Fund Styles Research Articles

Active factor investing: Hedge funds versus the rest of us

AbstractWe examine whether the success of hedge fund market timing strategies can be replicated. We develop a methodology for creating a portfolio of ETFs to capture risk factor exposures of market timing hedge funds identified using extant market timing measures. We find that the top market timing hedge funds outperform their ETF clone peers and the superior performance cannot be replicated. We show that the irreplicable market timing skills are more profound in certain hedge fund styles. Finally, we provide evidence that the success of market timing strategies is driven by non‐cloneable hedge funds that possess managerial skills.

Uncovering investment management performance using SPIVA data

AbstractWhich performs better, passive or active funds management, a question that both fund managers and academics fiercely debate. Why does fund size matter? These are a number of typical questions that puzzle practitioners and academics alike. To date, the data has been shown to be somewhat problematic. This paper exploits the SPIVA and passive fund datasets with several novel methods in order to build a foundation for unbiased fund performance analysis and comparison. For this, we address a number of questions including: passive versus active management, fund size, time horizon and fund style on performance. We find that in general, passive funds outperform active funds due to lower management costs, larger funds tend to perform better and funds with longer (3+ years) records of accomplishment tend to perform better. Short termism tends to have a significant detrimental effect on performance. We introduce Dynamic Generalized Method of Moments to show that competition has a significant effect on fund performance. Furthermore, this demonstrates that SPIVA data has a significant dynamic panel time series that was largely ignored by prior research. This integrated dataset and associated methods that we illustrate here, provide both academic researchers and industry analysts alike with an environment to investigate and potentially draw conclusions about the fund factors that affect performance without the inherent limitations of the original sources.

Style Drift among Value and Growth Funds

In the United States, retail investors rely heavily on the mutual fund industry to meet long-term objectives such as preparing for retirement. One segment of the mutual fund industry identifies as value stock funds, while another identifies as growth stock funds. Anecdotal evidence suggests that mutual funds in both categories deviate from their stated style for some of their purchases. We identify what proportion of each types’ funds are invested in crossover stocks that do not fit the funds’ stated investment styles and whether this deviation earns abnormal positive returns. We find that mutual fund managers frequently hold stocks outside their identified style; this effect is larger for value fund managers holding growth stocks. We find that, on average, growth fund managers earn higher returns than their index on these “crossover” stock holdings. We conduct a simple attribution analysis and provide weak evidence that managers of large-cap growth funds can time their investment in value stocks, and strong evidence that both large and small-cap growth fund managers can successfully select underpriced value stocks. The same is not true of value fund managers. <b>TOPICS:</b>Mutual fund performance, indexing exchange-traded, manager selection <b>Key Findings</b> • Managers of value funds, particularly small-cap value funds, frequently and consistently deviate from their stated fund objective. • Deviating from fund style does not generate excess returns for value fund managers by either timing or selection. • Growth fund managers generate excess returns by investing in value stocks, particularly from selecting underpriced value stocks, though they deviate from style less often.

A Network Approach to Analyzing Hedge Fund Connectivity

In this article, the authors investigate the hedge fund market as a network of interacting individual funds. The authors identify and analyze the most important hedge fund styles that could both affect the market and transmit systemwide shocks to other funds, individual asset classes, and beyond. The authors find that the most connected hedge fund database categories are global macro and equity long–short funds. A central result of the article is a classification of funds using clustering, in which seemingly different funds are shown to cluster based on their shared factor exposures. This finding demonstrates that investors should consider fund connectivity and their attendant importance scores rather than database classifications when measuring hedge fund risk across the business cycle. The authors also provide a forecasting framework that can be used to predict hedge fund network behavior and the impact of individual factors on the network. TOPICS:Analysis of individual factors/risk premia, factor-based models, style investing, performance measurement Key Findings • The hedge fund universe can be represented as a graph that depicts the relational structure and interaction among individual hedge funds. • Clustering algorithms are used to classify and evaluate hedge fund styles as well as individual hedge funds. Hedge fund networks are found to have different dynamics across different stages of the business cycle. • Various hedge funds are found to be connected based on factor exposure in ways that stand in contrast to their database categorizations; applying LASSO helps to identify factor exposure in the clusters and to predict network behavior.

