Bitcoin price prediction through Satoshi Nakamoto eyes (using Quantitative methodologies)
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In this presentation I explore the Stock To Flow Model used by some Bitcoin Lovers to predict the Bitcoin price based on halving plan, respect other two methodologies proposed by myself: The rate of adoption and the Hase Rate rewards function.
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Bitcoin price prediction through Satoshi Nakamoto eyes (using Quantitative methodologies)
- 1. Galzignano Terme, 20th February 2020Galzignano Terme, 20th February 2020 Bitcoin’s Price prediction through Satoshi Nakamoto’s eyes - using Quantitative methodologies.
- 2. Galzignano Terme, 20th February 2020 Starting from the basics • What is a Bitcoin? According to Satoshi Nakamoto’s Paper: Bitcoin is a Peer-to-Peer Electronic Cash System
- 3. Galzignano Terme, 20th February 2020 The Economic System behind Bitcoin • Mining Server Producers • Electricity Power • Software developers • Exchanges • Media and advertising • Companies developing Blockchain Technologies • …
- 4. Galzignano Terme, 20th February 2020 But really, what is a Bitcoin? • A Currency? • A Commodity? • An Intangible Asset? • A Fungible Asset? • A Digital Gold? • A P2P transaction system? • An Investible Asset? • A new Asset Class?
- 5. Galzignano Terme, 20th February 2020 More importantly: what is Bitcoin’s Fair Value Price? • What is Bitcoin’s Fair Value Price? • How can we predict its Value? • What are the dynamics that move the price?
- 6. Galzignano Terme, 20th February 2020 Four Methodologies to predict the price • Stock to Flow model [8] • Rate of Adoption [proprietary] • Hash Rate Remuneration [proprietary] • Montecarlo Simulations [11]
- 7. Galzignano Terme, 20th February 2020 The three Bitcoin’s Bubbles 0.01 0.1 1 10 100 23/07/10 23/09/10 23/11/10 23/01/11 23/03/11 23/05/11 23/07/11 23/09/11 Price +39611% -91% -91% +39611% 1 10 100 1000 10000 28/10/11 28/03/12 28/08/12 28/01/13 28/06/13 28/11/13 28/04/14 28/09/14 Price -82% +27184% 10 100 1000 10000 100000 16/01/15 16/07/15 16/01/16 16/07/16 16/01/17 16/07/17 16/01/18 16/07/18 Price -81% +7942% Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 8. Galzignano Terme, 20th February 2020 Stock to Flow model [8] Using Power Law
- 9. Galzignano Terme, 20th February 2020 Power Law (1) [7] • You can find the Power Law in many physical, mathematical, relational, and statistical examples. • It occurs when there is an exponential relationship between two variables • One example is the Pareto Law, that uses the Power Law to describe the relationship between people and their wealth • The description of the Power law in a Cartesian plane is made by using the logarithmic scale for both the axes
- 10. Galzignano Terme, 20th February 2020 Power Law (2) [7] Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 11. Galzignano Terme, 20th February 2020 Power Law (3) [7] (a) word frequency in a Moby Dick book (b) Citations on Scientific papers (c) Cumulative distribution of Web Hits (d) The 633 Books that sold more than 2 million copies (e) Cumulative distribution of phone calls by AT&T (f) Earthquake magnitude in California between 1910 and 1992 (j) Wealth of the richest people (k) Frequency of Family name (l) Cumulative distribution of population in US cities Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 12. Galzignano Terme, 20th February 2020 Power Law and Scarcity (1) Intuitively it appears that there is a relationship between the scarcity of a physical good and its value. Example: an old coin from the Roman Empire is very scarce, and despite the value of the coin being small when it was in use, today, purchasing such a coin would probably cost 100x or 1000x its original value. The questions are: are Commodities following this rule? Is there a link between the quantity extracted during a given year (flow) in relation to the total amount in circulation (stock) of the same commodity?
- 13. Galzignano Terme, 20th February 2020 Power Law and Stock to Flow models (2) Stock the reserved quantity Flow the quantity mined or extracted in a given year SF Years needed to duplicate the stock Supply Growth Percentage of flow related to the stock Price Actual price per unit Market Value Actual capitalization of the entire stock Stock (tn) Flow (tn) SF Supply Growth Price $/unit Market Value ($) GOLD 190,000 3,500 54 1,8% 1,425 9,476,250,000,000 DIAMONDS 2,812 150 19 5,3% 533 1,500,000,000,000 SILVER 71,000 25,000 3 35,2% 16 39,760,000,000 PALLADIUM 244 215 1 88,1% 1,488 12,707,520,000 Source:Medium–Elaborations:DIAMANPartnersLtd
- 14. Galzignano Terme, 20th February 2020 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000 100,000,000,000 1,000,000,000,000 10,000,000,000,000 100,000,000,000,000 1 10 100 MarketValue SF (years) Commodities - Stock to Flow power law chart Power Law and Stock to Flow models (3) Source:Medium–Elaborations:DIAMANPartnersLtd Gold Diamond Silver Palladium
- 15. Galzignano Terme, 20th February 2020 Difference between USD and Bitcoin USD + Inflationary supply = decreasing value Bitcoin + Deflationary supply = Stock to Flow model?
