Fig 2

Frequency analysis of the BTC price history.

a) Whole BTC price time series and associated periodogram for the whole dataset and b) and for the first 1000 days of the dataset (i.e. 2010 until 2014). High amplitudes levels (yellow to red) highlight discontinuities of price, change of frequency contents. One can note the high number of small event (green to yellow within blue areas) occurring in many occasions since 2012. Two classes of events can be distinguished: those which involve long-periods signals like it was seen for earthquake propagation [53], and those which are only small scale discontinuities (e.g. at approx. 700 days on panel b).

3. Results

3.1. First level analysis

As the analysis in the frequency domain does not require pre-processing steps (detrending, etc.), we first computed periodograms for both the complete and some subsets of the dataset. For this, we used openly distributed R packages [16-18]. The frequency domain approach allows the detection of discontinuities and periodic signals for a given time-series (for this reason, frequency analysis may be used to detect e.g. fraud such as price manipulations). Here, we use this tool in order to detect price peaks which may be hidden in either high-amplitude noise or low amplitudes. For example, in Fig 2B, we identify the events 1, 2 and 3 highlighting the correspondence between time and frequency analysis. Event 2 corresponds to the period during which BTC passed US$150 (those events are also highlighted in Fig 1). Fig 2 shows that there is no periodicity of the BTC prices (i.e. we cannot see continuous horizontal lines, please refer to S1–S9 Figs for a purely periodic time-series), indicating that the determination of the asset price does not include a cyclical component. The discontinuities visible in Fig 2A can be linked to price peaks of various amplitudes, therefore suggesting that the price peak of fall 2017-winter 2018 is not unique. Finally, Fig 2, by zooming in on specific periods, also suggests the peak prices to be in fact composed of collections of BTC prices discontinuities (green vertical lines or red areas in Fig 2). All those observations suggest the BTC may suddenly become less liquid, although the demand remains high, leading to a price increase by gain of interest from the investors. As those price changes are mostly positive, we can exclude the hypothesis that price variations were due to the discovery of large numbers of BTC (through mining). In order to provide the reader with a point of reference regarding this technique, we computed periodograms (S1–S9 Figs) for synthetic time-series and popular stocks (ABB, Tesla-TSLA, Gamestop-GME).

Secondly, we focus on the statistical characteristic of the price time-series. We first test whether BTC prices follow the Bendford’s law using the “BenfordTests” R library [19]. This method is commonly used for forensics analysis of price structure and income tax data analysis [20] and in the case of BTC it may highlight variations of liquidity. As BTC prices range 4 orders of magnitudes, they qualify for this kind of analysis. Chi2 test analysis shows that the prices of BTC do not comply with Benford’s law (χ2 = 357 for n = 8) as the occurrence of “6”s for the first digit is occurring too many times (approx. +50% excess; Fig 3). Further analyses would be necessary in order to identify the cause(s) of this observation.

Fig 3

Comparison between Benford law distribution (orange) and BTC prices (blue) since 17.08.2010.

3.2. The fall 2017 price peak

During the fall 2017 the BTC price tripled, its 30-day volatility was up to 180% (Fig 1), and daily returns sometimes exceeded 10%. Such price changes were faster than base-e exponential, soon looking like a Hockey Stick Function (HSF). HSF is a function parented to the Pascal triangle [21] and Fibonacci series; it can be understood as an exponential function of increasing base. Interestingly, and despite the many opportunities, this function is seldom used to describe natural phenomena. Concentration of CO2 in the Earth atmosphere [22], the global temperatures [23] but also financial transfers in the context of migrations of populations [24] or pandemic developments [25] are good candidates to be modelled by such a function. As HSF seems to be appropriate to variables resulting from crowds increasing in size, we choose to test it on the BTC time-series. We start by modelling the price peak of the fall 2017 as follows:

Where ith price is determined from the two previous (i-1 and i-2). This formulation is flexible because it can be tuned to fit amplitudes of the signal independently of the time interval used. Eq (1) is of a form similar to Fibonacci series, and like them it is bounded neither in time nor in amplitude. Therefore, the use of this function does not enable us to predict the maximum price of a BTC, nor the time of such a peak. Finally, we must select the periods of interest within the complete dataset to select the peak price of each data subset. In order to calibrate an HSF profile to our dataset and include information on time and amplitude, we have had to find two input parameters (P and P) which describe the evolution of the prices, as well as a sampling interval (dT) which helps define the speed of price increase. To reach a robust solution, we calibrate data against Eq (1), using a brute-force scaling approach, to match both the price amplitudes and the duration of development of each event. For instance, one can satisfactorily approximate the pricing episode of 2017 with P1 = 500 and P2 = 503 (this initial value for P1 fits well with the average value of BTC for the year 2016: approx. $550 +/- 150 USD) when using daily prices. We show the results of our analysis in Fig 5.

