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Volume 6 - Number 3 | November 2022

Influence of risk propensity, behavioural biases and demographic factors on equity investors' risk perception

Rangapriya Saivasan, and Madhavi Lokhande

02/11/2022 176 0 0
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Behavioural finance Risk perception Factor analysis Risk propensity Behavioural bias

Abstract:

Purpose
Investor risk perception is a personalized judgement on the uncertainty of returns pertaining to a financial instrument. This study identifies key psychological and demographic factors that influence risk perception. It also unravels the complex relationship between demographic attributes and investor's risk attitude towards equity investment.
Design/methodology/approach
Exploratory factor analysis is used to identify factors that define investor risk perception. Multiple regression is used to assess the relationship between demographic traits and factor groups. Kruskal–Wallis test is used to ascertain whether the factors extracted differ across demographic categories. A risk perception framework based on these findings is developed to provide deeper insight.
Findings
There is evidence of the relationship and influence of demographic factors on risk propensity and behavioural bias. From this study, it is apparent that return expectation, time horizon and loss aversion, which define the risk propensity construct, vary significantly based on demographic traits. Familiarity, overconfidence, anchoring and experiential biases which define the behavioural bias construct differ across demographic categories. These factors influence the risk perception of an individual with respect to equity investments.
Research limitations/implications
The reference for the framework of this study is limited as there has been no precedence of similar work in academia.
Practical implications
This paper establishes that information seekers make rational decisions. The paper iterates the need for portfolio managers to develop and align investment strategies after evaluation of investors' risk by including these behavioural factors, this can particularly be advantageous during extreme volatility in markets that concedes the possibility of irrational decision making.
Social implications
This study highlights that regulators need to acknowledge the investor's affective, cognitive and demographic impact on equity markets and align risk control measures that are conducive to market evolution. It also creates awareness among market participants that psychological factors and behavioural biases can have an impact on investment decisions.
Originality/value
This is the only study that looks at a three-dimensional perspective of the investor risk perception framework. The study presents the relationship between risk propensity, behavioural bias and demographic factors in the backdrop of “information” being the mediating variable. This paper covers five characteristics of risk propensity and eight behavioural biases, such a vast coverage has not been attempted within the academic realm earlier with the aforesaid perspective.

References:

  1. Abdellaoui, M., Bleichrodt, H. and Paraschiv, C. (2007), “Loss aversion under prospect theory: a parameter-free measurement”, Management Science, Vol. 53 No. 10, pp. 1659-1674.
  2. Abelson, R.P. (1985), “A variance explanation paradox: when a little is a lot”, Psychological Bulletin, Vol. 97 No. 1, pp. 129-133.
  3. Agnew, J.R. (2006), “Do behavioral biases vary across individuals? Evidence from individual level 401(k) data”, Journal of Financial and Quantitative Analysis, Vol. 41 No. 4, pp. 939-962.
  4. Ainia, N.S.N. and Lutfi, L. (2018), “The influence of risk perception, risk tolerance, overconfidence, and loss aversion towards investment decision making”, Journal of Economics, Business, and Accountancy Ventura, Vol. 21 No. 3, pp. 401-413.
  5. Al-Tamimi, H.A.H. and Kalli, A.A.B. (2018), “Financial literacy and investment decisions of UAE investors”, The Journal of Risk Finance, Vol. 10 No. 5, pp. 500-516.
  6. Arrow, K.J. (1951), “Alternative approaches to the theory of choice in risk - taking situations”, Econometrica, pp. 404-437, doi: 10.2307/1907465.
  7. Arrow, K.J. (1986), “Rationality of self and others in an economic system”, The Journal of Business, pp. 385-399, doi: 10.2307/2352770.
  8. Babalos, V., Caporale, G.M. and Philippas, N. (2015), “Gender, style diversity, and their effect on fund performance”, Research in International Business and Finance, Vol. 35, pp. 57-74.