- 16. Galzignano Terme, 20th February 2020 Bitcoin and Flow Deflation Satoshi Nakamoto designed a deflationary mining model to increase the value of Bitcoin over time Source:andelaborations:DIAMANPartnersLtd 0.01% 0.10% 1.00% 10.00% 100.00% 1000.00% 10000.00% 2009 2010 2012 2014 2015 2017 2019 2020 2022 2024 2025 2027 2029 2030 2032 2034 2035 2037 2039 2040 2042 2044 2045 2047 2049 2050 AnnualMinigRewardstoStock Bitcoin's Mining deflation over time We are here
- 17. Galzignano Terme, 20th February 2020 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000 100,000,000,000 1,000,000,000,000 10,000,000,000,000 100,000,000,000,000 1 10 100 MarketValue SF (Years) Bitcoin - Stock to Flow power law chart Bitcoin Power Law and Stock to Flow Model (1) Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd 1st Halving 2nd Halving
- 18. Galzignano Terme, 20th February 2020 y = 398098x3.879 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000 100,000,000,000 1,000,000,000,000 10,000,000,000,000 100,000,000,000,000 1 10 100 MarketValue SF (Years) Bitcoin - Stock to Flow power law chart Bitcoin Power Law and Stock to Flow Model (2) Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd 1st Halving 2nd Halving Next Halving
- 19. Galzignano Terme, 20th February 2020 Bitcoin’s Price Predictions using Stock to Flow 0 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 01/01/09 01/10/09 01/07/10 01/04/11 01/01/12 01/10/12 01/07/13 01/04/14 01/01/15 01/10/15 01/07/16 01/04/17 01/01/18 01/10/18 01/07/19 01/04/20 01/01/21 01/10/21 01/07/22 01/04/23 01/01/24 01/10/24 01/07/25 Stock to Flow Bitcoin's Price Predictions Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 20. Galzignano Terme, 20th February 2020 Rate of Adoption model Using Power Law
- 21. Galzignano Terme, 20th February 2020 Rate of Adoption (1) Is there an association between the exponential growth of the Bitcoin Price and the number of wallets around the world? 0 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 30,000,000 35,000,000 40,000,000 45,000,000 50,000,000 giu-12 ott-12 feb-13 giu-13 ott-13 feb-14 giu-14 ott-14 feb-15 giu-15 ott-15 feb-16 giu-16 ott-16 feb-17 giu-17 ott-17 feb-18 giu-18 ott-18 feb-19 giu-19 ott-19 Number of Wallets over time Source:Blockchain.com–Elaborations:DIAMANPartnersLtd
- 22. Galzignano Terme, 20th February 2020 Rate of Adoption (2) 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000 0.01 0.1 1 10 100 1000 10000 100000 01/07/10 01/12/10 01/05/11 01/10/11 01/03/12 01/08/12 01/01/13 01/06/13 01/11/13 01/04/14 01/09/14 01/02/15 01/07/15 01/12/15 01/05/16 01/10/16 01/03/17 01/08/17 01/01/18 01/06/18 01/11/18 01/04/19 01/09/19 01/02/20 NumberofWallets BTCPrice Relationship between Rate of Adoption and BTC Price y = 0.0081x4.818 Bitcoin Price Number of Wallets Power Law Source:Blockchain.com–Elaborations:DIAMANPartnersLtd
- 23. Galzignano Terme, 20th February 2020 Rate of Adoption (3) y = 85213x-0.845 100 1,000 10,000 100,000 1,000,000 3 8 13 18 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 98 103108113 Ratio Between Capitalization and N. of Wallets Source:Blockchain.com–Elaborations:DIAMANPartnersLtd
- 24. Galzignano Terme, 20th February 2020 Rate of Adoption (4) 0 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 01/01/09 01/10/09 01/07/10 01/04/11 01/01/12 01/10/12 01/07/13 01/04/14 01/01/15 01/10/15 01/07/16 01/04/17 01/01/18 01/10/18 01/07/19 01/04/20 01/01/21 01/10/21 01/07/22 01/04/23 01/01/24 01/10/24 01/07/25 Bitcoin Price Estimation using Rate of Adoptions Source:Blockchain.com–Elaborations:DIAMANPartnersLtd
- 25. Galzignano Terme, 20th February 2020 Hash Rate Remuneration model Using Power Law
- 26. Galzignano Terme, 20th February 2020 Hash Rate (1) Is there a relationship between the exponential growth of the Bitcoin Price and the Hash Rate Remuneration? 0 0 10 1,000 100,000 10,000,000 1,000,000,000 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Hash Rate over time Y = 2E-07x6.9026 Source:Blockchain.com–Elaborations:DIAMANPartnersLtd
