From expansion to recession: unraveling the performance of Chinese hedge funds through economic shifts

This work aims to contribute to the literature on investment funds in emerging markets to address the portfolio composition and performance of Brazilian hedge funds under the manager's perspective. This is because in emerging countries an efficient allocation of assets in the portfolios of the funds is subject to different risk characteristics of the active participants of these portfolios, in addition to the composition of the portfolios be a possible explanation for the differences in performance of the funds. Moreover, from the perspective of the manager, the choice of the assets that make up their portfolios and the performance of the funds it manages, may be subject to the influence of special features such as: experience, amount of funds under management, manager location and managers who have better performance than your peers. So the problem that prompted this research was: which variables related to the manager affect the portfolio composition and performance of hedge funds? Therefore, the Brazilian hedge funds registered with the CVM were analyzed, considering free survival bias samples for the period from September 2009 to January 2016. The first sample included 6,659 funds with 327,270 monthly observations and the second involved 5,309 funds, with an equal number of observations as contemplated indicators for the period. Hypothesis tests were conducted by econometric techniques in Stata® software. The results showed that the composition of the portfolio is influenced by characteristics of managers, as experience, amount of funds under management, manager and location managers who have better performance than their peers. These features of the manager, the experience and the amount of funds under management are also important to explain the performance achieved by hedge funds, beyond this be explained by the allocation of fixed and variable income assets. It should be noted that, when considering the confidence interval of the coefficients of these variables, the composition of the portfolios (fixed and variable income) is presented as the main factor that helps to explain a potential change of the performance of Brazilian hedge funds.

Journal of Derivatives AccountingVol. 01, No. 02, pp. 187-194 (2004) ARTICLESNo AccessTHE GLOBAL MACRO HEDGE FUND CEMETERYMASOUD ASGHARIAN, FERNANDO DIZ, GREG N. GREGORIOU, and FABRICE ROUAHMASOUD ASGHARIANDepartment of Mathematics and Statistics, McGill University, Montreal, QC, Canada Search for more papers by this author , FERNANDO DIZWhitman School of Management, Syracuse University, Syracuse, New York, USA Search for more papers by this author , GREG N. GREGORIOUSchool of Business and Economics, State University of New York, 101 Broad Street, Plattsburgh, New York 12901, USACorresponding author. Search for more papers by this author , and FABRICE ROUAHFaculty of Management, McGill University, Montreal, QC, Canada Search for more papers by this author https://doi.org/10.1142/S0219868104000154Cited by:3 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractThis study estimates the survival time distribution of the global macro class of hedge funds. We use methods of survival analysis to investigate how performance and nonperformance features of hedge funds could affect their lifetimes. We find that the effect of monthly returns and average assets under management is significant and has an impact on survival. We further discover that between 6 and 8 years of existence there is a sharp increase in the hazard of failure, which is most likely attributed to the Russian Ruble crisis of August 1998. The assumption by the media that many global macro hedge funds have been accused of failing due to their excessive leverage may in fact be wrong.Keywords:Hedge fund survivallife table estimatorKaplan–Meier estimator References Amin, G. S. and H. Kat (2002). Welcome to the dark side: hedge fund attrition and survivorship bias over the period 1994–2001. Working Paper, University of Reading, ISMA Centre, Reading, UK . Google Scholar Barès, P. A., R. Gibson and S. Gyger (2001). Style consistency and survival probability in the hedge fund industry. Working Paper, Swiss Federal Institute of Technology Lausanne EPFL and University of Zurich . Google Scholar Barry, R. (2002). Hedge funds: a walk through the graveyard. Working Paper, Applied Finance Center, Macquarue University, Sydney, Australia . Google Scholar Baquero, H., Horst, ter J. and M. Verbeek (2002). Survival, look-ahead bias and the performance of hedge funds. Working Paper, Erasmus University and Tilburg University, The Netherlands . Google Scholar Boyson, N. (2002). How are hedge fund manager characteristics related to performance, volatility and survival. Working Paper, Ohio State University . Google Scholar Brooks, C. and H. Kat (2001). The statistical properties of hedge fund index returns and their implications for investors. Working Paper, University of Reading ISMA Centre, Reading, UK . Google ScholarS. J. Brown, W. N. Goetzmann and J. Park, Journal of Finance 56(5), 1869 (2001), DOI: 10.1111/0022-1082.00392. Crossref, Google ScholarD. R. Cox, Journal of the Royal Statistical Society, Series B 34(2), 187 (1972). Google ScholarM. J. Howell, Journal of Alternative Investments 4(2), 57 (2001), DOI: 10.3905/jai.2001.319011. Crossref, Google Scholar Jen, P., C. Heasman and K. Boyatt (2001). Alternative asset strategies: early performance in hedge fund managers. Lazard Asset Management, London, UK . Google Scholar J. D. Kalbfleisch and R. L. Prentice , The Statistical Analysis of Failure Time Data , 2nd edn. ( John Wiley & Sons , New York, NY , 2002 ) . Crossref, Google ScholarB. Liang, The Journal of Financial and Quantitative Analysis 35(3), 309 (2000), DOI: 10.2307/2676206. Crossref, Google Scholar FiguresReferencesRelatedDetailsCited By 3Global MacroZura Kakushadze and Juan Andrés Serur14 December 2018References and Additional Reading17 August 2016Global Macro Investing3 October 2015 Recommended Vol. 01, No. 02 Metrics History KeywordsHedge fund survivallife table estimatorKaplan–Meier estimatorPDF download

