Analysis of Cryptocurrency as Investment Asset in Optimizing the Portfolio Performance - ITB SBM MBA...

ANALYSIS OF CRYPTOCURRENCY AS INVESTMENT ASSET

IN OPTIMIZING THE PORTFOLIO PERFORMANCE

(CASE STUDY: PT MANDIRI MANAJEMEN INVESTASI)

FINAL PROJECT

By:

I DEWA GEDE RAMA

29116537

Master of Business Administration Program

School of Business and Management

Institut Teknologi Bandung

2018

Institut Teknologi Bandung - SBM - MBA - 2018 - I Dewa Gede Rama - idegeram - [email protected]




I DEWA GEDE RAMA

NIM: 29116537

Date of Passing Thesis Defense Exam ( __ / __ / ______ )

Date of Graduation Ceremony ( __ / __ / ______ )

Master of Business Administration Program, Institut Teknologi Bandung, 2018

Thesis Advisor: Dr. Ir. Uke MMP Siahaan, MBA

ABSTRACT

Cryptocurrency is one of the most disruptive innovation that continuously emerge

from reinvention of digital currency to revolution of transaction mechanism. The

ease and efficiency offered by the cryptocurrency makes it an alternative currency

for some parties. The high fluctuation in cryptocurrency prices raises the idea that

these assets have a high level of risk, which is ultimately expected to provide high

return as well. By considering the cryptocurrency as a digital asset, and

understanding its growing value as an opportunity, the cryptocurrency can be

considered as one of the instruments that can be used to optimize the portfolio

performance. In this study, a set of performance comparison analysis was carried out

on portfolio that only involves common assets and portfolio with a number of

cryptocurrency in it. Performance measurement was carried out based on the Sharpe

ratio, and portfolio optimization was carried out using the Markowitz method.

Efficient frontier was also used as an analytical tool in determining the portfolio

efficiency. The obtained results and findings state that portfolio with

cryptocurrencies in its composition suggests a higher risk and return potential.

Through the Markowitz method, the portfolio Sharpe ratio can be optimized up to 20

times at 26.35% by the indexed cryptocurrencies, and up to 59 times at 76.11% by

the individual cryptocurrencies. CRYPTO5 index contributed with rate of return at

148.98%, as well as individual BTC, ETH, XRP, and XMR respectively contributed

with rate of return at 168.77%, 234.25%, 562.86%, and 422.13%. The optimal

compositions of cryptocurrency mixed portfolio for risk averse investor is 5.02% of

LQ45, 85.37% of FIFI, and 9.61% of CRYPTO5. While the optimal compositions of

the cryptocurrency mixed portfolio for risk seeking investor is 83.06% of BTC,

4.87% of ETH, 1.49% of XRP, and 10.57% of XMR.

Keywords: cryptocurrency, digital asset, investment analysis, Sharpe performance

ratio, Markowitz portfolio optimization, efficient frontier


ANALISIS MATA UANG KRIPTO SEBAGAI ASET INVESTASI

DALAM MENGOPTIMALKAN KINERJA PORTOFOLIO

(STUDI KASUS: PT MANDIRI MANAJEMEN INVESTASI)

I DEWA GEDE RAMA

NIM: 29116537

Tanggal Kelulusan Sidang ( __ / __ / ______ )

Tanggal Wisuda ( __ / __ / ______ )

Program Magister Administrasi Bisnis, Institut Teknologi Bandung, 2018

Pembimbing Tesis: Dr. Ir. Uke MMP Siahaan, MBA

ABSTRAK

Mata uang kripto adalah salah satu inovasi yang memunculkan beragam isu yang

terus meluas, mulai dari penemuan kembali mata uang digital sampai pada revolusi

mekanisme bertransaksi. Kemudahan dan efisiensi yang ditawarkan oleh mata uang

kripto menjadikannya sebagai mata uang alternatif bagi sejumlah pihak. Fluktuasi

harganya yang tinggi memunculkan gagasan bahwa instrumen tersebut memiliki

tingkat risiko yang tinggi, yang pada akhirnya diharapkan dapat memberikan imbal

hasil yang tinggi pula. Dengan mempertimbangkan mata uang kripto sebagai aset

digital, dan memahami pertumbuhan nilainya sebagai peluang, mata uang kripto

dapat dianggap sebagai salah satu instrumen yang dapat digunakan dalam

mengoptimalkan kinerja portofolio. Dalam studi ini, serangkaian analisis

perbandingan kinerja dilakukan pada portofolio yang hanya melibatkan aset umum

dan portofolio dengan sejumlah mata uang kripto di dalamnya. Pengukuran kinerja

dilakukan berdasarkan rasio Sharpe, dan optimalisasi portofolio dilakukan

menggunakan metode Markowitz. Kurva efisiensi juga digunakan sebagai alat

analisis dalam menentukan efisiensi portofolio. Hasil dan temuan yang diperoleh

menyatakan bahwa portofolio dengan mata uang kripto dalam komposisinya

memiliki potensi risiko dan imbal hasil yang lebih tinggi. Melalui metode

Markowitz, rasio Sharpe pada portofolio dapat dioptimalkan hingga 20 kali pada

26,35% oleh indeks mata uang kripto, dan hingga 59 kali pada 76,11% oleh satuan

mata uang kripto. Indeks CRYPTO5 berkontribusi dengan tingkat imbal hasil sebesar

148,98%, serta masing-masing BTC, ETH, XRP, dan XMR berkontribusi sebesar

168,77%, 234,25%, 562,86%, dan 422,13%. Komposisi optimal dari portofolio

campuran mata uang kripto untuk investor penghindar risiko adalah 5.02% LQ45,

85.37% FIFI, dan 9.61% CRYPTO5. Sedangkan komposisi optimal dari portofolio

campuran mata uang kripto untuk investor pencari risiko adalah 83,06% BTC, 4,87%

ETH, 1,49% XRP, dan 10,57% XMR.

Kata kunci: mata uang kripto, aset digital, analisis investasi, rasio kinerja Sharpe,

optimalisasi portofolio Markowitz, kurva efisiensi


VALIDATION PAGE




By:

I DEWA GEDE RAMA

NIM: 29116537

Master of Business Administration Program

School of Business and Management


Approved,

________________________________

Thesis Advisor

________________________________

Dr. Ir. Uke MMP Siahaan, MBA


DECLARATION OF NON PLAGIARISM

Plagiarism is …

Taking, using, and submitting a work of another, including

an idea, writing, or invention, as if it was her or his own.

Plagiarism includes but not limited to …

1. Quoting verbatim the work of another without acknowledgement of the source;

2. Paraphrasing the work of another without acknowledgement of the source;

3. Using the idea of another without acknowledgement of the source;

4. Submitting the work of another without identifying clearly who did the work;

5. Colluding the work of another as her or his own with consent from the other.

I understand that plagiarism is wrong, a breach of academic integrity,

and against Program, School, and University Policy and Regulation.

I declare that all material in this Final Project is original, my own

work, and does not involve plagiarism.

I have not allowed, and will not allow, another to copy my work with

the intention of submitting it as her or his own work.

I Dewa Gede Rama 29116537

Name ___________________________ Student ID _______________________

Signed __________________________ Date ____________________________


DISCLAIMER

This study aims to reveal the potential of cryptocurrency in optimizing the portfolio

performance. Investing and/or trading on cryptocurrency is a very risky decision. All

data, analysis, and solutions presented in this study are provided for informational

purposes only. Any consideration of investing and/or trading on cryptocurrency must

be a personal decision. The author is free from the responsibilities and/or benefits

obtained from any investing and/or trading decisions made.


FOREWORD

The author prepared this thesis as a final project report of the research that has been

carried out. In the preparation of this thesis, the author has received direction and

guidance from many parties, and thus, through this opportunity the author intends to

personally thank:

1. Dr. Ir. Uke MMP Siahaan, MBA, as the thesis advisor who at every

opportunity has provided direction so that the research activities and the

preparation of this thesis can be realized.

2. Dr. Ir. Asep Darmansyah, MSi, as the examiner chairman who has provided

meaningful inputs in improving the research quality and the reporting

method.

3. Dr. Ir. Sandhy Widyasthana, ST, MBA, PMP, as the examiner member who

has provided inputs in adjustment between the case study and the

implementation method.

4. Prof. Dr. Sudarso Kaderi Wiryono, as dean of the School of Business and

Management, along with the vice deans who have led this faculty in inspiring

their students.

5. Dr. Subiakto Sukarno, MBA, RFA, QWP, CFP, as the head of the Master of

Business Administration program, along with the staffs who have built and

embodied a high quality management education.

6. My classmates, executive MBA 56, who at all times always provide

motivation and consolation, and have helped in exchanging ideas during the

research activities and writing of this thesis.

