Relevant bibliographies by topics / Hedge Fund

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hedge Fund'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Hedge Fund"

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Agarwal, Vikas, Nicole M. Boyson, and Narayan Y. Naik. "Hedge Funds for Retail Investors? An Examination of Hedged Mutual Funds." Journal of Financial and Quantitative Analysis 44, no. 2 (April 2009): 273–305. http://dx.doi.org/10.1017/s0022109009090188.

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AbstractRecently, there has been rapid growth in the assets managed by “hedged mutual funds”—mutual funds mimicking hedge fund strategies. We examine the performance of these funds relative to hedge funds and traditional mutual funds. Despite using similar trading strategies, hedged mutual funds underperform hedge funds. We attribute this finding to hedge funds’ lighter regulation and better incentives. Conversely, hedged mutual funds outperform traditional mutual funds. Notably, this superior performance is driven by managers with experience implementing hedge fund strategies. Our findings have implications for investors seeking hedge-fund-like payoffs at a lower cost and within the comfort of a regulated environment.
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Kolisovas, Danielius, Gintarė Giriūnienė, Tomas Baležentis, Dalia Štreimikienė, and Mangirdas Morkūnas. "DETERMINANTS OF THE NORDIC HEDGE FUND PERFORMANCE." Journal of Business Economics and Management 23, no. 2 (March 29, 2022): 426–50. http://dx.doi.org/10.3846/jbem.2022.16170.

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Hedge funds have become an important part of the financial sector. The development of the hedge funds in the Nordic countries has been rather robust. Therefore, it is important to identify the determinants of the hedge fund performance and isolate the managerial performance, i.e., the Jensen’s alpha. To this end, this paper construct cross sectional and panel model for the Nordic hedge funds over 2005–2018. The Fung-Hsieh 8-factor model and other models are developed to identify the determinants of the Nordic hedge fund performance. The effects of crises of different nature (local to global, hedge funds to banking sector) are also tested. The results indicate that Nordic hedge funds are capable to generate positive alpha during the crisis even exceeding the alpha of the economically stable time periods.
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Acito, Christopher J., and F. Peter Fisher. "Fund of Hedge Funds." Journal of Alternative Investments 4, no. 4 (March 31, 2002): 25–35. http://dx.doi.org/10.3905/jai.2002.319029.

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Muhtaseb, Majed R. "A hedge fund collapse and diversification 101: lessons to stakeholders." Journal of Financial Crime 28, no. 3 (April 6, 2021): 774–83. http://dx.doi.org/10.1108/jfc-09-2020-0198.

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Purpose The purpose of this paper is events and analysis of present a hedge fund collapse, offer lessons to investors and hedge fund industry stakeholders and propose a possible remedy for mitigating operational risks and associated potential losses. Design/methodology/approach This study focused on one hedge fund case study and conducted a thorough investigation of the events that led to the collapse and eventual filing of the Securities and Exchange Commission (SEC) complaint. All articles and publications used for this research are available in the public domain and accessible. Findings Wood River Capital Management had concentrated the portfolios of its two hedge funds into one stock, EndWave Corp. Fund Manager violated terms of offering memorandum. Investors were not made aware of and did not discover the operational risks. Stock price of EndWave plummeted. There was no independent oversight over the funds. The values of the two funds dropped significantly. Investors attempted to redeem but the funds were not liquid. The SEC filed a complaint. Mr Whittier was sentenced for three years in jail. Research limitations/implications It is an analysis of US-based hedge fund, not an empirical paper. The article presents critical analysis and offers many valuable lessons to hedge fund industry stakeholders. Practical implications This paper helps investors in terms of identifying a hedge fund’s operational risks and conducting more effective due diligence while vetting a hedge fund. This could potentially save investors and constituents billions of dollars, by avoiding potential hedge fund collapses. This paper suggests that the scope of fiduciary duty be expanded to cover hedge fund industry vendors. Originality/value Thorough research of a hedge fund that collapsed because of poor investment decisions, not self-enrichment at expense of fund investors. This paper provides lessons to investors in terms of identifying a hedge fund’s critical operational risks and conducting value preserving due diligence. This could potentially save hedge funds investors billions of dollars, by avoiding potential hedge fund collapses. This paper recommends that the scope of fiduciary duty be expanded to cover hedge fund industry vendors.
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Cao, Charles, Bradley A. Goldie, Bing Liang, and Lubomir Petrasek. "What Is the Nature of Hedge Fund Manager Skills? Evidence from the Risk-Arbitrage Strategy." Journal of Financial and Quantitative Analysis 51, no. 3 (June 2016): 929–57. http://dx.doi.org/10.1017/s0022109016000387.

