Hedge Fund Portfolio Research Articles

The Brexit Unknown—Britain's Boom or Bust?

This case invites students to assess the impact that Brexit, the withdrawal of the United Kingdom from the European Union, might have on a New York–based hedge fund's portfolio and, specifically, its UK assets. The case is designed to prompt students to make market assumptions and investment hypotheses based on a combination of numerical data and qualitative information. It requires no numerical computations; instead, it asks the student to interpret both markets' short-term reactions to the Brexit vote and strategy shifts from UK and European business leaders in order to evaluate longer-term implications for the economies of the United Kingdom, Europe, and the world.

Improved Hedge Fund Portfolio Optimization Using 2-Step Covariance Matrix and Fund Transaction Costs

This study focuses on fund portfolio investments in the Chinese market. The application of classic portfolio optimization methods encounters several issues when applied to fund portfolios. For example, issues such as the non-normal distribution of returns on funds or fund portfolios, turnover rate limitations in fund investments, and liquidity constraints of fund assets, which can lead to transaction costs and opportunity costs, are prevalent challenges. The existence of these issues can compromise the effectiveness of classic portfolio optimization methods like Mean-Variance Optimization. This may result in a reduction of accuracy in determining the portfolios optimal weights, a deviation of actual trading results from the models optimal expectation, and may even render the optimal weights impractical in real-world scenarios. To address these challenges, this paper integrates the 2-Step covariance matrix method (2-Step method) and the measurement of fund transaction costs into the portfolio optimization process. The paper finds that the 2-Step method, compared to the baseline, can indeed improve the risk-return indicators of the potimal fund portfolio. The inclusion of the transaction cost can effectively control the turnover frequency of the portfolio. Even after accounting for these costs, the 2-Step method continues to exhibit a significant improvement effect compared to the baseline.

Design of Single Valued Neutrosophic Hypersoft Set VIKOR Method for Hedge Fund Return Prediction

The theory of neutrosophic hypersoft set (NHSS) is an appropriate extension of the neutrosophic soft set to precisely measure the uncertainty, anxiety, and deficiencies in decision-making and is a parameterized family that handles sub-attributes of the parameters. In contrast to recent studies, NHSS could accommodate more uncertainty, which is the essential procedures to describe fuzzy data in the decision-making method. Hedge funds are financial funds, finance institutions that increase funds from stockholders and accomplish them. Usually, they try to make certain predictions and work with the time sequence dataset. A hedge fund is heterogeneous in its investment strategies and invests in a different resource class with various return features. Furthermore, hedge fund strategy is idiosyncratic and proprietary to the hedge fund manager, and the correct skills of fund managers are not visible to the stockholders. These reasons, united, make hedge fund selection a complex task for the stockholders. Different techniques have been analyzed to select the portfolio of hedge funds for investment. Machine-learning (ML) models employed used for performing individual hedge fund selection within hedge fund style classifications and forecasting hedge fund returns. Therefore, this study designs a new Single Valued Neutrosophic Hypersoft Set VIKOR Model for Hedge Fund Return Prediction (SVNHSS-HFRP) technique. The presented SVNHSS-HFRP technique aims to forecast the hedge fund returns proficiently. In the SVNHSS-HFRP technique, two stages of operations are involved. At the initial stage, the SVNHSS-HFRP technique, the SVNHSS is used for forecasting the hedge funds. Next, in the second stage, the moth flame optimization (MFO) system is applied to optimally choose the parameter values of the SVNHSS model. The performance validation of the SVNHSS-HFRP model is verified on a benchmark dataset. The experimental values highlighted that the SVNHSS-HFRP technique reaches better performance than existing techniques

To Net or Not to Net, That Is the Question: A Regulatory Review for Calculating the ESG Impact of Hedge Fund Portfolios

Sustainable finance regulation has largely overlooked alternatives, particularly hedge funds, given the greater complexity of strategies and asset classes. However, regulators are now expanding their scope to recognize the role that hedge funds can play in sustainable finance. The role of short selling in sustainable finance, especially in a net zero context, has been increasingly discussed and debated among regulators, market participants, investor initiatives, investor trade organizations and ESG data providers. There is a concern that hedge funds may intentionally or unintentionally employ short selling to misrepresent their real-world impact which is distinct from exposure to financial risk. This article summarizes these arguments and traces the signals from UK, US, and European regulators. It contributes to the discourse by providing considerations to the channels of influence, specifically the efficacy of short selling among different asset classes to affect the cost of capital; the time-varying aspect of short selling; and the limitations that short sellers face when engaging corporates. Last, the EU—as the regulator with the most mature regulatory framework—appears to establish a compromise that balances safeguards against greenwashing with the mechanics of portfolio management and reporting.

