Conclusion de bitcoin

Financial Innovation volume 5 , Article number: 2 Cite this article. Metrics details. In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a VAR framework, a copulas approach, and non-parametric drawings, to capture a dependence structure. Using a weekly dataset from to , our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume.

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Shocks to search values have a positive effect, which persisted for at least a week. It is difficult to make a prediction, particularly about the future! Predictions of future technological changes and their implications for the socio-economic and financial outlook are areas of research that have never lost their glitter.

In the same vein, forecasting the dynamics of technology and its implications for financial asset prices and their returns have always been one of the most interesting aspects of research. In the twenty-first century, the perpetual evolutionary characteristics of financial and technological innovation have brought us to the age of cryptocurrencies, one of which is Bitcoin.

Crypto or digital currency is an asset that only exists electronically. The most popular cryptocurrencies, such as Bitcoin, were designed for transactional purposes; however, they are often held for speculation in anticipation of a rise in their values see Bank of England for detailed insight into digital currencies. Based on blockchain technology, Bitcoin is the most popular and used cryptocurrency, and in some cases, has been treated in tandem with conventional currencies see Kristoufek and Vosvrda, Bitcoin came with controversy and there are doubts about its future, yet the popularity of cryptocurrencies has been increasing since their inception Li and Wang, One aspect of this controversy is the debate on whether Bitcoin should be considered a safe financial asset.

A few recent studies have debated about the Bitcoin market and its dynamics; for example, Li and Wang argued that despite the intense discussion, our understanding regarding the values of cryptocurrencies is very limited. Some of the participants in this debate have appreciated the role of cryptocurrencies; for instance, Kim argued that the simpler infrastructure and lower transaction cost of Bitcoin are advantages compared to retail foreign exchange markets.

Blockchain: Criptomonedas y Contratos Inteligentes – ITBA

Similarly, Bouri et al. Financial innovation has been an important platform for the debate and implications of blockchain technology and cryptocurrencies for instance, see the special issue on blockchain 2. The emergence of cryptocurrencies has important implications for the global economy in general and emerging economies in particular. For instance, a study by Carrick argued that Bitcoin and cryptocurrencies have idiosyncratic features that make them suitable and complementary to the currencies of emerging markets.

Furthermore, the risk to Bitcoin technologies can also be minimized and concomitantly, cryptocurrencies have an important role to play in emerging economies. Similarly, on the importance of Bitcoin, Polasik et al. A study by Pazaitis et al. Similarly, from the technological as well economic perspective, Goertzel et al. Contrarily, some contemporary studies, for instance, Corbet et al. Similarly, Jiang reported the existence of long-term memory and inefficiency in the Bitcoin market.

Alvarez-Ramirez et al. They reported that the Bitcoin market exhibits periods of efficiency alternating with periods where the price dynamics are driven by anti-persistence. However, Bariviera et al. A later study by Bouri et al. Nonetheless, in the debate or controversy around cryptocurrencies, important factors that have been fairly underappreciated are their determinants and predictability. On this aspect, a study by Feng et al. In recent years, some studies have analyzed the ability of keyword analysis to forecast technological factors.

For instance, a study by Dotsika and Watkins used keyword network analysis to identify the potentially disruptive trends in emerging technologies3 and reported significant influence. Similarly, Dubey et al. Some studies have tested the effects of data availability on the internet and in print-media on financial asset returns. For instance, in equity markets, Tetlock analyzed the role of traditional media, whereas Bollen et al.

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Similarly, Moat et al. A study by Preis et al. Interestingly, search engines can influence portfolio diversification, as Google Trends are found to be connected with Bitcoin prices; there was also evidence of the asymmetric effect of an increased interest in the currency while it is above or below its trend value Kristoufek, Undoubtedly, this may result in asset bubbles or Minsky movements see Tavasci and Toporowski, ; however, overwhelming information is generated in the process involved in the decision-making that leads to cryptocurrency transactions.

The more frequently internet users conduct a search on a topic, the higher its indicator. A number of studies from social to health sciences have employed these figures4. Specific to the financial world, there is some limited evidence that suggests potential causal linkages; however, it requires further exploration. For instance, Preis et al.

A later study by Preis et al. Furthermore, portfolios constructed based on a high number of searches will outperform the market. Studies by Joseph et al. However, specific to Bitcoin, to the best of our knowledge, no study has explored this nexus. Keeping this concise evidence in context, there is a caveat in existing knowledge on the role of search engines and the data generated during their routine functioning process in predicting the dynamics of Bitcoin.

Blockchain: Criptomonedas y Contratos Inteligentes

Accordingly, this study is an endeavor to analyze the significance of search engines for predicting Bitcoin returns and volume. We employ a rich set of established empirical approaches including the VAR framework, a copulas approach, and nonparametric drawings for time series to calculate the dependence structure. Using a weekly dataset from to , our key results suggest that Google search values carry a remarkable amount of information for predicting Bitcoin returns. There was also a positive effect of Google search values on Bitcoin trading volume, although the estimates fell short of statistical significance.

