HomeInternational Journal of Multidisciplinary: Applied Business and Education Researchvol. 6 no. 9 (2025)

Involvement and Decisions of Young Professionals on Stock Investments

Mark Noel C. Medalla | Via Blanca Nacua | Emilie Joy F. Tabuelog | Mariza O. Jortil | Marvin Ian E. Niere | Hafsah D. Macaurao | Jasmine A. Sejuela | Shyra Mae Gaviola | Shan Chaira Gonzales | Reina Richa R. Jumao-as | Gwyndharrel E. Guy

Discipline: business studies

 

Abstract:

Young professionals in the Philippines show low stock market involve-ment due to behavioral biases, poor risk assessments, lack of confidence to invest, and limited understanding or trust in digital investment tools. This study examined the influence of determinants of stock market in-volvement to stock investment decisions among 385 young professionals in Cebu City aged 20-35, a demographic with growing financial capacity but limited involvement, while also accounting the impact of demo-graphic factors. Employing descriptive statistics, Pearson Correlation Co-efficient, and Chi-Square Tests, results revealed that age, monthly in-come, and years of stock investment experience significantly affect in-volvement and investment decisions. Strong correlations were found be-tween stock market awareness and investment behaviors, risk percep-tion, and technology adoption with key investment decision factors, in-cluding consideration of economic conditions (r=.696), technical indica-tors (r=.620), market volatility (r=.684), and stock market indices (r=.606). Results affirm the Theory of Planned Behavior, Prospect The-ory, and the Technology Acceptance Model, while supporting the hypoth-esis that a significant relationship exists between the levels of involve-ment and investment decisions. The findings underscore the importance of personalized financial education, improved digital literacy, and greater regulatory transparency to foster confident, data-driven investment de-cisions. These insights also provide a valuable basis for financial institu-tions, policymakers, and fintech developers to collaboratively design ac-cessible, behavior-sensitive, and tech-enabled programs that encourage deeper and smarter engagement in the stock market.



References:

  1. Ahadzadeh, M., Karimi, M., Asgari, G., & Asgari, N. (2024). Impact of artificial intelligence (AI) on stock market: A comprehensive systematic review.
  2. AI integration in investment management. (2024). Mercer (US) LLC. https://www.mercer.com/assets/global/en/shared-assets/global/attachments/pdf-2024-Mercer-AI-integration-in-investment-management-2024-global-manager-survey-report-03212024.pdf
  3. Ajzen, I. (1991). The theory of planned behav-ior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  4. Akhtar, F., & Das, N. (2019). Predictors of in-vestment intention in Indian stock mar-kets: Extending the theory of planned be-haviour. International Journal of Bank Marketing, 37(1), 97–119. https://doi.org/10.1108/IJBM-08-2017-0167
  5. Almenberg, J., & Dreber, A. (2015). Gender, stock market participation and financial literacy. Economics Letters, 137, 140–142. https://doi.org/10.1016/j.econlet.2015.10.009
  6. Čirkova, E. (2015). The Warren Buffett philos-ophy of investment: How a combination of value investing and smart acquisitions drives extraordinary success. McGraw-Hill Education.
  7. Davis, F. D. (1989). Perceived usefulness, per-ceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
  8. de Castro, N., Salamat, L. A., & Tabor, M. (2020). Financial Literacy of Young Pro-fessionals in the Philippines. EPRA Inter-national Journal of Research & Develop-ment (IJRD), 5(3), 4. https://doi.org/https://doi.org/10.36713/epra2016
  9. Gavrilakis, N., & Floros, C. (2022). The impact of heuristic and herding biases on portfo-lio construction and performance: The case of Greece. Review of Behavioral Fi-nance, 14(3), 436–462. https://doi.org/10.1108/RBF-11-2020-0295
  10. Godefroid, M.-E., Plattfaut, R., & Niehaves, B. (2023). How to measure the status quo bias? A review of current literature. Man-agement Review Quarterly, 73(4), 1667–1711. https://doi.org/10.1007/s11301-022-00283-8
  11. Hsiao, Y.-J., & Tsai, W.-C. (2018). Financial lit-eracy and participation in the derivatives markets. Journal of Banking & Finance, 88, 15–29. https://doi.org/10.1016/j.jbankfin.2017.11.006
  12. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263. https://doi.org/10.2307/1914185
  13. Karki, U., Bhatia, V., & Sharma, D. (2024). A systematic literature review on overcon-fidence and related biases influencing in-vestment decision making. Economic and Business Review, 26(2), 130–150. https://doi.org/10.15458/2335-4216.1338
  14. Kumar, P., Amandeep Singh, & Taneja, S. (Eds.). (2024). Artificial intelligence and machine learning-powered smart finance. IGI Global.
  15. Kumari, D. (2020). The impact of financial lit-eracy on investment decisions: With spe-cial reference to undergraduates in west-ern province, sri lanka. Asian Journal of Contemporary Education, 4(2), 110–126. https://doi.org/10.18488/journal.137.2020.42.110.126
  16. Onyenahazi, O. B., & Antwi, B. O. (2024). The role of artificial intelligence in investment decision-making: Opportunities and risks for financial institutions. International Journal of Research Publication and Re-views, 5(10), 70–85. https://doi.org/10.55248/gengpi.5.1024.2701
  17. Priantinah, D., Aisyah, M. N., & Nurim, Y. (2019). The analysis of technology ac-ceptance model (TAM) for personal fi-nancial management on mobile applica-tion technology. Proceedings of the Inter-national Conference on Banking, Account-ing, Management, and Economics (ICO-BAME 2018). Proceedings of the Interna-tional Conference on Banking, Account-ing, Management, and Economics (ICO-BAME 2018), Yogyakarta, Indonesia. https://doi.org/10.2991/icobame-18.2019.56
  18. Saeedi, A., & Hamedi, M. (2018). Financial lit-eracy: Empowerment in the stock market. Palgrave Macmillan.
  19. Sanghvi, P. (2024, August 28). Prospect Theory and Its Implications for Investor Behavior. Prospect Theory and Its Implications for Investor. https://site.financialmodelingprep.com/education/financial-analysis/Prospect-Theory-and-Its-Implications-for-Investor-Behavior
  20. Suresh G. (2024). Impact of financial literacy and behavioural biases on investment de-cision-making. FIIB Business Review, 13(1), 72–86. https://doi.org/10.1177/23197145211035481
  21. Tait, V., & Miller, H. L. (2019). Loss aversion as a potential factor in the sunk-cost fallacy. International Journal of Psychological Re-search, 12(2), 8–16. https://doi.org/10.21500/20112084.3951
  22. The Philippine Stock Exchange, Inc. — PSE. (n.d.). The Philippine Stock Exchange, Inc. — PSE. Retrieved February 18, 2025, from https://www.pse.com.ph/
  23. Trivedi, S. R., & Kyal, A. H. (2020). Effective trading in financial markets using tech-nical analysis (1st ed.). Routledge India. https://doi.org/10.4324/9780429316487
  24. Veni, P., & Kandregula, R. (2020). Evolution of behavioral finance. International Journal of Scientific Development and Research, 5(3), 209–215. https://ijsdr.org//viewpaperforall.php?paper=IJSDR2003039
  25. Yeo, K. H. K., Lim, W. M., & Yii, K.-J. (2024). Financial planning behaviour: A system-atic literature review and new theory de-velopment. Journal of Financial Services Marketing, 29(3), 979–1001. https://doi.org/10.1057/s41264-023-00249-1