LITERATURE REVIEW STUDY ON EFFICIENCY AND EFFECTIVENESS OF FINANCIAL MODELLING FOR INVESTMENT DECISIONS OF INDIVIDUAL INVESTORS

Authors

  • Shubhangi Gupta Research Scholar, Dr. D. Y. Patil School of Management (Research Centre), Savitribai Phule Pune University, Pune
  • Dr. Ganesh Sambhaji Lande Research Guide, Dr. D. Y. Patil School of Management (Research Centre), Savitribai Phule Pune University, Pune

DOI:

https://doi.org/10.29121/shodhkosh.v4.i2.2023.5607

Keywords:

Financial Modelling, Investment Decision-Making, Individual Investors, Financial Literacy, Fintech Tools, Robo-Advisors, Portfolio Planning

Abstract [English]

In today’s volatile and information-driven financial environment, individual investors are increasingly seeking tools that enhance the quality and reliability of their investment decisions. Financial modelling, once primarily used by institutional investors and analysts, is now gradually making its way into personal finance due to the rise of user-friendly digital platforms and fintech applications. This study investigates the efficiency and effectiveness of financial modelling tools in empowering individual investors to make informed, data-driven, and goal-oriented investment decisions. The research explores key dimensions such as the level of awareness, usage patterns, perceived effectiveness, and the actual benefits derived from financial modelling tools among retail investors. It also delves into the various barriers that hinder the widespread adoption of such tools, including lack of financial literacy, complexity in usage, and mistrust of algorithm-based advice. Data was collected through structured questionnaires and analyzed using descriptive statistics and hypothesis testing to assess correlations between investor behavior and tool usage. The findings reveal a clear positive correlation between financial literacy and effective use of modelling tools, with users reporting improved decision confidence, risk assessment, and goal planning. The study also highlights the need for simplified interfaces, personalized insights, and educational interventions to bridge the gap between potential and actual usage. By identifying critical gaps and actionable insights, this research contributes to the broader conversation on democratizing investment intelligence and equipping individual investors with strategic tools to navigate complex markets effectively.

References

Gerlach, J. D., Pournader, M., & Seuring, S. (2020). Big data analytics for strategic decision-making: A systematic review. Journal of Business Research, 115, 375–388.

Ghosh, D. (2021). Artificial intelligence in financial modelling: Potential and limitations. Finance Research Letters, 39, 101621. DOI: https://doi.org/10.1016/j.frl.2020.101621

Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics. W. W. Norton & Company.

Arner, D. W., Barberis, J. N., & Buckley, R. P. (2017). FinTech, RegTech, and the Reconceptualization of Financial Regulation. Northwestern Journal of International Law & Business, 37(3), 371–413.

Barberis, N., & Thaler, R. (2003). A Survey of Behavioral Finance. Handbook of the Economics of Finance, 1, 1053–1128. DOI: https://doi.org/10.1016/S1574-0102(03)01027-6

Jappelli, T., & Padula, M. (2021). Investment in Financial Literacy, Social Security, and Portfolio Choice. Journal of Pension Economics & Finance, 20(2), 232–250.

Lusardi, A., & Mitchell, O. S. (2014). The Economic Importance of Financial Literacy: Theory and Evidence. Journal of Economic Literature, 52(1), 5–44. DOI: https://doi.org/10.1257/jel.52.1.5

Sironi, P. (2016). FinTech Innovation: From Robo-Advisors to Goal-Based Investing and Gamification. Wiley Finance. DOI: https://doi.org/10.1002/9781119227205

Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics. W. W. Norton & Company.

Zhang, Z., Wang, Y., & Xu, Y. (2022). Understanding Retail Investors' Usage of Digital Financial Advice Tools: The Role of Trust, Transparency, and Personalization. International Journal of Bank Marketing, 40(6), 1012–1033.

Bose, I., & Sarkar, A. (2022). Fintech tools and the democratization of personal investing: A behavioral perspective. Journal of Behavioral Finance, 23(1), 15–28. https://doi.org/10.1080/15427560.2021.1984317

Ghosh, S. (2021). The rise of robo-advisors: Implications for retail investors in emerging economies. Asian Journal of Finance & Accounting, 13(2), 1–17.

Gerlach, J., Dutt, C., & Batmaz, S. (2020). Financial Modelling in Investment Decision Making: Theory and Practice in the Digital Age. International Review of Financial Analysis, 68, 101479.

Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics. W. W. Norton & Company.

Sharma, R., & Kumar, A. (2023). Adoption of AI-powered financial tools among millennials: Barriers and enablers. Journal of Financial Innovation, 7(2), 41–59.

Sundararajan, A., & Mukherjee, K. (2021). Exploring digital financial literacy and its impact on financial planning in India. Indian Journal of Economics and Development, 17(4), 215–228.

Bhimani, A., Sivabalan, P., & Soonawalla, K. (2019). A study of financial modelling in strategic corporate finance. Management Accounting Research, 44, 101415.

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Published

2023-12-31

How to Cite

Gupta, S., & Lande, G. S. (2023). LITERATURE REVIEW STUDY ON EFFICIENCY AND EFFECTIVENESS OF FINANCIAL MODELLING FOR INVESTMENT DECISIONS OF INDIVIDUAL INVESTORS. ShodhKosh: Journal of Visual and Performing Arts, 4(2), 4695–4701. https://doi.org/10.29121/shodhkosh.v4.i2.2023.5607