  9. Bahovec, V. (2015), “Testing the effects of financial literacy on debt behavior of financial consumers using multivariate analysis methods”, CRORR Journal Regular Issue, Vol. 6 No. 2, pp. 361-371.
  10. Baker, H.K., Kumar, S., Goyal, N. and Gaur, V. (2018), “How financial literacy and demographic variables relate to behavioral biases”, Managerial Finance, Vol. 45 No. 1, pp. 124-146, doi: 10.1108/MF-01-2018-0003.
  11. Banerjee, A., De, A. and Bandyopadhyay, G. (2018), “Impact of demographic profile on investor biases in India using OLAP and ANOVA”, Research Bulletin - ICAI, Vol. 43 No. 4, pp. 75-94.
  12. Barber, B.M. and Odean, T. (2001), “Boys will be boys: gender, overconfidence, and common stock investment”, The Quarterly Journal of Economics, Vol. 116 No. 1, pp. 261-292.
  13. Barsky, A.J., Peekna, H.M. and Borus, J.F. (2001), “Somatic symptom reporting in women and men”, Journal of General Internal Medicine, Vol. 16 No. 4, pp. 266-275.
  14. Bartlett, M.S. (1950), Tests of Significance in Factor Analysis, The British Psychological Society, pp. 77-85, doi: 10.1111/j.2044-8317.1950.tb00285.x.
  15. Bernstein, P.L. (1996), Against the Gods: The Remarkable Story of Risk, Wiley, New York, NY.
  16. Bhattacharjee, J., Singh, R. and K, K. (2021), “Risk perception in respect of equity shares: a literature review and future research agenda”, DLSU Business and Economics Review, Vol. 31 No. 2, pp. 101-119.
  17. Bondt, W.F.M.D. and Thaler, R. (1985), “Does the stock market overreact?”, The Journal of Finance, pp. 793-805, doi: 10.1111/j.1540-6261.1985.tb05004.x.
  18. Booth, A. and Nolen, P. (2012), “Salience, risky choices and gender”, Economics Letters, pp. 517-520, doi: 10.1016/j.econlet.2012.06.046.
  19. Bouteska, A. and Regaieg, B. (2018), “Loss aversion, overconfidence of investors and their impact on market performance evidence from the US stock markets”, Journal of Economics, Finance and Administrative Science, pp. 451-478, doi: 10.1108/JEFAS-07-2017-0081.
  20. Burton, L.J. and Mazerolle, S.M. (2011), “Survey instrument validity Part I: principles of survey instrument development and validation in athletic training education research”, Athletic Training Education Journal, pp. 27-35, doi: 10.4085/1947-380X-6.1.27.
  21. Campbell, S.D. and Sharpe, S.A. (2009), “Anchoring bias in consensus forecasts and its effect on market prices”, Journal of Financial and Quantitative Analysis, pp. 369-390, doi: 10.1017/S0022109009090127.
  22. Cao, H.H., Han, B., Hirshleifer, D. and Zhang, H.H. (2009), “Fear of the unknown: familiarity and economic decisions”, Review of Finance, Vol. 15 No. 1, pp. 173-206, doi: 10.1093/rof/rfp023.
  23. Cattell, R.B. (1966), “The scree test for the number of factors”, Multivariate Behavioral Research, pp. 245-276, doi: 10.1207/s15327906mbr0102_10.
  24. Chen, N.-F., Roll, R. and Ross, S.A. (1986), “Economic forces and the stock market”, The Journal of Business, pp. 383-403, doi: 10.2307/2352710.
  25. Collin-Dufresne, P., Johannes, M. and Lochstoer, L.A. (2016), “Asset pricing when ‘this time is different’”, Review of Financial Studies, pp. 505-535, doi: 10.1093/rfs/hhw084.
  26. Constantinides, G. (1979), “A note on the suboptimality of dollar-cost averaging as an investment policy”, Journal of Financial and Quantitative Analysis, pp. 443-450, doi: 10.2307/2330513.