7. My parents and all of my family members who endlessly provide me

continuous support, motivation, and guidance throughout the study period

until this thesis is realized.

8. My gratitude goes to Bodhisattva Avalokitesvara for her teaching and

compassion to all sentient beings in bringing the ultimate consciousness and

liberating every soul from samsara.

Finally, with all humility the author hopes this thesis can provide benefits for the

readers, as well as those who need it, especially in adding references to further

related studies.

Bandung, August 2018

I Dewa Gede Rama


TABLE OF CONTENTS

ABSTRACT i

ABSTRAK ii

VALIDATION PAGE iii

DECLARATION OF NON PLAGIARISM iv

DISCLAIMER v

FOREWORD vi

TABLE OF CONTENTS vii

LIST OF FIGURES ix

LIST OF TABLES x

LIST OF APPENDICES xi

CHAPTER I INTRODUCTION 1

1.1. Background 1

1.2. Company Profile 3

1.3. Business Issue 5

1.4. Research Question 5

1.5. Research Objective 5

1.6. Research Limitation 5

CHAPTER II BUSINESS ISSUE EXPLORATION 6

2.1. Conceptual Framework 6

2.2. Theoretical Foundation 7

2.2.1. Cryptocurrency Digital Asset 7

2.2.2. Efficient Frontier Graph 9

2.2.3. Sharpe Performance Measurement 10

2.2.4. Markowitz Portfolio Optimization 11

2.3. Research Methodology 12

2.4. Situation Analysis 12

2.4.1. Current Performance of Common Assets 13

2.4.2. Current Performance of Cryptocurrencies 14

CHAPTER III BUSINESS SOLUTION 15

3.1. Portfolio Preparation 15

3.1.1. Investment Instrument Selection 15

3.1.2. Historical Data Collection 16


3.2. Portfolio Valuation 17

3.2.1 Valuation of Portfolio with Common Assets Only 17

3.2.2. Valuation of Portfolio with Indexed Cryptocurrencies 24

3.2.3. Valuation of Portfolio with Individual Cryptocurrencies 31

3.3. Portfolio Analysis 38

3.3.1 Performance Comparison 38

3.3.2 Results and Findings 42

3.4. Proposed Solutions 42

3.4.1. Portfolio Composition for Risk Averse Investor 43

3.4.2. Portfolio Composition for Risk Seeking Investor 44

CHAPTER IV CONCLUSION AND RECOMMENDATION 45

4.1. Conclusion 45

4.2. Recommendation 45

4.3. Implementation Phase 46

4.3.1. Risk Profiling 47

4.3.2. Strategy Development 47

4.3.3. Strategy Implementation 47

4.3.4. Continuous Improvement 48

4.3.5. Risk Management 48

4.4. Implementation Schedule 48

4.5. Implementation Approach for PT Mandiri Manajemen Investasi 49

REFERENCES 50

APPENDICES 53


LIST OF FIGURES

Figure 1.1. Ownership Structure of PT Mandiri Manajemen Investasi 4

Figure 1.2. Organizational Structure of PT Mandiri Manajemen Investasi 4

Figure 2.1. Conceptual Framework 6

Figure 2.2. Peer to Peer Transaction in Blockchain 7

Figure 2.3. New Privacy Model Introduced by Blockchain 8

Figure 2.4. Efficient Frontier Graph 9

Figure 2.5. Performance of Common Assets in 2015-2017 13

Figure 2.6. Performance of Cryptocurrencies in 2015-2017 14

Figure 3.1. Risk and Return of Portfolio with Common Assets Only 18

Figure 3.2. Efficient Frontier Graph of Portfolio with Common Assets Only 20

Figure 3.3. Global Minimum Variance of Portfolio with Common Assets Only 21

Figure 3.4. Equally Weighted Value of Portfolio with Common Assets Only 22

Figure 3.5. Optimal Risky Value of Portfolio with Common Assets Only 23

Figure 3.6. Risk and Return of Portfolio with Indexed Cryptocurrencies 25

Figure 3.7. Efficient Frontier Graph of Portfolio with Indexed Cryptocurrencies 27

Figure 3.8. Global Minimum Variance of Portfolio with Indexed Cryptocurrencies 28

Figure 3.9. Equally Weighted Value of Portfolio with Indexed Cryptocurrencies 29

Figure 3.10. Optimal Risky Value of Portfolio with Indexed Cryptocurrencies 30

Figure 3.11. Risk and Return of Portfolio with Individual Cryptocurrencies 32

Figure 3.12. Efficient Frontier Graph of Portfolio with Individual Cryptocurrencies 34

Figure 3.13. Global Minimum Variance of Portfolio with Individual Cryptocurrencies 35

Figure 3.14. Equally Weighted Value of Portfolio with Individual Cryptocurrencies 36

Figure 3.15. Optimal Risky Value of Portfolio with Individual Cryptocurrencies 37

Figure 3.16. Comparison on Efficient Frontier Graph 38

Figure 3.17. Comparison on Global Minimum Variance 39

Figure 3.18. Comparison on Equally Weighted Portfolio Value 40

Figure 3.19. Comparison on Optimal Risky Portfolio Value 41

Figure 4.1. Implementation Phase 46


LIST OF TABLES

Table 3.1. Basic Portfolio and Selected Instruments 15

Table 3.2. Description of Historical Data Used in This Research 16

Table 3.3. Risk and Return of Portfolio with Common Assets Only 17

Table 3.4. Correlation Coefficient of Portfolio with Common Assets Only 19

Table 3.5. Instrument Covariance of Portfolio with Common Assets Only 19

Table 3.6. Efficient Frontier Plot of Portfolio with Common Assets Only 20

Table 3.7. Global Minimum Variance of Portfolio with Common Assets Only 21

Table 3.8. Equally Weighted Value of Portfolio with Common Assets Only 22

Table 3.9. Optimal Risky Value of Portfolio with Common Assets Only 23

Table 3.10. Risk and Return of Portfolio with Indexed Cryptocurrencies 24

Table 3.11. Correlation Coefficient of Portfolio with Indexed Cryptocurrencies 25

Table 3.12. Instrument Covariance of Portfolio with Indexed Cryptocurrencies 26

Table 3.13. Efficient Frontier Plot of Portfolio with Indexed Cryptocurrencies 27

Table 3.14. Global Minimum Variance of Portfolio with Indexed Cryptocurrencies 28

Table 3.15. Equally Weighted Value of Portfolio with Indexed Cryptocurrencies 29

Table 3.16. Optimal Risky Value of Portfolio with Indexed Cryptocurrencies 30

Table 3.17. Risk and Return of Portfolio with Individual Cryptocurrencies 31

Table 3.18. Correlation Coefficient of Portfolio with Individual Cryptocurrencies 32

Table 3.19. Instrument Covariance of Portfolio with Individual Cryptocurrencies 33

Table 3.20. Efficient Frontier Plot of Portfolio with Individual Cryptocurrencies 34

Table 3.21. Global Minimum Variance of Portfolio with Individual Cryptocurrencies 35

Table 3.22. Equally Weighted Value of Portfolio with Individual Cryptocurrencies 36

Table 3.23. Optimal Risky Value of Portfolio with Individual Cryptocurrencies 37

Table 3.24. Portfolio Composition for Risk Averse Investor 43

Table 3.25. Portfolio Composition for Risk Seeking Investor 44

Table 4.1. Implementation Schedule 48


LIST OF APPENDICES

Appendix A: Historical Data of Bank Indonesia Rate 53

Appendix B: Historical Data of LQ45 Stock Index 54

Appendix C: Historical Data of Infovesta FIFI Mutual Fund Index 55

Appendix D: Historical Data of USD Currency Price 56

Appendix E: Historical Data of GOLD Commodity Price 57

Appendix F: Historical Data of BTC Bitcoin Cryptocurrency Price 58

Appendix G: Historical Data of ETH Ethereum Cryptocurrency Price 59

Appendix H: Historical Data of XRP Ripple Cryptocurrency Price 60

Appendix I: Historical Data of LTC Litecoin Cryptocurrency Price 61

Appendix J: Historical Data of XMR Monero Cryptocurrency Price 62

Appendix K: Development of CRYPTO5 Index 63

Appendix L: Calculation of Portfolio with Common Assets Only 68

Appendix M: Calculation of Portfolio with Indexed Cryptocurrencies 74

Appendix N: Calculation of Portfolio with Individual Cryptocurrencies 80


REFERENCES

Alberts, Jeffrey, and Bertrand Fry. 2015. "Is Bitcoin a Security." Journal of Science

and Technology Law 21 (2): 1-21.

Alfonso, Alexander, Peter Langer, and Zintis Vandelis. 2016. The Future of

Cryptocurrency: Investor Comparison of Bitcoin and Ethereum. Investment

Case Study, Ryerson University.

