Abstract
The study's purpose is to examine the effect of herding, loss aversion, overconfidence, and fear of missing out (FOMO) biases on crypto investors’ investment decisions. The study also looks at how FOMO plays a mediating role between herding, loss aversion, overconfidence, and crypto investment decisions. To acquire data from crypto retail investors, the study used a questionnaire survey. A total of 473 responses were gathered and analyzed with SmartPLS. To achieve the study's aims, factor analysis and partial least square structural equation modelling were used. The study's findings found that FOMO, herding, loss aversion, and overconfidence biases have a substantial effect on the investment decisions of crypto investors, in respective order. In addition, FOMO bias establishes a complementary partial mediation on the relationship between herding, loss aversion, and crypto investors’ decision-making behavior. Ergo, the present study assisted individual and institutional cryptocurrency investors, crypto portfolio managers, policymakers, researchers, and market regulators in broadening their knowledge base about cryptocurrency and forecasting investors' behavior. Hence, this study contributes to the field of behavioral finance.
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References
Aalborg, H.A., Molnár, P., de Vries, J.E.: What can explain the price, volatility and trading volume of Bitcoin? Finance Res. Lett. 29, 255–265 (2019). https://doi.org/10.1016/j.frl.2018.08.010
Abel, J.P., Buff, C.L., Burr, S.A.: Social media and the fear of missing out: scale development and assessment. J. Bus. Econ. Res. JBER 14(1), 33–44 (2016). https://doi.org/10.19030/jber.v14i1.9554
Abubakar, M., Hassan, M.K., Haruna, M.A.: Cryptocurrency tide and Islamic finance development: any issue?. In: Disruptive Innovation in Business and Finance in the Digital World. Emerald Publishing Limited (2019). https://doi.org/10.1108/S1569-376720190000020019
Agarwal, S., Chiu, I.M., Liu, C., Rhee, S.G.: The brokerage firm effect in herding: evidence from Indonesia. J. Financ. Res. 34(3), 461–479 (2011). https://doi.org/10.1111/j.1475-6803.2011.01273.x
Akhtar, F., Das, N.: Investor personality and investment performance: from the perspective of psychological traits. Qual. Res.financ. Mark. 12(3), 333–352 (2019). https://doi.org/10.1108/QRFM-11-2018-0116
Arias-Oliva, M., Pelegrín-Borondo, J., Matías-Clavero, G.: Variables influencing cryptocurrency use: a technology acceptance model in Spain. Front. Psychol. 10, 475 (2019)
Auer, R., Tercero-Lucas, D.: Distrust or speculation? The socioeconomic drivers of US cryptocurrency investments. J. Financ. Stab. 62, 101066 (2022). https://doi.org/10.1016/j.jfs.2022.101066
Ayedh, A., Echchabi, A., Battour, M., Omar, M.: Malaysian Muslim investors’ behavioral towards the blockchain-based Bitcoin cryptocurrency market. J. Islam. Mark. (2020). https://doi.org/10.1108/JIMA-04-2019-0081
Baddeley, M.: Herding, social influence and economic decision-making: socio-psychological and neuroscientific analyses. Philos. Trans. R. Soc. B Biol. Sci. 365(1538), 281–290 (2010). https://doi.org/10.1098/rstb.2009.0169
Ballis, A., Drakos, K.: Testing for herding in the cryptocurrency market. Finance Res. Lett. (2020). https://doi.org/10.1016/j.frl.2019.06.008
Barberis, N., Thaler, R., Constantinides, G.M., Harris, M., Stulz, R.: Handbook of the Economics of Finance, vol. 1, pp. 1053–1128 (2003)