  27. Covrig, V., Lau, S.T. and Ng, L. (2006), “Do domestic and foreign fund managers have similar preferences for stock characteristics? A cross-country analysis”, Journal of International Business Studies, Vol. 37, pp. 407-429.
  28. Das, S. (2016), “Financial literacy among Indian millennial financial literacy among Indian millennial generation and their reflections on financial generation and their reflections on financial behaviour and attitude: an explanatory research”, The Indian Journal of Commerce, Vol. 69 No. 4, pp. 16-34.
  29. Dave, S. and Mascarenhas, R. (2022), “More women join the stock market party on D-street”, Economic Times, 16 January.
  30. Dickason, Z. and Ferreira, S. (2018), “Establishing a link between risk tolerance, investor personality and behavioural finance in South Africa”, Cogent Economics and Finance, pp. 1-13, doi: 10.1080/23322039.2018.1519898.
  31. Elizabeth, J., Murhadi, W. and Sutejo, B. (2020), “Investor behavioral bias based on demographic characteristics”, Advances in Economics, Business and Management Research, Vol. 115, pp. 6-12.
  32. Fama, E.F. (1970), “Efficient capital markets: a review of theory and empirical work”, The Journal of Finance, pp. 383-417, doi: 10.2307/2325486.
  33. Field, A. (2005), Discovering Statistics Using SPSS for Windows: Advanced Techniques for Beginners (Introducing Statistical Methods Series), 2nd ed., Sage Publications, New York, NY.
  34. Fillinger, C. (2017), Exploring the Notions of Investment Expertise and Strategies, University of Gloucestershire, Gloucestershire.
  35. Fulton, M., Kahn, B. and Sharples, C. (2012), Sustainable Investing: Establishing Long-Term Value and Performance, Deutsche Bank Group, New York.
  36. Galloway, A. (2005), “Non-probability sampling”, in Encyclopedia of Social Measurement, Elsevier, London, pp. 859-864.
  37. Ghosh, D. and Ray, M.R. (1997), “Risk, ambiguity, and decision choice: some additional evidence”, Decision Sciences, Vol. 28 No. 1, pp. 81-104.
  38. Goll, I. and Rasheed, A.A. (2005), “The relationships between top management demographic characteristics, rational decision making, environmental munificence, and firm performance”, Organisation Studies, Vol. 26 No. 7, pp. 999-1023.
  39. Goyal, N., Kumar, S. and Burton, B. (2016), “Evidence on rationality and behavioural biases in investment decision making”, Qualitative Research in Financial Markets, Vol. 8 No. 4, pp. 270-287.
  40. Grable, J.E. (2000), “Financial risk tolerance and additional factors that affect risk taking IN everyday money matters”, Journal of Business and Psychology, pp. 625-630, doi: 10.1023/A:1022994314982.
  41. Greenwood, R. and Shleifer, A. (2014), “Expectations of returns and expected returns”, Review of Financial Studies, pp. 714-746, doi: 10.1093/rfs/hht082.
  42. Grinblatt, M. and Han, B. (2005), “Prospect theory, mental accounting, and momentum”, Journal of Financial Economics, Vol. 78, pp. 311-339.
  43. Grinblatt, M., Titman, S. and Wermers, R. (1995), “Momentum investment strategies, portfolio performance, and herding: a study of mutual fund behavior”, American Economic Association, pp. 1088-1105, doi: 10.2307/2950976.
  44. Gultekin, M.N. and Gultekin, N.B. (1983), “Stock market seasonality: international evidence”, Journal of Financial Economics, Vol. 12 No. 4, pp. 469-481.
  45. Hair, J.F., Anderson, R., Black, B. and Babin, B. (2016), Multivariate Data Analysis, Pearson, Kennesaw.
  46. Hoffmann, A.O.I., Post, T. and Pennings, J.M.E. (2015), “How investor perceptions drive actual trading and risk-taking behavior”, Journal of Behavioral Finance, pp. 94-103, doi: 10.1080/15427560.2015.1000332.