- 27. Galzignano Terme, 20th February 2020 Hash Rate (2) 0 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000 100,000,000,000 01/08/10 01/06/11 01/04/12 01/02/13 01/12/13 01/10/14 01/08/15 01/06/16 01/04/17 01/02/18 01/12/18 01/10/19 01/08/20 01/06/21 01/04/22 01/02/23 01/12/23 01/10/24 01/08/25 Hash Rate Function Hash Rate Power Law Source:Blockchain.com–Elaborations:DIAMANPartnersLtd
- 28. Galzignano Terme, 20th February 2020 Hash Rate Remuneration (3) Source:Blockchain.com–Elaborations:DIAMANPartnersLtd 0 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000 100,000,000,000 01/08/10 01/06/11 01/04/12 01/02/13 01/12/13 01/10/14 01/08/15 01/06/16 01/04/17 01/02/18 01/12/18 01/10/19 01/08/20 01/06/21 01/04/22 01/02/23 01/12/23 01/10/24 01/08/25 Rewards ($) by Month Rewards by Month Hash Rate Power Law
- 29. Galzignano Terme, 20th February 2020 Hash Rate Remuneration (4) Source:Blockchain.com–Elaborations:DIAMANPartnersLtd 0 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000 100,000,000,000 01/08/10 01/06/11 01/04/12 01/02/13 01/12/13 01/10/14 01/08/15 01/06/16 01/04/17 01/02/18 01/12/18 01/10/19 01/08/20 01/06/21 01/04/22 01/02/23 01/12/23 01/10/24 01/08/25 Historical Rewards ($) VS Hash Rate Rewards by Month Historical Remuneration Hash Rate Power Law
- 30. Galzignano Terme, 20th February 2020 Hash Rate Remuneration (5) Source:Blockchain.com–Elaborations:DIAMANPartnersLtd y = 2E+308x-172 0 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000 100,000,000,000 01/08/10 01/06/11 01/04/12 01/02/13 01/12/13 01/10/14 01/08/15 01/06/16 01/04/17 01/02/18 01/12/18 01/10/19 01/08/20 01/06/21 01/04/22 01/02/23 01/12/23 01/10/24 01/08/25 Rewards ($) Function for Hash Rate Rewards by Month Historical Rewards Hash Rate Power Law Rewards Function
- 31. Galzignano Terme, 20th February 2020 Bitcoin Price Based on Hash Rate Remuneration 0 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 01/01/09 01/10/09 01/07/10 01/04/11 01/01/12 01/10/12 01/07/13 01/04/14 01/01/15 01/10/15 01/07/16 01/04/17 01/01/18 01/10/18 01/07/19 01/04/20 01/01/21 01/10/21 01/07/22 01/04/23 01/01/24 01/10/24 01/07/25 Bitcoin Price Estimation using Hash Rate Remunearion Source:Blockchain.com–Elaborations:DIAMANPartnersLtd
- 32. Galzignano Terme, 20th February 2020 Montecarlo Simulation model [11] Using Power Law
- 33. Galzignano Terme, 20th February 2020 Montecarlo Simulation (1) • Volatility estimation y = 12.746x-0.613 10% 100% 1000% 10000% 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 Power Law of Annualized Volatility Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 34. Galzignano Terme, 20th February 2020 Montecarlo Simulation (2) • Mean Returns estimation y = 1519.7x-1.495 10% 100% 1000% 10000% 100000% 1000000% 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101 Power Law of Annualized Returns Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 35. Galzignano Terme, 20th February 2020 Montecarlo Simulation (3) • Expected Volatility and Returns 10% 100% 1000% 10000% 100000% 1000000% 2011 2013 2014 2016 2018 2019 2021 2023 2024 2026 2028 2029 2031 2033 2034 2036 2038 2039 2041 2043 2044 2046 2048 2049 Power Law of Annualized Returns Expected Annual Volatility Expected Annual Returns In the sample estimations Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 36. Galzignano Terme, 20th February 2020 Montecarlo Simulation (5) 10,000 Montecarlo Simulations on Bitcoin Price 0 0 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 2010 2011 2011 2012 2012 2013 2013 2014 2014 2015 2015 2016 2016 2017 2017 2018 2018 2019 2019 2020 2020 2021 2021 2022 2022 2023 2023 2024 2024 2025 2025 Multiple Analysis For Bitcoin's Price Simulation 97% percentile Median 3% Percentile Historical BTC Price Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 37. Galzignano Terme, 20th February 2020 Conclusions Using all the models
- 38. Galzignano Terme, 20th February 2020 Bitcoin’s Prediction using Quantitative Models 0 0 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 2010 2011 2011 2012 2013 2013 2014 2015 2015 2016 2017 2017 2018 2019 2019 2020 2021 2021 2022 2023 2023 2024 2025 2025 USDDollars Multiple Analysis For Bitcoin's Price Simulation 97th Percentile Median 3rd Percentile Rate of Adoption Halving-Value Hash-Rate BTC Price Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 39. Galzignano Terme, 20th February 2020 Considerations Using Common Sense