Baur, D.G., Dimpfl, T.: Asymmetric volatility in cryptocurrencies. Econ. Lett. 173, 148–151 (2018). https://doi.org/10.1016/j.econlet.2018.10.008
Beyens, I., Frison, E., Eggermont, S.: “I don’t want to miss a thing”: adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook related stress. Comput. Hum. Behav. 64, 1–8 (2016). https://doi.org/10.1016/j.chb.2016.05.083
Bhasker, R.N.: Why we should be worried about North India’s demographic time-bomb (2016). http://www.firstpost.com/india/why-we-shouldbe-worried-about-north-indias-demographic-time-bomb-2731954.html
Bonaparte, Y.: Time horizon and cryptocurrency ownership: is crypto not speculative? J. Int. Financ. Mark. Inst. Money (2022). https://doi.org/10.1016/j.intfin.2022.101609
Bouri, E., Gupta, R., Roubaud, D.: Herding behavioral in cryptocurrencies. Finance Res. Lett. 29, 216–221 (2019). https://doi.org/10.1016/j.frl.2018.07.008
Caferra, R.: Good vibes only: the crypto-optimistic behavior. J. Behav. Exp. Finance (2020). https://doi.org/10.1016/j.jbef.2020.100407
Chadha, S.: Explainer: why crypto has come under India's anti-money laundering law (2023). https://timesofindia.indiatimes.com/business/cryptocurrency/bitcoin/explainer-why-crypto-has-come-under-indias-anti-money-laundering-law/articleshow/98515196.cms
Cheah, E.T., Fry, J.: Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Econ. Lett. 130, 32–36 (2015). https://doi.org/10.1016/j.econlet.2015.02.029
Chhatwani, M., Parija, A.K.: Who Invests in Cryptocurrency? The Role of Overconfidence Among American Investors. The Role of Overconfidence Among American Investors. https://doi.org/10.2139/ssrn.4419088
Christie, W.G., Huang, R.D.: Following the pied piper: do individual returns herd around the market? Financ. Anal. J. 51(4), 31–37 (1995)
Cochran, W.G.: Sampling Techniques, 2nd edn. Wiley, New York (1963)
CoinMarketCap (2022). https://coinmarketcap.com/
CoinSwitch: India’s Portfolio 2022. How India invests in crypto (2022). https://coinswitch.co/switch/crypto/india-crypto-adoption-2022/
da Gama Silva, P.V.J., Klotzle, M.C., Pinto, A.C.F., Gomes, L.L.: Herding behavior and contagion in the cryptocurrency market. J. Behav. Exp. Finance 22, 41–50 (2019). https://doi.org/10.1016/j.jbef.2019.01.006
Dar, F.A., Hakeem, I.A.: The influence of behavioral factors on investors investment decisions: a conceptual model. Int. J. Res. Econ. Soc. Sci. 5(10), 51–65 (2015)
Davcik, N.S.: The use and misuse of structural equation modeling in management research: a review and critique. J. Adv. Manag. Res. 11(1), 47–81 (2014). https://doi.org/10.1108/JAMR-07-2013-0043
Delfabbro, P., King, D.L., Williams, J.: The psychology of cryptocurrency trading: Risk and protective factors. J. Behav. Addict. 10(2), 201–207 (2021). https://doi.org/10.1556/2006.2021.00037
Denison, E., Lee, M., Martin, A.: What do cryptocurrencies do? J. Investig. 28(3), 57–61 (2019). https://doi.org/10.3905/joi.2019.28.3.057
Diamantopoulos, A., Siguaw, D.: Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration. Br. J. Manag. 17(4), 263–282 (2006). https://doi.org/10.1111/j.1467-8551.2006.00500.x
Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., Kaiser, S.: Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective. J. Acad. Mark. Sci. 40(3), 434–449 (2012). https://doi.org/10.1007/s11747-011-0300-3
Faul, F., Erdfelder, E., Buchner, A., Lang, A.G.: Statistical power analyses using G* Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods 41(4), 1149–1160 (2009)