Fig 5

BTC pricing events presented in this study.

Observed prices are plotted using grey diamonds triangles, modelled prices using red lines. Preceding events are shown using green lines. Residuals are shown using blue diamonds. All curves and residuals parameters are shown in Table 1. BTC pricing events presented in this study. Observed prices are plotted using grey diamonds triangles, modelled prices using red lines. Residuals are shown using blue diamonds. All curves and residuals parameters are shown in Table 1. BTC pricing events presented in this study. Observed prices are plotted using grey diamonds triangles, modelled prices using red lines. Preceding events are shown using green lines. Residuals are shown using blue diamonds. All curves and residuals parameters are shown in Table 1. On panel j), we show for comparison the fitted curve (in green) shown in panel i). BTC pricing events presented in this study. Observed prices are plotted using grey diamonds triangles, modelled prices using red lines. Preceding events are shown using green lines. Residuals are shown using blue diamonds. All curves and residuals parameters are shown in Table 1. Panel m) shows that more research needs to be done to understand the mechanism of each event. If the data of panel m) can be well modeled, the next peak is more difficult to fit (red line; Table 1). Here we have unique opportunity to see to simultaneous events and may need to be studied in more detail.

3.3. Data fit of secondary episodes (2010–2021)

While it is well known that BTC has been volatile during the fall of 2017, one rarely considers that BTC had already experienced similar pricing episodes composed of 1) a sharp rise of the price (~50–70 days), followed by 2) a short stagnation and 3) a readjustment over several weeks (>100 days). Such a price increase may seem similar to the development of a Minsky-Kindleberger bubble [26-28] except the final price level is not reduced to the pre-surge price. Three of those events have been documented so far: one in 2012 and two in 2013 [29, 30]. Using the approach described above, we found at least 10 additional events which occurred between 2010 and 2021. We have compared those events together by normalizing both their amplitudes and time frames. We show here that regardless of the maximum price, the relationship between duration and price change is strongly consistent, suggesting some self-similarity in the BTC time-series (Fig 4). This observation allows us to apply our approach to all new events found so far.

Fig 4

Time- and Price- normalized data segment preceding price peaks.

Average price history is plotted using a red line. From a price of less than USD1 to > USD 55k, events shows self-similarity. All prices increases are contained within a time-frame of 50–70 days (normalized time ~ 0.7).

We then approximate each event using a HSF by determining the values of P1, P2 and sampling rates. We display the results of all data fit in Fig 5. The quality of each data fit was assessed using their Root Mean Square (RMS) value; distance between each price (x) at the time i and the best-fitted model price (m) histories:

Table 1

Characteristics of time series and statistics of fit curves (Fig 5).

The Root Mean Square (RMS) difference between the modeled curve and original time-series never exceeds a ~26%. Average length of subdataset is approx. 190 days (median ~ 180).

