Investigation of gender differences in familiar portfolio choice
Abstract
The prevailing assumption holds that investors include in their portfolios securities that they know well, are located near their place of residence, or align with their fields of interest. This article analyse familiarity in investment through gender perspective and their fields of interest. Women and men field of interest is defined by enabling online magazines’ article’s themes. The aim of this paper is to investigate gender-based behavioural differences in investment decisions – i.e. to define women’s and men’s fields of interests and value investment portfolios. Portfolios differ according to whether they are formed from securities that are consistent with women’s fields of interest, men’s fields of interest or both women’s and men’s fields of interest. Textual analysis was employed to identify men’s and women’s fields of interest. Investment portfolios were built using mean variance (MV) and Black–Litterman (BL) models. The analysis revealed that portfolios built from men’s fields of interests are more diversified than are portfolios built either from women’s fields of interests or from both men’s and women’s fields of interest. Analysing 12 portfolios’ efficiency revealed that women’s portfolio returns are more stable than are men’s. Moreover, the study demonstrated that time impacts investment portfolio returns to a greater extent than do gendered fields of interest. The article complements the existing knowledge about bias in investor familiarity, which results from differences in men’s and women’s fields of interest.
Keyword : behavioural finance, investment psychology, gender differences, familiarity bias, investment decisions, media analytics, textual analysis, investment portfolio
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Akhter, S., & Alam, P. (2001). Information acquisition and investment decisions on the Internet: An empirical investigation. Marketing Management Journal, 11(1), 94–100.
Applegate, A. J., & Applegate, M. D. (2004). The Peter effect: Reading habits and attitudes of preservice teachers. The Reading Teacher, 57, 554–563.
Badulescu, D. (2016). Reading in the digital age. Philological Jassyensia, 12(1), 139–149.
Baron, N. S. (2017). Reading in a digital age. Phi Delta Kappan, 99(2), 15–20. https://doi.org/10.1177/0031721717734184
Bayyurt, N., Karisik, V., & Coskun, A. (2013). Gender differences in investment preferences. European Journal of Economic and Political Studies, 6(1), 71–83.
Black, F., & Litterman, R. (1990). Asset allocation: Combining investor views with market equilibrium. Goldman Sachs Fixed Income Research, 115.
Buzzetto-More, N., Guy, R., & Elobaid, M. (2007). Reading in a digital age: E-books are students ready for this learning object? Interdisciplinary Journal of E-Learning and Learning Objects, 3, 239–250. https://doi.org/10.28945/397
Coiro, J. (2011). Predicting reading comprehension on the Internet: Contributions of offline reading skills, online reading skills, and prior knowledge. Journal of Literacy Research, 43, 352–392. https://doi.org/10.1177/1086296X11421979
Dickason, Z., Nel, I., & Ferreira, S. J. (2017). Gender: Behavioural finance and satisfaction of life. Gender and Behaviour, 15(3), 9550–5559.
Dickason, Z., & Ferreira, S. (2019). Risk tolerance of South African investors: Marital status and gender. Gender and Behaviour, 16(3), 12999–13006.