Fonseca, V., Pacheco, L., Lobão, J.: Psychological barriers in the cryptocurrency market. Rev. Behav. Finance (2019). https://doi.org/10.1108/RBF-03-2019-0041
Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981). https://doi.org/10.1177/002224378101800104
Glaser, M., Weber, M.: Overconfidence and trading volume. Geneva Risk Insur. Rev. 32(1), 1–36 (2007). https://doi.org/10.1007/s10713-007-0003-3
Gonzalez-Igual, M., Corzo-Santamaría, M.T., Vieites, A.R.: Impact of education, age and gender on investor’s behavior: modeling confidence. In: Decodıng Behavioral Finance: The Practitioner’s View (Three Articles) Descifrando Las Finanzas Conductuales: La Visión Del, vol. 79 (2018)
Graham, J.R.: Herding among investment newsletters: theory and evidence. J. Finance 54(1), 237–268 (1999). https://doi.org/10.1111/0022-1082.00103
Gupta, M., Sharma, A.: Fear of missing out: A brief overview of origin, theoretical underpinnings and relationship with mental health. World J. Clin. Cases 9(19), 4881 (2021). https://doi.org/10.12998/wjcc.v9.i19.4881
Gupta, S., Shrivastava, M.: Herding and loss aversion in stock markets: mediating role of fear of missing out (FOMO) in retail investors. Int. J. Emerg. Mark. (2021). https://doi.org/10.1108/IJOEM-08-2020-0933
Gurdgiev, C., Corbet, S.: Ripples in the crypto world: Systemic risks in crypto-currency markets. Int. Bank. (2018)
Gurdgiev, C., O’Loughlin, D.: Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty. J. Behav. Exp. Finance (2020). https://doi.org/10.1016/j.jbef.2020.100271
Hair, J.F., Jr., Hult, G.T.M., Ringle, C., Sarstedt, M.: A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications, London (2016)
Hair, J.F., Jr., Sarstedt, M., Ringle, C.M., Gudergan, S.P.: Advanced issues in partial least squares structural equation modeling. Sage Publications, Thousand Oaks (2017)
Hair, J.F., Risher, J.J., Sarstedt, M., Ringle, C.M.: When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31(1), 2–24 (2019). https://doi.org/10.1108/EBR-11-2018-0203
Hairudin, A., Sifat, I.M., Mohamad, A., Yusof, Y.: Cryptocurrencies: a survey on acceptance, governance and market dynamics. Int. J. Finance Econ. (2020). https://doi.org/10.1002/ijfe.2392
Hälli, H.: Herding behavior in the US stock markets: does market capitalization matter? (2022)
Haryanto, S., Subroto, A., Ulpah, M.: Disposition effect and herding behavior in the cryptocurrency market. J. Ind. Bus. Econ. 47(1), 115–132 (2020). https://doi.org/10.1007/s40812-019-00130-0
Hassan, T.R., Khalid, W., Habib, A.: Overconfidence and loss aversion in investment decisions: a study of the impact of gender and age in Pakistani perspective. Res. J. Finance Acc. 5(11), 148–157 (2014)
Henseler, J., Hubona, G., Ray, P.A.: Using PLS path modeling in new technology research: updated guidelines. Ind. Manag. Data Syst. 116(1), 2–20 (2016). https://doi.org/10.1108/IMDS-09-2015-0382
Hershfield, H.: How availability bias and FOMO can impact financial decision-making. Avantis Invest. 24 (2020a)
Hershfield, H.: How Availability Bias and FOMO Can Impact Financial Decision-Making 1–3 (2020b). https://www.avantisinvestors.com/
Hidajat, T.: Behavioural biases in bitcoin trading. Fokus Ekonomi: Jurnal Ilmiah Ekonomi 14(2), 337–354 (2019)
Hira, T.K., Loibl, C.: Gender differences in investment behavior. In Handbook of consumer finance research, pp. 253–270. Springer, New York. https://doi.org/10.1007/978-0-387-75734-6_15 (2008)