Event	T_1	T_2	P_min	P_max	Price change ($US)	Date 1 (dd mm yyyy)	Date 2 (dd mm yyyy)	RMS	RMS Red (%)	Panel in Fig 5
1	89	186	0	1	1	13 Nov. 2010	18 Feb. 2011	0.08	85.07	a)
2	199	261	1	4	3	03 Mar. 2011	04 May 2011	0.2	87.32	b)
3	199	299	1	35	34	03 Mar. 2011	11 June 2011	2.	76.34	c)
4	517	699	4	13	8	15 Jan. 2012	15 Jul. 2012	0.9	86.02	d)
5	727	937	10	229	219	12 Aug. 2012	10 Mar. 2013	6	85.30	e)
6	1017	1174	66	1132	1065	29 May. 2013	02 Nov. 2013	28	91.19	f)
7	1917	2099	365	705	339	15 Nov. 2015	15 May 2016	21	95.17	g)
8	2191	2467	596	2953	2357	15 Aug. 2016	18 May 2017	165	86.78	h)
9	2454	2524	1933	4066	2133	05 May 2017	14 Jul. 2017	421	84.60	i)
10	2191	2647	596	17803	17206	15 Aug. 2016	14 Nov. 2017	1033	74.51	j)
11	2999	3204	3236	11007	7770	01 Nov. 2018	25 May 2019	1044	80.83	k)
12	3561	3728	9048	19104	10055	16 May 2020	30 Oct. 2020	1312	89.10	l)
13	3561	3773	9048	40789	31740	16May 2020	14 Dec. 2020	2503	84.70	m)
14	3561	3815	9048	57533	48484	16 May 2020	25 Jan. 2021	5621	75.25	n)

Table 1 shows the price changes and RMS for each event shown in Fig 5. In most cases, we can successfully explain approx. 75% of the data signal (Table 1) or more. The price change over each period is > 77% (median ~ +80%) with a minimal value of 48% for event 4. After each peak, BTC deprecated, but its value was never lower than the values preceding the peak. We can compare this remarkable behaviour to data histories (time-dynamics) observed in some natural phenomena. Similar to seismicity rate (see [31-33] for visual comparison) or volcano magma chamber inflation [34], BTC prices hold on to about 30–40% of the peak price after the pricing episode is over. The price decrease being of only approx. 1/3 of the asset volatility suggests that the confidence placed into the asset is never completely dissipated. The driving cause of price peak remains however unclear, and will require more research.

3.4. Similarities with assets under speculative pressure

On the financial market, rapid changes of asset prices may be explained by changes of confidence in the asset (e.g. bad and good results, scandal), by a temporary change in the liquidity of the asset, and by a variety of price manipulation schemes (e.g. pump and dump, insider trading, rumour propagation). In order to explore whether BTC price is driven by a fundamental cause or by external perception, we searched within financial market data if the constant time development of 50–70 days could be observed for other assets of various liquidity. Here, we state that an asset is liquid when any amount of the asset can be traded in a cash market without materially affecting its price. We also assume an orderly transaction in the sense of fair value measurement as defined by IFRS 13, i.e. a transaction is not forced and the agent making the transaction is able to conduct usual marketing activities (such as gathering a sufficient number of competitive bids). In that context, the liquidity can be interpreted as a measure of confidence, seeing how the seller–confident that the price of the same assets will be marginally changed in a near-future–is not afraid of losing wealth by selling their assets as they are. And of course, one should keep in mind that the level of investors’ confidence may be impacted at any time by changing market context (central banks interest rates raises, stock market volatility, etc.).

Because of their known broad liquidity regimes and/or high demand and/or speculation histories, we selected analogues of BTC time series such as gold (XAU), currencies pairs (TRY, and EUR), equities (ENRON, EXCITE), CC (ETHEREUM), bond yields (Greece 10-YR yields) and Tulip bulbs in the 17th century (Figs 6 and 7). Those assets of various classes span the complete range of liquidity in the market sense. Some are considered highly liquid (Gold-XAU), others experienced prominent periods of illiquidity (Turkish lira and Greek debt), others yet have reached terminal illiquidity (Enron and Excite). At last, we chose Ethereum as a reference because of its different governance mechanism and because of the high correlation between ETH and BTC prices (r2 = 0.91, N = 180) over the last six months. Prices variations of those asset values have of course different causes (from purely speculative versus business plan revaluation) but their consequences are very similar: quick change of price followed by a stagnation, and then a sudden price readjustment after more information becomes available to a wider audience.

Fig 6

Time series of Gold (XAU), currencies (TRY and EUR) and 10YR Greek bond yields (weekly data).

Currencies and bond yields show very diverse patterns due to different economic, market and political contexts. Only 10yr Greek Gov. bond yields time-series shows similarities with BTC pricing episodes until the peak is reached.

Fig 7

Time-shifted (x-axis) and normalized (y-axis) time-series corresponding to data shown in Fig 1.

