Metrics details Abstract This paper investigates the role of the frequency of price overreactions in the cryptocurrency market in the case of BitCoin over the period — Specifically, it uses a static approach to detect overreactions and then carries out hypothesis testing by means of a how to make bitcoin fluctuations of statistical methods both parametric and non-parametric including ADF tests, Granger causality tests, correlation analysis, regression analysis with dummy variables, ARIMA and ARMAX models, neural net models, and VAR models.
Specifically, the hypotheses tested are whether or not the frequency of overreactions i is informative about Bitcoin price movements H1 and ii exhibits no seasonality H2.
On the whole, the results suggest that it can provide useful information to predict price dynamics in the cryptocurrency market and for designing trading strategies H1 cannot be rejectedwhilst there is no evidence of seasonality H2 cannot be rejected. Introduction Cryptocurrencies have attracted considerable attention since their recent creation and experienced huge swings.
Such significant deviations of asset prices from their average values during certain periods of time are known as overreactions and have been widely analysed in the literature since the seminal paper of De Bondt and Thalervarious studies being carried out for different markets how to make bitcoin fluctuations, FOREX, commodities etc.
However, hardly any evidence is available to date on the cryptocurrency market, which is particularly interesting because of its extremely high volatility compared to the FOREX or stock market see Caporale and Plastun a for details. In the most recent years interest in the cryptocurrency market has increased even further, and price prediction has been investigated in various studies Ciaian et al.
However, the evidence is still mixed. Overreactions are detected by plotting the distribution of logreturns. Then, the following null hypotheses are tested: i the frequency of overreactions is informative about BitCoin price movements H1and ii it exhibits no seasonality H2.
The remainder of the paper is organised as follows. Literature review According to Hileman and Rauchs there were more than academic papers devoted to the cryptocurrency market published before the crypto boom; their number has increased further since then.
The cryptocurrency market is still relatively young and as a result papers have initially analysed some of its general features Dwyer ab ; Elbahrawy et al. There is only a limited number of studies examining instead its long memory and persistence Caporale et al. Bariviera finds evidence of long memory in the daily dynamics of BitCoin; they also show that persistence in the cryptocurrency market is decreasing. Similar conclusions are reached by Bouri et al.
Aggarwal examines Bitcoin returns and finds strong evidence of market inefficiency see also Urquhart Calendar anomalies in the cryptocurrency market are analysed by Kurihara and Fukushima and Caporale and Plastun cintraday patterns are explored by Eross et al.
Ma and Tanizaki analyse the day-of-the-week effect for both returns and their volatility in the cryptocurrency market, and find significantly high volatilities on Monday and Thursday. Similar results are reported by Aharon and Qadan Eross et al. Analysing overreactions in the case of the cryptocurrency market is particularly interesting because of its extreme volatility see Caporale and Plastun a ; Cheung et al. Catania and Grassi show that world of trading behaviour in the cryptocurrency market is quite complex, with outliers, asymmetries and nonlinearities that are difficult to model.
Al-Yahyaee et al. Balcilar et al. Aharon and Qadan show that normally used variables have limited forecasting power for Bitcoin prices. Khuntia and Pattanayak explore time-varying linear and nonlinear dependence in Bitcoin returns.
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Kristoufek finds that the trade-exchange ratio plays an essential role in driving Bitcoin price fluctuations in the long run.
Ciaian et al. Another issue investigated in the literature is whether overreactions exhibit seasonality. De Bondt and Thaler show that they tend to occur mostly in a specific month of the year, whilst Caporale and Plastun b do not find evidence of seasonal behaviour in the US stock market.
Note also that according to Khuntia and Pattanayak market efficiency in the cryptocurrency market is evolving over time. Caporale and Plastun a find evidence in favour of the overreaction hypothesis, whilst Bartos report that the cryptocurrency market immediately reacts to the arrival of new information and absorbs it; as a result prices are not affected by overreactions.
Whilst most studies examine abnormal returns and the subsequent price behaviour in general, contrarian movement for a given time interval day, week, and monththe current paper focuses on the frequency of abnormal price changes.
We will aim to show that the frequency of abnormal price changes can be a useful tool for price predictions in the cryptocurrency market.
What Determines the Price of 1 Bitcoin?
Methodology The first step in the analysis of overreactions is their detection. There are two main methods.
One is the dynamic trigger approach, which is based how to make bitcoin fluctuations relative values. Wong and Caporale and Plastun a in particular propose to define overreactions on the basis of the number of standard deviations to be added to the average return. The other is the static approach which uses actual price changes as an overreaction how to make bitcoin fluctuations.
Bitcoin fluctuations and the frequency of price overreactions
Caporale and Plastun b compare these two methods in the case of the US stock market and show that the static approach produces more reliable results. Therefore, this will also be used here. The static approach was introduced by Sandoval and Franca and developed by Caporale and Plastun b. The next step is analysing the frequency distribution by creating histograms. Thresholds are then obtained for both positive and negative overreactions, and periods can be identified when returns were above or equal to the threshold.
What Determines the Price of 1 Bitcoin?
Such a procedure generates a data set for the frequency of overreactions at a monthly frequencywhich is then divided into 3 subsets including, respectively, the frequency of negative and positive overreactions, and of them all. There is a body of evidence suggesting that typical price patterns appear in financial markets after abnormal price changes.
The relationship between the frequency of overreactions and BitCoin prices is investigated here by running the following regressions see Eqs. The size, sign and statistical significance of the excellent signals for binary options provide information about the possible influence of the frequency of overreactions on BitCoin log returns.
To assess the performance of the regression models a multilayer perceptron MLP method will be used Rumelhart and McClelland This method is based on neural networks modelling.
The algorithm is as follows. This procedure generates an optimal neural net. The results from the neural net are then compared with those from the regression analysis. Hypothesis 2 H2 The frequency of overreactions exhibits no seasonality.
Frequently Asked Questions
We perform a variety of statistical tests, both parametric ANOVA analysis and non-parametric Kruskal—Wallis testsfor seasonality in the monthly frequency of overreactions, which provides information on whether or not overreactions are more likely in some specific months of the year.
Empirical results The data used are BitCoin daily and monthly prices for the period As can be seen, two symmetric fat tails are present in the distribution. The next step is the choice of thresholds for detecting overreactions. Detailed results are presented in Appendix 2. Visual inspection of Figs. By contrast, there is a negative correlation in the case of returns and log returns.
The overreaction multiplier exhibits a rather strong negative correlation with BitCoin log returns. Finally, the overall number of overreactions has a rather weak 90 profitable options trades with prices.
The highest coefficient corresponds to lag length zero, which means that there is no need to shift the data. The next step is testing H1 by binary options in metatrader a simple linear regression and one with dummy variables see Sect.
In all three cases the specification with the highest explanatory power is the one including negative and positive overreactions as separate variables, though in the case of BitCoin closes the positive sign of the coefficient on negative overreactions is not what one would expect.