Hoang, H.T., Mørken, S.E.: Testing for Bubbles in the Bitcoin Market. Master's thesis, University of Stavanger, Norway (2018)
Hult, G.T.M., Hair, J.F., Jr., Proksch, D., Sarstedt, M., Pinkwart, A., Ringle, C.M.: Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. J. Int. Mark. 26(3), 1–21 (2018). https://doi.org/10.1509/jim.17.0151
Hwang, S., Salmon, M.: Market stress and herding. J. Empir. Finance 11(4), 585–616 (2004). https://doi.org/10.1016/j.jempfin.2004.04.003
Israel, G.D.: Determining Sample Size. University of Florida, Gainesville (1992)
Jain, R., Jain, P., Jain, C.: Behavioral biases in the decision making of individual investors. IUP J. Manag. Res. 14(3), 7 (2015)
Jain, J., Walia, N., Kaur, M., Singh, S.: Behavioural biases affecting investors’ decision-making process: a scale development approach. Manag. Res. Rev. (2021). https://doi.org/10.1108/MRR-02-2021-0139
Jalal, R.N.U.D., Leonelli, S.: Does self-attribution impact on investor perception about cryptocurrency market efficiency? The mediating role of overconfidence. In: Exploring Innovation in a Digital World, pp. 137–150. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87842-9_11
Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263–292 (1979)
Kaiser, L., Stöckl, S.: Cryptocurrencies: herding and the transfer currency. Finance Res. Lett. (2020). https://doi.org/10.1016/j.frl.2019.06.012
Kang, I., He, X., Shin, M.M.: Chinese consumers’ herd consumption behavior related to Korean luxury cosmetics: the mediating role of fear of missing out. Front. Psychol. 11, 121 (2020). https://doi.org/10.3389/fpsyg.2020.00121
Katper, N.K., Azam, M., Karim, N.A., Zia, S.Z.: Behavioral biases and investors’ decision-making: the moderating role of socio-demographic variables. Int. J. Financ. Eng. (2019). https://doi.org/10.1142/S2424786319500208
Kaur, A., Ahuja, P., Jain, J., Singh, S., Garg, A.: Is Youth ready for the looming technology frontier in healthcare? examining intentions and adoption of mobile health (mHealth). Bus. Perspect. Res. 22785337221091017 (2021)
Kim, K.T., Hanna, S.D.: Investment literacy, overconfidence and cryptocurrency investment (2021). https://doi.org/10.2139/ssrn.3953242
Kock, N.: Common method bias in PLS-SEM: A full collinearity assessment approach. Int. J. E-Collaboration (IJEC) 11(4), 1–10 (2015). https://doi.org/10.4018/ijec.2015100101
Korniotis, G.M., Kumar, A.: Do older investors make better investment decisions? Rev. Econ. Stat. 93(1), 244–265 (2011). https://doi.org/10.1162/REST_a_00053
Koutmos, D., Payne, J.E.: Intertemporal asset pricing with bitcoin. Rev. Quant. Finance Acc. 56(2), 619–645 (2021). https://doi.org/10.1007/s11156-020-00904-x
Kumar, S., Goyal, N.: Behavioural biases in investment decision making—a systematic literature review. Qual. Res. Financ. Mark. (2015). https://doi.org/10.1108/QRFM-07-2014-0022
Liu, W.: Portfolio diversification across cryptocurrencies. Finance Res. Lett. 29, 200–205 (2019b)
Liu, H.M.: Why do People Invest in Initial Coin Offerings (ICOs)? (2019a). https://doi.org/10.1016/j.frl.2018.07.010.
Luong, P.L., Thu Ha, D.T.: Behavioral factors influencing individual investors’ decision-making and performance: a survey at the Ho Chi Minh Stock Exchange (2011)
Maheshwari, R.: Why Is the Crypto Market Rising Today? (2023) https://www.forbes.com/advisor/in/investing/cryptocurrency/why-is-crypto-going-up/