We compare BTC price to BTC pricing periods, Excite stock price, Tulip bulb crisis, and ETH/MONERO prices observed in 2017–2018.

For the periods following each peak, one can distinguish two classes of assets: those which deflate completely (or return to their normal value as Greece 10-YR bond yield) and those which hold their value for various periods of time (XAU, TRY, BTC). In all cases, confidence plays a strong role in the price fixed by the market, and a given level of illiquidity is reached close to the price peak time.

Amongst this group of assets, we distinguish between those who experience illiquidity due to high demand (energy trader ENRON, search web engine EXCITE) and those with extremely low demand (TRY, Greeks). Meanwhile, some did not reach their lowest level due to confidence loss (TRY, XAU, Greeks), and others did because of fundamental business issues (Excite) or even accounting fraud (Enron) and, in the end, were revealed as valueless. Our comparison shows that BTC is not fundamentally different from assets under speculative pressure. What is specific to BTC is that, as for gold (XAU), the stabilization of prices following a price peak suggests that investors’ confidence level remains strong despite the intrinsic parameters indicate the risk of holding BTC for investor is high.

3.5. Correlation with other CC

For other types of assets, it has been observed that a sudden price rise for a given product may spill to others in the same sector; an effect amplified with the level of media attention [35-38]. Capital spilling suggests that investors aim at investing in alternative assets which are either cheaper or more liquid. This is also true for the most capitalized virtual currencies. News stories about the BTC performance increased the visibility of other crypto-currencies, and encouraged the risk-seeking investors to make a compromise between fund allocated and reputation of the CC in order to buy assets as cheap as possible while benefiting from the herd effect. If media attention and social network activity may impact the price of all CC, it seems that differences in design (i.e. centralization, number of coins available) might not have an influence on their price dynamics. Like in the past, contagion was made easier by the availability of common trading tools for a wide variety of trading financial products. The high correlation between virtual assets in general, and their correlated returns following a mediatic event (e.g., NBC Saturday Night Live) confirm that the level of confidence of investors plays a large role in the pricing of virtual assets.

4. Discussion

In this study we examine episodes of BTC price surges. We found that, in the past, BTC sudden price increases have lasted less than 100 days, were not followed by a full depreciation, BTC staying instead at a level close to 30–50% of its peak value, and that the successive BTC price changes were of quasi-exponential nature. We now hope that, when more data become available, analyses similar to those already applied to other kinds of financial returns [39, 40] shall be carried out for BTC and other CC. Put simply, BTC has a lot in common with hard-to-borrow assets, mostly because the market liquidity is limited during periods of price inflation. The valuation of BTC, however, remains a complex endeavor, and one should expect it to behave like any other investment instrument under the scrutiny of a wide population of investors. The overall consistency of each BTC pricing event might be what makes BTC unusual compared to other financial pricing events; few stocks experienced consecutive crises of increasing amplitude without disappearing, or suffering so much that their values never recovered. Finally, we have shown that observations made on the BTC price history could be extended to other types of assets (crypto or not), despite fundamental differences in governance models and price structures (centralization, emissions strategies, price models, underlying businesses). As the BTC also behaved like other equities under speculative pressure, BTC should not be seen purely as a virtual currency. This observation is also supported by the numerous uses of BTC by various owners (savings, speculative, purchase of services, political aims, taste for technologies, hedging, diversification, long-term investment etc.) as published in the recent literature.

But BTC prices are not only driven by pulses of trading. BTC price dynamics is also sensitive to causes outside its own pricing mechanisms (mining, validation, number of coins on the market). Those causes are regulatory framework(s), media attention, social media activity, market conditions, and this open list may be expanded in the future. The relative contribution of all effects is likely sensitive to international market conditions and technological sector influence. All along the BTC price history, prices and traded volumes were very much related to the opening/closing of trading platforms with the implementation of national regulations. For instance, when traded volumes were the largest, during the fall 2017, new platforms based in China, with more relaxed rules regarding funds origins and traders identifications, were very active [15]. From January 2018, trading volumes were dramatically reduced, from millions of coins traded daily to thousands, suggesting that while the price stabilized in the range of $8’000–10’000, the price was not driven by the amount of BTC traded nor by the media coverage (see Google Trends time-series in Supplementary Materials). Finally, new regulations focusing on publicizing the identities of traders and owners, or the provenance of funds, with an aim to prevent illegal use of coins, could obviously change the dynamics of CC in the near-future, as suggested before [41].