Andrianto, Yanuar, and Yoda Diputra. 2017. "The Effect of Cryptocurrency on

Investment Portfolio Effectiveness." Journal of Finance and Accounting 5

(6): 229-238.

Anyfantaki, Sofia, Stelios Arvanitis, and Nikolas Topaloglou. 2018. Diversification,

Integration and Cryptocurrency Market. Economic Analysis and Research

Report, Special Studies Division, Bank of Greece Working Paper.

Bodie, Zvi, Alex Kane, and Alan Marcus. 2011. Investments and Portfolio

Management, 9th Edition. New York: MGH Education Publisher.

Bruneau, Gabriel, and Kevin Moran. 2017. "Exchange Rate Fluctuations and Labor

Market Adjustments in Canadian Manufacturing Industries." Canadian

Journal of Economics 50 (1).

Burniske, Chris, Jack Tatar, and Dave Clark. 2017. Cryptoassets: The Innovative

Investor Guide to Bitcoin and Beyond. New York: MGH Education

Publisher.

Carter, John. 2018. Mastering the Trade: Proven Techniques for Profiting from

Intraday and Swing Trading Setups. New York: MGH Education Publisher.

Chan, Stephen, Jeffrey Chu, Saralees Nadarajah, and Joerg Osterrieder. 2017. "A

Statistical Analysis of Cryptocurrencies." Journal of Risk and Financial

Management 10 (1).

Chincarini, Ludwig, and Daehwan Kim. 2006. Quantitative Equity Portfolio

Management: An Active Approach to Portfolio Construction and

Management. New York: MGH Education Publisher.

Dave, Chetan, Catherine Eckel, Cathleen Johnson, and Christian Rojas. 2010.

"Eliciting Risk Preferences: When Is Simple Better." Journal of Risk and

Uncertainty 41 (3).

Dorresteijn, Floor. 2017. Which Socio Demographic Factors Determine Risk Taking

Behaviour of Investors. Master Thesis in International Economics and

Business, Nijmegen School of Management, Radboud University.


Dziwok, Ewa. 2014. "Asset Allocation Strategy in Investment Portfolio

Construction: A Comparative Analysis." Journal of Economics and

Management 18 (1): 124-132.

Fabozzi, Frank, and Harry Markowitz. 2011. The Theory and Practice of Investment

Management: Asset Allocation, Valuation, Portfolio Construction, and

Strategies. New Jersey: John Wiley & Sons.

Gikay, Asress. 2018. "Regulating Decentralized Cryptocurrencies Under Payment

Services Law: Lessons from EU Law." Journal of Law, Technology and

Internet 9 (1).

Gravetter, Frederick, Larry Wallnau, and Lori Forzano. 2017. Essentials of Statistics

for The Behavioral Sciences, 9th Edition. Boston: Cengage Learning.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2017. An

Introduction to Statistical Learning with Applications, 1st Edition. New

York: Springer Publishing.

Lambovska, Maya, and Angel Marchev. 2011. "Investment Portfolio Evaluation by

Fuzzy Approach." Journal of Competitiveness 3 (1): 13-26.

Lansky, Jan. 2018. "Possible State Approaches to Cryptocurrencies." Journal of

Systems Interation 8 (1): 19-31.

Lee, David, Li Guo, and Yu Wang. 2018. "Cryptocurrency: A New Investment

Opportunity." Journal of Alternative Investments 20 (3): 16-40.

Markowitz, Harry. 2016. Risk Return Analysis: The Theory and Practice of Rational

Investing. New York: MGH Education Publisher.

Mokhtarian, Edmund, and Alexander Lindgren. 2018. "Rise of the Crypto Hedge

Fund: Operational Issues and Best Practices for an Emergent Investment

Industry." Stanford Journal of Law, Business & Finance 23 (1): 112-158.

Parlapiano, Fabio, Vitali Alexeev, and Mardi Dungey. 2017. "Exchange Rate Risk

Exposure and the Value of European Firms." European Journal of Finance

23 (1).

Partono, Thomas, Widiyanto Widiyanto, Arief Yulianto, and Handika Vidayanto.

2017. "The Analysis of Optimal Portfolio Forming with Single Index Model

on Indonesian Most Trusted Companies." International Research Journal of

Finance and Economics 163 (1): 50-59.

Pecaut, Daniel, and Corey Wrenn. 2017. University of Berkshire Hathaway: Lessons

Learned from Warren Buffett and Charlie Munger at the Annual

Shareholders Meeting. Sioux City: Pecaut and Company.


Phillips, Ross, and Denise Gorse. 2018. "Mutual Excitation of Cryptocurrency

Market Returns and Social Media Topics." Third International Conference on

Knowledge Engineering and Applications, ICKEA. Moscow.

Schwartzkopff, Daniel, Luke Schwartzkopff, Raymond Botha, Matthew Finlayson,

and Frans Cronje. 2017. CRYPTO20: The First Tokenized Cryptocurrency

Index Fund. Cryptocurrency Review Whitepaper, Invictus Capital.

Srokosz, Witold, and Tomasz Kopyscianski. 2015. "Legal and Economic Analysis of

the Cryptocurrencies Impact on the Financial System Stability." Journal of

Teaching and Education 4 (2).

Swensen, David. 2009. Pioneering Portfolio Management: An Unconventional

Approach to Institutional Investment. Massachusetts: Free Press.

Takashima, Ikuya. 2017. Cryptocurrency Investing: The Ultimate Guide in Investing

and Trading. Seattle: Amazon Kindle Publishing.

Vassiliadis, Savvas, Perikles Papadopoulos, Maria Rangoussi, Tomasz Konieczny,

and Jacek Gralewski. 2017. "Bitcoin Value Analysis Based on Cross

Correlations." Journal of Internet Banking and Commerce 22 (1).

Warne, Russell. 2017. Statistics for the Social Sciences: General Linear Model

Approach, 1st Edition. Cambridge: Cambridge University Press.


APPENDICES

Appendix A: Historical Data of Bank Indonesia Rate

Year Month BI Rate

2015 Jan 7.75%

2015 Feb 7.50%

2015 Mar 7.50%

2015 Apr 7.50%

2015 May 7.50%

2015 Jun 7.50%

2015 Jul 7.50%

2015 Aug 7.50%

2015 Sep 7.50%

2015 Oct 7.50%

2015 Nov 7.50%

2015 Dec 7.50%

2016 Jan 7.25%

2016 Feb 7.00%

2016 Mar 6.75%

2016 Apr 6.75%

2016 May 6.75%

2016 Jun 6.50%

2016 Jul 6.50%

2016 Aug 5.25%

2016 Sep 5.00%

2016 Oct 4.75%

2016 Nov 4.75%

2016 Dec 4.75%

2017 Jan 4.75%

2017 Feb 4.75%

2017 Mar 4.75%

2017 Apr 4.75%

2017 May 4.75%

2017 Jun 4.75%

2017 Jul 4.75%

2017 Aug 4.50%

2017 Sep 4.25%

2017 Oct 4.25%

2017 Nov 4.25%

2017 Dec 4.25%

Source: Bank Indonesia, www.bi.go.id, 1 Aug 2018.


Appendix B: Historical Data of LQ45 Stock Index

Year Month Value Change

2015 Jan 912.05 0.00%

2015 Feb 946.88 3.82%

2015 Mar 961.93 1.59%

2015 Apr 869.44 -9.62%

2015 May 904.13 3.99%

2015 Jun 839.14 -7.19%

2015 Jul 813.10 -3.10%

2015 Aug 770.81 -5.20%

2015 Sep 704.98 -8.54%

2015 Oct 759.73 7.77%

2015 Nov 755.46 -0.56%

2015 Dec 792.03 4.84%

2016 Jan 799.99 1.01%

2016 Feb 834.74 4.34%

2016 Mar 840.35 0.67%

2016 Apr 832.51 -0.93%

2016 May 820.01 -1.50%

2016 Jun 860.72 4.96%

2016 Jul 892.84 3.73%

2016 Aug 924.96 3.60%

2016 Sep 922.20 -0.30%

2016 Oct 927.10 0.53%

2016 Nov 857.25 -7.53%

2016 Dec 884.62 3.19%

2017 Jan 877.35 -0.82%

2017 Feb 892.70 1.75%

2017 Mar 921.53 3.23%

2017 Apr 940.76 2.09%

2017 May 957.70 1.80%

2017 Jun 977.62 2.08%

2017 Jul 974.08 -0.36%

2017 Aug 977.33 0.33%

2017 Sep 979.44 0.22%

2017 Oct 992.22 1.30%

2017 Nov 992.16 -0.01%

2017 Dec 1,079.38 8.79%

Source: Investing, www.investing.com, 1 Aug 2018.