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AbstractTo understand the nature of hedge fund managers’ skills, we study the implementation of risk arbitrage by hedge funds using their portfolio holdings and comparing them with those of other institutional arbitrageurs. We find that hedge funds significantly outperform a naive risk-arbitrage portfolio by 3.7% annually on a risk-adjusted basis, whereas non–hedge fund arbitrageurs fail to outperform the benchmark. Our analysis reveals that hedge funds’ superior performance does not reflect fund managers’ ability to predict or affect the outcome of merger and acquisition deals; rather, hedge fund managers’ superior performance is attributed to their ability to manage downside risk.
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Caslin, J. J. "Hedge Funds." British Actuarial Journal 10, no. 3 (August 1, 2004): 441–521. http://dx.doi.org/10.1017/s1357321700002671.

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ABSTRACTThe paper opens by showing how certain types of hedge funds can reduce the risk and increase the return on a traditional balanced managed fund. One of the key characteristics of such a hedge fund is that it has a low correlation with the balanced managed fund. The paper puts forward a new way of explaining correlation so that it can be more readily understood, and suggests methods of analysis for dealing with the fact that correlation is unstable. Volatility correlation is also examined because of its importance in reducing the risk of a portfolio.An outline of the characteristics and risks of three types of hedge funds, namely, long/short equity, convertible arbitrage and merger arbitrage, together with some questions investors might put to prospective hedge fund managers is given in Section 5.Some of the very basic statistical analysis techniques used in assessing the past performance of hedge funds are given in Section 6. Considerable emphasis is put on the need to examine daily return data as an insight into the quality of the manager's IT systems, his risk management, evidence of smoothing of returns, and to gain access to a higher number of data points for assessing the repeatability of performance.An entire section of the paper is devoted to gaining a clear understanding of a prospective hedge fund manager's volatility management strategy because of its importance in the context of the fee structure of hedge funds and its importance for assessing the ability of a hedge fund to reduce the risk and increase the returns of a balanced managed fund.Funds of hedge funds are examined in the final section, and the section concludes that large sophisticated institutional investors may wish to create a portfolio of hedge funds rather than invest in a fund of hedge funds.
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Fadoua, Fadoua. "Design of Single Valued Neutrosophic Hypersoft Set VIKOR Method for Hedge Fund Return Prediction." International Journal of Neutrosophic Science 24, no. 2 (2024): 317–27. http://dx.doi.org/10.54216/ijns.240228.

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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
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Gregoriou, Greg, François-Éric Racicot, and Raymond Théoret. "The q-factor and the Fama and French asset pricing models: hedge fund evidence." Managerial Finance 42, no. 12 (December 5, 2016): 1180–207. http://dx.doi.org/10.1108/mf-01-2016-0034.