Regarding media (social or not), trading volume increases observed in 2017–2018 were comparable to those observed in the 1980’s in other contexts [42, 43]. This research described sudden price increases followed by warnings of market makers and regulators, resulting in the fall of the stock price of interest. Such a loss of enthusiasm in financial assets has been observed in the past, for instance during the bursting of the dot-com bubble, or following press releases on company performance. The fact that BTC survived various episodes of confidence loss during the last decade demonstrates that it is not purely speculative. Rather, its behavior results from a combination of owners’ trust in the future of BTC [44, 45], safety of the transaction system (block chain), and public interest into the asset [11, 46–50].

Whilst our observations are supported by more than 10 events over a decade and more than five order to price magnitude, some questions remain. We are not able to predict the time and amplitude of the next price peak. Also, we found that in some cases it is difficult to discern the starts and ends of peaks when they are close to each other (Fig 5. m/n). A more sophisticated analysis may be helpful in finding the origin of those “split peaks”, and also in linking trading volumes, platform activities and prices on platforms. Further research may help identify potential bottlenecks (trading delays, wrong prices, etc.) between banks involved in derivative products emissions and crypto-platforms trading coins which are used as hedge by those banks.

During our research, we faced some issues to explain our results from an economic perspective, because of the lack of research in some domains. First, further research should be carried around the role of platforms within the trading environment (banks, exchanges, retail investors, institutional investors), including in the Over-The-Counter (OTC) trades as initiated by [51]. As an extension, it would be useful to determine the floating quantity and the tracking of coins in order to constrain which portion of the asset is considered as reserve or long-term investment. In the financial domain, it would be necessary to establish clearly whether BTC prices (and CCs prices generally) correlate with other asset class prices, and over what time-scale. Regarding exchanges efficiency, we could not explore intra-day price variations because we were not able to access the necessary data so far. Those data are of particular importance to document price decrease episodes which span usually less than 5 days (see Fig 8 for episodes between 01 Jan. 2021 and 15 Jun. 2022). Finally, it would be highly useful to continue studying the sociological profile of the crypto investor (e.g.: age, date of entry in the crypto market, wealth level, country, trade volumes) as such information may help banks define the risk appetite of investors, provide better services, and guaranty the stability of the trading environment [52].

Fig 8

Preliminary search for price decreases for the period 01 Jan. 2021–14 Jun. 2022.

We show that the hockey stick function could explain price decreases as well; although the time of development is shorter (days) and likely rooted in the intra-day trading activity.

Periodogram for the sinus function; f = sin(t/30).

(PDF)

Click here for additional data file.

Periodogram for the sinus function; f = sin(t/30) plus a step (dz = 0.1) at t>1000.

(PDF)

Click here for additional data file.

Periodogram for the sinus function; f = sin(t/30) plus a step (dz = 1.5) at t>1000.

(PDF)

Click here for additional data file.

Periodogram for the sinus function [f = sin(t/30)] plus an additional sinus [f = sin((t-60)/5)] at t>1000.

(PDF)

Click here for additional data file.

Periodogram for the equity stock Wirecard (WDI).

(TIFF)

Click here for additional data file.

Periodogram for the equity stock ABB (ABB).

(TIFF)

Click here for additional data file.

Periodogram for the equity stock Tesla (TSLA).

(TIFF)

Click here for additional data file.

Periodogram for the equity stock GameStop (GME).

(TIFF)

Click here for additional data file.

Periodogram (zoom) for the equity stock GameStop (GME).

(PDF)

Click here for additional data file.

Google trend map by city (search = ‘bitcoin price’).

(CSV)

Click here for additional data file.

Google trend map by country (search = ‘bitcoin price’).

(CSV)

Click here for additional data file.

Google trend time series (search = ‘bitcoin price’).

(CSV)

Click here for additional data file.

Google trends related searches to search = ‘bitcoin price’.

(CSV)

Click here for additional data file.

3 Jun 2021

Submitted filename: P1_Ans2Rev.docx

Click here for additional data file.

