Data on daily log returns of top large-cap, mid-cap and small cap funds and NIFTY 50 index from 1st April 2015 to 31st March 2025

India has emerged as a strong long-term investment option among emerging markets, showing steady growth over the years. This dataset presents the daily log return percentages of fifteen mutual funds—comprising of large-cap, mid-cap and small cap funds which are among the top five funds in terms of Assets Under Management (AUM) in each category along with NIFTY 50 index from 1st April 2015 to 31st March 2025. The mutual funds included in the dataset are: Large-cap funds: ICICI Prudential Bluechip Fund–Growth, SBI Bluechip Fund–Regular Plan Growth, Mirae Asset Large Cap Fund–Growth Plan, HDFC Large Cap Fund–Growth Option Regular Plan, Aditya Birla Sun Life Frontline Equity Fund–Growth
Midcap funds: HDFC Mid-Cap Opportunities Fund–Growth Plan, Kotak Emerging Equity Scheme–Growth, Nippon India Growth Fund – Growth Option, SBI Magnum Midcap Fund–Regular Plan Growth, DSP Midcap Fund–Regular Plan Growth
Small-cap funds: Nippon India Small Cap Fund–Growth Option, SBI Small Cap Fund–Regular Plan Growth, HDFC Small Cap Fund–Growth Option, Quant Small Cap Fund–Direct Plan Growth Option,Axis Small Cap Fund–Direct Plan Growth

These funds offer an excellent avenue for retail investors to participate and benefit from the uptrends in capital markets. The daily log returns of a mutual fund is the logarithm of the ratio of its Net Asset Value (NAVs) on two consecutive days. The daily log return percentages are simply the log return values multiplied by hundred. The data are reported in one .xlsx file. For each date in the .xlsx file, the table provides the daily log returns of each fund and the NIFTY 50 index. The NAV data used to compute these returns were obtained from the AMFI website (https://www.amfiindia.com/net-asset-value/nav-history), while NIFTY 50 data were sourced from multiple sources such as Yahoo Finance and Investing.com.

Some of the empirical properties observed from the data were: The (linear) autocorrelations of the daily log returns for each fund and index seem to be insignificant whereas the autocorrelation of squared and absolute daily log returns are positive. Histograms and kernel density plots indicate that the daily return distributions are negatively skewed. Additionally, the calculated kurtosis values suggest that the daily return distributions have heavy tails.
Applications: These data can be used in financial research for a wide variety of purposes which includes modeling daily log returns and estimation of different measures of market risk, for instance Value at Risk (VaR) and Median Shortfall (MS). Using these log return data, we can estimate the parameters of different safety first criteria. These data satisfy the assumptions of the asset return model proposed by Dutta and Powdel (2023, https://doi.org/10.1007/s13571-023-00303-x; see Equation (1.2), p. 260 and Assumption 1, p. 262), allowing us to apply it for modeling the long-term returns of the funds and index and for estimating long-term market risk measures.