Our findings contribute to the recent literature and debate on cryptocurrencies, their role in developed and emerging economies, and understanding their dynamics as well as their predictability. We eliminated Google search values extracted before because these figures are unreliable see Challet and Ayed, , for details. Furthermore, we computed the logarithmic figure in the movement of Google search values and divided by standardization standard deviation to make this index compatible with changes in Bitcoin prices, which were already converted to returns Eq.

Due to the continuous trading in the cryptocurrencies market, it includes transactions carried out the weekend days. Therefore, we choose to collect the Bitcoins price data on Sunday as it is the last day in the week. Concomitantly this does not require correction for the insufficient data, for instance like stock markets which only open until Friday. Furthermore, Google Trends are completely extracted from the open-source provided by Google. In addition, we adjusted some of the insufficient data collected from Google Trends to have a continuous time series.

However, in the Weeks with no data were skipped and returns and volume were adjusted to balance the dataset. This approach was popularized by Campbell and Yogo and is used to construct the volume series, which is also tested for stationarity.

A number of studies focusing on volume and returns have followed this approach, most remarkably, Cooper , Odean , Cochrane , and Gebka and Wohar To begin, we performed a descriptive statistical analysis to gain insight into the features of the data. The results are presented in Table 1. After the brief description of data, we employed unit root tests to check if the data series is stationary, using the augmented Dickey-Fuller ADF and Phillips-Perron tests.

The results presented in Table 2 suggest that the dataset is stationary at levels, i. Next, we tested for co-integration using the Johansen cointegrated test for these pairs of variables. The results of the co-integration test presented in Table 3 suggest that there is no co-integrating relationship between any two pairs i. This suggests that the relationship between Google search values and Bitcoin returns and trading volume do not persist in the long run. This is intuitive, considering the volatility and dynamics of the market.

Hence, this leads us to a VAR estimation. Before proceeding, we selected the lag order based on the Akaike information criteria and chose three as the optimal number of lags6. To determine the direction of causality, we performed a Granger causality test and the results presented in Table 4. The results of the Granger causality test showed that there is strong evidence of causality for Bitcoin returns only for the SGSV. This was statistically unidirectional causality running from the SGSV only to returns.

This means that Bitcoin returns on can be predicted by the Google search value.

This is an intuitive finding, as investors looking for Bitcoin information on the Internet may lead to an increase in the price of Bitcoin, producing a cause-and-effect relationship with Bitcoin returns. Next, to take a broader perspective on the association among the variables being analyzed, we performed an impulse response function IRF analysis; the results are presented in Figs.

The response was also statistically significant and the surge in returns persisted for a period before starting to decline. This implies that a shock on the search value leads to an increase in returns immediately over the following week.

The resolution of the Bitcoin experiment

Afterwards, it sharply decreases and ends in the second week. On the other hand, stock returns did not lead to a surge in searches. Moreover, this shock triggered a gradual increase in trading volume over two weeks, and thereafter the effects started to diminish. The remaining pairs of analysis did not show any significant responses, indicating lack of association.

Exchanges news

Accordingly, we can only infer that one can confidently predict a surge in trading volume in response to a surge in the SGSV. However, the contribution of the SGSV to volume is comparatively trivial. Investors find more information about Bitcoin by searching, but their trading behavior is not explained by the action of searching. This also implies that those who search do not necessarily enter into transactions. We also employed a copulas approach with an estimated parameter to define how the dependency holds between the variables of interest.

The rationale for enriching our estimation with this approach is a manifested in the notion to perform an inclusive empirical analysis, and b that the assumptions for the previous test are quite strict, whereas copulas meet more requirements for testing dependence structures, including left tailed, right tailed, or normal distributions. The nonparametric approach is a good method for estimating the dependence structure for a pair of random variables, whereas the parametric copulas is the best indicator for identifying the position of tail dependence rather than structure Nguyen et al.

Instead of employing correlation or causality with the disadvantage of scalar measures of dependence or linear estimations, we employ Kendal-plots and copulas to determine the dependence relationship by joining the marginal distribution with the joint distribution of the variables being analyzed. Hence, this approach is an appropriate candidate for use as the framework of analysis. Furthermore, the fluctuation of Bitcoin prices is quite high, depicting substantial nonlinearities; using a traditional approach such as correlation or Granger Causality would be prone to producing spurious results for estimation.

For all these reasons we employed copulas and a nonparametric approach. The results are presented in Table 5 :. With the highest log likelihood, we choose the Gumbel copulas family for estimation. The results suggested that the Google search value has a strong relationship with returns but a comparatively weaker one with volume. In addition, the Gumbel copulas family right tail indicates joint probabilities for increasing values for both groups. Last, Kendall plots were adopted, which is a graphical approach based on rank statistics.

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