Covid-19 and asset management in EU: a preliminary assessment of performance and investment styles

The likelihood of pandemics has been perceived very low till very recently. Therefore, the exponential spread of Covid-19 was a major surprise that has resulted in a global rout of financial markets. In this study, we document some preliminary evidence of performance and investment styles of European funds during the evolution of Covid-19. We assess the period between January and May 2020 and categorized the spread of contagion in three phases. The results document that Social Entrepreneurship funds demonstrated positive returns across the three phases, while most of the other subcategories plunged into negative zone. Our findings on style analysis suggest that fund managers have been drifting from high risk option to low risk in terms of size and investment strategy. Similarly, there has been a switch from high risk to relatively less sensitive sectors and a transition of investment from countries with higher to those with lower number of cases.

The macroeconomic drivers in hedge fund beta management

We investigate how macroeconomic indicators alter the dynamic risk exposure of different hedge fund style strategies. We implement a multifactor model to estimate the unobservable time-varying risk exposure conditional on macroeconomic information and a VAR to measure the impact of macroeconomic predictors on different time horizons. Using monthly returns on a cross-section of 10 different style indices from February 1997 to August 2019, we find that, on average, macroeconomic indicators explain approximately 30%, 55%, and 75% of the variability of betas at 1-, 6-, and 36-month horizons, respectively. Although macroeconomic predictors play a critical role at every horizon, at 1 month, the dominating effect comes from idiosyncratic shocks, which indicates that in the short run, hedge fund managers rely mostly on their own reallocation signals. Moreover, consistent with the fundamental drivers of the smart beta factors, we find that the interest rate level and GDP growth similarly impact hedge fund exposures across styles.

Extremal Connectedness and Systemic Risk of Hedge Funds

We propose a dynamic measure of extremal connectedness across investment styles of hedge funds. Using multivariate extreme value regression techniques, we estimate this measure conditional on factors reflecting the economic uncertainty and the state of the financial markets, and derive several systemic risk indicators. Empirically, we study the dynamics of tail dependencies between investment strategies in the HFR database. We show that during crisis periods, some pairs of strategies display an increase in their extremal connectedness. Our results highlight that a proactive regulatory framework should account for the dynamic nature of the tail dependence and its link with financial stress.

Decomposing Tracking Error to Identify Mutual Fund Strategies

Decomposing mutual funds’ tracking error into its idiosyncratic and systematic components is informative about funds’ intention to implement picking and timing strategies. Accordingly, we define the fraction of a fund’s active return variance that is idiosyncratic as the fund’s degree of picking (dop), and document that pickers (high-dop funds) exhibit higher performance than timers (low-dop funds). We rule out that this outperformance is driven by different decreasing returns to scale across strategies, different levels of fund activeness, idiosyncratic risk, and fund styles. We also provide new evidence on the dynamics of fund strategies over business cycles and the fund life-cycle

How Do Bond Investors Measure Performance? Evidence from Mutual Fund Flows

Which factor model do investors in corporate bonds use? We examine this question by tracking investors’ decisions to invest in actively managed corporate bond mutual funds with a revealed preference approach. Our main result is that all bond factor models are dominated by the simple Sharpe ratio. For all major corporate bond mutual fund styles, the Sharpe ratio explains fund flows better than alphas from bond factor models. Since the Sharpe ratio can be easily manipulated in bond markets, our findings have potentially severe implications for fund mangers as well as active traders and buy-and-hold corporate bond mutual fund investors.