Author Response: Effect of Levetiracetam Use Duration on Overall Survival of Isocitrate Dehydrogenase Wild-Type Glioblastoma in Adults: An Observational Study.

Average leverage is often used as a measure of risk. However, average leverage in a limited liability context should not be computed as a simple arithmetic average of the underlying constituents. In fact, using a simple arithmetic average can give misleading results. For example, the simple arithmetic average leverage may give results which run counter to the actual risk exposure for certain portfolios. This article introduces a new measure, Limited Liability Leverage (<i>L</i>³), which corrects the simple average for the limited liability framework. This new measure is discussed in the context of a portfolio of hedge funds and compared to a multi-strategy hedge fund structure, where trading units do not have limited liability. This perspective can also be extended to reflect the lower risk of a financial system with many small institutions relative to a system with large, “too-big-to-fail” institutions. <b>TOPICS:</b>Real assets/alternative investments/private equity, legal/regulatory/public policy, risk management, portfolio construction

Dynamic Hedge Fund Portfolio Construction: Exponentially Weighted Returns Approach

This article proposes an exponentially weighted returns approach for constructing portfolios of hedge funds. This approach, which gives more weight to more recent observations, allows for volatility and higher moment dynamics in portfolio construction. Using monthly hedge fund index returns from the Hedge Fund Research Database for January 1990 to December 2020, we find that the proposed approach significantly improves portfolio performance in terms of enhanced returns, reduced risk, and improved risk-adjusted returns compared to the benchmark equally weighted approach. However, the exponentially weighted method generally requires more frequent portfolio rebalancing to capture the return distribution dynamics, and the turnover varies across different portfolio optimization models. Therefore, investors should select an appropriate optimization strategy when implementing this approach for hedge fund portfolio construction. Moreover, we show that the results are robust to the choice of decay factor for the exponential weighting, target returns, and estimation window size, and across low-volatility, high-volatility, and most recent evaluation periods.

Chance Constrained Programs with Gaussian Mixture Models

In this paper, we discuss input modeling and solution techniques for several classes of chance constrained programs (CCPs). We propose to use Gaussian mixture models (GMM) to fit the data available and to model the randomness. We demonstrate the merits of using GMM. We consider several scenarios that arise from practical applications and analyze how the problem structures could embrace alternative optimization techniques. More specifically, for several scenarios, we study how to assess the gradient of the chance constraint and incorporate the results into gradient-based nonlinear optimization algorithms, and for a class of CCPs, we propose a spatial branch-and-bound (BB) procedure and solve the problems to global optimality. We also conduct numerical experiments to test the efficiency of our approach and propose an example of hedge fund portfolio to illustrate the practical application of the method.

Hedge fund portfolio selection with fund characteristics

This paper examines hedge fund portfolio selection approaches in isolation and in the context of an investor’s overall portfolio. Characteristics-based portfolios that minimize risk delivers superior out-of-sample performance. For instance, a minimum variance portfolio tilting toward small funds with high alpha and strategy distinctiveness index and low systematic risk delivers an annualized Sharpe ratio of 2.03 with a maximum drawdown of 5.20%. Investors realize diversification benefits by shifting a portion of their wealth from 60 to 40 equity-bond portfolio to characteristics-based hedge fund portfolio. Investors recognize the attractiveness of characteristics-based portfolios, but do not target flows enough to erode their superior performance.

Applications of Game- Theory in Active Strategic Portfolio Management- the Case of Hedge - Funds Adaptation for the Real Constraints in Romanian Capital Market

Abstract The application is focused on strategies for portfolio management in the case of hedge-funds for emerging markets taking into account the severe constraints for a real-world implementation. In the case of Romanian capital market, the design of a hedge-fund architecture should respond to the typical constraints for using alternative strategies. Beyond the liquidity problems there exits only a limited set of alternative instruments and strategies with impact on diversification, on the functionality and efficiency. The objective is to develop a better understanding of alternative actions and innovations for real adaptation of the architecture of a hedge-fund at these emerging market conditions, especially the lack of short and hedging instruments and the liquidity problems. Based on this new innovative framework that could capture the value of multiple rotating satellite sub-portfolio paradigm, as an active strategy, it is possible to build a different paradigm for active portfolio management in a dynamic manner. Based on an adequate dynamic of rotation of these sub-portfolios it results an optimal risk- return-liquidity profile for the whole hedge-fund portfolio, adaptable for different contexts.

Do Hedge Funds Still Manipulate Stock Prices?

