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Cryptocurrency Valuation: An Explainable AI Approach (2201.12893v8)

Published 30 Jan 2022 in econ.GN, cs.AI, cs.CR, q-fin.CP, q-fin.EC, and stat.ML

Abstract: Currently, there are no convincing proxies for the fundamentals of cryptocurrency assets. We propose a new market-to-fundamental ratio, the price-to-utility (PU) ratio, utilizing unique blockchain accounting methods. We then proxy various existing fundamental-to-market ratios by Bitcoin historical data and find they have little predictive power for short-term bitcoin returns. However, PU ratio effectively predicts long-term bitcoin returns than alternative methods. Furthermore, we verify the explainability of PU ratio using machine learning. Finally, we present an automated trading strategy advised by the PU ratio that outperforms the conventional buy-and-hold and market-timing strategies. Our research contributes to explainable AI in finance from three facets: First, our market-to-fundamental ratio is based on classic monetary theory and the unique UTXO model of Bitcoin accounting rather than ad hoc; Second, the empirical evidence testifies the buy-low and sell-high implications of the ratio; Finally, we distribute the trading algorithms as open-source software via Python Package Index for future research, which is exceptional in finance research.

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References (68)
  1. IEEE access 6, 52,138–52,160 (2018)
  2. Alabi, K.: Digital blockchain networks appear to be following metcalfe’s law. Electronic Commerce Research and Applications 24, 23–29 (2017)
  3. Alabi, K.: A 2020 perspective on “digital blockchain networks appear to be following metcalfe’s law”. Electronic Commerce Research and Applications 40, 100,939 (2020)
  4. arXiv preprint arXiv:2206.08401 (2022). 10.48550/arXiv.2206.08401. URL https://arxiv.org/abs/2206.08401
  5. Athey, S.: Beyond prediction: Using big data for policy problems. Science 355(6324), 483–485 (2017)
  6. Athey, S.: 21. the impact of machine learning on economics. In: The economics of artificial intelligence, pp. 507–552. University of Chicago Press (2019)
  7. Journal of Economic Perspectives 31(2), 3–32 (2017)
  8. Annual Review of Economics 11, 685–725 (2019)
  9. Available at SSRN 3261063 (2020)
  10. Blau, B.M.: Price dynamics and speculative trading in bitcoin. Research in International Business and Finance 41, 493–499 (2017)
  11. Borri, N.: Conditional tail-risk in cryptocurrency markets. Journal of Empirical Finance 50, 1–19 (2019)
  12. International Group 432, 151–166 (1984)
  13. The Journal of finance 47(5), 1731–1764 (1992)
  14. The Review of Financial Studies 2(4), 527–551 (1989)
  15. The Quarterly Journal of Economics 131(3), 1181–1242 (2016)
  16. Applied Economics 48(19), 1799–1815 (2016)
  17. The Review of Financial Studies 34(3), 1105–1155 (2021)
  18. SSRN (2020)
  19. The Journal of Financial Data Science 3(1), 28–42 (2021)
  20. In: The Palgrave Handbook of FinTech and Blockchain, pp. 267–284. Springer (2021)
  21. Conlisk, J.: Why bounded rationality? Journal of economic literature 34(2), 669–700 (1996)
  22. Finance Research Letters 26, 81–88 (2018)
  23. The Journal of finance 42(3), 557–581 (1987)
  24. Journal of Financial Economics 33(1), 3–56 (1993)
  25. https://mpra.ub.uni-muenchen.de/71946/ (2016)
  26. Economics Letters 171, 225–229 (2018)
  27. International Review of Financial Analysis 47, 343–352 (2016)
  28. arXiv preprint arXiv:2212.06951 (2022). URL https://arxiv.org/abs/2212.06951
  29. Gartley, H.M.: Profits in the stock market. Health Research Books (1935)
  30. International Review of Financial Analysis 77, 101,847 (2021)
  31. Available at SSRN 2607167 (2015)
  32. Gersbach, H.: Flexible majority rules for cryptocurrency issuance (2019)
  33. Giang, P.H.: Decision making under ignorance. In: Fusion Methodologies in Crisis Management, pp. 435–454. Springer (2016)
  34. Cambridge university press (2009)
  35. The Review of Financial Studies 33(5), 2223–2273 (2020)
  36. Journal of Econometrics 222(1), 429–450 (2021)
  37. Science Robotics 4(37) (2019)
  38. Economic analysis of the digital revolution," J. Ganuza and G. Llobert,(eds)., FUNCAS (2018)
  39. Journal of Economic Literature forthcoming (2022)
  40. John Wiley & Sons (2021)
  41. Microsoft research Redmond, WA (2009)
  42. Journal of Behavioral and Experimental Economics 52, 23–28 (2014)
  43. Journal of Risk and Uncertainty 10(1), 15–36 (1995)
  44. Handbook of Game Theory with Economic Applications 4, 901–947 (2015)
  45. American Economic Review 103(7), 2790–2810 (2013)
  46. Review of Economics and Statistics 104(6), 1329–1340 (2022)
  47. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS ’22, p. 2099–2113. Association for Computing Machinery, New York, NY, USA (2022). 10.1145/3548606.3559341. URL https://arxiv.org/abs/2305.02552
  48. The Review of Financial Studies 34(6), 2689–2727 (2021)
  49. arXiv preprint arXiv:2103.00173 (2021)
  50. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, pp. 281–297. Oakland, CA, USA (1967)
  51. Journal of management information systems 35(1), 19–52 (2018)
  52. Malkiel, B.G.: Passive investment strategies and efficient markets. European Financial Management 9(1), 1–10 (2003)
  53. Maskin, E.: Decision-making under ignorance with implications for social choice. In: Game theory, social choice and ethics, pp. 319–337. Springer (1979)
  54. Metcalfe, B.: Metcalfe’s law after 40 years of ethernet. Computer 46(12), 26–31 (2013)
  55. Moskowitz, T.J.: Asset pricing and sports betting. The Journal of Finance 76(6), 3153–3209 (2021)
  56. Journal of Economic Perspectives 31(2), 87–106 (2017)
  57. Management science 60(7), 1772–1791 (2014)
  58. International Journal of Electronic Commerce 20(1), 9–49 (2015)
  59. Saleh, F.: Blockchain without waste: Proof-of-stake. The Review of financial studies 34(3), 1156–1190 (2021)
  60. Harvard Business Review Press (1998)
  61. Sharpe, W.F.: Likely gains from market timing. Financial Analysts Journal 31(2), 60–69 (1975)
  62. The Journal of finance 40(3), 777–790 (1985)
  63. Shilling, A.G.: Market timing: Better than a buy-and-hold strategy. Financial Analysts Journal 48(2), 46–50 (1992)
  64. Varian, H.R.: Big data: New tricks for econometrics. Journal of Economic Perspectives 28(2), 3–28 (2014)
  65. Royal Society open science 6(6), 180,538 (2019)
  66. arXiv preprint arXiv:2205.04256 (2022). 10.48550/arXiv.2205.04256. URL https://arxiv.org/abs/2205.04256
  67. In: 2022 IEEE International Conference on Blockchain (Blockchain), pp. 298–303 (2022). 10.1109/Blockchain55522.2022.00048
  68. arXiv preprint arXiv:2305.02552 (2023). 10.48550/arXiv.2305.02552. URL https://arxiv.org/abs/2305.02552
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