  47. Hung, K.-T. and Tangpong, C. (2010), “General risk propensity in multifaceted business decisions: scale development”, Journal of Managerial Issues, Vol. 22 No. 1, pp. 88-106.
  48. Hur, J. and Singh, V. (2019), “How do disposition effect and anchoring bias interact to impact momentum in stock returns?”, Journal of Empirical Finance, pp. 238-256, doi: 10.1016/j.jempfin.2019.07.007.
  49. Indro, D.C., Jiang, C.X., Hu, M.Y. and Lee, W.Y. (1999), “Mutual fund performance: does fund size matter?”, Financial Analysts Journal, pp. 74-87, doi: 10.2469/faj.v55.n3.2274.
  50. Jager, J., Putnick, D.L. and Bornstein, M.H. (2017), “More than just convenient: the scientific merits of homogeneous convenience samples”, Monographs of the Society for Research in Child Development, Vol. 82 No. 2, pp. 13-30.
  51. Jaggia, S. and Thosar, S. (2010), “Risk aversion and the investment horizon: a new perspective on the time diversification debate”, Journal of Psychology and Financial Markets, pp. 211-215, doi: 10.1207/S15327760JPFM0134_6.
  52. Jain, J., Walia, N. and Gupta, S. (2020), “Evaluation of behavioral biases affecting investment decision making of individual equity investors by fuzzy analytic hierarchy process”, Review of Behavioral Finance, pp. 297-314, doi: 10.1108/RBF-03-2019-0044.
  53. Kahneman, D. and Tversky, A. (1979), “Prospect theory: an analysis of decision under risk”, Econometrica, pp. 263-292, doi: 10.2307/1914185.
  54. Kahneman, D., Knetsch, J.L. and Thaler, R.H. (1991), “Anomalies: the endowment effect, loss aversion, and status quo bias”, Journal of Economic Perspectives, pp. 193-206, doi: 10.1257/jep.5.1.193.
  55. Kaiser, H.F. (1970), “A second-generation Little Jiffy”, Psychometrika, pp. 401-415, doi: 10.1007/BF02291817.
  56. Kaustia, M. (2018), Prospect Theory and the Disposition Effect, Aalto University, Finland.
  57. Kaustia, M., Alho, E. and Puttonen, V. (2008), “How much does expertise reduce behavioral biases? The case of anchoring effects in stock return estimates”, Financial Management, pp. 391-411, doi: 10.1111/j.1755-053X.2008.00018.x.
  58. Kelkar, N. (2022), “Stock trading in India sees massive growth, courtesy young investors”, The Week, 22 June.
  59. Kilka, M. and Weber, M. (2010), “Home bias in international stock return expectations”, Journal of Psychology and Financial Markets, Vol. 1 Nos 3-4, pp. 176-192.
  60. Korniotis, G.M. and Kumar, A. (2011), “Do older investors make better investment decisions?”, The Review of Economics and Statistics, Vol. 93 No. 1, pp. 244-265.
  61. Kruskal, W.H. and Wallis, W.A. (1952), “Use of ranks in One Criterion variance analysis”, Journal of the American Statistical Association, pp. 583-621, doi: 10.2307/2280779.
  62. Kübilay, B. and Bayrakdaroğ, A. (2016), “An empirical research on investor biases in financial decision-making, financial risk tolerance and financial personality”, International Journal of Financial Research, Vol. 7 No. 2, pp. 171-182.
  63. Kumar, S. and Goyal, N. (2018), “Behavioural biases in investment decision making – a systematic literature review”, Qualitative Research in Financial Markets, pp. 88-108, doi: 10.1108/QRFM-07-2014-0022.
  64. Lazányi, K., Virglerová, Z., Dvorský, J. and Dapkus, R. (2017), “An analysis of factors related to “taking risks”, according to selected SocioDemographic factors”, Acta Polytechnica Hungarica, Vol. 14 No. 7, pp. 35-50.
  65. Lee, K., Miller, S., Velasquez, N. and Wann, C. (2013), “The effect of investor bias and gender on portfolio performance and risk”, The International Journal of Business and Finance Research, Vol. 7 No. 1, pp. 1-16.
