Introduction

The most well-known cryptocurrency being traded is Bitcoin (BTC). Even though Bitcoin existed since 2009, it has recently become a hot topic for discussion. During the year 2017, Bitcoin price went up from a starting point at below USD 1,000 and reached its peak at above USD 19,000 - more than 1,800% of return in merely one year!

With the surge of Bitcoin activities, we would like to conduct a study to determine whether there are any drivers from financial markets, economic indicators or political triggers that could establish meaningful relationship or possible correlation with Bitcoin price movement.

Data and Method

Selected factors/variables under different domains will be used as data in this study to explore and determine their impact on Bitcoin price movements.

Financial Market Factors

Bloomberg is an accurate and reliable platform that will be used as the source to get financial data of the variables and/or important events happening in financial markets.

Bitcoin is recognized as one of currencies being traded in financial markets, priced in USD, with BTC being assigned as its ticker. Since 2017 was the year with most significant movements in Bitcoin prices, we limited the timeframe for our analysis to only one year. As a result, we obtained daily prices and calculated daily price returns on the selected variables in financial markets during 228 trading days in 2017.

Note: Daily return is calculated as [Today’s Price - Yesterday’s Price] / Yesterday’s Price

Selected factors included variables from the two segments operated within financial markets:

In addition to the above, we also included S&P 500 Index as it is widely used to measure performance of broad domestic economy.

Analytical methods to be used to study financial markets variables would be:

Below is the summary statistics of selected factors from financial markets.

##       Date                          BTC              S&P500    
##  Min.   :2017-01-03 00:00:00   Min.   :  789.1   Min.   :2258  
##  1st Qu.:2017-04-06 00:00:00   1st Qu.: 1216.1   1st Qu.:2367  
##  Median :2017-07-07 00:00:00   Median : 2597.7   Median :2438  
##  Mean   :2017-07-04 19:10:44   Mean   : 4052.5   Mean   :2451  
##  3rd Qu.:2017-09-28 00:00:00   3rd Qu.: 4741.4   3rd Qu.:2510  
##  Max.   :2017-12-28 00:00:00   Max.   :18674.5   Max.   :2690  
##                                                                
##      USDEUR           USDGBP           USDCNY          USDJPY     
##  Min.   :0.8308   Min.   :0.7358   Min.   :6.487   Min.   :107.8  
##  1st Qu.:0.8480   1st Qu.:0.7580   1st Qu.:6.627   1st Qu.:110.9  
##  Median :0.8763   Median :0.7729   Median :6.792   Median :112.2  
##  Mean   :0.8854   Mean   :0.7760   Mean   :6.755   Mean   :112.1  
##  3rd Qu.:0.9302   3rd Qu.:0.7974   3rd Qu.:6.884   3rd Qu.:113.3  
##  Max.   :0.9611   Max.   :0.8227   Max.   :6.964   Max.   :117.8  
##                                                                   
##       Gold          Silver          Nymex        Agriculture   
##  Min.   :1159   Min.   :15.62   Min.   :42.53   Min.   :368.9  
##  1st Qu.:1237   1st Qu.:16.63   1st Qu.:47.87   1st Qu.:384.6  
##  Median :1261   Median :17.02   Median :50.44   Median :411.3  
##  Mean   :1259   Mean   :17.06   Mean   :50.86   Mean   :409.8  
##  3rd Qu.:1282   3rd Qu.:17.49   3rd Qu.:53.33   3rd Qu.:426.0  
##  Max.   :1349   Max.   :18.54   Max.   :59.97   Max.   :462.6  
##                                                                
##      BTC %             S&P500 %             USDEUR %         
##  Min.   :-0.13308   Min.   :-0.0181783   Min.   :-0.0138656  
##  1st Qu.:-0.01490   1st Qu.:-0.0012418   1st Qu.:-0.0035381  
##  Median : 0.01306   Median : 0.0005753   Median :-0.0004675  
##  Mean   : 0.01315   Mean   : 0.0007736   Mean   :-0.0005936  
##  3rd Qu.: 0.03827   3rd Qu.: 0.0029257   3rd Qu.: 0.0025969  
##  Max.   : 0.21034   Max.   : 0.0124159   Max.   : 0.0140579  
##  NA's   :1          NA's   :1            NA's   :1           
##     USDGBP %             USDCNY %             USDJPY %         
##  Min.   :-0.0214851   Min.   :-0.0117995   Min.   :-0.0202440  
##  1st Qu.:-0.0034116   1st Qu.:-0.0012052   1st Qu.:-0.0035883  
##  Median :-0.0004434   Median :-0.0000293   Median :-0.0004445  
##  Mean   :-0.0003974   Mean   :-0.0002773   Mean   :-0.0001697  
##  3rd Qu.: 0.0029260   3rd Qu.: 0.0008868   3rd Qu.: 0.0036541  
##  Max.   : 0.0167120   Max.   : 0.0071838   Max.   : 0.0180252  
##  NA's   :1            NA's   :1            NA's   :1           
##      Gold %              Silver %             Nymex %          
##  Min.   :-0.0227569   Min.   :-0.0300917   Min.   :-0.0538201  
##  1st Qu.:-0.0041170   1st Qu.:-0.0067950   1st Qu.:-0.0080979  
##  Median : 0.0013111   Median : 0.0001802   Median : 0.0033410  
##  Mean   : 0.0005106   Mean   : 0.0002114   Mean   : 0.0007133  
##  3rd Qu.: 0.0045897   3rd Qu.: 0.0080610   3rd Qu.: 0.0109688  
##  Max.   : 0.0267564   Max.   : 0.0419818   Max.   : 0.0334484  
##  NA's   :1            NA's   :1            NA's   :1           
##  Agriculture %       
##  Min.   :-0.0360349  
##  1st Qu.:-0.0055557  
##  Median :-0.0008786  
##  Mean   :-0.0005629  
##  3rd Qu.: 0.0047577  
##  Max.   : 0.0358986  
##  NA's   :1