We find no evidence that hedge funds manipulate stock prices from 2011 to 2019, while confirming strong stock price manipulation pattern previously documented between 2000 and 2010. Stocks held by hedge funds exhibit positive abnormal returns and then reversals at quarter end in the earlier period; however, there is no relation between hedge fund ownership and end-of-quarter stock returns in the later period. Hedge fund market discipline is related to proactive actions of regulators. End-of-quarter stock price manipulation decreases with the number of the Security and Exchange Commission litigation cases involving hedge funds in that quarter. Investor flows, on the contrary, react positively on last-trading-day of a quarter hedge fund portfolio returns, creating incentives for stock price manipulation by hedge funds.

CAN WINNERS KEEP WINNING? AN ANALYSIS OF PERFORMANCE PERSISTENCE OF MUTUAL FUNDS AND HEDGE FUNDS IN CHINA

This paper investigates the performance persistence of both mutual funds and hedge funds using China’s data covering the period from January 2010 to December 2019. We first use a nonparametric method to examine the performance persistence with net return and Sharpe ratio over monthly, quarterly, semiannual, and annual intervals. We first find evidence that performance persistence exists in some years for both mutual funds and hedge funds. Then we construct portfolios based on lagged one-year returns and estimate their performance. We find top-performing portfolios of hedge funds that can get significantly positive alpha while no portfolio of mutual funds can get positive alpha.

Replicas: Have Hedge Funds Re-Resurrected as Traditional Beta?

We find market risk factors explain up to 81% of North American and Asian hedge fund portfolios’ variance, while out-of-sample clones have up to 98% correlation with realized returns. So-called “market neutral” funds take on significant short-term market bets, where rolling beta clones display up to 74% correlation with realized returns. Overall, returns attributable to skill declined systematically, with clones outperforming hedge fund indices. In our novel event study, we demonstrate clones are resilient to drawdowns, outperforming the market during China's COVID-19 lockdown and the WHO's pandemic announcement; 10-day market-adjusted CARs ranged from 2.46% – 9.71% for these dates.

Hedge funds portfolio optimisation using a vine copula-GARCH-EVT-CVaR model

This paper investigates the conditional value-at-risk (CVaR) hedge funds portfolio optimisation approach using a univariate GARCH type model, extreme value theory (EVT) and the vine copula to determine the optimal allocation for hedge funds portfolio. First, we apply the generalised pareto distribution (GPD) to model the tails of the innovation of each hedge funds strategy return. Second, we capture the interdependence structure between hedge funds strategies and construct vine copula-GARCH-EVT model. Then, we combine it with Monte Carlo simulation and mean-CVaR model to optimise hedge funds portfolio, in order to estimate the risk more accurately. The empirical results of five Hedge funds indexes show that the C-vine copula can better characterise the interdependence structure between the different hedge funds strategies and the performance of C-vine copula-GARCH-EVT-CVaR model is better that of multivariate copulas-GARCH-EVT-CVaR models in portfolio optimisation.

Hedge funds portfolio optimisation using a vine copula-GARCH-EVT-CVaR model

This paper investigates the conditional value-at-risk (CVaR) hedge funds portfolio optimisation approach using a univariate GARCH type model, extreme value theory (EVT) and the vine copula to determine the optimal allocation for hedge funds portfolio. First, we apply the generalised pareto distribution (GPD) to model the tails of the innovation of each hedge funds strategy return. Second, we capture the interdependence structure between hedge funds strategies and construct vine copula-GARCH-EVT model. Then, we combine it with Monte Carlo simulation and mean-CVaR model to optimise hedge funds portfolio, in order to estimate the risk more accurately. The empirical results of five Hedge funds indexes show that the C-vine copula can better characterise the interdependence structure between the different hedge funds strategies and the performance of C-vine copula-GARCH-EVT-CVaR model is better that of multivariate copulas-GARCH-EVT-CVaR models in portfolio optimisation.

Alternative Asset Fees, Returns, and Volatility of State Pension Funds: A Case Study of the New Jersey Pension Fund

This case study provides new information about alternative asset fees to many institutional investors by tapping a relatively unknown data source: state pension fund annual reports. Examining the few state pension funds annual reports that track both fixed fees and carried interest fees of private equity funds and hedge funds, we find that average alternative asset fees were 2.48% of the relevant pension fund assets for the fiscal year ended June 30, 2017. In addition, as New Jersey provides the most detailed alternative asset data, this study discusses New Jersey pension fund’s private equity and hedge fund (a) returns, (b) fees, and (c) volatility, compared to verifiable and public benchmarks for the five years ended June 30, 2017. Both private equity and hedge fund portfolios underperformed the benchmarks, and the alternative asset industries’ claim of higher returns and lower risks than traditional assets is not supported in this study. To the degree that other state pension funds follow the same investment policies and controls as the state of New Jersey, this study concludes that state pension funds should reduce their holdings of alternative asset substantially. TOPICS:Wealth management, retirement, pension funds, private equity Key Findings • The New Jersey pension plan’s private equity fund and hedge fund portfolios (i) are reasonable proxies for both asset classes and (ii) are similar to those of other state pension funds. • PE five-year annualized returns (net of fees) were the same as the S&P 500. Hedge fund returns were significantly below the 60–40 index and equivalent to LIBOR+5%. • PE return volatility was similar to the S&P 500. HF volatility was greater than the 60-40 and LIBOR+5%. Average annual PE and HF fees were 3.29% and 3.08% respectively.