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Purpose The purpose of this paper is to test the new Fama and French (2015) five-factor model relying on a thorough sample of hedge fund strategies drawn from the Barclay’s Global hedge fund database. Design/methodology/approach The authors use a stepwise regression to identify the factors of the q-factor model which are relevant for the hedge fund strategy analysis. Doing so, the authors account for the Fung and Hsieh seven factors which prove very useful in the explanation of the hedge fund strategies. The authors introduce interaction terms to depict any interaction of the traditional Fama and French factors with the factors associated with the q-factor model. The authors also examine the dynamic dimensions of the risk-taking behavior of hedge funds using a BEKK procedure and the Kalman filter algorithm. Findings The results show that hedge funds seem to prefer stocks of firms with a high investment-to-assets ratio (low conservative minus aggressive (CMA)), on the one hand, and weak firms’ stocks (low robust minus weak (RMW)), on the other hand. This combination is not associated with the conventional properties of growth stocks – i.e., low high minus low (HML) stocks – which are related to firms which invest more (low CMA) and which are more profitable (high RMW). Finally, small minus big (SMB) interacts more with RMW while HML is more correlated with CMA. The conditional correlations between SMB and CMA, on the one hand, and HML and RMW, on the other hand, are less tight and may change sign over time. Originality/value To the best of the authors’ knowledge, the authors are the first to cast the new Fama and French five-factor model in a hedge fund setting which account for the Fung and Hsieh option-like trading strategies. This approach allows the authors to better understand hedge fund strategies because q-factors are useful to study the dynamic behavior of hedge funds.
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Butowsky, Michael R., and Michele L. Gibbons. "Hedge fund marketing by broker‐dealers questions and comments in response to recent developments." Journal of Investment Compliance 4, no. 3 (July 1, 2003): 7–12. http://dx.doi.org/10.1108/15285810310813158.

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This article discusses the implications of heightened regulatory attention to hedge funds by focusing on the practical questions that are on the minds of many in the hedge fund industry and, possibly, even in the thoughts of the regulators themselves. The primary regulatory condition relevant to the offer and sale of interests in hedge funds is the prohibition on general solicitation or general advertising by the sponsor of the hedge fund. Under NASD rules, brokers must (1) provide balanced disclosures in their promotional efforts; (2) perform reasonable‐basis suitability determinations; (3) perform customer‐specific suitability determinations; (4) supervise associated persons selling hedge funds and funds of hedge funds; and (5) train associated persons regarding the features, risks, and suitability of hedge funds and funds of hedge funds. Internal controls, including supervision and compliance, must include written procedures to ensure that sales of hedge funds and funds of hedge funds comply with all relevant NASD and SEC rules. Promotion of hedge funds must be balanced by a fair presentation of the risks and potential disadvantages of hedge fund investing
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Li, Haitao, Xiaoyan Zhang, and Rui Zhao. "Investing in Talents: Manager Characteristics and Hedge Fund Performances." Journal of Financial and Quantitative Analysis 46, no. 1 (November 22, 2010): 59–82. http://dx.doi.org/10.1017/s0022109010000748.

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AbstractUsing a large sample of hedge fund manager characteristics, we provide one of the first comprehensive studies on the impact of manager characteristics, such as education and career concern, on hedge fund performances. We document differential ability among hedge fund managers in either generating risk-adjusted returns or running hedge funds as a business. In particular, we find that managers from higher-SAT (Scholastic Aptitude Test) undergraduate institutions tend to have higher raw and risk-adjusted returns, more inflows, and take fewer risks. Unlike mutual funds, we find a rather symmetric relation between hedge fund flows and past performance, and that hedge fund flows do not have a significant negative impact on future performance.
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Dissertations / Theses on the topic "Hedge Fund"

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Palma, Kelly. "Hedge funds and the SEC regulation of Hedge Fund Advisers : /." Staten Island, N.Y. : [s.n.], 2006. http://library.wagner.edu/theses/business/2006/thesis_bus_2006_palma_hedge.pdf.

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Adlersson, Patrik, and Patrik Blomdahl. "Hedge Fund Style Allocation : A Risk Adjusted Fund of Hedge Fund Perspective." Thesis, Linköping University, Department of Production Economics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2758.

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The purpose of the thesis has been to explore the use of hedge fund styles when constructing portfolios of hedge funds (i.e. funds of hedge funds). The central question is if the use of hedge fund styles can significantly explain and improve risk adjusted returns (characterized by Sharpe ratios). The study has been done in collaboration with Optimized Portfolio Management AB who desire further knowledge and evaluation of hedge fund styles for their fund of hedge funds.

