Narrow Results

Purpose: This research aims to examine and study the direct impact of change in COVID-19 cases and IIP index on the performance of NIFTY and selected mutual funds NAV performance for one year from the date WHO declared COVID-19 pandemic on 11 March 2020.

Approach/Methodology/Design: The study applies using daily NAV series of four mutual funds across sectors. ANOVA, Correlation, Regression, Descriptive Statistics.

Findings: There is a strong relation among NIFTY 50 and all the four mutual funds’ NAV performance, however, the relation between daily reported COVID-19 cases and NAV performance couldn’t be established.

Practical Implications: Economic turmoil has affected the disposable income of investors, the capital market is very volatile. This study will help the researchers and analysts to understand the relationship between COVID-19, NIFTY 50 index and select mutual funds NAV movements. And they can make a better decision under a similar situation.

We propose a new determinant of mutual fund performance persistence. We argue that different funds have different abilities to generate persistent performance and that such heterogeneity across funds can be explained by fund manager access to market information. To justify this hypothesis, we construct a network of mutual funds based on the commonality of their stock holdings and use network features to characterize how well a fund acquires and utilizes market information. Based on a sample of U.S. equity funds from 2001 to 2014, we find that a mutual fund with more complete information is more likely to possess momentum in performance.

Using a discrete-delay nonlinear dynamic system, we model the time evolution of a stock market price index and net stock of savings in mutual funds. The proposed deterministic model has a unique steady-state, so its time evolution is determined by nonlinear effects acting out of equilibrium. For this model, we find the local stability properties and the local bifurcations conditions, given the parameter space. Specifically, we find that in both versions of the model (with and without delay) a Neimark–Sacker bifurcation can occur. Moreover, we show that the system without delay has a chaotic behavior. Finally, we formulate the associated discrete stochastic model and establish the conditions for asymptotic stability. Several numerical simulations are finally performed for both the deterministic and the stochastic model to justify the theoretical results.

We analyze portfolio strategies which are locally optimal, meaning that they maximize the Sharpe ratio in a general continuous time jump-diffusion framework. These portfolios are characterized explicitly and compared to utility based strategies. We show that in the presence of jumps, maximizing the Sharpe ratio is generally inconsistent with maximizing expected utility, in the sense that a utility maximizing individual will not choose a strategy which has a maximal Sharpe ratio. This result will hold unless markets are incomplete or jump risk has no risk premium. In case of an incomplete market we show that the optimal portfolio of a utility maximizing individual may "accidentally" have maximal Sharpe ratio. Furthermore, if there is no risk premium for jump risk, a utility maximizing investor may select a portfolio having a maximal Sharpe ratio, if jump risk can be hedged away. We note that uncritical use of the Sharpe ratio as a performance measure in a world where asset prices exhibit jumps may lead to unreasonable investments with positive probability of ruin.

The main purpose of this paper is to examine whether "newly raised funds" (NRFs) can actually achieve the performance targets. The targets are usually provided to investors by the fund companies at the time when such new funds are introduced. Our results show that the performance of the new funds is weakly inferior to that of existing funds. Furthermore, the results also indicate that NRFs with lower cash flows tend to outperform those with higher cash flows. We also find that reports appearing in the newspapers relating to such NRFs have significantly positive effects on cash flows. Thereby, it indicates that more promotional efforts can attract more money.

In this paper, we introduce a conditional measure of skill, the correlation between funds’ residual trades, net of common trading motives, and future news about the stocks traded. Using this measure, we show that the average mutual fund manager in the cross-section has stock-picking skill. This result is robust to different benchmarks and is mainly driven by the manager’s ability to predict a firm’s cash-flow news. This skill has short-term persistence and is distinctly related to traditional measures of performance. Importantly, consistent with Berk and Green [2004, Mutual Fund Flows and Performance in Rational Markets, Journal of Political Economy 112(6), 1269–1295] fund flows are increasing with respect to managerial skill after controlling for fund performance.

This paper proposes a new approach to style analysis of mutual funds in a general state space framework with particle filtering and generalized simulated annealing (GSA). Specifically, we regard the exposure of each style index as a latent state variable in a state space model and employ a Monte Carlo filter as a particle filtering method, where GSA is effectively applied to estimating unknown parameters.

An empirical analysis using data of three Japanese equity mutual funds with six standard style indexes confirms the validity of our method. Moreover, we create fund-specific style indexes to further improve estimation in the analysis.

Mutual Funds give a platform for everyone to participate within the Indian capital market with skilled fund management no matter the number endowed. In the past few years, among the various financial products in India, Mutual Funds have emerged as the favorite. There is no doubt that acceptance of mutual funds as an investment vehicle has certainly increased among investors as many investors are earning from mutual fund — as result of increase in information and awareness among investors. Smaller amount of risk is associated with mutual fund investment than directly investing in stocks. Fund manager needs to provide returns in order to construct a diversified portfolio. They take into account numerous factors like, fund size, scheme type, returns, risk, etc. The paper attempts to analyze portfolio evaluation of selected equity diversified schemes using volatility measures such as quantitative factors like Standard Deviation, Beta and the ratios such as Sharpe, Treynor, Jensen’s Alpha, Information ratio, Fama’s Measure, Expense ratio measures. Data for research are collected from the secondary data sources and selected from 30 Mutual Fund schemes 10 AMCs.

Financial technology (Fintech) has become an emerging and powerful tool that contributes to the advancement of finance research. With the recent development of Fintech, machine-learning (ML) techniques have been continuously deployed for developing more effective models in financial research. This chapter aims to provide in-depth details of ML implementation in fund performance evaluation and prediction. Building on the theoretical basics of ML, we first introduce several widely applied ML algorithms, including linear regression, least absolute shrinkage and selection operator (LASSO), ridge, K nearest neighbors (KNN), decision trees (DT), random forest (RF), support vector machine (SVM), deep learning (DL), and artificial neural networks (ANN). We then focus on each method’s applicable conditions and how it contributes to forecasting and evaluating fund performance. The advantages of using ML methods over traditional methods in evaluating fund performance are also discussed.

Building on the work of Barras, Scaillet and Wermers (BSW, 2010), we propose a modified approach to inferring performance for a cross-section of investment funds. Our model assumes that funds belong to groups of different abnormal performance or alpha. Using the structure of the probability model, we simultaneously estimate the alpha locations and the fractions of funds for each group, taking multiple testing into account. Our approach allows for tests with imperfect power that may falsely classify good funds as bad, and vice versa. Examining both mutual funds and hedge funds, we find smaller fractions of zero-alpha funds and more funds with abnormal performance, compared with the BSW approach. We also use the model as prior information about the cross-section of funds to evaluate and predict fund performance.

This study investigates theoretically and empirically mutual fund managers’ risk-taking behavior due to ranking objectives. We argue that managers can not only choose the riskiness of their portfolio but can also determine how hard to work (their effort). The combination of risk and effort depends on the interim performance gap and the effort cost level. Both interim winner and loser gamble by taking high risk and spending low effort when the interim performance gap is below a certain threshold. Only the interim loser gambles when the interim performance gap is small and the effort cost is sufficiently high. Otherwise, managers adopt the same choice of risk-effort. In many cases, high (low) risk-taking induces higher (lower) effort. Empirically, we find that managerial effort is strongly and positively linked to their risk-shifting level. The worst-performers behave differently from the others but are not necessarily riskier and lazier.