21 Jul 2021

PONE-D-21-18020

BITCOIN: a life in crisis

PLOS ONE

Dear Dr. Houlie,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Though Reviewer 2 rejected PONE-D-21-18020, the reviewer provided many valuable and constructive comments. Considering three reviewers’ useful comments and the interesting topic of the manuscript, I would like to give you a chance to revise your manuscript during the special period. The revised manuscript will undergo the next round of review by the same reviewers.

Please submit your revised manuscript by Sep 04 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see:  http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at  https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Baogui Xin, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

3. Please amend either the title on the online submission form (via Edit Submission) or the title in the manuscript so that they are identical.

4. Please upload a copy of Figure A3, to which you refer in your text on page 5 and 17. If the figure is no longer to be included as part of the submission please remove all reference to it within the text.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is an interesting paper, however, after reviewing the paper, I do not think it is appropriate for publishing. The paper still needs to be comprehensively improved before publishing. Some detailed comments are shown in the attachment file.

Reviewer #2: The questions raised in this article are interesting, but the methods used are not sufficient to support the conclusions reached, and the conclusions are not analyzed in depth. In addition, the writing of the paper is not standardized, the literature is not reviewed, and the marginal contribution of the paper is not highlighted.

Main concerns

1. Compared with previous articles on Bitcoin prices, what is the motivation and contribution of this article?

2. This paper found that the increase of Bitcoin price follows a similar “Hockey-Stick Shaped” pattern, but the authors did not analyze what caused this pattern, what characteristics of Bitcoin price this pattern reflects, and what implications the discovery of this pattern has on predicting Bitcoin prices.

3. This paper suggested that Bitcoin price is not only driven by the number of coins to be mined but also depends on both the investor’s confidence into the asset and the level of media attention. However, in the article, the authors did not provide data on investor confidence and media attention, but just made assumptions based on previous literature conclusions. This is not the attitude that academic research should have.

4. This paper recognized that Bitcoin should be classified as an illiquid asset, but this conclusion is inconsistent with the public perception. In particular, the authors’ argument for insufficient liquidity is insufficient, and it is difficult to persuade others to agree with the conclusion.

5. The authors of the paper should firmly grasp the purpose of the research, adopt sophisticated methods, reliable data, and fully demonstrate and analyze the conclusions.

Reviewer #3: The paper study the price change of BTC and found they follow a similar patten which called "Hockey-Stick Shaped" events. I find the paper very interesting . It can be accepted after some revision.

The data set is not detailed enough. The data set description section says that the data used in this paper is from 2017， but in the abstract the data set is related to 2018-2020.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: comments.docx

Click here for additional data file.

4 Nov 2021

see attached document, point by points response are listed

9 Dec 2021

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Baogui Xin, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is an interesting research paper regarding the Bitcoin market. After reviewing the whole manuscript, I think the paper needs to do some necessary revisions before the official publishing.

1. Discussion should be the part to illustrate the rationality of the results. However, in the current manuscript, the current discussion seems to the outlook for the future. I suggest the authors to revise the current discussion.

2. Descriptions should make some revisions. For example, “Finally” in Line 277 and “Finally” in Line 280.

Reviewer #2: This article investigated the Bitcoin price dynamics and found that there were eight events with similar durations during the sample period. Studying the price of Bitcoin is a very interesting topic, but I think the authors’ research is not innovative enough and contributions are not enough, and the research design is not rigorous enough.

Main concerns

1. The authors should emphasize the research motivation. Why are you writing this article? What conclusion do you want to get?

2. The conclusion of the article is not credible. The author said that a function similar to the Fibonacci sequence can be used to approximate the price of Bitcoin. I find it hard to believe, and the author did not make further analysis.

3. The research design of the article is not rigorous. The authors’ conclusions all come from observations and intuitions of price dynamics, and they have not analyzed and discussed the conclusions in an economic sense. In addition, the author did not do a robustness test. Will the conclusion of the paper still hold for the period after January 1, 2020?

4. The writing of the paper is also not standardized. The table in the text is not a standard three-line table, the letters in the formula are not explained further, and the abscissa of the time series graph is not indicated by date. Therefore, I think this is not a qualified academic paper.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

23 Jan 2022

See attached file

Submitted filename: ANS_2_rev_R2.docx

Click here for additional data file.