To be able to create successful ex ante portfolios we have explored various prediction models for both risk and return. Our findings indicate that return prediction is problematic using simple models such as regression since the risk exposure of the indices appear to change significantly over time. One can however using exponentially weighted moving averages (EWMA) achieve relatively promising estimations of future returns.

Covariance matrix estimation seems to be more straightforward. We have achieved promising results using both traditional EWMA models as well as improved estimators using principal component analysis.Covariance prediction models were evaluated separately using a minimum-variance portfolio optimization technique and provided a significant risk reduction compared to the aggregated hedge fund universe (represented by a naively diversified portfolio). Combinations of risk and return prediction models were evaluated using traditional mean-variance portfolio construction methods, which were optimized for Sharpe ratios. These provided a significant increase in risk adjusted returns relative to the aggregated hedge fund universe. The allocation is however discouraging due to serious instability over time.

Our findings indicate that there indeed is an advantage of taking hedge fund styles into consideration when constructing funds of hedge funds in a risk adjusted perspective. However, further research into return prediction needs to be done in order to stabilize portfolio allocation. An alternative seems to be tactical style allocation on a more fundamental analysis basis.

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Samiev, Sarvar, and Yaqian Wu. "Do hedge fund investment strategies matter in hedge fund performance?" Thesis, Umeå universitet, Handelshögskolan vid Umeå universitet, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-37518.

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Our study aims at analyzing the performance of 1455 live hedge funds in the chosen timeframe from 2004 and 2010. Our work is of great importance both forindividual and institutional investor which finds alternative investments as aninvestment choice. By decomposing hedge funds into different strategies we implementour analysis. To answer to our research question “Do hedge fund investing strategiesmatter in hedge fund performance?” our findings based on single and multipleregression models on risk-adjusted basis, show that different hedge investmentstrategies have different risk and return characteristics.Our multiple regression analysis in which we have included sub-category indices asfactor has provided the high R squared (99%). Managerial skill (alpha) is lower in caseof single regression using benchmarks compared to market (S&P 500), which isreasonable since our benchmark is homogenous funds included and measures theaverage performance of specific hedge fund sub category. The beta values in case ofbenchmark used is higher compared to market due to the same reason. The difference inR squared values is quite fluctuating. For some hedge funds, the explanatory power ofbenchmark is higher while for others is lower. We would like to emphasize that Rsquared values in case of market (S&P 500) are more stable compared to benchmark.H test showed that the differences existed among the performance of hedge fundinvestment strategies. LSD test showed that there are some strategies having significantdifferences on performance among different investment strategies. The multipleregression analysis using dummy variables showed that to some extent hedge fundstrategies matter on hedge fund performance. Risk-adjusted performance measuresshow the highest sharp ratio to PIPES (2,88) and Statistical Arbitrage (1,55).
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Xiao, Li. "Valuing Hedge Fund Fees." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2931.

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This thesis applies a Partial Integral Differential Equation model, along with a Monte Carlo approach to quantitatively analyze the no arbitrage value of hedge fund performance fees. From a no-arbitrage point of view, the investor in a hedge fund is providing a free option to the manager of the hedge fund. The no-arbitrage value of this option can be locked in by the hedge fund manager using a simple hedging strategy. Interpolation methods, grid construction techniques and parallel computation techniques are discussed to improve the performance of the numerical methods for valuing this option.
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MacDonald, Lynn M. (Lynn Marie). "Hedge fund structured products." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33556.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2005.
Includes bibliographical references (leaves 58-63).
In the aftermath of the bear market and one of the most volatile periods in recent financial history, individual and institutional investors worldwide are reevaluating their asset allocation strategies. Interest in hedge funds and alternative investment styles is growing as investors realize these investments offer better return potential with relatively low correlation to traditional asset classes. However, returns of hedge funds have been somewhat lackluster recently, on average, and several factors indicate investors should expect similarly muted performance in the future. Hedge funds also expose investors to non-traditional risks, such as lack of transparency, lack of regulatory oversight, and limited liquidity. Structured products mitigate these risks and allow for flexibility in portfolio construction. They can help reduce the risk of an investment in exchange for a reduction in the potential upside. Additionally, they can provide a greater chance of a good return through the use of leverage. Because structured products can be designed to meet a variety of investment objectives they have become an increasingly popular way to gain exposure to and benefit from a variety of hedge fund strategies. The discussion of hedge funds and the ways in which structured products can be utilized to enhance return and mitigate risk is a broad and expansive topic. This paper is a primer on what hedge fund structured products are and how they can be used to enhance the risk/return profile of a portfolio. The focus is on the US market.
by Lynn M. MacDonald.
S.M.
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Mokoma, Kaibe. "Strategic asset selection taxonomy : fund of hedge funds." Master's thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/9037.

