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PurposeMutual funds are the second most preferred investment option in India and have garnered considerable research interest. The focus of Indian studies thus far has been restricted to the bottom-up approach of investing which rewards a fund manager for picking winner stocks and generates superior returns. While changing portfolio allocation as per varying macro-trends has been instrumental in generating superior returns, it has not been given the desired attention. This study addresses this important research gap.Design/methodology/approachThe authors analyze the industry selection ability of the fund manager on a robust sample by decomposing alpha into alpha due to industry selection and alpha attributable to stock selection. Alpha estimates are computed on a robust sample of 34 open-ended Indian equity mutual funds for a 10-year duration 2011–2020 using three base models of asset pricing – single-factor, four-factor and five-factor alpha under panel data methodology.FindingsThe study leads us to four major findings. One, industry selection explains more than two-fifth of the alpha both in cross-section and time series of returns; two, industry selection exhibits persistence for more than four quarters across asset pricing model; third, younger funds have level playing when alpha from picking right industries is concerned; four, broad industry allocation continues to explain superior returns as sector allocation undergoes consolidation during ongoing COVID-19 pandemic and funds increase exposure to defensive stocks, consistent with folio allocations as per macroeconomic conditions.Research limitations/implicationsThe authors find strong evidence of persistence in the case of alpha attributable to the industry selection component, and the findings are consistent with the persistence results reported in the empirical literature. While some funds excel in stock-picking skills and others excel in picking the right industries, both skills together make for winner funds that attract larger investor flows as investors chase superior performance. The authors also find no evidence of diseconomies of scale in the case of industry allocation alpha generated by the fund managers.Practical implicationsThe results suggest a fresh approach for investors while making mutual fund investment decisions; the investors can achieve superior returns by assessing industry selection skills as it tends to provide a more holistic picture concerning a perennial question – why some funds outperform and continue to contribute to investor's wealth?Social implicationsMutual funds have become a favored investment option for Indian investors more so as a disciplined investment option owing to dismal financial literacy rates. The study throws light on a relatively unaddressed dimension of choosing winner funds. The significance of right sector allocation assumed even more significance with the onset of the pandemic which lends further credence to the findings of the study.Originality/valueResearch has been conducted on secondary data extracted from a well-cited database for Indian mutual funds. Empirical analysis and conclusion drawn are based on authentic statistical analysis and adds to the existing literature.

In its latest financial stability report, dated 11 January 2021, the Reserve Bank of India (RBI) emphasised the significance of balanced mutual funds in risk transmission. We investigate the transmission of volatility and contagion effect from Indian balanced funds to representative indices—Bank, PSU Bank, Private Bank, Financial Services, Broader Market, Services Sector and Fixed Income—using three established models: Diagonal BEKK (1995), Dynamic Conditional Correlation (DCC GARCH (2002)) and network model. In analyses of financial time series data, the COVID-19 pandemic has been widely regarded as a structural break. We may better understand the dynamism and scale of spillover before and during a crisis by dividing the study into two periods: pre-COVID-19 (January 2011–29 December 2019) and during COVID-19 (30 December 2019–20 April 2021). The results of all three models support our hypothesis of statistically significant spillover from balanced funds to chosen indices, with strong persistence and a marked increase in long-term volatility spillover, showing the presence of contagion effects. The findings of this paper can assist fund managers in diversifying their portfolios while also benefiting investors educationally. JEL Classification: C23, G12, G23

Since their inception in 2002, exchange-traded funds (ETFs) in India have made significant progress. The lacklustre record of active investing and the dual benefits of low-cost diversification have elevated the asset class’s credibility. During the COVID-19 crisis, equities ETFs have demonstrated greater resilience than other investment options. Our examination of 35 ETFs that mirror broader indices indicates the performance of Indian ETFs amid the economic shock caused by COVID-19. A comprehensive analysis of the daily NAV, trading price, and benchmark price data from 2015 to the present, post-identification of the pandemic period, indicates three significant inferences: First, tracking errors persist, with significant increases in all three tracking measures throughout the COVID-19 period. Second, during the COVID-19 period, the existence of pricing inefficiencies transitions from discount to premium, with an average daily value of 57.485. Nonetheless, the rate of short-term disequilibrium corrections accelerates dramatically during the COVID-19 phase. Thirdly, it took only eight days during COVID-19 to erase 50% of the disequilibrium between NAV and market price, compared to 170 days before COVID-19. During the COVID-19 era, we also notice a rise in the co-integration between ETF returns and benchmark performance and an increase in the adjustment rate from 18% to 61% per unit of time. The research findings provide portfolio managers and regulators with a comprehensive view of the performance of ETFs before and after an economic shock and offer educational benefits to a growing investor base.

<p>Mutual Funds are the second most preferred financial investment option in India amongst households, corporate and private investors alike. Managed funds bring with them the expertise of fund managers along with the benefits of diversification and lower costs. The sensitivity of fund flows defines the ability of the fund manager in offering expected future returns. Mutual fund flows exhibit time series characteristics, it being financial data collected at regular intervals over a time period. This paper studies the dynamics of mutual fund flows by utilising time series regression modelling. Monthly fund flows data for a sample of 142 equity open-ended growth orientation across major marketcap categories – Large Cap, Large and Mid Cap, Multi Cap, Mid Cap, and Small Cap have been analysed using ARIMA Modelling in the R software package. Appropriate lag length and the presence of a unit root have been investigated with the help of established techniques coupled with suitable checks of robustness. Model of best fit has been used to forecast monthly fund flows for a lag length of 60. Our study leads us to two major outcomes. One, unlike many developed and emerging markets, fund flows in the chosen sample do not confirm to positive feedback trading hypothesis. This lends credible support to the absence of irrational exuberance in mutual fund investments. Second, equity-based funds in Large Cap, Large and Mid Cap, and Multi Cap category exhibit strong trend component while funds in Mid Cap and Small Cap category have a strong random component. Beginner investors can take advantage of alpha offered by fund managers possessing effective market -timing skills, an indicator of trend-investing strategy. Funds belonging to these categories are also lesser prone to market volatility in comparison to Mid Cap and Small Cap funds, being more suitable for experienced investors</p>