Martin, B.A., Chrysochou, P., Strong, C., Wang, D., Yao, J.: Dark personalities and Bitcoin®: the influence of the Dark Tetrad on cryptocurrency attitude and buying intention. Personal. Ind. Differ. 188 (2022)
Mikhaylov, A.: Cryptocurrency market analysis from the open innovation perspective. J. Open Innov. Technol. Mark. Complex. 6(4), 197 (2020). https://doi.org/10.3390/joitmc6040197
Mokni, K., Youssef, M., Ajmi, A.N.: COVID-19 pandemic and economic policy uncertainty: the first test on the hedging and safe haven properties of cryptocurrencies. Res. Int. Bus. Finance (2022). https://doi.org/10.1016/j.ribaf.2021.101573
Mosteanu, N.R., Faccia, A.: Digital systems and new challenges of financial management—FinTech, XBRL, blockchain and cryptocurrencies. Qual. Access Success J. 21(174), 159–166 (2020)
Nardi, P.: Doing Survey Research: A Guide to Quantitative Methods. Pearson Education, Boston (2006)
Netemeyer, R.G., Bearden, W.O., Sharma, S.: Scaling Procedures: Issues and Applications. Sage Publications, Thousand Oaks (2003)
Nitzl, C., Roldan, J.L., Cepeda, G.: Mediation analysis in partial least squares path modeling: helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 116(9), 1849–1864 (2016). https://doi.org/10.1108/IMDS-07-2015-0302
Nurbarani, B.S., Soepriyanto, G.: Determinants of investment decision in cryptocurrency: evidence from indonesian investors. Univ. J. Acc. Finance 10, 254–266 (2022). https://doi.org/10.13189/ujaf.2022.100126
Park, S., Gupta, S.: Handling endogenous regressors by joint estimation using copulas. Mark. Sci. 31(4), 567–586 (2012). https://doi.org/10.4018/ijec.2015100101
Pompian, M.: Behavioral finance and wealth management-how to build optimal portfolios that account for investor biases. Financ. Mark. Portf. Manag. 21(4), 491 (2006)
Popova, I.: Cryptocurrencies are for Daring Investors (2019)
Porterfield, J.: Julian Assange and WikiLeaks. The Rosen Publishing Group Inc, New York (2012)
Poyser, O.: Herding behavior in cryptocurrency markets. arXiv preprint arXiv:1806.11348 (2018)
Przybylski, A.K., Murayama, K., DeHaan, C.R., Gladwell, V.: Motivational, emotional, and behavioral correlates of fear of missing out. Comput. Hum. Behav. 29(4), 1841–1848 (2013). https://doi.org/10.1016/j.chb.2013.02.014
Pulford, B.D., Colman, A.M.: Overconfidence: feedback and item difficulty effects. Personal. Individ. Differ. 23(1), 125–133 (1997). https://doi.org/10.1016/S0191-8869(97)00028-7
Rau, H.A.: The disposition effect and loss aversion: Do gender differences matter? Econ. Lett. 123(1), 33–36 (2014). https://doi.org/10.1016/j.econlet.2014.01.020
Ringle, C., Da Silva, D., Bido, D.: Structural equation modeling with the SmartPLS (2015)
Rubbaniy, G., Tee, K., Iren, P., Abdennadher, S.: Investors’ mood and herd investing: a quantile-on-quantile regression explanation from crypto market. Finance Res. Lett. (2022). https://doi.org/10.1016/j.frl.2021.102585
Sarstedt, M., Ringle, C.M., Cheah, J.H., Ting, H., Moisescu, O.I., Radomir, L.: Structural model robustness checks in PLS-SEM. Tour. Econ. 26(4), 531–554 (2020). https://doi.org/10.1177/1354816618823921
Saundal, S.: Cryptocurrencies: analysis of the technology and need for its regulation (2021)
Savelyev, A.: Copyright in the blockchain era: Promises and challenges. Comput. Law Secur. Rev. 34(3), 550–561 (2018)
Sharma, R., Arora, M., Mehta, K.: Psychological traits and investors' cryptocurrency behavior. In: Handbook of Research on Stock Market Investment Practices and Portfolio Management, pp. 215–228. IGI Global (2022).
