Abstract :

Cryptocurrency, an innovative asset class that is widely adopted by investors around the world. Indonesia is no exception to this, increasing the investor adoption up to 12 million investors in 2022. This number is very significant compared to Indonesia stock market investors that is only around 7 million investors. Various literatures have covered cryptocurrency in terms of pricing strategy and technicalities, so this paper extends the understanding of cryptocurrency dynamics from a behavioral finance perspective that is still less developed in Indonesia. This paper aims to explore the relationship between financial literacy, behavioral bias as well as its implication on the investment decision making process and investment performance from the perspective of investors based on Indonesia’s cryptocurrency investors at online communities. This paper used Structural Equation Modeling (SEM) to predict the relation between variables. Our results show that financial literacy has an impact on each behavioral bias. While the behavioral biases that investigate in this study have different result in term of impact on decision-making process and the investment performance. Overconfidence, herding and anchoring are the biases that significantly influence invertor’s decision making in scope of cryptocurrency market in Indonesia. This study outcome may help investors understand and increase the awareness of investor’s investment behavior and decision-making process, and parallel to that the regulators and other stakeholders may use the insight to improve investor’s protection.

---

Keywords :

Behavioral Bias, Cryptocurrency, Financial Literacy, Investment Decision, Investment Performance

---

References :

