Individual Fund Level Research Articles

The market timing ability of bond mutual funds

We apply the nonparametric methodology of Jiang (Journal of Empirical Finance 10:399–425, 2003) to examine whether bond mutual funds can time the bond market by adjusting their portfolios' market exposure based on anticipated market movement. This approach offers several advantages over the widely used regression-based tests such as Treynor and Mazuy (Harvard Business Review 44(4):131–136, 1966) and Henriksson and Merton (The Journal of Business 54(4):513–533, 1981). In a comprehensive study covering the USA, UK, and China, we find some evidence of positive market timing of bond funds at the individual fund level. On average, bond funds show neutral to slightly negative market timing abilities. After controlling for public information, we find that a smaller number of bond funds successfully time the market based on private timing signals. In terms of categories, we find strong evidence of positive market timing for Government bond funds as a group, consistent with the findings of Huang and Wang (Management Science 60:2091–2109, 2014).

Performance Evaluation, Factor Models, and Portfolio Strategies: Evidence from Chinese Mutual Funds

Actively managed stock mutual funds in the Chinese A-share market offer good investment opportunities. In aggregate, they outperform stock market indexes. Individual funds show a wide cross-sectional dispersion in performance, with the best funds offering economically large risk-adjusted returns that persist over time. Performance persistence observed at the individual fund level is robust to various factor-model specifications. A portfolio of the top-performing funds offers an improved investment opportunity compared with a portfolio of all funds. Hedging out market risk in this portfolio increases the Sharpe ratio, lowers volatility, and reduces tail risk. These results suggest that, in the young and still-developing Chinese A-share market, mutual funds can add value for investors.

An Explainable Machine Learning Framework for Cross-Sectional Forecast-Based Fund Selection

Since the 1990’s the global hedge fund industry has seen a rapid expansion. Its growing presence in financial markets ranging from equity, fixed income and derivative markets has inextricably linked it to the broader financial industry, with larger funds effectively acting as a market makers and liquidity providers in many markets. For both academics and practitioners, the space has established itself as a key area of research given the vast heterogeneity of investment styles and the high mutability of the industry. In this thesis, a cross sectional fund selection approach which builds upon the paradigm of explainable machine learning is proposed in a fully systematic setting. Four fund performance metrics, Sharpe and Sortino ratios, fund alpha and its t-statistic, are used as the ranking and selection metric, at investable inter-regime forecast horizons of 24 and 36 months. We find that quintile portfolios constructed from machine learning and deep learning approaches outperform linear models and benchmark portfolios constructed exclusively based on historical realizations of the forecast metric, in terms of absolute and risk-adjusted performance. We find that the extreme quintile portfolios realize a high (resp. low) value of the performance metric employed as forecast metric in model training. We find forecasting on the Sortino ratio to yield the most consistent overall performance, and find particular benefit in employing machine learning methods for bottom quintile fund selection (consistent identification of under-performers) in the case of forecasting on fund alpha. Explainability, achieved via the use of SHAP values further serves the purpose of outlining feature importance both at the aggregate and the individual fund level. At the aggregate level, all methods agree on a subset of statistically consistent predictors across investment style and forecast horizon; with discernible relevance of predictors constructed from interactions of fund returns with nowcasters, and management quality indicators. This consistency enables a discretionary fund selection process to be complemented by model forecasts and SHAP value-based feature importance delineations. There is thus evidence that proposed approach may be valuable for a discretionary fund manager looking to incorporate machine learning based signals into their selection process.

Mutual fund managers’ market timing abilities: Indian evidence

In this paper, apart from traditional return timing, we examine the timing abilities of Indian mutual fund managers from two new perspectives, i.e., liquidity timing and volatility timing. Using a sample of 183 equity-oriented funds from April 2000 to March 2018, we find evidence of liquidity timing ability among fund managers at both portfolio and individual level. Our result is robust to different liquidity measure, sub-period test, excluding the crisis period and at the individual fund level analysis. No strong evidence is observed regarding market return timing and volatility timing of fund managers.