The purpose of this chapter is to critically evaluate the methods used to examine hedge fund performance, review and synthesize studies that attempt to explain the inconsistencies associated with the performance of hedge funds and to attempt to compare the returns of hedge funds against more liquid investments. In fact, research related to hedge fund performance seems to have been focused on whether hedge fund managers manipulate their performance and what investors should think about this performance manipulation; however, recent studies have questioned whether this perceived performance manipulation is manipulation per se or something else. In general, researchers have used a number of different techniques to attempt to model hedge fund performance and the relative opacity and latency that is evident in the reporting of hedge fund returns. Nevertheless, the very nature of the structure of a hedge fund makes it difficult to mark the returns to market on a frequent basis and even if managers wanted their performance marked to market, which would unveil their positioning through time, the relative illiquidity and stale pricing associated with some of the investments that are held by hedge funds make pricing the hedge fund a difficult and somewhat pointless exercise. To this end, studies that attempt to analyze and evaluate aggregate performance for hedge fund returns have focused on identifying the true determinates of hedge fund performance, attempted to account for and explain the relative staleness of pricing in hedge fund returns, and to relate the performance of hedge funds to more liquid and transparent investments. This chapter offers key suggestions for financial market participants such as hedge funds managers, portfolio managers, risk managers, regulatory bodies, financial analysts, and investors about their evaluation and interpretation of hedge fund performance. In addition, this critical review chapter can benefit investors, portfolio managers, and researchers in the establishment of a yardstick for the assessment of hedge fund performance and the performance of assets that have stale pricing and are relatively opaque.

Hedge Fund Performance and Issues

PurposeThis article seeks to explain and empirically test the relationship between managerial social capital and hedge fund performance.Design/methodology/approachThis article uses a capital asset pricing model (CAPM)‐style five factor model to estimate excess returns for the top 25 hedge funds.FindingsThe results show that hedge funds managers with more affiliation diversity have higher annualised rate of return. This result seems to suggest managers with more social networks and affiliation have access to market niche of wealthy investors to increase their investor base. Hedge fund managers' prior skills sets and repertoire of knowledge significantly influence their risk taking attitude.Research limitations/implicationsThe sample consists of the top 25 hedge funds.Originality/valueThe article discusses the role and implications of managerial social capital in hedge fund marketing and performance.

PurposeThis research aims to examine hedge funds’ performance, risk and flow before and after the implementation of the Stop Trading on Congressional Knowledge (STOCK) Act.Design/methodology/approachThis paper includes the use of different factor models to highlight the performance and risk of hedge funds before and after the implementation of the STOCK Act. Hedge fund holdings are retrieved from Thomson Reuters Lipper Hedge Fund Database (TASS).FindingsThis study finds significant differences before and after the implementation of the STOCK Act. The results for the entire sample period indicate that hedge funds suffered lower-alpha, standard deviation and idiosyncratic risk after the implementation of the STOCK Act.Originality/valueThe paper’s originality and value lie in addressing the relationship gap between the STOCK Act and hedge fund performance.

In this paper, the effect of leverage on hedge fund performance is measured. TASS data from 1994 to 2016 are used to measure the impact of leverage on hedge fund performance. Three hedge fund performance measurements are regressed on degree of leverage with eight control variables including fund size, strategies, and use of derivatives. The results show that for strategyadjusted return as a performance measurement, hedge fund leverage has a negative impact on fund performance. Also there is evidence of diseconomies of scale where funds with medium-sized assets under management (AUM) tend to show better performance than funds with high AUM. No significant relation between use of leverage and performance is observed for other performance measurements, including the Fung and Hsieh seven and eight-factor alpha and style-adjusted return.

