Executive Summary
Analyzing the Performance of Indian Mutual Funds: An Exploratory Data Analysis
Data analyst: Ayush Vishwakarma
Objective: The objective of this Exploratory Data Analysis (EDA) was to gain insights into the performance of mutual funds in India, with a focus on understanding returns over various periods, and fund categories, and considering other parameters like shape ratio, beta, alpha, expense ratio, etc. This EDA aims to provide valuable information for investors seeking to make informed decisions in the Indian mutual fund market.
Key Findings:
- Top-performing Mutual funds in 2023
Quant Infrastructure Fund Kotak Infrastructure & Ecoc. Reform-SP-DirGrowth Canara Robeco Consumer Trends Fund Mirae Asset Midcap Fund - Equity funds generally outperform hybrid funds over the 10-year periods.
- Subcategory – Sectoral / Thematic Mutual Funds is the most popular among all subcategories considering the last 3 and 5-year returns.
- Funds with higher ratings tend to yield better returns, although exceptions exist.
Methodology:
- Data preprocessing involved handling missing values and ensuring data consistency.
- Exploratory data analysis techniques, including data visualization and statistical analysis, were applied.
Conclusion: This EDA offers a comprehensive view of the Indian mutual fund landscape, shedding light on performance trends and critical insights. Investors can use this information to make informed decisions about fund selection and allocation. This analysis also underscores the importance of considering fund categories, sub-categories, and ratings when evaluating investment options in the Indian market.
Recommendations:
- Investors should diversify their portfolios based on their risk tolerance and investment goals.
- Further research and due diligence are advised before making investment decisions.
- Regular monitoring of fund performance is essential to ensure alignment with investment objectives.
Origin: This dataset has been meticulously curated through web scraping from various Indian mutual funds. It encompasses comprehensive information about each fund, including scheme name, category, sub-category, and crucial investment metrics such as returns over 1 year, 3 years, and 5 years, along with associated ratings.
The dataset proves invaluable for individuals interested in delving into the performance of mutual funds within the Indian financial landscape. Analysts can leverage this dataset to identify trends, conduct comparative analyses across various funds, and glean insightful perspectives into the dynamic Indian mutual fund industry.
Data Fields:
- Scheme Name: The title of the mutual fund scheme.
- Category: The broader classification of the mutual fund (e.g., equity, debt, hybrid).
- Sub-category: Further categorization, including labels such as Small Cap, Large Cap, ELSS, and more.
- 1-Year Return (%): The fund’s percentage return over 1 year.
- 3-Year Return (%): The fund’s percentage return over 3 years.
- 5-Year Return (%): The fund’s percentage return over 5 years.
- Rating: The rating assigned to the fund.
Data Volume: The dataset comprises information on a substantial number of mutual funds accessible in the Indian market.
Data Source: This dataset was meticulously compiled through web scraping from various online sources. Downloaded from kaggle
Conclusion:
This EDA offers a comprehensive view of the Indian mutual fund landscape, shedding light on performance trends and key insights. Investors can use this information to make informed decisions about fund selection and allocation. This analysis also underscores the importance of considering fund categories, sub-categories, and ratings when evaluating investment options in the Indian market.