Eckel, C. C., & Grossman, P. J. (2008). Men, women, and risk aversion: Experimental evidence. Handbook of Experimental Economic Results, 1(113), 1061–1073. https://doi.org/10.1016/S1574-0722(07)00113-8
Estes, R., & Hosseini, J. (2001). The gender gap on Wall Street: An empirical analysis of confidence in investment decision-making. The Journal of Psychology, 122(6), 577–590. https://doi.org/10.1080/00223980.1988.9915532
Farinosi, M., Lim, C., & Roll, J. (2016). Book or screen, pen or keyboard? A cross-cultural sociological analysis of writing and reading habits basing on Germany, Italy, and the UK. Telematics and Informatics, 33(2), 410–421. https://doi.org/10.1016/j.tele.2015.09.006
Frank, S. G., Souza de Souzab, D. V., Duarte Ribeiroa, J. L., & Echeveste, M. E. (2013). A framework for decision-making in investment alternatives selection. International Journal of Production Research, 51, 5866–5883. https://doi.org/10.1080/00207543.2013.802393
French, K. R., & Poterba, J. M. (1991). Investor diversification and international equity markets. American Economic Review, 81, 222–226. https://doi.org/10.3386/w3609
Dong, Y., Young, D., & Zhang, Y. (2021). Familiarity bias and earningsbased equity valuation. Review of Quantitative Finance and Accounting, 57, 795–818. https://doi.org/10.1007/s11156-020-00949-y
Gentjan, C., Khan, K. A., Rowland, Z., & Ribeiro, H. N. R. (2021). Financial advice, literacy, inclusion and risk tolerance: The moderating effect of uncertainty avoidance. E&M Economics and Management (E&M), 24(4), 105–123. https://doi.org/10.15240/tul/001/2021-4-007
Guthrie, J. T., & Wigfield, A. (1999). How motivation fits into a science of reading. Scientific Studies of Reading, 3, 199–205. https://doi.org/10.1207/s1532799xssr0303_1
Hala, Y., Abdullah, M. W., Andayani, W., Ilyas, G. B., & Akob, M. (2020). The financial behavior of investment decision making between real and financial assets sectors. Journal of Asian Finance, Economics and Business, 7(12), 635–645. https://doi.org/10.13106/jafeb.2020.vol7.no12.635
Idzorek, T. (2007). A step-by-step guide to the Black–Litterman model: Incorporating user-specified confidence levels. In S. Satchell (Ed.), Forecasting expected returns in the financial markets (pp. 17–38). Academic Press. https://doi.org/10.1016/B978-075068321-0.50003-0
Jaiswal, B., & Kamil, N. (2012). Gender, behavioral finance, and investment decision. Business Review, 7(2), 8–22. https://doi.org/10.54784/1990-6587.1201
Jureviciene, D., & Ivanova, O. (2013). Behavioural finance: Theory and survey. Science – Future of Lithuania, 5(1), 53–58. https://doi.org/10.3846/mla.2013.08
Kahneman, D., & Tversky, A. (2013). Prospect theory: An analysis of decision under risk. In L. C. MacLean & W. T. Ziemba (Eds.), Handbook of the fundamentals of financial decision making: Part I (pp. 99–127). https://doi.org/10.1142/9789814417358_0006
Kang, J.-K., & Stulz, R. A. M. (1997). Why is there a home bias? An analysis of foreign portfolio equity ownership in Japan. Journal of Financial Economics, 46, 3–28. https://doi.org/10.1016/S0304-405X(97)00023-8
Karim, N. S. A., & Hasan, A. (2007). Reading habits and attitude in the digital age. The Electronic Library, 25, 285–298. https://doi.org/10.1108/02640470710754805
Kent Baker, H., & Ricciardi, V. (2015). Understanding behavioural aspects of financial planning and investing. Journal of Financial Planning, 28(3), 22–26.
Krashen, S. (2005). A special section on reading research: Is in school free reading good for children? Why the national reading panel report is (still) wrong. Phi Delta Kappan, 86(6), 444–447. https://doi.org/10.1177/003172170508600607
Liu, Y., Park, J. L., & Sohn, B. (2018). Foreign investment in emerging market: International diversification or familiarity bias. Emerging Markets Finance & Trade, 54(10), 2169–2191. https://doi.org/10.1080/1540496X.2017.1369403
Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7, 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Markowitz, H. M. (Ed.). (2009). Harry Markowitz: Selected works (Vol. 1). World Scientific. https://doi.org/10.1142/6967
MathWorks. (2022). https://ch.mathworks.com/support.html
MATLAB Code 1. (2021, December 10–20). https://ch.mathworks.com/help/textanalytics/ug/visualize-text-data-using-word-clouds.html
MATLAB Code 2. (2022, January 4–12). https://ch.mathworks.com/help/finance/black-litterman-portfolio-optimization.html
McKenna, M. C., Conradi, K., Lawrence, C., Jang, B. G., & Meyer, J. P. (2012). Reading attitudes of middle school students: Results of US survey. Reading Research Quarterly, 47(3), 283–306. https://doi.org/10.1002/rrq.021
Mikelionyte, M., & Lezgovko, A. (2021). Gender impact on personal investment strategies. Economics and Culture, 18(1), 32–45. https://doi.org/10.2478/jec-2021-0003
Min, L., Dong, J., Liu, D., & Kong, X. (2021). A Black–Litterman portfolio selection with investor opinions generating from machine learning algorithms. Engineering Letters, 29(2), 710–721.
