Cryptocurrency investment portfolio evaluation

Abstract

The emergence of Cryptocurrencies in recent years has attracted the attention of some investors and researchers. Since the number of literature is growing on this subject, new concerns are emerging around this topic. Cryptocurrencies are considered financial products. Therefore, the financial industry includes most of the issues. Investors worldwide are looking for new opportunities and investment areas to make more diversification in their portfolios. Cryptocurrencies seem to have the potential to add advantages to the investment portfolios, such as diversification, acting like safe heaven, and tools for increasing the portfolio's return. Hence this study's hypothesis rises around the impact of Cryptocurrencies on the performance of investment portfolios. This study investigates the performance of four portfolios, three of which include Cryptocurrency indexes among traditional assets, and one consists of only traditional assets. Cryptocurrency indexes used in this study have different characteristics where the first one is unique to this study and constructed on the dynamic Laspeyres model, the second one is an index of the top ten Cryptocurrencies by market capitalization, and the third one is the index of the top ten Cryptocurrencies by market capitalization excluding Bitcoin as the largest Cryptocurrency. Traditional assets included in portfolios cover a vast class of financial asset types such as equity market, commodities, precious metals, real estate, and fixed income. Furthermore, different asset allocation strategies are applied to all four portfolios of this study to compare their performance under different conditions. The data period for this study varies for different assets. Still, to include them in the portfolios, daily data from January 2017 through June 2022 is acquired where all of the assets are dynamic. To follow the literature on this topic, all portfolios are rebalanced on a weekly basis for the data period, and optimal weights for each week of the portfolios are unique. Then moving window with the size of one year is applied to the data from the beginning week until it satisfied the date period. Portfolios are optimized under various asset allocation strategies such as equal-weighted portfolio, maximum Sharpe portfolio, minimum variance, maximum return, risk parity, maximum diversification, and portfolio optimization using higher moments using the SciPy library of python. Results of the optimizations are being evaluated with five performance measurement metrics, including Sharpe ratio, adjusted Sharpe ratio, Certainty Equivalent, Sortino ratio, and Omega ratio. Eventually, for the test on the robustness of the findings, ANOVA analyses are applied to test the consistency of the results. Based on the findings, Cryptocurrencies increase the return and risk of the portfolio. Still, portfolios including Cryptocurrency indexes in their structure give out better performance measurement results suggesting that Cryptocurrencies improve the performance of the investment portfolios for the data period of this study.