The Lost Decade for Hedge Funds: Three Threats

The last decade has challenged the paradigm of the hedge fund industry as a unique performer. We identify three main factors that have affected the operation of hedge funds: competition from mutual funds, the market environment, and tighter regulation. Recent trends in the financial industry have moved asset managers closer to hedge funds by introducing similar underlying strategies, such as liquid alternative funds, to directly compete with hedge funds. We find that such strategies can achieve performance similar to that of hedge funds, thus introducing more competition for hedge funds. Moreover, we find that several hedge fund styles that have traditionally worked well in crisis times—even in the last decade—are also strategies that can be replicated by liquid alternatives. Together with tighter regulation and a strong market environment, these developments continue to put pressure on the hedge fund industry. Our empirical findings add to the existing debate on the performance of hedge funds and the direct competition from liquid alternatives.

The devil in the style: Mutual fund style drift, performance and common risk factors

Style drift contributes to mutual fund performance at the cost of additional risks associated with styles. We find that, although style drift enhances the net return of Chinese mutual funds by 3.03% per month relative to peer funds, the profit is at risk. That is, applying the Carhart four-factor model with style drift leaves fund investors no significant premium (i.e. α=0.02% per month, t=0.20). Both active and passive style drifts can leverage up fund performance, but risk-adjusted returns show that low passive style drift funds benefit more from subsequent active style drifts than high passive style drift funds. Cross-sectional results are consistent with portfolio results, and further show that the predictive power of style drift is neither driven by insider information nor by idiosyncratic volatility.

Trends everywhere? The case of hedge fund styles

This paper investigates empirically whether time-series momentum returns can explain the performance of hedge funds in the cross section. Relying on the trend-following literature, a volatility-adjusted time-series momentum signal is applied on a daily basis across a large set of futures, covering the major asset classes. We build a hierarchical set of trend factors: the full version TREND can be split in summable factors across two dimensions: the horizon of the signals and the traded asset class. We show that Managed Futures, Global Macro and Fund of Hedge Funds strategies can be partly explained by a TREND exposure. Moreover, a TREND exposure is a significant determinant of hedge funds returns at the fund level, for Managed Futures and Global Macro but also, and more surprisingly, for the other styles.

Benchmarking Mutual Fund Returns

We question the appropriateness of using time-invariant indices as benchmarks and propose a regime-switching methodology to identify the time-varying de facto benchmarks from a pool of the market-based indices, with or without a risk-free asset. We highlight the benchmark mismatch phenomenon and the role of risk-free rate as an additional benchmark. Our de facto benchmark better capture fund styles than other benchmark choices, substantially improves the identification of significant fund alphas, and provides better out-of-sample forecasts. Importantly, we uncover several new findings in terms of fund performance evaluation using our identified benchmarks in this paper.

The Relationship between Hedge Fund Performance and Stock Market Sentiment

We examine the dynamic effect of aggregate stock market sentiment on the performance of various hedge fund styles. We find that hedge funds typically perform better during periods of optimistic sentiment and that for different hedge fund styles there is a differential response of hedge fund returns to positive and negative sentiment shocks. We also find that changes in aggregate investor sentiment have a larger effect on hedge fund performance during periods of high conditional volatility. Our results suggest there is a strong asymmetry in the relationship between hedge fund performance and investor sentiment.

Trends Everywhere? The Case of Hedge Fund Styles

This paper investigates empirically whether time-series momentum returns can explain the performance of hedge funds in the cross-section. Following the trend following literature, a volatility-adjusted time-series momentum signal is applied on a daily basis across a large set of futures, covering the major asset classes. We build a hierarchical set of trend factors: the full version TREND can be split in summable factors across two dimensions, the horizon of the signals and the traded asset class. We show that Managed Futures, Global Macro and Fund of Hedge Funds strategies can be partly explained by a TREND exposure, whereas Equity Market Neutral and Quantitative Directional are only exposed to long term trend factors. Moreover, a TREND expo- sure is a significant determinant of hedge funds returns at the aggregate level, as well as at the fund level. Finally, funds with high TREND beta outperform by 41 basis points of alpha the funds with low Trend beta. These results prove useful when managing the risk of a portfolio of hedge funds strategies, since assessment of the Trend exposure is easier. Another contribution of this study is related to the understanding of the CTA space, composed of pure trend funds as well as funds that do not exhibit any TREND exposure.