Mitchell, A., Gottfried, J., Barthel, M., & Shearer, E. (2016). The modern news consumer: News attitudes and practices in the digital age. Pew Research Centre.
De Naeghel, J., Van Keer, H., Vansteenkiste, M., & Rosseel, Y. (2012). The relation between elementary students’ recreational and academic reading motivation, reading frequency, engagement, and comprehension: A self-determination theory perspective. Journal of Educational Psychology, 104(4), 1006–1021. https://doi.org/10.1037/a0027800
Ng, F. C., Li, W. K., & Yu, P. L. (2014). A Black–Litterman approach to correlation stress testing. Quantitative Finance, 14(9), 1643–1649. https://doi.org/10.1080/14697688.2013.843022
Pfost, M., Dofler, T., & Artelt, C. (2013). Students’ extracurricular reading behaviour and the development of vocabulary and reading comprehension. Learning and Individual Differences, 26, 89–102. https://doi.org/10.1016/j.lindif.2013.04.008
Pool, V. K., Stoffman, N., & Yonker, S. E. (2012). No place like home: Familiarity in mutual fund manager portfolio choice. Review of Financial Studies, 25(8), 2563–2599. https://doi.org/10.1093/rfs/hhs075
Putro, N. H. P. S. & Lee, J. (2017). Reading interest in a digital age. Reading Psychology, 38(8), 778–807. https://doi.org/10.1080/02702711.2017.1341966
Riff, S., & Yagil, Y. (2016). Behavioural factors affecting the home bias phenomenon: Experimental tests. Journal of Behavioural Finance, 17(3), 267–279. https://doi.org/10.1080/15427560.2016.1203324
Skonieczna, A., & Castellano, L. (2020). Gender smart financing investing in and with women: Opportunities for Europe (No. 129). Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
Soni, K., & Desai, M. (2021). Stock price effect: Effect of behavioral biases on investor’s mindset in Gujarat state, India. Copernican Journal of Finance & Accounting, 10(1), 67–79. https://doi.org/10.12775/CJFA.2021.004
Stavytskyy, A., Kharlamova, G., Giedraitis, V. R., Cheberyako, O., & Nikytenko, D. (2020). Gender question: Econometric answer. Economics and Sociology, 13(4), 241–255. https://doi.org/10.14254/2071-789X.2020/13-4/15
Tang, M.-L., Wu, F.-Y., & Hung, M.-C. (2021). Multi-asset allocation of exchange traded funds: Application of Black–Litterman model. Investment Analysts Journal, 50(4), 273–293. https://doi.org/10.1080/10293523.2021.2010387
Tesar, L. L., & Werner, I. M. (1995). Home bias and high turnover. Journal of International Money and Finance, 14, 467–492. https://doi.org/10.1016/0261-5606(95)00023-8
Tversky, A., & Kahneman, D. (1985). The framing of decisions and the psychology of choice. In G. Wright (Ed.), Behavioural decision-making (pp. 25–41). Springer. https://doi.org/10.1007/978-1-4613-2391-4_2
Tversky, A., & Kahneman, D. (1989). Rational choice and the framing of decisions. In B. Karpak & S. Zionts (Eds.), Multiple criteria decision-making and risk analysis using microcomputers (pp. 81–126). Springer. https://doi.org/10.1007/978-3-642-74919-3_4
Xiao, Y., & Valdez, E. A. (2015). A Black–Litterman asset allocation model under elliptical distributions. Quantitative Finance, 15(3), 509–519. https://doi.org/10.1080/14697688.2013.836283
Wu, W., Kou, G., Peng, Y., & Ergu, D. (2012). Improved AHP-group decision-making for investment strategy selection. Technology and Economic Development of Economy, 18, 299–316. https://doi.org/10.3846/20294913.2012.680520