Mutual Fund Performance: Evidence from India

We empirically examine fund managers’ stock selection and market timing ability using various risk-adjusted measures such as CAPM and multifactor models of Fama-French (1993) and Carhart (1997) to gauge mutual fund performance in India. The sample consists of 183 actively managed equity-oriented funds and covers the period from April 2000 to March 2018. The study, on the whole, documents some evidence of positive and significant stock selection ability but fails to yield any notable evidence of market timing ability of fund managers. Our results are robust according to various risk-adjusted performance evaluation techniques, sub-period analysis, excluding the crisis period and at the individual fund level. The findings of our study are in line with the previous studies that report limited selectivity skill and market timing ability among fund managers. The main implication of the study is that active portfolio management may not be very rewarding in comparison to a passive investment strategy.

Sentimental Mutual Fund Flows

We show that many stylized empirical patterns for mutual fund flows are driven by investor sentiment. Specifically, when sentiment is high, investors exhibit a stronger tendency of chasing past fund performance; fund flows are less sensitive to fund expenses; and investors are attracted more to funds with sheer visibility. Moreover, the well-documented positive relation between fund flows and future fund performance is significant only during high sentiment periods and is mainly driven by expected component of fund flows. Finally, we show that mutual fund investors exhibit a significantly negative timing ability at the individual fund level when sentiment is high.

Sentimental mutual fund flows

AbstractWe show that many stylized empirical patterns for mutual fund flows are driven by investor sentiment. Specifically, when sentiment is high, investors exhibit a stronger tendency of chasing past fund performance; fund flows are less sensitive to fund expenses; and investors are attracted more to funds with sheer visibility. Moreover, the well‐documented positive relation between fund flows and future fund performance is significant only during high sentiment periods and is mainly driven by expected component of fund flows. Finally, we show that mutual fund investors exhibit a significantly negative timing ability at the individual fund level when sentiment is high.

Detecting Repeatable Performance

Past fund performance does a poor job of predicting future outcomes. The reason is noise. Using a random effects framework, we reduce the noise by pooling information from the cross-sectional alpha distribution to make density forecasts for each individual fund’s alpha. In simulations, we show that our method generates parameter estimates that outperform alternative methods, both at the population and at the individual fund level. An out-of-sample forecasting exercise also shows that our method generates improved alpha forecasts. Received November 23, 2016; editorial decision November 1, 2017 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web Site next to the link to the final published paper online.

Assessment of Chinese green funds: Performance and industry allocation

Abstract: Facing the challenge of resource and environmental constraints, China has been adopted green development as the national strategy for pursuing sustainable long-term target at the stage of rapid industrialization, which naturally promotes green finance. The considerable growth in green investment within China has motivated intense debate on the implications of incorporating financial performance and green criteria into the portfolio selection process. However, few literature studies newly-emerged green funds' performance. This article tackles the financial performance of China green funds as well as analyzing the allocation difference across industries of green funds. We find that the performance of China green funds is sensitive to the market factor, small cap and value stocks for the full period. In contrast, after entering the new normal period, China green funds have not been sensitive to the market index, only to value stocks. Green funds play an important role in supporting the main direction of Chinese industrial restructuring and upgrading for the purpose of gaining new competitiveness in the markets. The study finds also that green funds exhibit different industry betas consistent with different portfolio positions. At the individual fund level, green funds have a high weight on manufacturing industries, which shows that China green funds have a characteristic of industry concentration instead of diversification; and steadily confirms that green funds' industry preference has a positive correlation with innovation indicators, indicating that innovation and R&D activities of enterprises are driving the new economy. Especially, the percentage of new intelligent manufacturing has an increasing trend in the new normal. Our findings suggest that green funds can work as a financial power to promote green technological innovation for industries, and to benefit small and medium-sized enterprises with new smart and green technologies.