27 Jan 2022

PONE-D-21-18020R2

BITCOIN: a life in crises

PLOS ONE

Dear Dr. Houlie,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected.

Although you have an interesting and valuable paper, the paper needs to be substantially improved before it can be considered for PLOS ONE. Since one of reviewers still advise us to reject your manuscript, I will save your time and hope you can consider improving the manuscript accordingly and resubmitting it as a new article. And I would like to be the Academic Editor of the revised version again.

I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision.

Yours sincerely,

Baogui Xin, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

- - - - -

For journal use only: PONEDEC3

1 Apr 2022

We have answered to latest comments. We have also attached the materials linked to the appeal.

Submitted filename: ANS_2_comments_R3.docx

Click here for additional data file.

2 Jun 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Baogui Xin, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #4: All comments have been addressed

Reviewer #5: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #4: Yes

Reviewer #5: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #4: Yes

Reviewer #5: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #4: Yes

Reviewer #5: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #4: Yes

Reviewer #5: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #4: This is an interesting paper. It is well motivated and generally well written. The topic is interesting and the results can have interesting policy implications for both policymakers and investors. Compared with the previous two versions, I can see the quality of this paper improves a lot. I therefore recommend that the paper be published in PLOS ONE. Congratulations to the authors.

Reviewer #5: It seems a very simple use of some equations. The topic at the beginning of the papar is very interesting but fail to be technically sound. I do not find a contribution around some diffeent ideas or matehmatically different. The conclusions are very naive.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #4: No

Reviewer #5: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

21 Jun 2022

please see attached files.

Reviewer #4: All comments have been addressed

Reviewer #5: All comments have been addressed

ANSWER 01: From this comment we consider we do not need to answer the comments attached to the current revision request. It seems to us that this file is inherited from another revision set and these comments have been answered in September 2021. We consider this as a mistake of the editor assistant. However, we update our answer to question 5 as highlighted in the cover letter.

Reviewer #4: This is an interesting paper. It is well motivated and generally well written. The topic is interesting and the results can have interesting policy implications for both policymakers and investors. Compared with the previous two versions, I can see the quality of this paper improves a lot. I therefore recommend that the paper be published in PLOS ONE. Congratulations to the authors.

ANSWER 02: We thank R1 for his positive feedback. We continued to research in the domain of price reduction since the last version. We show this latest finding in the new version and mention this in the discussion of the paper.

Reviewer #5: It seems a very simple use of some equations. The topic at the beginning of the papar is very interesting but fail to be technically sound. I do not find a contribution around some diffeent ideas or matehmatically different. The conclusions are very naive.

ANSWER 03: We proposed in our paper that price increases and stabilization were driven at least partially by confidence. Recent events (price fall) have shown that confidence is driving the dynamics of the BTC price (wrt. Luna) and BTC price was not free being impacted by adverse market conditions (central banks interest rises). Since the last version of the manuscript, we have added a figure showing that our model is also true for price decreases (including the one of 15 Jun 2022). We thank R5 for his comments and hope the latest additional improvements will allow decrease the perceived level of naiveness of our research.

Submitted filename: 20220614_ANS_2_rev.docx

Click here for additional data file.

24 Aug 2022

BITCOIN: a life in crises

PONE-D-21-18020R4

Dear Dr. Houlie,

I've been trying to support non-economists to employ some non-econometric approaches from their scientific fields to explore financial problem from some different perspectives. I also believe that my efforts mentioned above will better promote the diversified development of economics. I can understand Reviewer 6's motivation and worry because Reviewer 6 fears that this paper falls into the same trap that many similar mainstream currency papers trigger. To sum up, to promote the diversified development of economics, I still recommend this manuscript to be accepted.