Appendix C: Historical Data of Infovesta FIFI Mutual Fund Index


2015 Jan 3,070.59 0.00%

2015 Feb 3,094.68 0.78%

2015 Mar 3,064.76 -0.97%

2015 Apr 3,045.31 -0.63%

2015 May 3,028.93 -0.54%

2015 Jun 3,027.51 -0.05%

2015 Jul 3,017.72 -0.32%

2015 Aug 2,996.62 -0.70%

2015 Sep 2,929.12 -2.25%

2015 Oct 3,025.70 3.30%

2015 Nov 3,069.50 1.45%

2015 Dec 3,061.44 -0.26%

2016 Jan 3,120.39 1.93%

2016 Feb 3,139.53 0.61%

2016 Mar 3,218.12 2.50%

2016 Apr 3,260.07 1.30%

2016 May 3,251.60 -0.26%

2016 Jun 3,299.63 1.48%

2016 Jul 3,372.84 2.22%

2016 Aug 3,373.93 0.03%

2016 Sep 3,398.85 0.74%

2016 Oct 3,370.65 -0.83%

2016 Nov 3,275.89 -2.81%

2016 Dec 3,307.11 0.95%

2017 Jan 3,340.88 1.02%

2017 Feb 3,364.18 0.70%

2017 Mar 3,433.97 2.07%

2017 Apr 3,453.55 0.57%

2017 May 3,476.57 0.67%

2017 Jun 3,495.26 0.54%

2017 Jul 3,503.06 0.22%

2017 Aug 3,561.91 1.68%

2017 Sep 3,599.09 1.04%

2017 Oct 3,569.98 -0.81%

2017 Nov 3,625.23 1.55%

2017 Dec 3,661.58 1.00%

Source: Infovesta, www.infovesta.com, 1 Aug 2018.


Appendix D: Historical Data of USD Currency Price


2015 Jan 12,667.50 0.00%

2015 Feb 12,925.00 2.03%

2015 Mar 13,075.00 1.16%

2015 Apr 12,962.50 -0.86%

2015 May 13,224.00 2.02%

2015 Jun 13,332.50 0.82%

2015 Jul 13,527.50 1.46%

2015 Aug 14,050.00 3.86%

2015 Sep 14,650.00 4.27%

2015 Oct 13,687.50 -6.57%

2015 Nov 13,835.00 1.08%

2015 Dec 13,787.50 -0.34%

2016 Jan 13,775.00 -0.09%

2016 Feb 13,372.00 -2.93%

2016 Mar 13,260.00 -0.84%

2016 Apr 13,185.00 -0.57%

2016 May 13,660.00 3.60%

2016 Jun 13,212.50 -3.28%

2016 Jul 13,098.50 -0.86%

2016 Aug 13,267.50 1.29%

2016 Sep 13,051.00 -1.63%

2016 Oct 13,048.00 -0.02%

2016 Nov 13,552.50 3.87%

2016 Dec 13,472.50 -0.59%

2017 Jan 13,352.00 -0.89%

2017 Feb 13,336.00 -0.12%

2017 Mar 13,325.50 -0.08%

2017 Apr 13,329.00 0.03%

2017 May 13,322.50 -0.05%

2017 Jun 13,327.50 0.04%

2017 Jul 13,325.00 -0.02%

2017 Aug 13,343.00 0.14%

2017 Sep 13,471.50 0.96%

2017 Oct 13,562.50 0.68%

2017 Nov 13,526.00 -0.27%

2017 Dec 13,567.50 0.31%



Appendix E: Historical Data of GOLD Commodity Price


2015 Jan 16,195,398.75 0.00%

2015 Feb 15,672,855.00 -3.23%

2015 Mar 15,469,032.50 -1.30%

2015 Apr 15,326,860.00 -0.92%

2015 May 15,728,625.60 2.62%

2015 Jun 15,619,023.75 -0.70%

2015 Jul 14,811,259.75 -5.17%

2015 Aug 15,898,980.00 7.34%

2015 Sep 16,342,075.00 2.79%

2015 Oct 15,624,281.25 -4.39%

2015 Nov 14,745,343.00 -5.63%

2015 Dec 14,618,886.25 -0.86%

2016 Jan 15,378,410.00 5.20%

2016 Feb 16,499,710.80 7.29%

2016 Mar 16,365,492.00 -0.81%

2016 Apr 16,998,102.00 3.87%

2016 May 16,594,168.00 -2.38%

2016 Jun 17,419,360.00 4.97%

2016 Jul 17,669,876.50 1.44%

2016 Aug 17,339,295.75 -1.87%

2016 Sep 17,139,878.30 -1.15%

2016 Oct 16,590,532.00 -3.21%

2016 Nov 15,867,267.00 -4.36%

2016 Dec 15,493,375.00 -2.36%

2017 Jan 16,137,227.20 4.16%

2017 Feb 16,704,673.60 3.52%

2017 Mar 16,620,896.15 -0.50%

2017 Apr 16,875,846.90 1.53%

2017 May 16,946,220.00 0.42%

2017 Jun 16,535,429.25 -2.42%

2017 Jul 16,877,445.00 2.07%

2017 Aug 17,562,056.60 4.06%

2017 Sep 17,263,727.25 -1.70%

2017 Oct 17,183,687.50 -0.46%

2017 Nov 17,221,303.20 0.22%

2017 Dec 17,723,225.25 2.91%



Appendix F: Historical Data of BTC Bitcoin Cryptocurrency Price


2015 Jan 2,753,914.50 0.00%

2015 Feb 3,304,922.50 20.01%

2015 Mar 3,194,222.50 -3.35%

2015 Apr 3,060,446.25 -4.19%

2015 May 3,024,328.80 -1.18%

2015 Jun 3,505,114.25 15.90%

2015 Jul 3,848,573.75 9.80%

2015 Aug 3,251,170.00 -15.52%

2015 Sep 3,464,725.00 6.57%

2015 Oct 4,325,250.00 24.84%

2015 Nov 5,214,411.50 20.56%

2015 Dec 5,914,837.50 13.43%

2016 Jan 5,034,762.50 -14.88%

2016 Feb 5,872,982.40 16.65%

2016 Mar 5,516,160.00 -6.08%

2016 Apr 5,888,421.00 6.75%

2016 May 7,249,362.00 23.11%

2016 Jun 8,914,473.75 22.97%

2016 Jul 8,169,534.45 -8.36%

2016 Aug 7,644,733.50 -6.42%

2016 Sep 7,975,466.10 4.33%

2016 Oct 9,187,096.80 15.19%

2016 Nov 10,015,297.50 9.01%

2016 Dec 13,022,518.50 30.03%

2017 Jan 12,900,702.40 -0.94%

2017 Feb 15,857,837.60 22.92%

2017 Mar 14,414,193.35 -9.10%

2017 Apr 19,129,780.80 32.71%

2017 May 29,200,255.50 52.64%

2017 Jun 32,261,879.25 10.48%

2017 Jul 38,056,200.00 17.96%

2017 Aug 62,954,942.60 65.43%

2017 Sep 58,830,040.50 -6.55%

2017 Oct 87,590,693.75 48.89%

2017 Nov 134,002,082.00 52.99%

2017 Dec 187,231,500.00 39.72%



Appendix G: Historical Data of ETH Ethereum Cryptocurrency Price


2015 Jan 144,536.18 0.00%

2015 Feb 147,474.25 2.03%

2015 Mar 149,185.75 1.16%

2015 Apr 147,902.13 -0.86%

2015 May 150,885.84 2.02%

2015 Jun 152,123.83 0.82%

2015 Jul 154,348.78 1.46%

2015 Aug 160,310.50 3.86%

2015 Sep 167,156.50 4.27%

2015 Oct 156,174.38 -6.57%

2015 Nov 157,857.35 1.08%

2015 Dec 157,315.38 -0.34%

2016 Jan 157,172.75 -0.09%

2016 Feb 152,574.52 -2.93%

2016 Mar 151,296.60 -0.84%

2016 Apr 116,950.95 -22.70%

2016 May 191,376.60 63.64%

2016 Jun 165,156.25 -13.70%

2016 Jul 155,348.21 -5.94%

2016 Aug 154,301.03 -0.67%

2016 Sep 173,578.30 12.49%

2016 Oct 143,006.08 -17.61%

2016 Nov 116,822.55 -18.31%

2016 Dec 107,780.00 -7.74%

2017 Jan 142,999.92 32.68%

2017 Feb 211,508.96 47.91%

2017 Mar 662,543.86 213.25%

2017 Apr 1,113,104.79 68.00%

2017 May 2,908,834.65 161.33%

2017 Jun 3,673,325.55 26.28%

2017 Jul 2,675,793.25 -27.16%

2017 Aug 5,167,743.90 93.13%

2017 Sep 4,087,791.96 -20.90%

2017 Oct 4,118,795.63 0.76%

2017 Nov 5,846,072.46 41.94%

2017 Dec 9,996,126.98 70.99%


Data available from Mar 2016, initial value was used for previous data.