Practical Applications of The Myth of Hedge Fund Fee Diversification

Practical Applications Summary In The Myth of Hedge Fund Fee Diversification, published in the Fall 2019 issue of The Journal of Alternative Investment, Fei Meng, David Saunders (both at the University of Waterloo), and Luis Seco (at the University of Toronto) provide clear insights for hedge fund investors. Recent developments in the hedge fund industry have made more types of fee arrangements available. This study examines the optimality of alternative hedge fund fee structures from an investor’s perspective. Optimal fee structures correspond to the weights in a hedge fund portfolio that maximize its Sharpe ratio. The authors consider three types of hedge fund portfolios: one with a traditional fee structure, one with a first-loss fee structure, and one that is a blend of the other two (effectively a portfolio with a shared-loss fee structure). Results show that the optimal fee structure depends on a variety of factors—most notably, a hedge fund’s volatility. TOPICS:Real assets/alternative investments/private equity, manager selection

Are college education and job experience complements or substitutes? Evidence from hedge fund portfolio performance

We examine, for various educational characteristics of hedge fund managers, the performance profile of hedge fund portfolios along their managers’ professional experience path. We find that during the initial years following their graduation, hedge fund managers who majored in business or economics outperform other managers. However, in subsequent years, hedge fund managers who studied science or engineering improve their performance and eventually outperform all other managers. These results are consistent with the view that business education is a substitute for, while science education is a complement to, job experience in acquiring the skills that help hedge fund managers generate excess returns. Compared with cross section studies of the impact of educational variables on productivity, our performance measures are more likely to represent individual productivity, and our estimates are less likely to be biased because of a correlation between educational variables and time-invariant characteristics, such as ability.

Dedicated Short Bias Hedge Funds Diversification and Alpha during Financial Crises

During the recent financial crisis dedicated short bias (DSB) hedge funds exhibited extremely strong results while many other hedge fund strategies suffered badly. This study, prompted by this recent episode, investigates DSB hedge fund performance over an extended sample period, from January 1994 to December 2008. Performance and risk evaluation is carried out on an equally weighted DSB hedge fund portfolio using three different factor model specifications and both linear and nonlinear estimation techniques. We conclude that DSB hedge funds are a significant source of diversification for equity market investors and produce statistically significant levels of alpha. Our findings are robust to the specification of traditional and alternative risk factors, nonlinearity and the omission of the crisis periods, which are particularly favorable for the DSB strategy.

Practical Applications of A Best Practice Protocol for the Risk Measurement of a Portfolio of Hedge Funds

<h3>Practical Applications Summary</h3> In <b><i>A Best Practice Protocol for the Risk Measurement of a Portfolio of Hedge Funds</i></b>, from the Winter 2019 issue of <b>the <i>Journal of Alternative Investments</i></b>, authors <b>Shubeur Rahman</b> and <b>Ranjan Bhaduri</b> review the limitations of current risk measurement practices for funds of hedge funds. They describe and formalize an emerging best practice approach for risk measurement and enhancements for market and operational risk management. <b>TOPICS:</b>Real assets/alternative investments/private equity, portfolio construction, risk management

Guidance to a Goldman Sachs alumnus Hedge Fund with $400 Billion-$500 Billion AUM: Alpha Trading Strategies Analysis, Maximizing Alpha for Hedge Funds, and, High Frequency Econometrics for Analyzing Price Impact of Trades, Liquidity, and, Market Microstructure

The current report resulted from an engagement with a Goldman Sachs alumnus Hedge Fund with $400 Billion-$500 Billion AUM at the time of the engagement. Given the overriding roles of Liquidity and Volatility in the structural Market Microstructure shifts impacting Alpha of Hedge Fund Portfolios in the post-Crisis period, the report made specific recommendations for Hedge Funds strategy and execution. The specific recommendations were based upon: Alpha Trading Strategies Analysis for 400 Trading Strategies; ‘Post-Financial Crisis’ Strategies for Maximizing Alpha of Hedge Funds, and, High Frequency Econometrics for Analyzing Price Impact of Trades, Liquidity, and, Market Microstructure.