  66. Lin, H.-W. (2012), “How herding bias could be derived from individual investor types and risk tolerance?”, International Scholarly and Scientific Research and Innovation, Vol. 6 No. 6, pp. 1395-1400.
  67. Lopes, L.L. and Oden, G.C. (1999), “The role of aspiration level in risky choice: a comparison of cumulative prospect theory and SP/A theory”, Journal of Mathematical Psychology, Vol. 43 No. 2, pp. 286-313.
  68. Lobosco, A. (1999), “Style/risk-adjusted performance”, The Journal of Portfolio Management, Vol. 25 No. 3, pp. 65-68.
  69. Madan, C.R., Ludvig, E.A. and Spetch, M.L. (2017), “The role of memory in distinguishing risky decisions from experience and description”, Quarterly Journal of Experimental Psychology, Vol. 70 No. 10, pp. 2048-2059.
  70. Maheshwari, S. and Mittal, D.M. (2017), “Effect of age on financial decisions”, International Journal of Advance Research and Innovative Ideas in Education, Vol. 3 No. 6, pp. 1445-1454.
  71. Malkiel, B.G. and Xu, Y. (1997), “Risk and return revisited”, Journal of Portfolio Management, Vol. 23 No. 3, pp. 9-14.
  72. Manglik, G. (2006), “Countering over-confidence and over-optimism by creating awareness and experiential learning amongst stock market players”, SSRN, pp. 1-31, doi: 10.2139/ssrn.954861.
  73. March, J.G. (1996), “Learning to be risk averse”, Psychological Review, Vol. 103 No. 2, pp. 309-319.
  74. Mathanika, T., Tharshiga, P. and Yogendrarajah, R. (2018), “Demographic factors and individual investor's decision making”, European Journal of Business and Management, Vol. 9 No. 15, pp. 175-185.
  75. Mazzoli, C., Marinelli, N. and Palmucci, F. (2017), “How does gender really affect investment behavior?”, Economic Letters, Vol. 152, pp. 58-61.
  76. Mishra, K. and Metilda, M.J. (2015), “A study on the impact of investment experience, gender, and level of education on overconfidence and self-attribution bias”, IIMB Management Review, Vol. 27 No. 4, pp. 228-239.
  77. Mouna, A. and Jarboui, A. (2015), “Financial literacy and portfolio diversification: an observation from the Tunisian stock market”, International Journal of Bank Marketing, pp. 808-822, doi: 10.1108/IJBM-03-2015-0032.
  78. Newton, D.C.J., Carlos, M. and Sergio, D.S. (2008), “Disposition effect and gender”, Applied Economics Letters, Vol. 15 No. 6, pp. 411-416.
  79. Noussair, C. and Wu, P. (2006), “Risk tolerance in the present and the future: an experimental study”, Managerial and Decision Economics, pp. 401-412, doi: 10.1002/mde.1278.
  80. NSE (2021), “Market pulse - monthly review”, Market Pulse, 31 Dec, p. 15.
  81. Olive, D.J. (2017), “Multiple linear regression”, in Linear Regression, Springer International Publishing, IL, pp. 17-83.
  82. Olsen, R.A. (2014), “Investment risk: the experts' perspective”, Financial Analysts Journal, pp. 62-66, doi: 10.2307/4479986.
  83. Oreng, M., Yoshinaga, C.E. and Junior, W.E. (2021), “Disposition effect, demographics and risk taking”, RAUSP Management Journal, Vol. 56 No. 2, pp. 217-233.
  84. Osipovich, A. (2020), “Individual-investor boom reshapes U.S. Stock market”, The WallStreet Journal, Vol. 31 August, pp. 1-6, available at: https://ofdollarsanddata.com/wp-content/uploads/2020/08/wsj_feature_nmaggiulli.pdf.
  85. Ostertagová, E., Ostertag, O. and Kováč, J. (2014), “Methodology and application of the Kruskal-Wallis test”, Applied Mechanics and Materials, pp. 115-120, doi: 10.4028/www.scientific.net/AMM.611.115.