Appendix H: Historical Data of XRP Ripple Cryptocurrency Price


2015 Jan 184.31 0.00%

2015 Feb 32.31 -82.47%

2015 Mar 46.55 44.05%

2015 Apr 147.64 217.19%

2015 May 150.62 2.02%

2015 Jun 153.32 1.79%

2015 Jul 74.54 -51.39%

2015 Aug 77.42 3.86%

2015 Sep 80.72 4.27%

2015 Oct 62.96 -22.00%

2015 Nov 57.97 -7.93%

2015 Dec 82.73 42.71%

2016 Jan 84.72 2.41%

2016 Feb 108.31 27.85%

2016 Mar 98.12 -9.41%

2016 Apr 90.71 -7.55%

2016 May 76.09 -16.12%

2016 Jun 87.20 14.61%

2016 Jul 78.59 -9.88%

2016 Aug 79.61 1.29%

2016 Sep 112.24 40.99%

2016 Oct 104.12 -7.23%

2016 Nov 92.02 -11.62%

2016 Dec 87.71 -4.69%

2017 Jan 83.58 -4.70%

2017 Feb 73.75 -11.77%

2017 Mar 283.57 284.51%

2017 Apr 733.10 158.53%

2017 May 3,152.10 329.97%

2017 Jun 3,250.58 3.12%

2017 Jul 2,171.98 -33.18%

2017 Aug 3,453.17 58.99%

2017 Sep 2,680.83 -22.37%

2017 Oct 2,685.38 0.17%

2017 Nov 3,208.37 19.48%

2017 Dec 26,727.98 733.07%



Appendix I: Historical Data of LTC Litecoin Cryptocurrency Price


2015 Jan 47,376.45 0.00%

2015 Feb 48,339.50 2.03%

2015 Mar 48,900.50 1.16%

2015 Apr 48,479.75 -0.86%

2015 May 49,457.76 2.02%

2015 Jun 49,863.55 0.82%

2015 Jul 50,592.85 1.46%

2015 Aug 52,547.00 3.86%

2015 Sep 54,791.00 4.27%

2015 Oct 51,191.25 -6.57%

2015 Nov 51,742.90 1.08%

2015 Dec 51,565.25 -0.34%

2016 Jan 51,518.50 -0.09%

2016 Feb 50,011.28 -2.93%

2016 Mar 49,592.40 -0.84%

2016 Apr 49,311.90 -0.57%

2016 May 51,088.40 3.60%

2016 Jun 49,414.75 -3.28%

2016 Jul 48,988.39 -0.86%

2016 Aug 49,620.45 1.29%

2016 Sep 49,724.31 0.21%

2016 Oct 51,931.04 4.44%

2016 Nov 52,041.60 0.21%

2016 Dec 58,740.10 12.87%

2017 Jan 54,342.64 -7.49%

2017 Feb 51,343.60 -5.52%

2017 Mar 96,609.88 88.16%

2017 Apr 215,929.80 123.51%

2017 May 344,786.30 59.68%

2017 Jun 526,302.98 52.65%

2017 Jul 563,247.75 7.02%

2017 Aug 977,107.89 73.48%

2017 Sep 744,435.09 -23.81%

2017 Oct 751,498.13 0.95%

2017 Nov 1,160,801.32 54.46%

2017 Dec 3,082,128.98 165.52%


Data available from Aug 2016, initial value was used for previous data.


