Mutual Funds

Mutual funds are one of the most paramount investment mechanisms in financial markets. By playing a financial intermediary role, they give nonprofessionals access to professionally managed portfolios of securities and provide numerous benefits for both the capital market and investors simultaneously. This study evaluated and investigated the systemic risk of mutual funds in the Iran capital market by adopting a Conditional Value at Risk (CoVaR) approach and employing quantile regression. In the finance literature, systemic risk is the probability of a downfall in the financial system when a segment or an individual component gets in distress. This risk can trigger instability or shock in financial markets and the real part of the economy. The results revealed that stock (equity) mutual funds were systemically more important than other funds, including fixed-income and balanced mutual funds, due to the high volatility in their return, which makes them riskier. To compare systemic risk and market risk among mutual funds, funds classified into five different groups based on their systemic risk. According to this categorization, analysis of variance illuminated that the market risk of mutual funds had a direct relationship with their systemic risk, such that a higher systemic risk of a fund stood for higher market risk.

Net Asset Value (NAV) has long been a key performance metric for mutual fund investors. Due to the considerable fluctuation in the NAV value, it is risky for investors to make investment decisions. As a result, accurate and reliable NAV forecasts can help investors make better decisions and profit. In this research, we have analysed and compared the NAV prediction performance of our proposed deep learning models, such as N-BEATS and NBSL, with the FLANN model in both univariate and multivariate settings for five Indian mutual funds for forecast periods of 15, 20, 45, 63, 126, and 252 days using RMSE, MAPE, and R2 as evaluation metrics. A large forecast horizon was chosen to assess the model's consistency, reliability, and accuracy. The result reveals that the N-BEATS model outperforms the FLANN and NBSL models in the univariate setting for all datasets and all prediction horizons. In a multivariate setting, the outcome demonstrates that the N-BEATS model outperforms the FLANN model across all datasets and prediction horizons. The result also shows that, as the number of forecast days grew, our suggested models, notably N-BEATS, maintained consistency and attained the highest R2 value throughout the longest forecast duration.