Hedge Fund Return Predictability To Combine Forecasts Or Combine Information
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Hedge Fund Return Predictability To Combine Forecasts Or Combine Information
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Author : Ekaterini Panopoulou
language : en
Publisher:
Release Date : 2014
Hedge Fund Return Predictability To Combine Forecasts Or Combine Information written by Ekaterini Panopoulou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance.
Hedge Fund Return Predictability In The Presence Of Model Risk
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Author : Christos Argyropoulos
language : en
Publisher:
Release Date : 2019
Hedge Fund Return Predictability In The Presence Of Model Risk written by Christos Argyropoulos and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Hedge funds implement elaborate investment strategies that include a variety of positions and assets. As a result, there is significant time variation in the set of risk factors and their respective loadings which in turn introduces severe model risk in any attempt to model and forecast hedge fund returns. In this study, we investigate the statistical and economic value of incorporating heteroscedasticity, non-normality, time-varying parameters, model selection risk and parameter estimation risk jointly in hedge fund return forecasting and fund of funds construction. Parameter estimation risk is dealt with a time-varying parameter structure, while model selection uncertainty is mitigated by model averaging or model selection. We adopt a dynamic model averaging approach along with the conventional Bayesian averaging technique. Our empirical results suggest that accounting for model risk can significantly improve the forecasting accuracy of hedge fund returns and, consequently, the performance of funds of hedge funds.
A Cross Sectional Machine Learning Approach For Hedge Fund Return Prediction And Selection
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Author : Wenbo Wu
language : en
Publisher:
Release Date : 2019
A Cross Sectional Machine Learning Approach For Hedge Fund Return Prediction And Selection written by Wenbo Wu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
We apply four machine learning methods to cross-sectional return prediction for hedge fund selection. We equip the forecast model with a set of idiosyncratic features, which are derived from historical returns of a hedge fund and capture a variety of fund-specific information. Evaluating the out-of-sample performance, we find that our forecast method significantly outperforms the four styled HFR Indices in almost all situations. Among the four machine learning methods, we find that deep neural network appears to be overall most effective. Investigating the source of methodological advantage of our method using a case study, we find that cross-sectional forecast outperforms forecast based on time series regression in most cases. Advanced modeling capabilities of machine learning further enhance these advantages. We find that the return-based features lead to higher returns than the benchmark of a set of macro-derivative features, and our forecast method yields best performance when the two sets of features are combined.
Portfolio Structuring And The Value Of Forecasting
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Author : Jacques Lussier
language : en
Publisher: CFA Institute Research Foundation
Release Date : 2016-10-10
Portfolio Structuring And The Value Of Forecasting written by Jacques Lussier and has been published by CFA Institute Research Foundation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-10 with Business & Economics categories.
Using Investment Portfolio Return To Combine Forecasts
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Author : Mark T. Leung
language : en
Publisher:
Release Date : 2006
Using Investment Portfolio Return To Combine Forecasts written by Mark T. Leung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
This study investigates the usefulness and efficacy of a multiobjective decision method for financial trading guided by a set of seemingly diverse analysts' forecasts. The paper proposes a goal programming (GP) approach which combines various forecasts based on the performance of their previous investment returns. In our experiment, several series of financial analysts' forecasts are generated by different forecasting techniques. Investment returns on each series of forecasts are measured and then evaluated by three performance criteria, namely, mean, variance, and skewness. Subsequently, these distributional properties of the returns are used to construct a GP model. Results of the GP model provide a set of weights to compose an investment portfolio using various forecasts. To examine its practicality, the approach is tested on several major stock market indices. The performance of the proposed GP approach is compared with those of individual forecasting techniques and a number of forecast combination models suggested by previous studies. This comparison is conducted with respect to different levels of investor preference over return, variance, and skewness. Statistical significance of the results are accessed by bootstrap re-sampling. Empirical results indicate that, for all examined investor preference functions and market indices, the GP approach is significantly better than all other models tested in this study.
Predicting Stock Market Returns By Combining Forecasts
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Author : Laurence Fung
language : en
Publisher:
Release Date : 2009
Predicting Stock Market Returns By Combining Forecasts written by Laurence Fung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
The predictability of stock market returns has been a challenge to market practitioners and financial economists. This is also important to central banks responsible for monitoring financial market stability. A number of variables have been found as predictors of future stock market returns with impressive in-sample results. Nonetheless, the predictive power of these variables has often performed poorly for out-of-sample forecast. This study utilises a new method known as quot;Aggregate Forecasting Through Exponential Re-weighting (AFTER)quot; to combine forecasts from different models and achieve better out-of-sample forecast performance from these variables. Empirical results suggest that, for longer forecast horizons, combining forecasts based on AFTER provides better out-of-sample predictions than the historical average return and also forecasts from models based on commonly used model selection criteria.
Using Investment Portfolio Return To Combine Forecasts
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Author : An-Sing Chen
language : en
Publisher:
Release Date : 2001
Using Investment Portfolio Return To Combine Forecasts written by An-Sing Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with categories.
In recent years, there has been a growing trend of using multiobjective techniques. The primary advantage of using multiobjective techniques in decision making is, as stated in Spronk (1981), quot;that most of these (single objective) models and methods are unsuitable for decision situations in which multiple and possibly conflicting objectives play a role, because they are concentrated on the optimal fulfilment of only one objective.quot; Given this notion, we attempt to explore the possibility of taking the multiobjective approach to solve a typical problem encountered by many financial and investment managers, namely, making investment trading decisions based on a set of potentially incompatible forecasts supplied by different analysts. In our experiment, several series of financial analysts' forecasts are generated by different forecasting techniques. The approach examines the historical performance of the various series of forecasts and combines them based on the average, variance, and skewness of investment returns. Through the use of a goal programming model, an investor can construct a portfolio which matches his or her preference. This portfolio-based approach also adds the benefits of diversification in trading. We test our proposed approach with three widely traded broad market indices, Samp;P 500, FTSE 100, and Nikkei 225. Improved performance of the multiobjective portfolio approach relative to those of individual forecasting techniques and some previously suggested forecast-combining models is measured The empirical results indicates that the performance of the proposed approach statistically outperforms the others at a significance level of 0.05. Moreover, we find that the benefits of our approach becomes more apparent when the market exhibits higher volatility and instability.
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes
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Author : Cheng Few Lee
language : en
Publisher: World Scientific
Release Date : 2020-07-30
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng Few Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-30 with Business & Economics categories.
This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
Hedge Fund Activism
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Author : Alon Brav
language : en
Publisher: Now Publishers Inc
Release Date : 2010
Hedge Fund Activism written by Alon Brav and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Business & Economics categories.
Hedge Fund Activism begins with a brief outline of the research literature and describes datasets on hedge fund activism.
Artificial Intelligence In Asset Management
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Author : Söhnke M. Bartram
language : en
Publisher: CFA Institute Research Foundation
Release Date : 2020-08-28
Artificial Intelligence In Asset Management written by Söhnke M. Bartram and has been published by CFA Institute Research Foundation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-28 with Business & Economics categories.
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.