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Includes bibliographical references (leaves 68-70).
This thesis develops a logical methodology to be used to assess the hedge fund managers' return time series in comparison with their peers. This enables Fund of Hedge Funds portfolio manager to identify those with required factors to be included in a portfolio. The models that had been used as the industry standard for some time are derived on the assumption of normal distribution. Hence they use only mean and standard deviation to explain all data phenomenal attributes of time series. This study project uses higher order moments and some performance measures to rank order feasible portfolios of different hedge fund strategies based on their calculated metrics. Then determine the significance of t-Statistics, thus to observe the likelihood of achieving a particular return level relative to the downside associated with that target return and also on the behavioral hypothesis that investors prefer more to less. The study proposes and examines an alternative performance measures to facilitate the investment decision making. An indication of how this may be applied across a broad range of problems in hedge funds analysis. Some performance measures capture the higher order moments of the return distributions. This method makes intuitive sense since one of the key mandates of the hedge funds is to seek to capture most upside while protecting against downside.
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Palaro, Helder Parra. "Essays in hedge fund replication, evaluation and synthetic funds." Thesis, City University London, 2007. http://openaccess.city.ac.uk/8541/.

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In this thesis it is developed and demonstrated the workings of a copula-based technique that allows the derivation of dynamic trading strategies, which generate returns with statistical properties similar to hedge funds. It is shown that this technique is not only capable of replicating fund of funds returns, but is equally well suited for the replication of individual hedge fund returns. Since replication is accomplished by trading futures on traditional assets only, it avoids the usual drawbacks surrounding hedge fund investments, including the need for extensive due diligence, liquidity, capacity, transparency and style drift problems, as well as excessive management fees. This replication technique is also used to evaluate the net-of-fee performance of 875 funds of hedge funds and 2073 individual hedge funds, up to an including November 2006. Comparing fund returns with the returns on dynamic futures trading strategies with the same risk and dependence characteristics, no more than 18.6% of the funds of funds and 22.5% of the individual hedge funds in the data sample convincingly beat the benclunark. Besides the replication and evaluation of funds which already exist in the market, this technology can also be used to create new funds with previously unavailable return characteristics, the so-called `synthetic funds'. In a set of four out-ofsample tests over the period January 1998 - February 2007, it is shown that the replication-based strategies are indeed capable of accurately generating returns with a variety of properties, including negative correlation with stocks and bonds and high positive skewness. The synthetic funds also produce impressive average excess returns. Disappointing performance is leading hedge fund investors to look for cheaper alternatives to invest, such as indices of hedge funds. Unfortunately, investable hedge fund indices are nothing more than funds of funds in disguise, with performance similar or even worse than real funds of funds. The replication technology generates returns with statistical properties very similar to those of hedge fund indices, and a higher average return for most hedge fund categories, but without actually investing in hedge funds.
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Schaub, Nic. "Persistence of Hedge Fund Performance." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/02060515001/$FILE/02060515001.pdf.

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DeVault, Luke, and Richard Sias. "Hedge fund politics and portfolios." ELSEVIER SCIENCE BV, 2017. http://hdl.handle.net/10150/623039.

