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Prior research has documented inconclusive and/or mixed empirical evidence on the timing performance of hybrid funds. Their performance inferences generally do not efficiently control for fixed-income exposure, conditioning information, and cross-correlations in fund returns. This study examines the stock and bond timing performances of hybrid funds while controlling and accounting for these important issues. It also discusses the inferential implications of using alternative bootstrap resampling approaches.

We examine the stock and bond timing performances of hybrid funds using (un)conditional multi-factor benchmark models with robust estimation inferences. We also rely on the block bootstrap method to account for cross-correlations in fund returns and to separate the effects of luck or sampling variation from manager skill.

We find that the timing performance of portfolios of funds is neutral and sensitive to controlling for fixed-income exposures and choice of the timing measurement model. The block-bootstrap analyses of funds in the tails of the distributions of stock timing performances suggest that sampling variation explains the underperformance of extreme left tail funds and confirms the good and bad luck in the bond timing management of tail funds. We report inference changes based on whether the Kosowski et al. or the Fama and French bootstrap approach is used.

This study provides extensive and robust evidence on the stock and bond timing performances of hybrid funds and their sensitivity based on (un)conditional linear multi-factor benchmark models. It examines the timing performances in the extreme tails funds using the block bootstrap method to efficiently identify (un)skilled fund managers. It also highlights the sensitivity of inferences to the choice of testing methodology.

This study aims to test mutual fund superiority, comparing the performance of 646 Islamic mutual funds with 475 ethical funds and conventional proxies.

This study uses statistical methods including paired t-statistics of independent samples, one-way Bonferroni test–analysis of variance–F-statistic for testing means equality, the chi-squared test for median equality and regression models corrected for heteroscedasticity. These methods are used to identify superiority of mutual funds and to validate the significance of the results.

The findings confirm the superiority of conventional funds over ethical funds and ethical funds over Islamic funds. Both ethical and Islamic funds, however, outperform conventional proxies during some recessionary periods. Moreover, stronger performance is recorded for Islamic funds in Europe and North America regions and across age and asset allocation categories, but limited support for reversal fund size, composition focus and reversed price effect.

These findings should assist investors when deciding to invest and motivate Islamic and ethical funds to improve their portfolio formation and asset allocation strategies set by their professional managers.

The originality of this study is in its comprehensive approach in that it compares the performance of funds after accounting for such characteristics as fund objectives, size, age, asset allocation, geographical investment focus, fund composition focus, share price levels and the effect of global crises. This study approach is not only original and productive in documenting Islamic funds’ performance for the past three decades (1990–2022) but can also update the literature on these characteristics collectively and individually.

The purpose of this study is to measure mutual funds' manager performance by attributing it to their abilities to choose better securities (selectivity effect) and to allocate these securities better than their benchmarks (allocation effect). The study enables the authors to examine the relative contributions of the commonly known asset-pricing factors in mutual funds' performance.

To examine managers' ability to steer funds' returns, the authors conduct a two-dimensional holdings-based analysis using factor-specific decomposition of funds' excess returns into their ability to select and allocate securities better than their benchmarks. Subsequently, the authors conduct an analysis of the covariance (ANCOVA) due to these factors in explaining funds' excess returns over time.

While managers' ability to choose better securities than the benchmarks (the selectivity effect) appears modest, some funds (especially the winners) allocate securities in their portfolios better than their benchmarks (the allocation effect) based on their exposures to certain factors (e.g. the momentum factor for the winner funds). However, although funds consistently gain through their ability to predict the size and value factors well, they do not consistently possess the skills to predict the momentum factor.

Although the paper analyzes all the available diversified funds, the sample excludes several other categories, such as thematic and international funds. Further, the analysis is based on equity-oriented Indian funds. Broader studies of changes in factor exposures and the inclusion of more factors apart from those conventionally used may shed more light on the managers' ability to maneuver these factors.

The results show that mutual fund managers lack persistence in their performance, even though some of them could predict specific factors well. Since the activity in active mutual funds could not lead to superior performance over time, investors could be better off by selecting cheaper passive funds for their long-term investments.

The paper presents a novel approach to studying funds' performance by conducting a two-dimensional holdings-based analysis to capture the relative contributions of common asset-pricing factors in the cross-section as well as over time.

This study aims to examine the role of fund family size on the money flow of Saudi Arabian open-end equity mutual funds. The author also investigates whether the relationship between fund flow and past return varies based on the fund's family size.

The study analyses 256 equity funds that operated in Saudi Arabia from 2006 until 2017. Pooled and fixed-effect regression models are used to test the relationship between mutual fund flow and family size.

The results indicate that fund flow is higher for large size family funds. The results also show that the relationship between mutual fund flow and past performance is more pronounced for large size families, which supports the concept that investors pay extra attention to funds' return and size.

The author provides evidence of the significant effect of family size of mutual funds on future money flow, which helps fund managers to understand investors' motivations for allocating their cash.

This paper contributes to the literature by examining the impact of family size level on the interaction between fund flow and past performance. This study is believed to be the first to investigate the family size factor in Saudi Arabia using a comprehensive data set.

The paper examines how equity mutual funds manage their liquidity. Specifically, the authors investigate what strategies fund managers use to meet investor redemption demand, whether these strategies vary over time, whether different type of funds employ different liquidation practices in response to fund outflows, and whether liquidity strategies impact fund performance.

This study uses a sample of U.S. actively managed equity funds over the period 1990–2019. The authors use three different measures to capture funds' liquidity management practices. The authors examine the relationship between fund liquidity measures and net flow by estimating panel regressions over the entire sample period, on 2 sub-sample periods of different market conditions measured by the magnitude of implied market volatility (VIX), and on 2 sub-samples of funds with different liquidity profiles. The authors also examine the relationship between funds liquidity status and near-term performance through both a portfolio approach and regression analysis.