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Baogui Xin, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #5: All comments have been addressed

Reviewer #6: (No Response)

Reviewer #7: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #5: Yes

Reviewer #6: Partly

Reviewer #7: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #5: Yes

Reviewer #6: No

Reviewer #7: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #5: Yes

Reviewer #6: Yes

Reviewer #7: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #5: Comments and onservations have been included in the document. As a future line of research maybe you can develop this research for ore cryptocurrencies

Reviewer #6: I fear that this paper falls into the same trap that many similar mainstream currency papers trigger: applying time series methodologies and callibration techniques to financial price data to find a pattern, without a detailed analysis of the components and dynamics of the underlying systems contributing to the price behaviour. The universe of time series functions is sufficiently large that something can usually be found, but this doesn´t really help with understanding the behaviour of the underlying system. This is a particularly nasty trap in BTC´s case, since very limited data is available about other components of its price behaviour, in particular the amount of explicit and disguised fractional reserve banking that is being performed with BTC (f.ex. tether). Without a clear justification based on possible shared mechanisms, which is absent, I don´t think it is valid to blindly apply techniques from seismological or meterological data in the way suggested here. (I would invite the authors to consider their opinion of a paper which took methods from financial analysis and calibrated them to fit short term seismic data.)

As an aside, since the Nyquist theorm applies to the frequency analysis here, this implies that at least twice the time period of the underlying system/data must be available in order to avoid aliasing. The relevant time period for Bitcoin, if there is one, is not known. If the economic "business cycle" period, which is typically estimated between 10-20 years is taken as a rough subsitute, then BTC hasn´t been in existence long enough for any conclusion to be drawn from that form of analysis.

Overall there are a number of occasions in the paper where I think claims are being made without sufficient substantation, and that the paper is also not robust to a suggestion of some degree of cherry picking. For example, why Greek bonds in particular? There is a very large set of different bond instruments of different ratings to be selected from here, has an analysis been done on all of them? Similarly Enron is an interesting example of an instrument of systemic fraud, but not the only one from that period. No evidence is provided to support the assertation that Chinese mining was responsible for the peak in bitcoin volumes in 2016-17, and other causes have been suggested for this, including Tether manipulation, which is also more likely given the size of the deviation. Probably the most likely explanation for the price behaviour after price peaks is the sale of coins derived from mining to finance electricity and other related costs, but this factor is not mentioned.

A general problem with any financial time series analysis (which often gets overlooked) is finding satisfactory methods to compensate for changes in the underlying quantities of the unit of measurement (money). In principle, this is something that bitcoin was supposedly designed to avoid, but in practice this has not been the case so far. To this point in time (mid-2022) the supply of bitcoin has expanded relatively rapidly (as designed), and also nothing about bitcoin´s design prevented it from being used as an asset for fractional reserve lending, and as recent events have shown, this feature of the existing financial system duely emerged within the cryptocurrency financial system and has proceeded to wreak its own brand of financial chaos. Since the changes in quantity of BTC due to mining are now (as designed) stabilising, this should at least be highlighted in the paper as previous behaviour may not be a guide to future dynamics now that this is finally occurring.

Correcting (normalising) for changes in the quantity of the unit of measurements involved here (USA M3 and BTC) would make some of these affects clearer, and probably make some of the BTC price behaviour reported here more extreme, but would still run into the problem mentioned by the authors in the discussion in that there is also a very large amount of dormant BTC to be factored in. Whilst I think there might be an interesting data paper here, in terms of presenting the various time series and quantitative analysis that the author have performed, and an interesting historical paper, if a timeline of critical bitcoin events was added, for example the bitcoin mining rate date changes, first posting on slackdot.com, etc., I don´t find the financial price time series analysis compelling, or adding anything to the literature.

Reviewer #7: The paper investigates a hot topic in finance nowadays.

Reading the reviewers’ comments and the authors’ answers and the latest version of the paper, I found it interesting and it shed light to some aspects in the history of Bitcoin prices that worth investigation.

The authors mention they investigate the “BTC price time-series (17 August 2010 – 27 June 2021)” and “All data listed in this article can be found here: https://finance.yahoo.com/”

However, nowadays, in Yahoo finance data is not available for the entire period – older than 2014. In Yahoo finance “Date shouldn't be prior to '2014-09-17'”

The affirmation on row 275 (page 13) – “as published in the recent literature” – should be followed by some citation(s).

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #5: No

Reviewer #6: Yes: Jacky Mallett

Reviewer #7: No

**********

20 Sep 2022

PONE-D-21-18020R4

BITCOIN: a life in crises

Dear Dr. Houlié:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Professor Baogui Xin

Academic Editor

PLOS ONE