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Consistent with the well-documented relation between political orientation and psychological traits, hedge funds' political orientations are related to their portfolio decisions. Relative to politically conservative hedge funds, politically liberal hedge funds exhibit a preference for smaller stocks, less mature companies, volatile stocks, unprofitable companies, non-dividend paying companies, and lottery-type securities. Politically liberal hedge funds are also more likely to enter new positions or fully exit existing positions, and make larger adjustments to their U.S. equity market exposure. Our results suggest that psychological characteristics can influence the portfolio decisions of even those at the very top of the financial sophistication ladder.
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Mattes, Achim [Verfasser]. "Three Essays on Hedge Fund Risk Taking, Hedge Fund Herding, and Audit Experts / Achim Mattes." Konstanz : Bibliothek der Universität Konstanz, 2014. http://d-nb.info/1058825747/34.

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Books on the topic "Hedge Fund"

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McCrary, Stuart A. Hedge Fund Course. New York: John Wiley & Sons, Ltd., 2004.

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Gregoriou, Greg N., and Maher Kooli, eds. Hedge Fund Replication. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230358317.

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Scharfman, Jason A. Hedge Fund Compliance. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119240242.

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Travers, Frank J., ed. Hedge Fund Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119204855.

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Mirabile, Kevin R., ed. Hedge Fund Investing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119205074.

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Kiev, Ari, ed. Hedge Fund Leadership. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119197775.

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Lederman, Scott J. Hedge fund regulation. New York City: Practising Law Institute, 2006.

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Boncompagni, Tatiana. Hedge Fund Wives. New York: HarperCollins, 2009.

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Ang, Andrew. Hedge fund leverage. Cambridge, MA: National Bureau of Economic Research, 2011.

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Lederman, Scott J. Hedge fund regulation. New York City: Practising Law Institute, 2006.

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Book chapters on the topic "Hedge Fund"

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Capocci, Daniel. "Hedge Fund Characteristics." In The Complete Guide to Hedge Funds and Hedge Fund Strategies, 55–134. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9781137264442_2.

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Chatterjee, Rupak. "Hedge Fund Replication." In Practical Methods of Financial Engineering and Risk Management, 333–48. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6134-6_9.

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Savona, Roberto. "Hedge Fund Performance." In Asset Management and Institutional Investors, 355–71. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32796-9_12.

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Kooli, Maher, and Sameer Sharma. "Can We Really “Clone” Hedge Fund Returns? Further Evidence." In Hedge Fund Replication, 1–14. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230358317_1.

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Barone-Adesi, Giovanni, and Simone Siragusa. "Linear Model for Passive Hedge Fund Replication." In Hedge Fund Replication, 133–45. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230358317_10.

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Markov, Iliya, and Nils S. Tuchschmid. "Can Hedge Fund-Like Returns be Replicated in a Regulated Environment?" In Hedge Fund Replication, 146–58. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230358317_11.

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Rossi, Marco, and Sergio L. Rodríguez. "A Factor-Based Application to Hedge Fund Replication." In Hedge Fund Replication, 159–90. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230358317_12.

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Teïletche, Jérôme. "Hedge Fund Replication: Does Model Combination Help?" In Hedge Fund Replication, 15–29. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230358317_2.

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Harris, Richard D. F., and Murat Mazibas. "Factor-Based Hedge Fund Replication with Risk Constraints." In Hedge Fund Replication, 30–47. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230358317_3.

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Ravi, Anthony, Peter Mayall, and John Simpson. "Takeover Probabilities and the Opportunities for Hedge Funds and Hedge Fund Replication to Produce Abnormal Gains." In Hedge Fund Replication, 48–60. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230358317_4.

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Conference papers on the topic "Hedge Fund"

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Wang, Yu. "Analysis about China Hedge Fund and Global Hedge Fund in the Same Type." In ICEBI 2021: 2021 5th International Conference on E-Business and Internet. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3497701.3497715.

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Barbosa, Rui Pedro, and Orlando Belo. "The Agent-Based Hedge Fund." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.149.

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Johnston, Douglas E., and Petar M. Djuric. "Estimating hedge fund risk factor exposures." In 2012 IEEE 13th Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2012). IEEE, 2012. http://dx.doi.org/10.1109/spawc.2012.6292961.