Correlated Flows, Portfolio Overlap and Mutual Fund Liquidity Management

We contend that confluence of portfolio overlap and correlated liquidity shocks within mutual fund styles can exacerbate fund exposure to flow-driven liquidity risk. We show that funds mitigate such liquidity risk by reducing portfolio overlap with peer funds when their flows become more correlated (Overlap Management). Overlap Management is a highly persistent fund attribute and less costly compared to other liquidity management tools. It contributes to fund performance by avoiding correlated trades and thus alleviates the negative feedback effect of outflows on future performance. The effect of Overlap Management on performance is economically significant and is independent of that of portfolio uniqueness or alternative liquidity management tools. Using several different approaches, we show that our findings cannot be explained by potential endogeneity between flow correlation and portfolio overlap.

Gamma Trading Skills in Hedge Funds

This paper explores the gamma trading, timing and managerial skills of individual hedge funds across categories. We replicate the non-linear payoffs of hedge funds with traded options, with the option features being endogenously defined in our replication model. On top of providing a flexible tool to create individual benchmarks for the payoff curvature of hedge fund, the model helps assigning hedge fund styles into three categories: directional with market timing skills, non-directional and market timers. Overall, our empirical results show that, on 30% of replicated funds in our sample (10,958 funds), there is no evidence of the presence of selection skills once a fund performance is adjusted with respect to the option-based benchmark and the traditional option-based factors of Agarwal and Naik (2004). This research has an incremental potential to stimulate additional research in the field of hedge funds performance replication through passive strategies.

Correlated Flows, Portfolio Similarity and Mutual Fund Liquidity Management

We contend that the confluence of portfolio similarity and correlated liquidity shocks within mutual fund styles can exacerbate fund exposure to liquidity risk. We find that mutual funds mitigate such liquidity risk exposure by systematically reducing portfolio overlap with peer funds when their flows become more correlated (Overlap Management). Overlap Management is highly persistent and is more pronounced among funds with liquidity concerns. Funds engaging in overlap management tend to avoid stocks that are more vulnerable to flow-driven trading when facing correlated flows. As a result, this type of liquidity management independently contributes to higher fund performance after controlling for the direct relation between portfolio overlap and fund performance. The benefit of Overlap Management is therefore distinct from that of strategy uniqueness as studied in prior literature. An examination of the channel through which Overlap Management contributes to fund performance suggests that overlap management effectively alleviates the negative feedback effect of outflows on future performance.

Stock composition of mutual funds and fund style: a time series decomposition approach towards testing for consistency

In this paper, we present a generic approach for checking the consistency between the proclaimed style of a mutual fund and the actual fund composition. We use a method of time series decomposition of stock prices to ascertain whether their inclusion in a particular style of fund is justified. It has been our contention that some share prices have a strong trend component in their time series, some show seasonality, while some share prices exhibit strong random component. We have chosen a sample of eleven equity- based mutual funds of varying styles, from Indian financial market and analyzed whether the style of the fund matches with the stock composition of the fund. We feel that the retail investors, who buy into certain funds on the basic trust that fund managers have the requisite expertise, should know whether the portfolio matches what they promise. A detailed analysis of the results show that, while in majority of cases the actual allocation of funds is consistent with the corresponding fund style, there have been some notable deviations too.

Stock composition of mutual funds and fund style: a time series decomposition approach towards testing for consistency

In this paper, we present a generic approach for checking the consistency between the proclaimed style of a mutual fund and the actual fund composition. We use a method of time series decomposition of stock prices to ascertain whether their inclusion in a particular style of fund is justified. It has been our contention that some share prices have a strong trend component in their time series, some show seasonality, while some share prices exhibit strong random component. We have chosen a sample of 11 equity-based mutual funds of varying styles, from Indian financial market and analysed whether the style of the fund matches with the stock composition of the fund. We feel that the retail investors, who buy into certain funds on the basic trust that fund managers have the requisite expertise, should know whether the portfolio matches what they promise. A detailed analysis of the results show that, while in majority of cases the actual allocation of funds is consistent with the corresponding fund style, there have been some notable deviations too.