Forecasting Mutual Fund Performance Under Market Scenarios: A Regression-Based Sensitivity Analysis of Nippon India Large Cap Fund and Nifty 500

References

[1] Alexandra, J. (2025). NAV of equity fund forecasting using ARIMA and RNN. Journal of Computer and Information Systems Applications, 1(1), 1–8.

[2] Association of Mutual Funds in India (AMFI). (2025). Monthly SIP Contribution Report — December 2025. Mumbai: AMFI.

[3] Bank of Singapore. (2025). Navigating drawdowns: Strategies for long-term investors. Investment Strategy Report.

[4] Barras, L., Scaillet, O., & Wermers, R. (2010). False discoveries in mutual fund performance. Journal of Finance, 65(1), 179–216.

[5] Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.

[6] Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. San Francisco: Holden-Day.

[7] Brinson, G. P., Hood, L. R., & Beebower, G. L. (1986). Determinants of portfolio performance. Financial Analysts Journal, 42(4), 39–44.

[8] Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57–82.

[9] Constantinides, G. M. (1979). A note on the suboptimality of dollar-cost averaging as an investment policy. Journal of Financial and Quantitative Analysis, 14(2), 443–450.

[10] CRISIL Fund Analytics. (2024). Indian Mutual Fund Performance Review 2024. Mumbai: CRISIL.

[11] Cremers, M., & Petajisto, A. (2009). How active is your fund manager? Review of Financial Studies, 22(9), 3329–3365.

[12] Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383–417.

[13] Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3–56.

[14] Fama, E. F., & French, K. R. (2010). Luck versus skill in the cross-section of mutual fund returns. Journal of Finance, 65(5), 1915–1947.

[15] Goswami, G. (2021). Cross-sectional return predictability in Indian stock market. SSRN Electronic Journal.

[16] Jensen, M. C. (1968). The performance of mutual funds in the period 1945–1964. Journal of Finance, 23(2), 389–416.

[17] Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.

[18] Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292.

[19] Kumar, R., & Singh, A. (2023). Performance persistence in Indian equity mutual funds. Journal of Asset Management, 24(3), 245–262.

[20] Li, B., & Rossi, A. (2024). Selecting mutual funds from the stocks they hold: A machine learning approach. Journal of Financial Economics, 142(3), 1215–1245.

[21] Malkiel, B. G. (2013). Asset management fees and the growth of finance. Journal of Economic Perspectives, 27(2), 97–108.

[22] Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.

[23] Minsky, H. P. (1986). Stabilizing an unstable economy. New Haven: Yale University Press.

[24] Morningstar. (2023). Understanding capture ratios in fund analysis. Chicago: Morningstar Investment Management.

[25] Müller, S., Pugachyov, N., & Weigert, F. (2024). Forecasting mutual fund performance: Combining return-based with portfolio holdings-based predictors. SSRN Electronic Journal.

[26] Narayan, P. K. (2015). Are Indian stock returns predictable? Journal of Banking & Finance, 58, 506–531.

[27] National Stock Exchange of India (NSE). (2025). Nifty 500 return distribution analysis 2000–2025. Mumbai: NSE.

[28] Reserve Bank of India (RBI). (2025). Financial Stability Report — December 2025. Mumbai: RBI.

[29] Securities and Exchange Board of India (SEBI). (2024). Annual Report 2023–24. Mumbai: SEBI.

[30] Sharma, M. (2022). Fund size and performance: Evidence from Indian mutual funds. International Journal of Finance and Economics, 27(4), 4321–4338.

[31] Sharpe, W. F. (1966). Mutual fund performance. Journal of Business, 39(1), 119–138.

[32] Sharpe, W. F. (1992). Asset allocation: Management style and performance measurement. Journal of Portfolio Management, 18(2), 7–19.

[33] Shiller, R. J. (2000). Irrational exuberance. Princeton, NJ: Princeton University Press.

[34] Sortino, F. A., & Price, L. N. (1994). Performance measurement in a downside risk framework. Journal of Investing, 3(3), 59–64.

[35] Treynor, J. L. (1965). How to rate management of investment funds. Harvard Business Review, 43(1), 63–75.

[36] Value Research. (2024). Large cap fund analysis report. New Delhi: Value Research.

[37] Young, T. W. (1991). Calmar ratio: A smoother tool. Futures Magazine, 20(1), 40–42.

@article{1718884,
      author = {Raghav Somani P, Dr. P. A. Manoj Kumar},
      title = {Forecasting Mutual Fund Performance Under Market Scenarios: A Regression-Based Sensitivity Analysis of Nippon India Large Cap Fund and Nifty 500},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {1540-1551},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718884.pdf},
      abstract = {This paper proposes and tests an integrated forecasting model for predicting the trajectory of the mutual fund Net Asset Value (NAV) using regression-based transmission mechanisms as the backbone. Based on 156 monthly observations from January 2013 to December 2025, the study investigates the empirical link between the Nifty 500 index of the national stock exchange and the Nippon India Large Cap Fund (NILK) which is one of the most well-known equity funds in India that follows a large-cap strategy. The ordinary least squares (OLS) regression model shows an excellent explanatory power (R2 = 0.936) and the estimated transmission coefficient (β = 0.916) indicates that 93.6% of fund NAV variations can be attributed to variations of the benchmark index. The base case returns to funds over 12 months are 9.41%, the optimistic case returns are 77.19%, and the recessionary case returns are a negative −32.44%—a combined range of 109.6 percentage points and strong scenario discrimination. The ratios of upside capture (86.1%) and downside capture (96.0%) indicate an asymmetric risk-return profile with the fund sacrificing a disproportionate amount of upside participation compared to downside protection. The worst-case analysis of maximum drawdown is nearly identical to the drawdown of the benchmark, which bottomed at 33.79%, while it measures a 32.44% drawdown, indicating that the asset class may offer some protection from downside risk. The results are robust through subsample stability tests and alternative scenarios and beta sensitivity analyses. The findings have immediate relevance to individual investors, financial advisors and fund managers in the evolving retail investment landscape in India.},
      keywords = {Mutual Fund Performance Forecasting, Scenario Analysis, Capture Ratios, NAV Projection, Drawdown Resilience, Regression Transmission Mechanism, Nifty 500, Indian Equity Markets},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1718884}
  }