Appendix J: Historical Data of XMR Monero Cryptocurrency Price


2015 Jan 3,673.58 0.00%

2015 Feb 4,523.75 23.14%

2015 Mar 4,053.25 -10.40%

2015 Apr 4,407.25 8.73%

2015 May 6,479.76 47.03%

2015 Jun 5,866.30 -9.47%

2015 Jul 6,222.65 6.07%

2015 Aug 6,884.50 10.64%

2015 Sep 5,713.50 -17.01%

2015 Oct 5,201.25 -8.97%

2015 Nov 5,118.95 -1.58%

2015 Dec 6,755.88 31.98%

2016 Jan 7,163.00 6.03%

2016 Feb 12,034.80 68.01%

2016 Mar 18,564.00 54.25%

2016 Apr 11,998.35 -35.37%

2016 May 12,840.40 7.02%

2016 Jun 20,875.75 62.58%

2016 Jul 23,577.30 12.94%

2016 Aug 112,508.40 377.19%

2016 Sep 111,194.52 -1.17%

2016 Oct 66,805.76 -39.92%

2016 Nov 123,327.75 84.61%

2016 Dec 185,785.78 50.64%

2017 Jan 173,843.04 -6.43%

2017 Feb 163,632.72 -5.87%

2017 Mar 269,574.87 64.74%

2017 Apr 336,557.25 24.85%

2017 May 536,497.08 59.41%

2017 Jun 574,548.53 7.09%

2017 Jul 531,667.50 -7.46%

2017 Aug 1,875,225.22 252.71%

2017 Sep 1,293,937.58 -31.00%

2017 Oct 1,186,718.75 -8.29%

2017 Nov 2,367,455.78 99.50%

2017 Dec 4,502,103.53 90.17%



Appendix K: Development of CRYPTO5 Index

Year Month BTC ETH

Price Volume Price Volume

2015 Jan 2,753,914.50 89,120 144,536.18 285,140

2015 Feb 3,304,922.50 749,170 147,474.25 285,140

2015 Mar 3,194,222.50 1,240,000 149,185.75 285,140

2015 Apr 3,060,446.25 733,270 147,902.13 285,140

2015 May 3,024,328.80 476,350 150,885.84 285,140

2015 Jun 3,505,114.25 591,710 152,123.83 285,140

2015 Jul 3,848,573.75 753,530 154,348.78 285,140

2015 Aug 3,251,170.00 706,780 160,310.50 285,140

2015 Sep 3,464,725.00 294,470 167,156.50 285,140

2015 Oct 4,325,250.00 571,390 156,174.38 285,140

2015 Nov 5,214,411.50 1,640,000 157,857.35 285,140

2015 Dec 5,914,837.50 1,150,000 157,315.38 285,140

2016 Jan 5,034,762.50 893,300 157,172.75 285,140

2016 Feb 5,872,982.40 643,440 152,574.52 285,140

2016 Mar 5,516,160.00 416,350 151,296.60 285,140

2016 Apr 5,888,421.00 366,050 116,950.95 656,560

2016 May 7,249,362.00 604,030 191,376.60 2,060,000

2016 Jun 8,914,473.75 1,680,000 165,156.25 6,150,000

2016 Jul 8,169,534.45 657,720 155,348.21 4,650,000

2016 Aug 7,644,733.50 203,640 154,301.03 1,200,000

2016 Sep 7,975,466.10 129,690 173,578.30 1,100,000

2016 Oct 9,187,096.80 197,710 143,006.08 871,300

2016 Nov 10,015,297.50 129,400 116,822.55 555,360

2016 Dec 13,022,518.50 288,280 107,780.00 1,120,000

2017 Jan 12,900,702.40 699,430 142,999.92 1,270,000

2017 Feb 15,857,837.60 477,670 211,508.96 1,380,000

2017 Mar 14,414,193.35 1,040,000 662,543.86 8,240,000

2017 Apr 19,129,780.80 422,010 1,113,104.79 3,630,000

2017 May 29,200,255.50 744,190 2,908,834.65 4,960,000

2017 Jun 32,261,879.25 649,180 3,673,325.55 4,090,000

2017 Jul 38,056,200.00 942,610 2,675,793.25 7,730,000

2017 Aug 62,954,942.60 951,950 5,167,743.90 5,150,000

2017 Sep 58,830,040.50 1,500,000 4,087,791.96 8,400,000

2017 Oct 87,590,693.75 1,360,000 4,118,795.63 4,440,000

2017 Nov 134,002,082.00 2,120,000 5,846,072.46 5,790,000

2017 Dec 187,231,500.00 2,260,000 9,996,126.98 9,390,000


Year Month XRP LTC


2015 Jan 184.31 2,230 47,376.45 18,600

2015 Feb 32.31 700 48,339.50 18,600

2015 Mar 46.55 72,330 48,900.50 18,600

2015 Apr 147.64 3,330 48,479.75 18,600

2015 May 150.62 10,030 49,457.76 18,600

2015 Jun 153.32 41,160 49,863.55 18,600

2015 Jul 74.54 8,190 50,592.85 18,600

2015 Aug 77.42 1,630 52,547.00 18,600

2015 Sep 80.72 8,630 54,791.00 18,600

2015 Oct 62.96 83,580 51,191.25 18,600

2015 Nov 57.97 174,540 51,742.90 18,600

2015 Dec 82.73 999,050 51,565.25 18,600

2016 Jan 84.72 336,560 51,518.50 18,600

2016 Feb 108.31 2,320,000 50,011.28 18,600

2016 Mar 98.12 3,100,000 49,592.40 18,600

2016 Apr 90.71 1,350,000 49,311.90 18,600

2016 May 76.09 4,500,000 51,088.40 18,600

2016 Jun 87.20 51,820,000 49,414.75 18,600

2016 Jul 78.59 13,960,000 48,988.39 18,600

2016 Aug 79.61 12,350,000 49,620.45 18,600

2016 Sep 112.24 28,620,000 49,724.31 28,510

2016 Oct 104.12 20,040,000 51,931.04 25,610

2016 Nov 92.02 10,300,000 52,041.60 34,840

2016 Dec 87.71 8,080,000 58,740.10 35,740

2017 Jan 83.58 52,670,000 54,342.64 92,400

2017 Feb 73.75 52,940,000 51,343.60 156,400

2017 Mar 283.57 750,830,000 96,609.88 1,160,000

2017 Apr 733.10 1,560,000,000 215,929.80 8,020,000

2017 May 3,152.10 1,250,000,000 344,786.30 16,680,000

2017 Jun 3,250.58 502,310,000 526,302.98 15,750,000

2017 Jul 2,171.98 455,050,000 563,247.75 12,460,000

2017 Aug 3,453.17 813,350,000 977,107.89 9,560,000

2017 Sep 2,680.83 505,330,000 744,435.09 23,960,000

2017 Oct 2,685.38 470,050,000 751,498.13 10,220,000

2017 Nov 3,208.37 672,630,000 1,160,801.32 17,410,000

2017 Dec 26,727.98 1,590,000,000 3,082,128.98 37,480,000


Year Month XMR Sum


2015 Jan 3,673.58 380 2,949,685.01 395,470

2015 Feb 4,523.75 1,750 3,505,292.31 1,055,360

2015 Mar 4,053.25 80 3,396,408.55 1,616,150

2015 Apr 4,407.25 260 3,261,383.02 1,040,600

2015 May 6,479.76 270 3,231,302.78 790,390

2015 Jun 5,866.30 7,030 3,713,121.25 943,640

2015 Jul 6,222.65 1,180 4,059,812.56 1,066,640

2015 Aug 6,884.50 1,120 3,470,989.42 1,013,270

2015 Sep 5,713.50 630 3,692,466.72 607,470

2015 Oct 5,201.25 630 4,537,879.84 959,340

2015 Nov 5,118.95 8,100 5,429,188.67 2,126,380

2015 Dec 6,755.88 8,580 6,130,556.73 2,461,370

2016 Jan 7,163.00 4,040 5,250,701.47 1,537,640

2016 Feb 12,034.80 24,260 6,087,711.31 3,291,440

2016 Mar 18,564.00 49,170 5,735,711.12 3,869,260

2016 Apr 11,998.35 69,890 6,066,772.91 2,461,100

2016 May 12,840.40 41,000 7,504,743.49 7,223,630

2016 Jun 20,875.75 133,300 9,150,007.70 59,801,900

2016 Jul 23,577.30 54,570 8,397,526.94 19,340,890

2016 Aug 112,508.40 410,530 7,961,242.98 14,182,770

2016 Sep 111,194.52 350,210 8,310,075.47 30,228,410

2016 Oct 66,805.76 126,400 9,448,943.80 21,261,020

2016 Nov 123,327.75 159,950 10,307,581.42 11,179,550

2016 Dec 185,785.78 129,750 13,374,912.08 9,653,770

2017 Jan 173,843.04 334,880 13,271,971.58 55,066,710

2017 Feb 163,632.72 244,350 16,284,396.63 55,198,420

2017 Mar 269,574.87 1,430,000 15,443,205.52 762,700,000

2017 Apr 336,557.25 532,120 20,796,105.74 1,572,604,130

2017 May 536,497.08 766,490 32,993,525.63 1,273,150,680

2017 Jun 574,548.53 439,540 37,039,306.88 523,238,720

2017 Jul 531,667.50 671,050 41,829,080.48 476,853,660

2017 Aug 1,875,225.22 1,030,000 70,978,472.78 830,041,950

2017 Sep 1,293,937.58 736,030 64,958,885.95 539,926,030

2017 Oct 1,186,718.75 204,680 93,650,391.63 486,274,680

2017 Nov 2,367,455.78 609,060 143,379,619.93 698,559,060

2017 Dec 4,502,103.53 480,310 204,838,587.45 1,639,610,310


Year Month Base Indexing Method

Common Paasche Laspeyer Drobisch Fisher

2015 Jan 1.0000 1.0000 0.00000252864 0.5000 0.001590

2015 Feb 1.1884 1.1884 0.00000112602 0.5942 0.001157

2015 Mar 0.9689 0.9689 0.00000059953 0.4845 0.000762

2015 Apr 0.9602 0.9602 0.00000092278 0.4801 0.000941

2015 May 0.9908 0.9908 0.00000125353 0.4954 0.001114

2015 Jun 1.1491 1.1491 0.00000121774 0.5746 0.001183

2015 Jul 1.0934 1.0934 0.00000102506 0.5467 0.001059

2015 Aug 0.8550 0.8550 0.00000084377 0.4275 0.000849

2015 Sep 1.0638 1.0638 0.00000175121 0.5319 0.001365

2015 Oct 1.2290 1.2290 0.00000128104 0.6145 0.001255

2015 Nov 1.1964 1.1964 0.00000056265 0.5982 0.000820

2015 Dec 1.1292 1.1292 0.00000045876 0.5646 0.000720

2016 Jan 0.8565 0.8565 0.00000055701 0.4282 0.000691

2016 Feb 1.1594 1.1594 0.00000035225 0.5797 0.000639

2016 Mar 0.9422 0.9422 0.00000024350 0.4711 0.000479

2016 Apr 1.0577 1.0577 0.00000042978 0.5289 0.000674

2016 May 1.2370 1.2370 0.00000017125 0.6185 0.000460

2016 Jun 1.2192 1.2192 0.00000002039 0.6096 0.000158

2016 Jul 0.9178 0.9178 0.00000004745 0.4589 0.000209

2016 Aug 0.9480 0.9480 0.00000006684 0.4740 0.000252

2016 Sep 1.0438 1.0438 0.00000003453 0.5219 0.000190

2016 Oct 1.1370 1.1370 0.00000005348 0.5685 0.000247

2016 Nov 1.0909 1.0909 0.00000009758 0.5454 0.000326

2016 Dec 1.2976 1.2976 0.00000013441 0.6488 0.000418

2017 Jan 0.9923 0.9923 0.00000001802 0.4962 0.000134

2017 Feb 1.2270 1.2270 0.00000002223 0.6135 0.000165

2017 Mar 0.9483 0.9483 0.00000000124 0.4742 0.000034

2017 Apr 1.3466 1.3466 0.00000000086 0.6733 0.000034

2017 May 1.5865 1.5865 0.00000000125 0.7933 0.000044

2017 Jun 1.1226 1.1226 0.00000000215 0.5613 0.000049

2017 Jul 1.1293 1.1293 0.00000000237 0.5647 0.000052

2017 Aug 1.6969 1.6969 0.00000000204 0.8484 0.000059

2017 Sep 0.9152 0.9152 0.00000000170 0.4576 0.000039

2017 Oct 1.4417 1.4417 0.00000000296 0.7208 0.000065

2017 Nov 1.5310 1.5310 0.00000000219 0.7655 0.000058

2017 Dec 1.4286 1.4286 0.00000000087 0.7143 0.000035


Year Month Index

Value Change

2015 Jan 4,690.50 0.00%

2015 Feb 4,054.83 -13.55%

2015 Mar 2,588.66 -36.16%

2015 Apr 3,070.02 18.60%

2015 May 3,601.08 17.30%

2015 Jun 4,392.36 21.97%

2015 Jul 4,297.98 -2.15%

2015 Aug 2,948.07 -31.41%

2015 Sep 5,039.85 70.95%

2015 Oct 5,693.81 12.98%

2015 Nov 4,454.47 -21.77%

2015 Dec 4,412.42 -0.94%

2016 Jan 3,626.66 -17.81%

2016 Feb 3,890.43 7.27%

2016 Mar 2,747.30 -29.38%

2016 Apr 4,090.38 48.89%

2016 May 3,454.11 -15.56%

2016 Jun 1,442.61 -58.23%

2016 Jul 1,752.44 21.48%

2016 Aug 2,004.15 14.36%

2016 Sep 1,577.69 -21.28%

2016 Oct 2,330.07 47.69%

2016 Nov 3,362.93 44.33%

2016 Dec 5,585.69 66.10%

2017 Jan 1,774.74 -68.23%

2017 Feb 2,689.33 51.53%

2017 Mar 530.31 -80.28%

2017 Apr 706.18 33.17%

2017 May 1,467.02 107.74%

2017 Jun 1,817.80 23.91%

2017 Jul 2,163.22 19.00%

2017 Aug 4,180.47 93.25%

2017 Sep 2,558.49 -38.80%

2017 Oct 6,122.66 139.31%

2017 Nov 8,305.46 35.65%

2017 Dec 7,227.13 -12.98%


Appendix L: Calculation of Portfolio with Common Assets Only

a. Risk and Return

Statistics LQ45 FIFI USD GOLD

Avg 0.55% 0.50% 0.21% 0.31%

Var 0.17% 0.02% 0.04% 0.11%

Stdev 4.11% 1.25% 2.01% 3.31%

Avg, Annual 6.66% 5.98% 2.53% 3.66%

Var, Annual 2.02% 0.19% 0.48% 1.32%

Stdev, Annual 49.29% 14.99% 24.10% 39.78%

Label

X Y

(Exp Risk) (Exp Return)