Mutual Fund Voting and Pension Ties : Evidence from Proxy Voting

Because mutual funds are the largest equity holders and because the retirement assets that are managed by mutual funds have been growing, mutual fund managers may have more incentives to support management in order to attract and retain pension business. I explore whether pension business ties have an impact on voting behaviors of mutual funds by examining the link pension business ties between mutual funds and the firms to actual mutual fund voting outcomes. At the fund family level, I find a positive relation between pension ties and mutual funds’ voting support for management. This relation becomes stronger when there is a voting divergence among funds within the same families. At the individual fund level, I find that individual funds are more likely to vote with management if they are included as one of the investment options of the pension plan of their portfolio firms. This suggests that the SEC should at least consider the recent petition from the AFL-CIO proposing that the SEC require mutual funds to disclose business ties with the firms in which they invest.

Hedge fund allocation: Evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques

We propose a model for constructing Asian funds of hedge funds. We compare the accuracy of forecasts of hedge fund returns using an ordinary least squares (OLS) regression model, a nonparametric regression model, and a nonlinear nonparametric model. We backtest to assess these forecasts using three different portfolio construction processes: an “optimized” portfolio, an equally-weighted portfolio, and the Kelly criterion-based portfolio. We find that the Kelly criterion is a reasonable method for constructing a fund of hedge funds, producing better results than a basic optimization or an equally-weighted portfolio construction method. Our backtests also indicate that the nonparametric forecasts and the OLS forecasts produce similar performance at the hedge fund index level. At the individual fund level, our analysis indicates that the OLS forecasts produce higher directional accuracy than the nonparametric methods but the nonparametric methods produce more accurate forecasts than OLS. In backtests, the highest information ratio to predict hedge fund returns is obtained from a combination of the OLS regression with the Fung–Hsieh eight-factor variables as predictors using the Kelly criterion portfolio construction method. Similarly, the highest information ratio using forecasts generated from a combination of the nonparametric regression using the Fung–Hsieh eight-factor model variables is achieved using the Kelly criterion portfolio construction method. Simulations using risk-adjusted total returns indicate that the nonparametric regression model generates superior information ratios than the analogous backtest results using the OLS. However, the benefits of diversification plateau with portfolios of more than 20 hedge funds. These results generally hold with portfolio implementation lags up to 12months.

Real Estate Fund Flows and the Flow-Performance Relationship

Convexity in the flow-performance relationship of traditional asset class mutual funds is widely documented, however it cannot be assumed to hold for alternative asset classes. This paper addresses this shortcoming in the literature by examining the flow-performance relationship for real estate funds, specifically open-end, direct-property funds. This investment vehicle is designed to provide the risk-return benefits of private market real estate and is available to retail investors in many countries across the globe. An understanding of fund flow dynamics associated with this investment vehicle is of particular interest due to the liquidity risk associated with holding an inherently illiquid asset in an open-end structure. Our analysis draws on the theoretical foundations provided in the literature on mutual fund flows, performance chasing, liquidity risk, participation costs and dynamics across market cycles. We focus on German real estate funds from 1990 to 2010 as this is the largest market globally and there is a high level of confidence in the data. The results show that real estate fund investors chase past performance at the aggregate level and the relationship between flows and relative performance is asymmetric (i.e., convex) at the individual fund level. Fund-level liquidity risk tends to weaken convexity, while sensitivity increases with higher participation costs. We find the flow-performance relationship varies across time, though our interpretation is asset and investment vehicle specific. The implications are applicable to investors and fund managers of open-end, direct-property funds and, more broadly, other alternative asset funds where the underlying asset may not be liquid.

Rethinking Performance Evaluation

The standard equation-by-equation OLS that is routinely used in performance evaluation ignores information in the alpha population and leads to severely biased estimates for the alpha population. Recent research has proposed a new approach -- essentially rethinking performance evaluation. Our contribution is a framework that treats fund alphas as random effects. This allows us to make inference on the alpha population while controlling for various sources of estimation risk. At the individual fund level, our method pools information from the entire alpha distribution to make density forecasts for each fund's alpha. In simulations, we show that our method generates parameter estimates that universally dominate the OLS estimates, both at the population and at the individual fund level. We also show the advantage of our approach compared to recently proposed alternative methods. An out-of-sample forecasting exercise also shows that our method generates superior alpha forecasts.