Shefrin, H., Statman, M.: The disposition to sell winners too early and ride losers too long: theory and evidence. J. Finance 40(3), 777–790 (1985). https://doi.org/10.1111/j.1540-6261.1985.tb05002.x
Shen, D., Urquhart, A., Wang, P.: Does twitter predict Bitcoin? Econ. Lett. 174, 118–122 (2019). https://doi.org/10.1016/j.econlet.2018.11.007
Shiller, R.C.: Irrational exuberance. Philos. Public Policy Q. 20(1), 18–23 (2000)
Shiva, A., Narula, S., Shahi, S.K.: What drives retail investors‟ investment decisions? Evidence from no mobile phone phobia (Nomophobia) and investor fear of missing out (I-FoMo). J. Content Community Commun. 10(6), 2–20 (2020)
Shmueli, G., Sarstedt, M., Hair, J.F., Cheah, J.H., Ting, H., Vaithilingam, S., Ringle, C.M.: Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict. Eur. J. Mark. 53(11), 2322–2347 (2019). https://doi.org/10.1108/EJM-02-2019-0189
Shrotryia, V.K., Kalra, H.: Herding in the crypto market: a diagnosis of heavy distribution tails. Rev. Behav. Finance 14(5), 566–587 (2022)
Singh, N., Srivastava, S., Sinha, N.: Consumer preference and satisfaction of M-wallets: a study on North Indian consumers. Int. J. Bank Mark. 35(6), 945–965 (2017). https://doi.org/10.1108/RBF-02-2021-0021
Singh, J.E., Babshetti, V., Shivaprasad, H.N.: Efficient market hypothesis to behavioral finance: a review of rationality to irrationality. Mater. Today Proc. (2021). https://doi.org/10.1016/j.matpr.2021.03.318
Sood, K., Singh, S., Behl, A., Sindhwani, R., Kaur, S., Pereira, V.: Identification and prioritization of the risks in the mass adoption of artificial intelligence-driven stable coins: the quest for optimal resource utilization. Resour. Policy 81, 103235 (2023a). https://doi.org/10.1016/j.resourpol.2022.103235
Sood, K., Pathak, P., Jain, J., Gupta, S.: How does an investor prioritize ESG factors in India? An assessment based on fuzzy AHP. Manag. Financ. 49(1), 66–87 (2023b). https://doi.org/10.1108/MF-04-2022-0162
Sood, K., Singh, S.: Marin Laboure and Nicolas Deffrennes (2022): democratizing finance—the Radical promises of Fintech (2022). https://doi.org/10.1007/s00191-022-00789-0
Stead, H., Bibby, P.A.: Personality, fear of missing out and problematic internet use and their relationship to subjective well-being. Comput. Hum. Behav. 76, 534–540 (2017). https://doi.org/10.1016/j.chb.2017.08.016
Steinmetz, F., Von Meduna, M., Ante, L., Fiedler, I.: Ownership, uses and perceptions of cryptocurrency: results from a population survey. Technol. Forecast. Soc. Change 173, 121073 (2021). https://doi.org/10.1016/j.techfore.2021.121073
Streukens, S., Leroi-Werelds, S., Willems, K.: Dealing with nonlinearity in importance-performance map analysis (IPMA): an integrative framework in a PLS-SEM context. In: Partial Least Squares Path Modeling, pp. 367–403 (2017). https://doi.org/10.1007/978-3-319-64069-3_17
Subramaniam, S., Chakraborty, M.: Investor attention and cryptocurrency returns: evidence from quantile causality approach. J. Behav. Finance 21(1), 103–115 (2020). https://doi.org/10.1080/15427560.2019.1629587
Sudzina, F., Dobes, M., Pavlicek, A.: Towards the psychological profile of cryptocurrency early adopters: overconfidence and self-control as predictors of cryptocurrency use. Curr. Psychol. (2021). https://doi.org/10.1007/s12144-021-02225-1
Sun, W., Dedahanov, A.T., Shin, H.Y., Li, W.P.: Factors affecting institutional investors to add crypto-currency to asset portfolios. N. Am. J. Econ. Finance (2021). https://doi.org/10.1016/j.najef.2021.101499
Tambe, N., Jain, A.: All You Need To Know About India’s Crypto Bill (2023). https://www.forbes.com/advisor/in/investing/cryptocurrency/crypto-bill/
Tarjanne, V.: Cognitive Biases Among Millennial Investors in Finland and Their Effect on Trading Activity in Volatile Market Conditions (2020). https://lutpub.lut.fi/bitstream/handle/10024/161346/ProGradu_Visa_Tarjanne.pdf?sequence51
Tech2: Indians Spend More Time Shopping Than Social Networking When Online: Survey (2016). http://tech.firstpost.com/news-analysis/indians-spend-more-time-shopping-than-social-networking-when-online-survey-308828.html
Thies, S., Molnár, P.: Bayesian change point analysis of Bitcoin returns. Finance Res. Lett. 27, 223–227 (2018). https://doi.org/10.1016/j.frl.2018.03.018
Tripathi, V.: Investment strategies in Indian stock market: a survey (2008). SSRN 1134668
Tversky, A., Kahneman, D.: Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty. Science 185(4157), 1124–1131 (1974). https://doi.org/10.1126/science.185.4157.1124