Economic and Political Factors

As part of our analysis, we also wanted to further understand whether any key economic and political factors, reported by the World Bank, would correlate with the volume of Bitcoin traded in the different markets. We chose four economic and political factors that, from an initial qualitative analysis, showed the most relevant impact/correlation with Bitcoin. The four factors studied had the following descriptions as outlined by the Worldwide Governance Indicators (WGI) project:

In order to compare the different index, we research the biggest traders by volume for Bitcoin and estimated a Volume/Country as represented in the table below (Source: https://data.bitcoinity.org). To compile the data, we assumed that the headquarters location for each of the trading companies was the market they mainly operated from.

Market Volume (In Million last 6 months)
China 1.05
Hong Kong 10.4
Japan 3.83
Luxemburg 3.65
UK 0.78
US 9.36

To visualize the data collected we developed two different graph types, one map to show the different markets involved as categorical variables and four graphs to understand the correlation between each index and the volume of bitcoin traded. The visual representation and the discussion of the results will be addressed in the following sections.

Analysis and Discussion

Financial Market Factors

The above table summarized correlation coefficients between the two financial market variables' daily prices in 2017. The correlation coefficient is used to determine the direction and the strength the relationship of the selected variables. We could see that Bitcoin established quite strong positive relationship with S&P 500 index with correlation coefficient of 0.9. However, its relationship with others, both currencies and commodities, was varied with correlation coefficients ranging from -0.7 to 0.6. These coefficients were useful to determine the overall relationship; however, they were not sufficient to conclude that they were Bitcoin's price drivers.

The line graph showed the comparison of daily price movement throughout 2017 for all selected financial market variables. We could see that Bitcoin, represented by orange line, demonstrated much higher volatility in its price movement. For others, their daily price change was clustered around 0% and within the range of -/+ 5%, while it could range from more than -10% to over 20% for Bitcoin. Looking at the trend, we could not detect any corresponding patterns and, therefore, not able to reasonably draw meaningful relationship between Bitcoin's and others' price movements.

This box plot showed the shape of distribution, central value and variability of daily returns in 2017 for all selected financial market variables. As we could see, Bitcoin's box spans, representing the interquartile range of its daily returns, was much wider than those of others', meaning that Bitcoin had much higher variability in its returns distribution. In addition, Bitcoin's median daily return was also higher than others'. Looking at the span of daily return distribution, we could not identify any comparable pattern of other tickers with that of Bitcoin. Bitcoin seemed to follow its own distribution pattern with many instances falling out of interquartile range. 

In addition to looking at the distribution of daily price returns, we used the density plot to examine the distribution pattern of each ticker's price movement and determine whether their prices followed normal distribution. As we had 10 tickers of interest, we grouped them based on their segments within financial markets for easier interpretation, assuming that the same segment should share the same/similar distribution. From the density plot, we could see that none of them followed normal distribution and did not even establish similar distribution patterns. Each group's distribution was quite different, meaning that the price movement of each group did not follow one another.

Economic and Political Factors

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To understand the trading regions and the coverage, we have represented the highest volume trading markets in the worldwide map above. Plotting this categorical variable clearly shows that markets with financial services background and/or tax benefits are key players. Additionally, all main economic powers are represented and covered through the main traders evaluated.

To understand if these key economic and political index had any relationship with the Bitcoin volume traded in the different markets, we plot 4 different graphs that represented the impact of each of the factors towards the volume traded in Millions (last 6 months)

The graphs above did not show a clear correlation among these four factors and the volume of Bitcoin traded. The only conclusions we can obtain from this high-level comparison are:

- The market with the highest volume also ranks highest in three of of the four index.
- There is one clear outlier (China) in the four graphical representations that has the lowest ratings for the four index.
- Regulatory Quality, Rule of Law and Government Effectiveness have a very similar correlation with Volume while Political stability is differently represented.

Conclusion

With Bitcoin's relatively high volatility in price movements during 2017, we could not establish any meaningful relationship between Bitcoin and several variables representing financial markets, including overall economy index, currencies and commodities. Bitcoin seemed to follow its own distribution pattern with none of our selected financial markets variables could reasonably be identified as possible Bitcoin's price drivers.

In addition, we could conclude that there was a relationship between markets with strong economic and political factors and Bitcoin trading volume. However, given these indicators were only estimated on a yearly basis, this part of our analysis did not successfully contribute to further understand the price of Bitcoin and its key drivers. As next steps, we would research further other publicly available indexes calculated at a market level by month and, if not available, we would need to conduct a primary research analysis.