1. com, “investing.com,” 1 December 2022. [Online]. Available: https://www.investing.com/crypto/currencies.
2. Association, “Indonesia Crypro Outlook,” Indonesia Blockchain Association, Jakarta, 2022.
3. Crawley, “Indonesia to Establish ‘Crypto Stock’ Exchange by 2022-End: Report,” Coindesk, Jakarta, 2022.
4. Publisher, “Gemini 2022 State of Crypto Global,” Gemini Pulisher, -, 2022.
5. Laycock, “Finder.com,” 30 November 2022. [Online]. Available: https://www.finder.com/id/finder-cryptocurrency-adoption-index.
6. C. Phillips and D. Gorse, “Predicting Cryptocurrency Price Bubbles Using Social Media Data and Epidemic Modelling,” IEEE Symposium Series on Computational Intelligence, 2018.
7. Gurdgiev and D. O’Loughlin, “Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty,” Journal of Behavioral and Experimental Finance Elsevier, vol. 25(C)., 2020.
8. Corbet, A. Meegan, C. Larkin, B. Lucey and L. Yarovaya, “Exploring the dynamic relationships between cryptocurrencies and other financial assets,” Economics Letters, 2018.
9. D.A.T, “THE IMPACT OF FINANCIAL LITERACY ON INVESTMENT DECISIONS: WITH SPECIAL REFERENCE TO UNDERGRADUATES IN WESTERN PROVINCE, SRI LANKA,” Asian Journal of Contemporary Education, 2020.
10. Awais, M. Laber, N. Rasheed and A. Khursheed, “Impact of Financial Literacy and Investment Experience on Risk Tolerance and Investment Decisions: Empirical Evidence from Pakistan,” International Journal of Economics and Financial Issues. 6. , pp. 73-79. , 2016.
11. Yang, “Behavioral Anomalies in Cryptocurrency,” Capital Markets: Market Efficiency eJournal, 2019.
12. M. Lammer, T. Hanspal and A. Hackethal, “Who are the Bitcoin investors? Evidence from indirect cryptocurrency investments,” SAFE Working Paper Series 277, Leibniz Institute for Financial Research SAFE, 2019.
13. Ciaian, M. Rajcaniova and d. Kancs, “The economics of BitCoin price formation,” Applied Economics, 2014.
14. Liu and A. Tsyvinski, “Risks and Returns of Cryptocurrency,” Technical Report, National Bureau of Economic Research, 2018.
15. K. Lee, Handbook of Digital Currency Bitcoin, Innovation, Financial Instruments, and Big Data, Elsevier Inc, 2015.
16. Baek and M. Elbeck, “Bitcoins as an investment or speculative vehicle? A first look,” Applied Economics Letters , 2015.
17. Cheah and J. Fry, “Speculative Bubbles in Bitcoin Markets? An Empirical Investigation into the Fundamental Value of Bitcoin,” Economics Letters, 2015.
18. Baur, K. Hong and A. Lee, “Bitcoin: Medium of Exchange or Speculative Assets?,” Journal of International Financial Markets, Institutions and Money. 54, 2017.
19. Carrick, “Bitcoin as a Complement to Emerging Market Currencies,” merging Markets Finance and Trade 52, 2016.
20. Ajaz and A. Kumar, “HERDING IN CRYPTO-CURRENCY MARKETS,” Annals of Financial Economics, 2018.
21. Bouri, R. Gupta and D. Roubaud, “Herding behaviour in cryptocurrencies,” Finance Research Letters, Elsevier, vol. 29(C), 2019.
22. Murray Leclair, “Herding in the cryptocurrency market,” 10.13140/RG.2.2.26154.11204. , 2018.
23. Poyser, “Herding behavior in cryptocurrency market,” 2019.
24. Craggs and A. Rashid, “Poster: The Role of Confirmation Bias in Potentially Undermining Speculative Cryptocurrency Decisions,” IEEE Euro Security & Privacy, 2016.
25. Noctor, S. Stoney and R. Stradling, “Financial Literacy,” Report Prepared for the National Westminster Bank, 1992.
26. v. Rooij, A. Lusardi and R. Alessie, “Financial literacy and stock market participation,” Journal of Financial Economics vol. 101, 2011.
27. Takeda, T. Takemura and T. Kozu, “Investment Literacy and Individual Investor Biases: Survey Evidence in the Japanese Stock Market,” The Review of Socionetwork Strategies , 2013.
28. Dhar and N. Zu, “Up close and personal: Investor sophistication and the disposition effect,” Management Science, 2006.
29. Jonsson, I. L. Söderberg and M. Wilhelmsson, “An investigation of the impact of financial literacy, risk attitude, and saving motives on the attenuation of mutual fund investors’ disposition bias.,” Managerial Finance. Vol.43, 2017.
30. T. Lin, C. U. T. Bumcrot, A. Lusardi, G. Mottola, C. Kieffer and G. (. Walsh, “Financial capability in the United States 2016.,” FINRA Investor Education Foundation, 2016.