<p>This thesis consists of three chapters and investigates the issues related to liquidity risk, credit market contagion, and corporate cash holdings. The first chapter is coauthored work with Professor Jay Sa-Aadu and Associate Professor Ashish Tiwari and is titled ‘Market Liquidity, Funding Liquidity, and Hedge Fund Performance.’ The second chapter is sole-authored and is titled ‘Credit Market Contagion and Liquidity Shocks.’ The third chapter is coauthored with Steven Savoy and titled ‘Ambiguity Aversion and Corporate Cash Holdings.’</p> <p>The first chapter examines the interaction between hedge funds’ performance and their market liquidity risk and funding liquidity risk. Using a 2-state Markov regime switching model we identify regimes with low and high market-wide liquidity. While funds with high market liquidity risk exposures earn a premium in the high liquidity regime, this premium vanishes in the low liquidity states. Moreover, funding liquidity risk, measured by the sensitivity of a hedge fund’s return to the Treasury-Eurodollar (TED) spread, is an important determinant of fund performance. Hedge funds with high loadings on the TED spread underperform low-loading funds by about 0.49% (10.98%) annually in the high (low) liquidity regime, during 1994-2012.</p> <p>The second chapter provides evidence on credit market contagion using CDS index data and identifies the channels through which contagion propagates in credit markets. The results show that funding liquidity and market liquidity are significant channels of contagion during periods with widening credit spreads and adverse liquidity shocks. These results provide support for the theoretical model proposed by Brunnermeier and Pedersen (2009) according to which negative liquidity spirals can lead to contagion across various asset classes. Furthermore, during periods with tightening credit spreads and positive liquidity shocks, the results indicate that a prime broker index and a bank index are important channels contributing to co-movement in credit spreads. This suggests that financial intermediaries play an important role in spreading market rallies across credit markets.</p> <p>The third chapter investigates the link between investors’ ambiguity aversion and precautionary corporate cash holdings. Investors’ ambiguity aversion is measured by the proportion of individual investors in a firm’s investor base who are hypothesized to be more ambiguity averse compared to institutional investors. We show that the value of cash holdings is negatively associated with the extent of ambiguity aversion in a firm’s shareholder base for firms that are financially constrained. Our results also show that financially constrained firms with a higher proportion of ambiguity averse investors hold less cash. These results provide support for models in which ambiguity averse investors dislike the cash holdings of firms, that are held for precautionary reasons to fund long term projects, given that the returns on long term projects are ambiguous.</p>

Market Liquidity, Funding Liquidity, and Hedge Fund Performance

Contrarian Hedge Funds and Momentum Mutual Funds

Performance Evaluation of Hedge Funds with Option-Based and Buy-and-Hold Strategies

In recent years, hedge funds have become very popular, with institutional investors. This article contributes to the debate on hedge fund performance by quantifying the omission non-normality risks and tactical trading when evaluating hedge fund returns. This is done by evaluating hedge fund performance using a model that treats systematic non-normality risks as a potential source of hedge fund returns. In addition, the tactical asset allocation decisions of fund managers are explicitly modeled. The results show that the arrival of public information alters the asset allocation of hedge fund managers and induces a change in the risk profile and performance of hedge funds. Further they indicate that failure to account for these two features leads to incorrect statistical inference on the performance of 1 out of 4 hedge funds and overstates alpha estimates. Overall, non-normality risks and tactical asset allocation explain no less than 23.1% of the commonly perceived abnormal performance of hedge funds. <b>TOPICS:</b>Real assets/alternative investments/private equity, risk management, performance measurement, portfolio construction

Sell-side Analyst Recommendation Revisions and Institutional Trading before and after Regulation FD

We examine institutional trading in relation to changes in consensus recommendations over time. We find that pre‐Reg FD's positive contemporaneous relation between hedge fund trading and change in consensus becomes negative after Reg FD, but the positive relation between nonhedge fund trading and change in consensus continues even after Reg FD. Furthermore, during post‐Reg FD, while the performance of hedge funds’ trades that contradict with analysts improve, nonhedge funds’ trades that agree with analysts significantly deteriorate. Our evidence suggests that hedge funds have better information processing skill and are more cognizant about the role of selective information in analyst recommendations.

The Impact of Portfolio Disclosure on Hedge Fund Performance, Fees, and Flows