Stdev, Annual Avg, Annual

LQ45 49.29% 6.66%

FIFI 14.99% 5.98%

USD 24.10% 2.53%

GOLD 39.78% 3.66%

Standard Deviation of Volatility Risk

Av

erag

e A

nnu

al R

etu

rn

0% 20% 40% 60% 80% 100%

2%

4%

6%

8%

10%

0%

LQ45

FIFI

USD

GOLD


b. Correlation Coefficient

Asset LQ45 FIFI USD GOLD

LQ45 1.00000 0.57913 -0.53225 0.06456

FIFI 0.57913 1.00000 -0.69031 0.10301

USD -0.53225 -0.69031 1.00000 -0.08009

GOLD 0.06456 0.10301 -0.08009 1.00000

c. Instrument Covariance


LQ45 0.00169 0.00030 -0.00044 0.00009

FIFI 0.00030 0.00016 -0.00017 0.00004

USD -0.00044 -0.00017 0.00040 -0.00005

GOLD 0.00009 0.00004 -0.00005 0.00110

d. Efficient Frontier Graph


Return Constant 6.66% 5.98% 2.53% 3.66%

Highest Return 6.66%

Lowest Return 2.53%

Required Plot 10

Value Distance 0.46%


LQ45 0.02025 0.00357 -0.00527 0.00105

FIFI 0.00357 0.00187 -0.00208 0.00051

USD -0.00527 -0.00208 0.00484 -0.00064

GOLD 0.00105 0.00051 -0.00064 0.01319


Graph Plot Asset Weight Sum

X Y LQ45 FIFI USD GOLD

24.10%

17.78%

13.84%

10.32%

7.78%

7.29%

9.31%

12.89%

23.46%

49.29%

2.53%

2.99%

3.45%

3.91%

4.37%

4.82%

5.28%

5.74%

6.20%

6.66%

0.00%

6.73%

6.95%

4.60%

2.24%

0.00%

0.00%

0.00%

32.73%

100.00%

0.00%

0.00%

14.14%

31.50%

48.86%

66.09%

79.86%

93.17%

67.27%

0.00%

100.00%

77.26%

66.23%

54.98%

43.73%

32.51%

20.14%

6.83%

0.00%

0.00%

0.00%

16.01%

12.68%

8.92%

5.17%

1.40%

0.00%

0.00%

0.00%

0.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%


Av

erag

e A

nnu

al R

etu

rn

0% 20% 40% 60% 80% 100%

2%

4%

6%

8%

10%

0%

efficient frontier graph


e. Global Minimum Variance


Weight 0.62% 60.83% 35.97% 2.57%

Exp Risk 7.16%

Exp Return 4.68%

Portfolio Variance 0.51%


Av

erag

e A

nnu

al R

etu

rn

0% 20% 40% 60% 80% 100%

2%

4%

6%

8%

10%

0%


global minimum variance

efficient portfolio area

inefficient portfolio area


f. Sharpe Performance Measurement

Asset Count 4

Prorate Amount 0.25

Weight Sum 1


Weight 25.00% 25.00% 25.00% 25.00%

Exp Risk 16.07%

Exp Return 4.71%

Risk Free Rate 6.03%

Sharpe Ratio -8.23%



Av

erag

e A

nnu

al R

etu

rn

0% 20% 40% 60% 80% 100%

2%

4%

6%

8%

10%

0%


equally weighted portfolio value


g. Markowitz Portfolio Optimization

Asset Count 4

Prorate Amount 0.25

Weight Sum 1


Weight 100.00% 0.00% 0.00% 0.00%

Exp Risk 49.29%

Exp Return 6.66%


Sharpe Ratio 1.27%



Av

erag

e A

nnu

al R

etu

rn

0% 20% 40% 60% 80% 100%

2%

4%

6%

8%

10%

0%


optimal risky portfolio value


Appendix M: Calculation of Portfolio with Indexed Cryptocurrencies

a. Risk and Return

Statistics LQ45 FIFI USD GOLD CRYPTO5

Avg 0.55% 0.50% 0.21% 0.31% 12.42%

Var 0.17% 0.02% 0.04% 0.11% 21.99%

Stdev 4.11% 1.25% 2.01% 3.31% 46.89%

Avg, Annual 6.66% 5.98% 2.53% 3.66% 148.98%

Var, Annual 2.02% 0.19% 0.48% 1.32% 263.89%

Stdev, Annual 49.29% 14.99% 24.10% 39.78% 562.73%

Label

X Y



LQ45 49.29% 6.66%

FIFI 14.99% 5.98%

USD 24.10% 2.53%

GOLD 39.78% 3.66%

CRYPTO5 562.73% 148.98%


Av

erag

e A

nnu

al R

etu

rn

0% 59% 132% 296% 667%

2%

8%

31%

125%

500%

0% log

log

1500%

CRYPTO5

LQ45 FIFI

USD GOLD



Asset LQ45 FIFI USD GOLD CRYPTO5

LQ45 1.00000 0.57913 -0.53225 0.06456 -0.13643

FIFI 0.57913 1.00000 -0.69031 0.10301 -0.27651

USD -0.53225 -0.69031 1.00000 -0.08009 0.09862

GOLD 0.06456 0.10301 -0.08009 1.00000 -0.03149

CRYPTO5 -0.13643 -0.27651 0.09862 -0.03149 1.00000



LQ45 0.00169 0.00030 -0.00044 0.00009 -0.00263

FIFI 0.00030 0.00016 -0.00017 0.00004 -0.00162

USD -0.00044 -0.00017 0.00040 -0.00005 0.00093

GOLD 0.00009 0.00004 -0.00005 0.00110 -0.00049

CRYPTO5 -0.00263 -0.00162 0.00093 -0.00049 0.21991



Return Constant 6.66% 5.98% 2.53% 3.66% 148.98%


Lowest Return 2.53%

Required Plot 10



LQ45 0.02025 0.00357 -0.00527 0.00105 -0.03153

FIFI 0.00357 0.00187 -0.00208 0.00051 -0.01944

USD -0.00527 -0.00208 0.00484 -0.00064 0.01114

GOLD 0.00105 0.00051 -0.00064 0.01319 -0.00587

CRYPTO5 -0.03153 -0.01944 0.01114 -0.00587 2.63890



X Y LQ45 FIFI USD GOLD CRYPTO5

24.10%

48.49%

111.35%

175.00%

238.84%

303.13%

367.80%

432.66%

497.66%

562.73%

2.53%

18.81%

35.08%

51.35%

67.62%

83.89%

100.17%

116.44%

132.71%

148.98%

0.00%

3.87%

23.77%

43.67%

57.16%

45.73%

34.30%

22.87%

11.43%

0.00%

0.00%

87.18%

56.00%

24.81%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

100.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

8.95%

20.24%

31.52%

42.84%

54.27%

65.70%

77.13%

88.57%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

17.70%

9.71%

6.95%

7.42%

9.19%

11.70%

14.58%

17.65%

20.83%

24.07%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

10.00%

11.00%

12.00%

6.93%

4.12%

0.72%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

34.99%

60.51%

66.69%

71.88%

76.31%

80.65%

84.99%

89.32%

93.66%

77.08%

52.72%

35.88%

31.10%

26.62%

21.75%

16.83%

11.91%

7.00%

2.08%

15.98%

8.17%

2.67%

1.42%

0.14%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.22%

0.79%

1.36%

1.94%

2.52%

3.10%

3.68%

4.26%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%


Av

erag

e A

nnu

al R

etu

rn

0% 59% 132% 296% 667%

2%

8%

31%

125%

500%

0% log

log

1500%





Weight 0.58% 61.73% 34.93% 2.42% 0.33%

Exp Risk 6.91%

Exp Return 5.20%



Av

erag

e A

nnu

al R

etu

rn

0% 59% 132% 296% 667%

2%

8%

31%

125%

500%

0% log

log

1500%







Asset Count 5

Prorate Amount 0.20

Weight Sum 1


Weight 20.00% 20.00% 20.00% 20.00% 20.00%

Exp Risk 111.32%

Exp Return 33.56%


Sharpe Ratio 24.73%



Av

erag

e A

nnu

al R

etu

rn

0% 59% 132% 296% 667%

2%

8%

31%

125%

500%

0% log

log

1500%





Asset Count 5

Prorate Amount 0.20

Weight Sum 1


Weight 5.02% 85.37% 0.00% 0.00% 9.61%

Exp Risk 52.08%

Exp Return 19.75%


Sharpe Ratio 26.35%



Av

erag

e A

nnu