31. Fujiki, “Who adopts crypto assets in Japan? Evidence from the 2019 financial literacy survey,” Journal of the Japanese and International Economies, 2020.
32. Hurd, M. V. Rooij and D. J. Winte, “STOCK MARKET EXPECTATIONS OF DUTCH HOUSEHOLDS,” Journal of Applied Econometrics, 2011.
33. Kezdi and R. Willis, “Household Stock Market Beliefs and Learning,” NBER Working Papers from National Bureau of Economic Research, Inc, 2011.
34. A. Panos, T. Karkkainen and A. Atkinson, “Financial Literacy and Attitudes to Cryptocurrencies,” Working Papers in Responsible Banking & Finance WP Nº 20-002, 2020.
35. F. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” The Journal of Finance , 1970.
36. M. Barber and T. Odean, “The Behavior of Individual Investors,” Handbook of the Economics of Finance, 2013.
37. K. Sahi, A. P. Arora and N. Dhameja, “An Exploratory Inquiry into the Psychological Biases in Financial Investment Behavior,” Journal of Behavioral Finance , 2013.
38. Caparrelli, a. M. D’Arcangelis and A. Cassuto, ” Herding in the Italian Stock Market: A Case of Behavioral Finance,” The Journal of Behavioral Finance , 2004.
39. O. Fogel and T. Berry, “The Disposition Effect and Individual Investor Decisions: The Roles of Regret and Counterfactual Alternatives,” Journal of Behavioral Finance , 2006.
40. Bannier, T. Meyll, F. Röder and A. Walter, “The Gender Gap in ‘Bitcoin Literacy’,” SSRN Electronic Journal, 2018.
41. Kumar and N. Goyal, “Behavioural biases in investment decision making – a systematic literature review,” Qualitative Research in Financial Markets, Vol. 7 , 2015.
42. Haykir and I. Yagli, “Speculative bubbles and herding in cryptocurrencies,” Financial Innovation , 2022.
43. Devasagayam, K. J K and S. Sabarinathan, “Does Herding Behaviour Among Traders Increase During Covid 19 Pandemic? Evidence from the Cryptocurrency Market,” Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation, 2020.
44. Fellner and S. Krügel, “Judgmental overconfidence: Three measures, one bias?,” Journal of Economic Psychology, 2012.
45. Gervais and T. Odean, “Learning to Be Overconfident,” The Review of Financial Studies Vol. 14, No. 1 (Spring, 2001), 2001.
46. J. Elton, M. J. Gruber, A. d. Souza and C. R. Blake., “Target Date Funds: Characteristics and Performance,” The Review of Asset Pricing Studies 5(2), 2015.
47. Murad, M. Sefton and C. Starmer, “How do risk attitudes affect measured confidence?,” Journal of Risk and Uncertainty, 2016.
48. Tversky and D. Kahneman, “Judgment under uncertainty: Heuristics and biases.,” Science, 185(4157), 1974.
49. Kudryavtsev and G. Cohen, “Behavioral Biases in Economic and Financial Knowledge: Are They the Same for Men and Women?,” Advances in Management and Applied Economics, 2011.
50. Kaustia, E. Alho and V. Puttonen, “How Much Does Expertise Reduce Behavioral Biases? The Case of Anchoring Effects in Stock Return Estimates,” Financial Management Association International, 2008.
51. Bokhari and D. Geltner, “Loss Aversion and Anchoring in Commercial Real Estate Pricing: Empirical Evidence and Price Index Implications,” Real Estate Economics, 2011.
52. Dougal, J. Engelberg, C. Parsons and E. Wesep, “Anchoring on Credit Spreads,” The Journal of Finance, 2015.
53. Wang, “Anchoring Effect and Loss Aversion: Evidence from the Non-Fungible Token Market,” SSRN, 2022.
54. Shefrin, “Beyond Greed and Fear: Understanding Behavioural Finance and the Psychology of Investing,” Oxford University Press, 2002.
55. Zielonka, “Technical analysis as the representation of typical cognitive biases,” International Review of Financial Analysis, 2004.
56. Jayaraj, “The Factor Model for Determining the Individual Investment behavior in India.,” IOSR Journal of Economics and Finance , 2013.
57. Waweru, E. Munyoki and E. Uliana, “The effects of behavioural finance in investment decision making: a survey of institutional investors operating at the Nairobi stock exchange,” Int. J. Business and Emerging Markets, Vol. 1, 2008.
58. Sharpe, “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk,” Journal of Finance, 1964.
59. Li, W. T. Cui and J. H. Feng, “Investment Decision-Making Method of Medium Voltage Distribution Network Considering Project Attributes,” Proc. CSU-EPSA 30, 2018.
60. Merton, “A Simple Model of Capital Market Equilibrium with Incomplete Information,” Journal of Finance, 1987.