Window dressing in mutual fund portfolios: fact or fiction?

PurposeCongress and the Securities and Exchange Commission (SEC) have mandated mutual fund disclosure regimes to help investors make better investment decisions to strike an optimal balance between the investors' interest in more timely and accurate portfolio holdings disclosure and the cost associated with making and disclosing the holdings information available to investors. Many academics and practitioners point out that, despite all the regulations on portfolio disclosure, fund managers can still engage in practices that go against the spirit of the rules without violating the letter of the law. The purpose of this paper is to address the empirical question of whether the practice exists, using holdings data for more than 3,000 equity mutual funds during the time period from 1995 to 2004.Design/methodology/approachIn this paper, the authors examine window dressing by mutual fund portfolio managers, using holdings data covering more than 3,000 equity mutual funds from 1995 to 2004. The authors first investigate whether the fund holdings are materially different from universe holdings across performance quintiles based on holdings in the month of disclosure and in the following month. The second part of the analysis examines funds' patterns of buying and selling. Finally, the measure of “Buying Intensity” and “Selling Intensity” is examined, with a specific focus on the holdings data for the fourth quarter.FindingsAn examination of fund holdings finds no statistically significant evidence of systematic window dressing, either at the aggregate level or within subsamples of funds based on size or past performance. Rather, it was found that fund managers tend to chase momentum. A combination of investor sophistication and market oversight may serve to be effective in dissuading fund managers from engaging in the practice.Originality/valueThe authors' data are at the individual fund level, based on equity mutual funds holdings data provided to Morningstar on a quarterly or monthly basis (according to Elton et al., the Morningstar database provides timely and accurate mutual fund holdings information). These data allow us to infer better the investment manager intent vis‐à‐vis using 13F data, which is aggregate data across various fund families and separate accounts, or aggregate pension fund equity holdings data that includes aggregate holdings of multiple portfolio managers. In addition, the authors comment on the significance of the regulatory checks and balances that are designed to restrict fund managers' ability to window‐dress their portfolios. In summary, the combination of quantitative evidence from empirical tests and an examination of the legal framework under which mutual fund portfolio managers operate, lead to the conclusion that window dressing is not prevalent in the industry.

Do mutual fund managers time market liquidity?

This paper examines mutual fund managers' ability to time market-wide liquidity. Using the CRSP mutual fund database, we find strong evidence that over the 1974–2009 period, mutual fund managers demonstrate the ability to time market liquidity at both the portfolio level and the individual fund level. Liquidity timing predicts future fund performance and the difference in the risk-adjusted returns between top and bottom liquidity-timing funds is approximately 2% per year. Funds exhibiting liquidity-timing ability tend to have longer histories, higher expense ratios, and higher turnover rates.

Capacity Effects and Winner Fund Performance: The Relevance and Interactions of Fund and Family Characteristics

In this paper, we investigate the profitability of characteristics-based mutual fund investment strategies. We analyze the existence of capacity effects on the individual fund level and its interactions with fund flows, family TNA, and family size. Recent studies suggest the existence of diseconomies of scale on the individual fund level while assets under management on the family level may not have detrimental effects for fund performance per se. By analyzing a sample of US equity mutual funds between 1992 and 2007 we provide evidence for performance persistence among recent winner funds. After controlling for fund size, we are able to document positive yet insignificant four-factor alphas for the sub-group of small winner funds. When net inflows into these funds are additionally considered, an investment strategy buying small winner funds with below median inflows yields significantly positive four-factor alphas of 2.76 percent per year. Moreover, if the fund family manages few other funds in the same segment, small winner funds weakly outperform the four-factor benchmark by 2.40 percent per year. In contrast, family TNA only provides sufficient information for selecting persistent outperformers among the subset of extremely small winner funds. Overall, the results are rather consistent with the divergence of scale effects on the fund and family level, respectively, and suggest the presence of organizational complexity costs and agency conflicts in large fund families.