Vidal-Tomás, D., Ibáñez, A.M., Farinós, J.E.: Herding in the cryptocurrency market: CSSD and CSAD approaches. Finance Res. Lett. 30, 181–186 (2019). https://doi.org/10.1016/j.frl.2018.09.008
Virigineni, M., Rao, M.B.: Contemporary developments in behavioral finance. Int. J. Econ. Finance Issues 7(1), 448–459 (2017)
Yarovaya, L., Matkovskyy, R., Jalan, A.: The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets. J. Int. Financ. Mark. Inst. Money (2021). https://doi.org/10.1016/j.intfin.2021.101321
Zhang, S., Mani, G.: Popular cryptoassets (Bitcoin, Ethereum, and Dogecoin), Gold, and their relationships: Volatility and correlation modeling. Data Sci. Manag. 4, 30–39 (2021). https://doi.org/10.1016/j.dsm.2021.11.001
Zhang, J., Zhang, C.: Do cryptocurrency markets react to issuer sentiments? Evidence from Twitter. Res. Int. Bus. Finance 61, 101656 (2022). https://doi.org/10.1016/j.ribaf.2022.101656
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Appendix: Questionnaire
Appendix: Questionnaire
1.1 Section I: demographic profile of the respondent
1.2 Section II: survey Questions
The following questions are connected to how behavioral biases like herding, loss aversion, and herding impact the decision-making behavior of crypto investors in the presence of FOMO as a mediator. Please indicate your level of agreement/disagreement with the following statements on a scale of 1–7, where 1 indicates “strongly disagree”, 2 indicates “disagree”, 3 indicates “somewhat disagree”, 4 indicates “neither agree nor disagree”, 5 indicates “somewhat agree”, 6 indicates “agree”, and 7 indicates “strongly agree."
S. no. | Statements | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|---|
1 | Other investors’ decisions to choose cryptocurrency have an impact on my investment decisions | |||||||
2 | Other investors’ decisions regarding cryptocurrency volume have an impact on my investment decisions | |||||||
3 | I usually react quickly to the changes in other investors’ decisions and follow their reactions to the crypto market | |||||||
4 | Other investors’ decisions on buying and selling crypto currencies have an impact on my investment decisions | |||||||
5 | When faced with a sure gain, I am a risk-averse | |||||||
6 | When faced with a sure loss, I am a risk-taker | |||||||
7 | I avoid selling those cryptocurrencies that have decreased in value and readily sell those cryptocurrencies that have increased in value | |||||||
8 | I believe that my skills and knowledge of the crypto market can help you outperform the market | |||||||
9 | I know the best times to enter and exit my investment position in the crypto market | |||||||
10 | I feel more confident in my own investment opinion than in the opinions of my family members, friends, and colleagues | |||||||
11 | I trade frequently in cryptocurrency than other people | |||||||
12 | It upsets me when I don't hear any news about my crypto investments | |||||||
13 | I would like to get quick updates on the trends of the cryptocurrencies in which I have invested | |||||||
15 | It bothers me when I miss out an investment opportunity in cryptocurrency | |||||||
16 | I'm afraid of being the last to hear about significant news that is relevant to my crypto portfolio | |||||||
17 | The more I see values of cryptocurrency sky rocketing the more I don’t want to miss out on the gains | |||||||
18 | Cryptocurrency is a new way to make me a millionaire | |||||||
19 | In general, I am satisfied with my cryptocurrency investment decisions | |||||||
20 | My crypto investment decisions help me to achieve my investment objectives | |||||||
21 | I believe that I can make crypto investment decisions accurately | |||||||
22 | I mostly earn more than the average return generated by the crypto market | |||||||
23 | I make all the crypto investment decisions at my own | |||||||
24 | Return on my crypto portfolio justifies my investment decisions |
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Kaur, M., Jain, J. & Sood, K. “All are investing in Crypto, I fear of being missed out”: examining the influence of herding, loss aversion, and overconfidence in the cryptocurrency market with the mediating effect of FOMO. Qual Quant 58, 2237–2263 (2024). https://doi.org/10.1007/s11135-023-01739-z
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DOI: https://doi.org/10.1007/s11135-023-01739-z