- 40. Galzignano Terme, 20th February 2020 Considerations • Where there are High Returns there are also High Risks • Satoshi Nakamoto designed a Deflationary model • The probability of Bitcoin Price following the Power Law is 95% [9] • Different Quantitative models design a brilliant future for BTC • We have to consider that BTC can rise to 100.000 or 1m • This opportunity deserves between 2 to 5% of the SAA • The future of Bitcoin will be drawn over multiple bubbles • A Quant Alternative Investment Fund is the best solution
- 41. Galzignano Terme, 20th February 2020 Notified Alternative Investment Fund of Crypto Funds Cayman Islands Crypto Hedge Fund Sub-Investment Manager Investment Manager Just an update:
- 42. Galzignano Terme, 20th February 2020 One more thing for Asset Managers
- 43. Galzignano Terme, 20th February 2020 You might consider to introduce Bitcoin in SAA[10] BITCOIN 2% CASH 98% BITCOIN 5% CASH 95% BITCOIN 10% CASH 90% SP500 GOLDT-10Yr 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% AnnualReturns Annual Standard Deviation Risk Adjusted Returns Source:Analyser4crypto–Elaborations:DIAMANPartnersLtd
- 44. Galzignano Terme, 20th February 2020 And another one more thing for all the attendees
- 45. Galzignano Terme, 20th February 2020 …Read my book, maybe it can change your life
- 46. Galzignano Terme, 20th February 2020 Bibliography 1. Satoshi Nakamoto (2009). Bitcoin White Paper: A Peer-to-Peer Electronic Cash System 2. Daniele Bernardi e Ruggero Bertelli (2014): The DIAMAN Ratio 3. Mebane Faber (2013), A Quantitative Approach to Tactical Asset Allocation 4. Didier Sornette (2018): Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe’s Law and the LPPLS Model 5. Didier Sornette (2018): Can We Use Volatility to Diagnose Financial Bubbles? Lessons from 40 Historical Bubbles 6. Didier Sornette (2010): Bubbles Everywhere in Human Affairs 7. M.E.J. Newman (2006): Power laws, Pareto distributions and Zipf’s law 8. PlanB (2019): Modeling Bitcoin’s Value with Scarcity 9. Nick (2019): Falsifying Stock-to-Flow As a Model of Bitcoin Value 10. PlanB (2020): Efficient Market Hypothesis and Bitcoin Stock-to-Flow Model 11. Samik Raychaudhuri 82008): Introduction on Montecarlo Simulation
- 47. Galzignano Terme, 20th February 2020 DISCLAIMER: This document is prepared by Daniele Bernardi, CEO of Diaman Partners Limited (‘Diaman’). Diaman is a Category 2 Management Company authorized by the MFSA pursuant to article 6 of the Investment Services Act, 1994, and certified to operate throughout Europe. The Digital Asset Fund is a NAIF structures notified by MFSA. Framont Partners & Management Limited (the ‘Company’) is an AIF Manager authorized by MFSA, and allowed to operate throughout Europe according to European regulation. The Company would act as Management Company of the NAIF structure. Additional information about the Company Licenses and Authorizations can be found in the MFSA Financial Register. This document is intended as information reserved for professional and/or institutional clients, and disclosure to the public is strictly prohibited and is not intended for distribution; the information contained is exclusively directed towards investors who reside in the Countries where the AIFM is recognized as Fund Manager. This presentation doesn’t reflect any strategies used by Diaman Partners Ltd, it is only a didactical study of the Bitcoin’s Price prediction.. Only the public versions of the Global Series – Digital Asset Fund’s offering documents will be effective for any potential investment decisions. Any potential investors should refer to the offering documentation of the fund once available and should consult with their respective independent financial and tax advisors. Investment decisions should be made exclusively on an independent assessment on the basis of the investor’s financial position and investment objectives, as well as the investor’s personal interpretation together with the fund’s offering documentation. The fund is licensed to be sell in EU. In Switzerland the fund has Open Fund Investment Services AG as legal representative to be compliant with Swiss and FINMA Rules.