CAPM

The portfolios are plotted in a scatter plot to construct the CAPM with the respective efficient frontier and CML, Figure 21. The efficient frontier is constructed by the connected dots on the plot and reveals the superior portfolios of the portfolio analysis. Portfolio 1-7 are all inferior as there are other portfolios that are significantly superior in terms of annual expected return given a relatively equal expected standard deviation. The slope of the CML is significantly steep in which may be interpreted as additional weighting of Bitcoin in the portfolios tend to increase the annual expected return with relatively low impact on standard deviation.

Figure 21: Capital Asset Pricing Model, Efficient Frontier and CML of Portfolios

6 Discussion

The empirical results presented and interpreted in the previous chapter are assessed and discussed in this chapter. The assessment and discussion of the results is based on the overall understanding of the Bitcoin market reflecting the financial theory, models and economic thoughts presented. The significant findings of the quantitative analyses and the academic work is assumed to be sufficient in order to answer the set of research questions sought to be answered.

RQ1: “What is driving the Bitcoin market?”

RQ2: “How does the future look like for the Bitcoin market”?

6.1 Cost of Production Analysis of Bitcoin

The findings of the cost of production analysis reveals a significant relationship between the Bitcoin spot price and the estimated cost of production. Based on the given time period of research the estimated cost of production is trending closer to the Bitcoin spot price.

Assuming Bitcoin as a commodity in this analysis, the results are rather interesting as the price of Bitcoin and really any commodity tends to gravitate towards the production cost. If the Bitcoin spot price is below the estimated cost of production the incentive of production decelerate. If the Bitcoin spot price is above the estimated cost of production the incentive of production accelerates. The increased incentive of production would increase network difficulty and electricity consumption and ultimately creating an equilibrium of the Bitcoin spot price and the estimated cost of production. However, this price relationship may not be present at all times as it could also be argued that miners would continue to operate their business for short time periods at a slight loss given that they may sell Bitcoin at a higher price in the future. Additionally, there is also a possibility for miners to negotiate futures contracts and hedge price risk by selling at fixed rates in advance.

The model of estimated cost of production may be rather optimistic as the model ultimately model a higher price of the Bitcoin spot price holding the demand for Bitcoin constant in terms of electricity consumption and electricity costs. As the block reward is cyclically cut in half so are miners revenue and this may cause a potential threat to the model if demand for Bitcoin experience a prolonged significant drop. If demand for Bitcoin decrease the Bitcoin spot price would decrease and the incentive for miners to continue their business will

decelerate. This situation may offer a potential mining death spiral for the Bitcoin network as

miners would eventually capitulate and shut down their business as it is no longer profitable.

However, a mining death spiral is rather unlikely as the main contributors to the mining business are aware of this potential threat and are acting accordingly with long-term perspectives and strategies.

A reasonable significant finding, in reference to the research questions, for this particular analysis is the assumption of that the Bitcoin spot price is highly influenced by the underlying production costs, likewise other commodities. Even though the Bitcoin spot price diverge significantly at certain time periods compared to the model, the price tends to correct to the fundamental value in which is the estimated cost of production.

6.2 Regression Analysis

The regression analysis discloses the relationship between the variables of interest. For the first simple linear regression equation a 1% increase in ln(Network Difficulty) equals a 0.66% increase in ln(Bitcoin Spot Price). These results are rather interesting as one would expect that increase in ln(Network Difficulty) would be a more significant determinant of change in ln(Bitcoin Spot Price). A possible explanation for these results may be that the increasing efficiency of mining hardware outperforms the increase in network difficulty. As demand for Bitcoin mining increase the network difficulty increase, electricity consumption increase, and similarly the Bitcoin spot price is expected to increase. However, the price increase less than expected as the Bitcoin spot price is less dependent on increase in network difficulty as the miners are able to solve the PoW relatively more efficiently and consume less energy.

For the second linear regression equation a 1% increase in

ln(Average Electricity Consumption) equals 1.06% increase in ln(Bitcoin Spot Price). The coefficients of the linear regression equation are statistically significant and confirms the results of the cost of production analysis. The changes in Bitcoin spot price may be explained by the underlying cost of production in terms of electricity consumption. The slight premium of change in ln(Bitcoin Spot Price) explained by ln(Average Electricity Consumption) may be explained by the relationship of the network difficulty and the Bitcoin spot price. For the average electricity consumption to increase the network difficulty would need to increase with a greater magnitude in which may reflect significant demand for both Bitcoin mining and Bitcoin itself.

Finally, the last linear equation presents a 1% increase in ln(Network Difficulty) equals a 0.63% increase in ln(Average Electricity Consumption). The results of this linear equation confirm the assumptions stated. Increase in network difficulty offers less change in electricity consumption. However, this relationship may be inverted in the future if the network

difficulty increases to a level where miners would have difficulties keeping up solving the PoW. If the network difficulty increases and miners are unable to solve the PoW efficiently the miners would either need to acquire more efficient hardware or consume more energy in which would increase the average electricity consumption.

6.3 Portfolio Analysis

The results of the portfolio analysis are rather interesting in terms of hedging and

diversification properties. As presented by the variance – covariance – matrix the correlation of Bitcoin and the other assets and indices is arguably low and rather inconsistent. This feature of Bitcoin offers a unique feature in terms of hedging and diversification properties.

Traditionally a negative correlation is assumed to be decisive for a hedge, however it could also be argued that this risk exposure may be constructed in the financial markets by short-selling assets. The unique feature of Bitcoin is the zero correlation that is arguably impossible to construct in the same way by futures contracts. As of this unique feature Bitcoin may offer as a great diversifier in an investment portfolio.

For the time period of research, the annual expected return for the portfolios varies

significantly given the different allocation of weights of Bitcoin in the portfolios. As of the exponential growth of Bitcoin the annual expected return for the portfolios increase

significantly, despite fairly limited weighting. These results in themselves may not be as surprising given the preconditions of the data and it is more a question of whether this trend continues. The Sharpe Ratio of the respective portfolios are lower than expected and this may be explained by the negative annual expected return of Brent Crude Oil and United States 10-Year Bond Yield. The negative annual expected return for the time period of research for these assets is arguably not representative for the longer-term perspective and thereby the respective ratios of the portfolios should be weighted accordingly in the assessment. Despite lower expected risk-adjusted returns for the portfolios, with the respective weighting of Bitcoin, the CML supports the assumption of Bitcoin as great diversifier.