  86. Oxford Learner's Dictionary (2021), “Oxford learner's dictionary”, available at: https://www.oxfordlearnersdictionaries.com/ (accessed 29 October 2021).
  87. Pablo, A.L. (1997), “Reconciling predictions of decision making under risk: insights from a reconceptualized model of risk behaviour”, Journal of Managerial Psychology, Vol. 12 No. 1, pp. 4-20.
  88. Parker, K. and Fry, R. (2020), More than Half of U.S. Households Have Some Investment in the Stock Market, PEW Research Centre, New York.
  89. Pompian, M.M. (2012), Behavioral Finance and Investor Types, 1st ed., John Wiley & Sons, NJ.
  90. Pompian, M. (2016), Risk Profiling through a Behavioral Finance Lens, 1st ed., CFA Institute Research Foundation, New York.
  91. Princeton University Library (2021), “Princeton university library”, available at: https://dss.princeton.edu/online_help/analysis/interpreting_regression.htm#coefficients (accessed 01 November 2021).
  92. Quail, R. and Belluz, D.D.B. (2012), “Risk appetite and risk tolerance − what's the difference?”, RIMS Canada conference, Canada.
  93. Ramanujam, V. and Chitradevi, K. (2012), “A study on impact of socio –economic profile on investment pattern of salaried & business people in Coimbatore city”, International Journal of Management and Information Technology, Vol. 2 No. 1, pp. 67-77.
  94. Ramsey, C.A. and Hewitt, A.D. (2005), “A methodology for assessing sample representativeness”, Environmental Forensics, Vol. 6, pp. 71-75.
  95. Renn, O. (1998), “The role of risk perception for risk management”, Reliability Engineering and System Safety, pp. 49-62, doi: 10.1016/S0951-8320(97)00119-1.
  96. Ricciardi, V. (2008), The Psychology of Risk: The Behavioral Finance Perspective, John Wiley & Sons, KY.
  97. Robin and Angelina, V. (2020), “Analysis of the impact of anchoring, herding bias, overconfidence and ethical consideration towards investment decision”, Jurnal Ilmiah Manajemen Fakultas Ekonomi, Vol. 6 No. 2, pp. 253-264.
  98. Rockwell, R.C. (1975), “Assessment of multicollinearity: the Haitovsky test of the determinant”, Sociological Methods and Research, Vol. 3 No. 3, pp. 308-320.
  99. Sachse, K., Jungermann, H. and Belting, J.M. (2012), “Investment risk – the perspective of individual investors”, Journal of Economic Psychology, pp. 437-447, doi: 10.1016/j.joep.2011.12.006.
  100. Samson, A. and Voyer, B.G. (2014), “Emergency purchasing situations: implications for consumer decision-making”, Journal of Economic Psychology, Vol. 44, pp. 21-33.
  101. Samuelson, W. and Zeckhauser, R. (1988), “Status quo bias in decision making”, Journal of Risk and Uncertainty, pp. 7-59, doi: 10.1.1.632.3193.
  102. Sanna, L.J. and Schwarz, N. (2007), “Metacognitive experiences and hindsight bias: it's not just the thought (content) that counts!”, Social Cognition, Vol. 25 No. 1, pp. 185-202.
  103. Savage, I. (1992), “Demographic influences on risk perceptions”, Risk Analysis, Vol. 13 No. 4, pp. 413-420.
  104. Saxena, A. (2020), “Does aging impacts on financial behavior and investment decisions”, Global Journal of Enterprise Information System, Vol. 12 No. 3.
  105. Schaafsma, S.M., Pfaff, D.W., Spunt, R.P. and Adolphs, R. (2015), “Deconstructing and reconstructing theory of mind”, Trends in Cognitive Science, Vol. 19 No. 2, pp. 65-72.
  106. Seasholes, M.S. and Zhu, N. (2010), “Individual investors and local bias”, The Journal of Finance, pp. 1987-2010, doi: 10.1111/j.1540-6261.2010.01600.x.