The authors find that on average, mutual funds reduce their cash position and the most liquid asset holdings to meet investor redemption demand. Furthermore, the authors find that fund managers choose different liquidity strategies under different market conditions. During highly volatile markets, mutual funds use cash and their most liquid assets to meet redemption demand while maintaining their portfolio liquidity. During low volatility markets, mutual funds rely heavily on cash but less on liquidity assets and tend to increase their portfolio illiquidity. Upon further examination of funds across portfolio liquidity profiles, the authors find that liquid funds increase portfolio liquidity when facing outflows, whereas illiquid funds maintain their portfolio liquidity position. The different liquidity strategies have significant impact on funds' near-term performance. Specifically, liquid funds underperform illiquid funds following the increase in their portfolio liquidity.

This paper contributes to the literature on liquidity management by asset managers by taking a holistic approach to examine funds liquidation practice at the portfolio holdings level. Considering the recent increase in market volatility, mutual fund liquidity management has drawn an increasing share of interest and attention from policy makers, investment professionals, and academia. This study covers both uncertain and stable market states during a long sample period and provides empirical evidence on the flow-induced liquidation decisions by equity mutual funds. In addition, this paper also contributes to the literature on mutual fund performance by providing evidence that funds' liquidity strategies significantly impact their near-term performance.

This paper examines whether target returns act as specific goals that impact risk-taking when individuals make investment decisions.

Using an experimental setting, the authors assign either a low or a high target return to participants and ask them to make independent investment decisions as the risk-free rate fluctuates around their target return and, for some of them, becomes negative.

Building on cumulative prospect theory, the authors find that the prevailing reference point of participants is the target return, regardless of the level of the risk-free rate. This result still holds even when the risk-free rate is negative, suggesting that (1) the target return drives risk-taking more than does a zero-threshold and (2) negative rates are limited as a tool to stimulate appetites for risk. In a follow-up study, the authors show that these conclusions remain valid when the target return is endogenously determined.

The authors' original approach, which pioneers the use of target returns in both the positive and negative interest rate contexts, provides insightful results about the “reach for yield” among regular people.

The purpose of this study is to evaluate the performance of mutual funds during the COVID-19 pandemic with environmental, social and governance (ESG) criteria. The main research question is whether mutual fund performance differs with respect to the level of the mutual fund’s ESG score.

The data set contains global fund data, and mutual fund performance is analyzed using two types of data envelopment analysis (DEA) models: the DEA portfolio index (DPEI) and the range direction measure (RDM) DEA. Propensity score matching and logistic regression are also applied.

The results reveal that: nonequity mutual funds present significantly higher performance compared to the performance of equity mutual funds; mutual funds with high ESG scores are associated with significantly higher performance compared to those with low to medium ESG scores; funds with high ESG scores experience higher performance irrespective of their type; and efficiency scores derived from the RDM DEA are significantly higher than those derived from the DPEI model.

Investors, fund managers and market participants can benefit from the findings of this study and improve their investment decision-making process, including more sustainable funds in their portfolios. Regulators and policymakers should further promote or even require the inclusion of more sustainable investments in the financial products offered by institutional investors. The main limitation of the study is related to data availability regarding the ESG score of mutual funds.

To the best of the authors’ knowledge, this is the first study that provides robust evidence in support of a positive association between ESG scores and mutual fund performance during the pandemic-induced crisis applying a DEA methodology.

The purpose of this paper is to explore and evaluate the main segments of existing empirical literature related to Sovereign Wealth Funds (SWFs) and provide a thorough investigation of their research questions, theoretical frameworks, data selections and research methodologies.

The literature on SWFs has been split into three main streams: qualitative studies with theoretical contributions aiming to conceptualize the phenomenon of SWFs; normative assessments of the optimal asset allocations of SWFs; and empirical works that aim to investigate different perspectives of SWFs. The paper attempts to review the state of existing literature relating to these areas by answering specific questions.

Despite their significant size and potential impact, the literature on SWFs seems to be still in its infancy. The paper collects insights from previous literature, addresses its difficulties and challenges.

The characteristics of the previous empirical literature and the challenges facing this line of research offer an insightful thought for the future research works in this topic.

The paper offers a thorough assessment of the existing empirical research on SWFs and shade some light on the techniques and procedures used.

The author proposes an update to the mean variance (MV) framework that replaces a constant risk aversion parameter using a dynamic risk aversion indicator. The contribution to the literature is made through making the static risk aversion parameter operational using an indicator of market sentiment. Results suggest that Sharpe ratios improve when the author replaces the traditional risk aversion parameter with a dynamic sentiment indicator from the behavioral finance literature when allocating between a risky portfolio and a risk-free asset. However, results are mixed when using the behavioral framework to allocate between two risky assets.

The author includes a dynamic risk aversion parameter in the mean variance framework and back test using the traditional and updated behavioral mean variance (BMV) framework to see which framework leads to better performance.

The author finds that the behavioral framework provides superior performance when allocating between a risky and risk-free asset; however, it under performs when allocating between risky assets.

The research is based on back testing; therefore, it cannot be concluded that this strategy will perform well in real-time circumstances.

Portfolio managers may use this strategy to optimize the allocation between a risky portfolio and a risk-free asset.

An improved allocation between risk-free and risky assets that could lead to less leverage in the market.

The study is the first to use such a sentiment indicator in the traditional MV framework and show the math.