A Fund Flow Study of Investor Migration between Islamic and Conventional Managed Funds in Malaysia

The Malaysian managed fund market provides investors with greater choice between conventional and Islamic equity funds. Widening choice also pose challenges to investors in relation to selection of fund type, manager and portfolio optimization. While aggregate market information reveals the extent of investment in funds, not much is known on investors’ migration between fund types in this dual market. Using net fund flow analyses, this paper addresses this paucity through a comparative study of the movement of investable funds between fund types and managers. Net fund flow studies at individual fund and fund manager level provides a method of overcoming analytical deficiency designed to determine trends in investor’s choice. A sample of large and small Islamic and conventional funds and managers in a new Malaysian economic period is used for the purpose. The methodology compares net fund flows (subscriptions less redemptions) between different classes of funds, expressed through net increase/reduction in cash flow using yearly time intervals. This method follows similar methods in other studies since fund managers are generally loath to provide quantitative data on movements in fund membership for reasons associated with proprietary and management performance. Using reported financial statements and market data, fund flows are determined to evaluate the effect of fund type and size based on market concentration. The analysis is used to determine the extent to which investors’ exercise preference between Islamic and conventional funds. For the selected period, lagged fund flows indicate large net outflows from conventional funds compared to Islamic funds, while net outflows from Islamic funds are insignificant. The mean inflows to Islamic funds are weak for one year but significant for the two years during which negative flows were recorded against conventional funds. Market concentration ratios indicate that three large Malaysian fund managers collectively manage 57% of total funds invested in the retail equity fund market. They also manage 71% of all Islamic equity funds. The concentration ratios also show that two-thirds (68%) of all funds are controlled by five large fund managers. The remaining twenty small market managers account for a mere 13% of the market.

Capacity Effects and Winner Fund Performance: The Relevance and Interactions of Fund and Family Characteristics

In this paper, we investigate the profitability of characteristics-based mutual fund investment strategies. We analyze the existence of capacity effects on the individual fund level and its interactions with fund flows, family TNA, and family size. Recent studies suggest the existence of diseconomies of scale on the individual fund level while assets under management on the family level may not have detrimental effects for fund performance per se. By analyzing a sample of US equity mutual funds between 1992 and 2007 we provide evidence for performance persistence among recent winner funds. After controlling for fund size, we are able to document positive yet insignificant four-factor alphas for the sub-group of small winner funds. When net inflows into these funds are additionally considered, an investment strategy buying small winner funds with below median inflows yields significantly positive four-factor alphas of 2.76 percent per year. Moreover, if the fund family manages few other funds in the same segment, small winner funds weakly outperform the four-factor benchmark by 2.40 percent per year. In contrast, family TNA only provides sufficient information for selecting persistent outperformers among the subset of extremely small winner funds. Overall, the results are rather consistent with the divergence of scale effects on the fund and family level, respectively, and suggest the presence of organizational complexity costs and agency conflicts in large fund families.

A Dynamic Style Analysis Model for Hedge Funds

In this paper, we develop a dynamic style analysis model designed to identify hedge fund risk structures. Through the estimation of implied risk exposures, it reconciles the concepts of dynamics and non-linearity. We show that our model, coupled with specific estimation and selection methods, deliver significant results in identifying complex and non-linear risk exposures, such as straddle-like exposures implied by trend following strategies. Applied at the individual fund level, our dynamic model exhibit superior explanatory power in comparison to Asset-Based Style Factor models proposed in previous literature. The dynamic risk structures estimated are in line with the investment strategies that are specific to the various hedge fund styles.

Do Mutual Fund Managers Time Market Liquidity?

Extant research has focused on mutual fund managers’ ability to time market returns or volatility. In this paper, we offer a new perspective on the traditional timing issue by examining fund managers’ ability to time market wide liquidity. Using the CRSP mutual fund database, we find strong evidence that over the 1974 to 2009 period, mutual fund managers demonstrate the ability to time market liquidity at both the portfolio level and the individual fund level. Liquidity timing predicts future fund performance and the difference in the risk-adjusted returns between top and bottom liquidity timing funds is approximately 2% per year. Funds exhibiting liquidity timing ability tend to have longer histories, higher expense ratios, and higher turnover rates.