al R

etu

rn

0% 59% 132% 296% 667%

2%

8%

31%

125%

500%

0% log

log

1500%


optimal risky portfolio value


Appendix N: Calculation of Portfolio with Individual Cryptocurrencies

a. Risk and Return

Statistics … BTC ETH XRP LTC XMR

Avg

Var

Stdev

Avg, Annual

Var, Annual

Stdev, Annual

…

…

…

…

…

…

14.06%

3.93%

19.81%

168.77%

47.10%

237.74%

19.52%

24.68%

49.68%

234.25%

296.19%

596.18%

46.90%

206.43%

143.68%

562.86%

2477.11%

1724.10%

16.99%

15.49%

39.35%

203.87%

185.82%

472.21%

35.18%

60.57%

77.82%

422.13%

726.79%

933.89%

Label

X Y



LQ45

FIFI

USD

GOLD

BTC

ETH

XRP

LTC

XMR

49.29%

14.99%

24.10%

39.78%

237.74%

596.18%

1724.10%

472.21%

933.89%

6.66%

5.98%

2.53%

3.66%

168.77%

234.25%

562.86%

203.87%

422.13%


Av

erag

e A

nnu

al R

etu

rn

0% 54% 176% 572% 1852%

1%

8%

51%

320%

2000%

0% log

log

6000%

BTC

LQ45 FIFI

GOLD

ETH

XRP

LTC

XMR

USD



Asset … BTC ETH XRP LTC XMR

LQ45

FIFI

USD

GOLD

BTC

ETH

XRP

LTC

XMR

…

…

…

…

…

…

…

…

…

0.23271

0.10486

-0.15211

-0.03832

1.00000

0.32027

0.25856

0.46718

0.21554

0.18097

0.23947

0.02889

0.08794

0.32027

1.00000

0.58531

0.67006

0.24217

0.25504

0.09305

-0.06461

0.16060

0.25856

0.58531

1.00000

0.79834

0.17204

0.30998

0.17954

0.00577

0.12946

0.46718

0.67006

0.79834

1.00000

0.28619

0.21873

0.08455

0.04278

0.06523

0.21554

0.24217

0.17204

0.28619

1.00000



LQ45

FIFI

USD

GOLD

BTC

ETH

XRP

LTC

XMR

…

…

…

…

…

…

…

…

…

0.00189

0.00026

-0.00061

-0.00025

0.03925

0.03152

0.07360

0.03642

0.03323

0.00369

0.00149

0.00029

0.00145

0.03152

0.24682

0.41779

0.13100

0.09363

0.01505

0.00167

-0.00186

0.00765

0.07360

0.41779

2.06426

0.45137

0.19236

0.00501

0.00088

0.00005

0.00169

0.03642

0.13100

0.45137

0.15485

0.08764

0.00699

0.00082

0.00067

0.00168

0.03323

0.09363

0.19236

0.08764

0.60566



Return Constant … 168.77% 234.25% 562.86% 203.87% 422.13%


Lowest Return 2.53%

Required Plot 10



LQ45

FIFI

USD

GOLD

BTC

ETH

XRP

LTC

XMR

…

…

…

…

…

…

…

…

…

0.02273

0.00311

-0.00726

-0.00302

0.47102

0.37828

0.88320

0.43707

0.39879

0.04432

0.01784

0.00346

0.01738

0.37828

2.96189

5.01349

1.57198

1.12360

0.18062

0.02004

-0.02237

0.09179

0.88320

5.01349

24.77107

5.41638

2.30836

0.06013

0.01059

0.00055

0.02026

0.43707

1.57198

5.41638

1.85820

1.05173

0.08391

0.00987

0.00802

0.02019

0.39879

1.12360

2.30836

1.05173

7.26787



X Y LQ45 FIFI USD GOLD

24.10%

80.83%

159.90%

247.13%

344.23%

491.94%

656.43%

827.77%

1109.76%

1724.10%

2.53%

64.79%

127.05%

189.31%

251.57%

313.83%

376.08%

438.34%

500.60%

562.86%

0.00%

10.48%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

19.07%

22.12%

16.53%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

100.00%

21.66%

16.45%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

17.71%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

---

---

---

---

---

---

---

---

---

---

17.70%

9.71%

7.14%

7.26%

9.92%

15.26%

21.34%

33.91%

46.80%

66.29%

3.00%

4.00%

5.00%

6.00%

10.00%

15.00%

20.00%

30.00%

40.00%

55.00%

6.93%

4.12%

0.40%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

34.99%

60.76%

60.93%

59.96%

58.99%

56.87%

54.93%

51.65%

46.74%

77.08%

52.72%

36.01%

35.66%

35.12%

34.52%

33.57%

32.29%

30.61%

28.09%

15.98%

8.17%

2.64%

2.60%

2.02%

1.13%

1.61%

0.00%

0.00%

0.00%

---

---

---

---

---

---

---

---

---

---



X Y BTC ETH XRP LTC XMR

24.10%

80.83%

159.90%

247.13%

344.23%

491.94%

656.43%

827.77%

1109.76%

1724.10%

2.53%

64.79%

127.05%

189.31%

251.57%

313.83%

376.08%

438.34%

500.60%

562.86%

0.00%

26.11%

51.34%

59.57%

70.60%

50.23%

29.23%

8.22%

0.00%

0.00%

0.00%

1.91%

2.78%

8.58%

0.79%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.21%

0.92%

2.14%

6.94%

13.48%

19.89%

26.31%

55.76%

100.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

2.85%

6.39%

13.17%

21.68%

36.29%

50.88%

65.47%

44.24%

0.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

17.70%

9.71%

7.14%

7.26%

9.92%

15.26%

21.34%

33.91%

46.80%

66.29%

3.00%

4.00%

5.00%

6.00%

10.00%

15.00%

20.00%

30.00%

40.00%

55.00%

0.00%

0.00%

0.20%

0.81%

2.71%

4.83%

7.11%

11.23%

15.51%

21.93%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.04%

0.10%

0.07%

0.28%

0.40%

0.58%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.16%

0.44%

0.78%

1.27%

1.82%

2.65%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%


Av

erag

e A

nnu

al R

etu

rn

0% 54% 176% 572% 1852%

1%

8%

51%

320%

2000%

0% log

log

6000%





Weight 0.33% 60.82% 35.97% 2.63%

Asset BTC ETH XRP LTC XMR

Weight 0.24% 0.00% 0.00% 0.00% 0.00%

Exp Risk 7.14%

Exp Return 5.07%



Av

erag

e A

nnu

al R

etu

rn

0% 54% 176% 572% 1852%

1%

8%

51%

320%

2000%

0% log

log

6000%







Asset Count 9

Prorate Amount 0.11

Weight Sum 1


Weight 11.11% 11.11% 11.11% 11.11%


Weight 11.11% 11.11% 11.11% 11.11% 11.11%

Exp Risk 334.87%

Exp Return 178.97%


Sharpe Ratio 51.64%



Av

erag

e A

nnu

al R

etu

rn

0% 54% 176% 572% 1852%

1%

8%

51%

320%

2000%

0% log

log

6000%





Asset Count 9

Prorate Amount 0.11

Weight Sum 1


Weight 0.00% 0.00% 0.00% 0.00%


Weight 83.06% 4.87% 1.49% 0.00% 10.57%

Exp Risk 260.95%

Exp Return 204.63%


Sharpe Ratio 76.11%



Av

erag

e A

nnu

al R

etu

rn

0% 54% 176% 572% 1852%

1%

8%

51%

320%

2000%

0% log

log

6000%

efficient frontier graph optimal risky portfolio value

Analysis of Cryptocurrency as Investment Asset in Optimizing the Portfolio Performance - ITB SBM MBA...

Documents

Transcript of Analysis of Cryptocurrency as Investment Asset in Optimizing the Portfolio Performance - ITB SBM MBA...