| Issue |
ITM Web Conf.
Volume 68, 2024
2024 First International Conference on Artificial Intelligence: An Emerging Technology in Management (ICAETM 2024)
|
|
|---|---|---|
| Article Number | 01017 | |
| Number of page(s) | 6 | |
| Section | Engineering Technology & Management | |
| DOI | https://doi.org/10.1051/itmconf/20246801017 | |
| Published online | 12 December 2024 | |
Evaluating Mutual Funds for Retirement: Insights from Primary Data Analysis on Investor Preferences and Performance
1 Professor, Department of Management Studies, Dayananda Sagar College of Engineering, Bangalore, India
2 Department of Management Studies, Dayananda Sagar College of Engineering, Bangalore, India
* e-mail: metilda-mba@dayanandasagar.edu
This study evaluates the effectiveness of mutual funds in a retirement planning context by analyzing primary data collected from 204 participants, including investors and financial advisors. The survey focused on four important variables, diversification of funds, long term performance of the chosen fund, impact on retirement savings and the risk return relationship. A descriptive analysis and simple statistical tests like correlation to study the strength of relationship between the variables and regression to understand the predictive ability of the variables, demonstrates positive relationships in all the factors. The correlation analysis demonstrates positive relationships in all the factors. . Long-term performance is highly correlated with diversification, and also strongly related to impact on retirement savings and risk. This interconnectedness indicates that respondents who value one aspect of investment are likely to value others as well. The findings also suggest a positive impact of diversification on mutual fund long term performance and also a significant effect of long-term performance on ‘impact on retirement saving’.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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