International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
CLUSTERING MODELS FOR MUTUAL FUND RECOMMENDATION Aayush Shah1, Aayushi Joshi2, Dhanvi Sheth3, Miti Shah4, Prof. Pramila M Chawan5 1,2,3,4 B.Tech Student, Dept. of Information Technology, VJTI College, Mumbai, Maharashtra, India
5Associate Professor, Dept. of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India
---------------------------------------------------------------------***--------------------------------------------------------------------efficient and reliable recommendation system that can Abstract - The mutual fund industry has expanded consider the individual preferences and risk tolerance of investors to provide tailored recommendations for mutual fund investments.
significantly, providing investors with several investment options. Mutual fund information is necessary for investors to make prudent investments. Yet, novice investors may find the financial environment to be complex owing to the abundance of information. A mutual fund recommendation system based on machine learning and data analytics overcomes this issue. We have proposed a clustering models for recommending mutual funds by analyzing theories regarding mutual fund investments and returns.
2.2 Problem elaboration With the rise of online trading platforms, retail investors now have access to a wider variety of investment options, but the sheer number of options can be overwhelming. Additionally, many investors may lack the financial expertise to evaluate the risks and returns of different mutual funds effectively.
Key Words: Mutual funds, Clustering models, K-means, DBSCAN, Hierarchal, Agglomerative
A mutual fund recommendation system could provide personalized investment advice based on a user's investment goals, risk tolerance, and other relevant factors. However, designing an effective system would require addressing several challenges. One of the primary challenges is building a model that can accurately predict the performance of different mutual funds based on historical data. This requires identifying relevant features that are predictive of mutual fund returns and developing algorithms that can effectively learn from this data.
1. INTRODUCTION Mutual fund investing has become an integral component of portfolio management for investors and financial institutions. Yet, choosing the best mutual funds to invest in may be difficult owing to the vast number of possibilities and the complexity of the elements that affect their performance. It is essential to accurately forecast the performance of mutual funds in order to make educated investing selections. In this paper, we have described clustering models for recommending mutual fund investments. The suggested model takes into consideration a number of implicit and explicit parameters, such as expense ratios, fund manager experience, past performance, and net asset values, in order to create investment recommendations that correspond to an investor's preferences and risk profile. The models such as K-means, hierarchical clustering, and DBSCAN group mutual funds based on their comparable traits and performance. This allows the models to offer suggestions based not just on the characteristics of individual funds, but also on the performance and behavior of funds with comparable characteristics. Using cutting-edge clustering techniques, our models provides a complete solution for investors seeking to improve their mutual fund investments.
Another challenge is ensuring that the system can provide personalized recommendations that reflect each user's unique investment goals and preferences. This requires developing effective methods for capturing user preferences and incorporating them into the recommendation process. Finally, it is important to ensure that the system is transparent and easy to use for novice investors. This means designing an intuitive user interface that explains the rationale behind each recommendation and provides users with the information they need to make informed decisions. Overall, a mutual fund recommendation system has the potential to empower novice investors and help them navigate the complex world of mutual fund investments. However, designing an effective system requires addressing several technical and user-facing challenges.
2. PROBLEM 2.1 Problem statement
3. DATA
To propose clustering models for recommending mutual funds. Today, there is a lack of personalized and accurate recommendations for investors due to the vast amount of data and the complex nature of mutual funds. The existing approaches are limited and may not provide a satisfactory solution for novice investors. Hence, there is a need for an
© 2023, IRJET
|
Impact Factor value: 8.226
3.1 Data collection We acquired our data from the Value Research Online website. It is a well-established website that provides financial information and analysis to help investors make
|
ISO 9001:2008 Certified Journal
|
Page 435