61. Bikhchandani, D. Hirshleifer and I. Welch, “Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades,” JOURNAL OF ECONOMIC PERSPECTIVES, 1998.
62. Ajmal, S.Muti and Z. A. M.Shah, “Impact of Illusion,” Abasyn Journal of Social Sciences Vol. 5 No. 2, 2011.
63. Baker and J. R. Nofsinger, “Psychological Biases of Investors,” Financial Services Review, 2002.
64. U. Abdin, O. Farooq, N. Sultana and M. Farooq, “The impact of heuristics on investment decision and performance: Exploring multiple mediation mechanisms,” Research in International Business and Finance, 2017.
65. Luong and D. Thu Ha, “Behavioral Factors Influencing Individual Investors’ Decision-Making and Performance. A Survey at the Ho Chi Minh Stock Exchange,” Semantic, 2011.
66. Lusardi and O. S. Mitchell, “Financial Literacy around the World: An Overview,” Journal of Pension Economics and Finance, Cambridge University Press, vol. 10, 2011.
67. Kengatharan and N. Kengatharan, “The Influence of Behavioral Factors in Making Investment Decisions and Performance: Study on Investors of Colombo Stock Exchange, Sri Lanka,” Asian Journal of Finance & Accounting , 2014.
68. K. Baker, S. Kumar, N. Goyal and V. Gaur, “How financial literacy and demographic variables relate to behavioral biases,” Managerial Finance, 2019.
69. Khan, I. Naz, F. Qureshi and A. Ghafoor, “Heuristics and stock buying decision: evidence from Malaysian and Pakistani stock markets,” Borsa Istanbul Review, 2017.
70. Shanmugham and K. Ramya, “Impact of Social Factors on Individual Investors’ Trading Behaviour,” Procedia Economics and Finance, 2012.
71. Siraji, “Heuristics Bias and Investment Performance: Does Age Matter? Evidence from Colombo Stock Exchange,” Asian Journal of Economics, Business and Accounting, 2019.
72. G. Scott and R. A. Bruce, “Decision making style: the development of a new measure,” Educational and Psychological Measurement, 1995.
73. Khan, “Cognitive Driven Biases, Investment Decision Making: The Moderating Role of Financial Literacy,” SSRN, 2020.
74. Musundi, “.The Effects Of Financial Literacy On Personal Investment Decisions In Real Estate In Nairobi County,” 2014.
75. -W. Lin, “Elucidating rational investment decisions and behavioral biases: evidence from the Taiwanese stock market,” African Journal of Business Management, Academic Journals, 2011.
76. Schumacker and R. Lomax, ” A Beginner’s Guide To Structural Equation Modeling,” 10.4324/9781410610904. , 2016.
77. Hair, C. Ringle and M. Sarstedt, “Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning,” 10.1016/j.lrp.2013.08.016. , 2013.
78. Bagozzi and Y. Yi, “On the Evaluation of Structural Equation Models,” Journal of the Academy of Marketing Science, 1988.
79. Hair, W. Black, B. Babin, R. Anderson and R. Tatham, “Multivariate Data Analysis,” 6th Edition Pearson Prentice Hall, 2006.
80. R. Hocking and O. Pendleton, “The regression dilemma,” Communications in Statistics-theory and Methods, 1983.
81. Fernandes, J. Lynch and R. Netemeyer, “Financial Literacy, Financial Education, and Downstream Financial Behaviors,” Management Science, 2014.
82. Dima, S. Al-Abdallah and N. Abualjarayesh, “Behavioral Biases and Investment Performance: Does Gender Matter? Evidence from Amman Stock Exchange,” 2018.
83. Hayat and M. Anwar, “Impact of Behavioral Biases on Investment Decision; Moderating Role of Financial Literacy,” 10.2139/ssrn.2842502.. , 2016.
84. Khalid, M. Javed and K. Shahzad, “mpact of Behavioral Biases on Investment Decision Making with Moderating Role of Financial Literacy,” Jinnah Business Review, 2018.
85. -M. Chen, K. Kim, J. Nofsinger and O. Rui, “Behavior and performance of emerging market investors: Evidence from China,” 2004.
86. Aziz and Khan, “Behavioral factors influencing individual investor’s investment decision and performance, Evidence from Pakistan Stock Exchange,” 2016.
87. Chin and G. Marcoulides, “The Partial Least Squares Approach to Structural Equation Modeling,” Modern Methods for Business Research. 8. , 1998.
88. K. Baker, S. Kumar, N. Goyal and V. Gaur, “How Financial Literacy and Demographic Variables Relate to Behavioral Biases,” Managerial Finance, 2019.
89. T. Publisher, “Gemini 2022 State of Crypto Global,” Gemini Team Publisher, -, 2022.
90. Yeoh and A. Wood, “Overconfidence, Competence and Trading Activity,” 2011.