  107. Seiler, M.J., Seiler, V.L., Harrison, D.M. and Lane, M.A. (2013), “Familiarity bias and perceived future home price movements”, The Journal of Behavioral Finance, Vol. 14 No. 1, pp. 9-24.
  108. Seppälä, A. (2009), Behavioral Biases of Investment Advisors - The Effect of Overconfidence and Hindsight Bias, Helsinki School of Economics, Finland.
  109. ShareSoc (2021), UK Stock Market Statistics, WEST MALLING : ShareSoc.
  110. Shefrin, H. and Statman, M. (1984), “The disposition to sell winners too early and ride losers too long: theory and evidence”, The Journal of Finance, pp. 777-790, doi: 10.2307/2327802.
  111. Shefrin, H. and Statman, M. (2000), “Behaviour portfolio theory”, The Journal of Financial and Quantitative Analysis, Vol. 35 No. 2, pp. 127-151.
  112. Simon, H.A. (1972), “Theories of bounded rationality”, in McGuire, C.A.R.R. (Ed.), Decision and Organisation, The MIT Press, London, pp. 161-176.
  113. Singh, R. and Bhattacharjee, J. (2019), “Measuring equity share related risk perception of investors in economically backward regions”, Risks, Vol. 7 No. 1, pp. 1-20, doi: 10.3390/risks7010012.
  114. Sitkin, S.B. and Weingart, L.R. (1995), “Determinants of risky decision-making behavior: a test of the mediating role of risk perceptions and propensity”, The Academy of Management Journal, pp. 1573-1592, doi: 10.2307/256844.
  115. Spyrou, S. (2013), “Herding in financial markets: a review of the literature”, Review of Behavioral Finance, pp. 175-194, doi: 10.1108/RBF-02-2013-0009.
  116. Starr, C. (1969), “Social benefit versus technological risk”, American Association for the Advancement of Science, pp. 1232-1238, doi: 10.1126/science.165.3899.1232.
  117. Taherdoost, H., Sahibuddin, S. and Jalaliyoon, N. (2014), “Exploratory factor Analysis; concepts and theory”, Advances in Applied and Pure Mathematics, pp. 75-382, hal-02557344.
  118. Teijlingen, E.R.v. and Hundley, V. (2002), “The importance of pilot studies”, Art and Science, pp. 33-36, doi: 10.7748/ns2002.06.16.40.33.c3214.
  119. Tranmer, M., Murphy, J., Elliot, M. and Pampaka, M. (2020), Multiple Linear Regression, 2nd ed., Cathie Marsh Institute Working Paper, Manchester.
  120. Weber, E.U. and Milliman, R.A. (1997), “Perceived risk attitudes: relating risk perception to risky choice”, Management Science, Vol. 43 No. 2, pp. 123-144.
  121. Wilde, G.J.S. (1998), “Risk homeostasis theory: an overview”, Risk Homeostasis Theory: An Overview, Vol. 4, pp. 89-91.
  122. Williams, B., Brown, T. and Onsman, A. (2012), “Exploratory factor analysis: a five-step guide for novices”, Australian Journal of Paramedicine, Vol. 8 No. 3, pp. 1-13, doi: 10.1.1.475.8594.
  123. Yeomans, K.A. and Golder, P.A. (2016), “The Guttman-Kaiser criterion as a predictor of the number of common factors”, Journal of the Royal Statistical Society, pp. 221-229, doi: 10.2307/2987988.
  124. Yong, A.G. and Pearce, S. (2013), “A beginner's guide to factor analysis: focusing on exploratory factor Analysis”, Tutorials in Quantitative Methods for Psychology, Vol. 9 No. 2, pp. 79-94.
  125. Zahera, S.A. and Bansal, R. (2018), “A study of prominence for disposition effect: a systematic review”, Qualitative Research in Financial Markets, pp. 210-251, doi: 10.1108/QRFM-07-2018-0081.

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