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Agarwal, Aditya. "Performance Overview of Indian Hedge Fund Industry." In 2nd International Conference on Business, Management and Economics. acavent, 2019. http://dx.doi.org/10.33422/2nd.icbmeconf.2019.06.1026.

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Buckley, Muneer, Adam Ghandar, Zbigniew Michalewicz, and Ralf Zurbruegg. "Evaluation of intelligent quantitative hedge fund management." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983205.

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Budík, Jan, Radek Doskočil, and Lenka Niebauerová. "Proposal of Investment Portfolio of Hedge Fund." In The 7th International Scientific Conference "Business and Management 2012". Vilnius, Lithuania: Vilnius Gediminas Technical University Publishing House Technika, 2012. http://dx.doi.org/10.3846/bm.2012.003.

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Cru, David, and Jiaqiao Hu. "Dynamic hedge fund asset allocation under multiple regimes." In 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2010. http://dx.doi.org/10.1109/allerton.2010.5707074.

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Boudt, K., B. G. Peterson, and P. Carl. "Hedge fund portfolio selection with modified expected shortfall." In COMPUTATIONAL FINANCE 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/cf080101.

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Rong, Runsheng, Yongwei Yang, Mengru He, Dingyin Hu, and Zhenting Gu. "Identification of Optimal Risky Portfolios for Hedge Fund." In 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.211209.433.

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Johnston, Douglas E., Inigo Urteaga, and Petar M. Djuric. "Replication and optimization of hedge fund risk factor exposures." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639367.

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Reports on the topic "Hedge Fund"

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Ang, Andrew, Sergiy Gorovyy, and Gregory van Inwegen. Hedge Fund Leverage. Cambridge, MA: National Bureau of Economic Research, February 2011. http://dx.doi.org/10.3386/w16801.

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Chen, Joseph, Samuel Hanson, Harrison Hong, and Jeremy Stein. Do Hedge Funds Profit From Mutual-Fund Distress? Cambridge, MA: National Bureau of Economic Research, February 2008. http://dx.doi.org/10.3386/w13786.

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Boyson, Nicole, Christof Stahel, and Rene Stulz. Hedge Fund Contagion and Liquidity. Cambridge, MA: National Bureau of Economic Research, June 2008. http://dx.doi.org/10.3386/w14068.

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Boyson, Nicole, Christof Stahel, and Rene Stulz. Is There Hedge Fund Contagion? Cambridge, MA: National Bureau of Economic Research, March 2006. http://dx.doi.org/10.3386/w12090.

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Lim, Jongha, Berk Sensoy, and Michael Weisbach. Indirect Incentives of Hedge Fund Managers. Cambridge, MA: National Bureau of Economic Research, March 2013. http://dx.doi.org/10.3386/w18903.

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Ben-David, Itzhak, Justin Birru, and Andrea Rossi. The Performance of Hedge Fund Performance Fees. Cambridge, MA: National Bureau of Economic Research, June 2020. http://dx.doi.org/10.3386/w27454.

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Dor, Arik Ben, and Ravi Jagannathan. Understanding Mutual Fund and Hedge Fund Styles Using Return Based Style Analysis. Cambridge, MA: National Bureau of Economic Research, August 2002. http://dx.doi.org/10.3386/w9111.

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Brav, Alon, Wei Jiang, Song Ma, and Xuan Tian. How Does Hedge Fund Activism Reshape Corporate Innovation? Cambridge, MA: National Bureau of Economic Research, May 2016. http://dx.doi.org/10.3386/w22273.

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Bebchuk, Lucian, Alon Brav, and Wei Jiang. The Long-Term Effects of Hedge Fund Activism. Cambridge, MA: National Bureau of Economic Research, June 2015. http://dx.doi.org/10.3386/w21227.

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Gupta, Arpit, and Kunal Sachdeva. Skin or Skim? Inside Investment and Hedge Fund Performance. Cambridge, MA: National Bureau of Economic Research, July 2019. http://dx.doi.org/10.3386/w26113.

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You might also be interested in the extended bibliographies on the topic 'Hedge Fund' for particular source types:
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