We show that the Hedge Algorithm, a method widely used in Machine Learning, can be interpreted as a particular subgradient algorithm for minimizing a well-chosen convex function, namely a Mirror Descent Scheme. Using this reformulation, we can improve slightly the worstcase convergence guarantees of the Hedge Algorithm. Recently, Nesterov has introduced the class of Primal-Dual Subgradient Algo...

We use a new dataset of hedge fund returns from a separate account platform to examine (1) how much of hedge fund return smoothing is due to main-fund specific factors, such as managerial reporting discretion (2) the costs of removing hedge fund share restrictions. These accounts trade pari passu with matching hedge funds but feature third-party reporting and permissive share restrictions. We u...

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 ...

Characterizing the instantaneous investment opportunity set by the real interest rate and the maximum Sharpe ratio, a simple model of time varying investment opportunities is posited in which these two variables follow correlated Ornstein-Uhlenbeck processes, and the implications for stock and bond valuation are developed. The model suggests that the prices of certain portfolios that are relate...

Hedge detection is used to distinguish uncertain information from facts, which is of essential importance in biomedical information extraction. The task of hedge detection is often divided into two subtasks: detecting uncertain cues and their linguistic scope. Hedge scope is a sequence of tokens including the hedge cue in a sentence. Previous hedge scope detection methods usually take all token...

This paper investigates hedge funds’ exposures to various financial and macroeconomic risk factors through alternative measures of factor betas and examines the performance of these factor betas in predicting the cross-sectional variation in hedge fund returns. The results indicate a positive and significant link between default premium beta (DEF beta) and future hedge fund returns as well as a...

This paper investigates the performance of hedge funds adjusted for higher order risk factors. Traditional risk-adjusted performance measures are subject to size distorted in the presence of skewness and kurtosis. A residual augmented least squares approach to model higher order risk moments in returns allows us to estimate a robust risk-adjusted performance measure for hedge funds. In a compar...

We provide novel evidence that hedge fund performance is persistent following periods of relative hedge fund market weakness, but not following periods of relative market strength. Specifically, we construct two performance measures, DownsideReturns and UpsideReturns, conditioning on whether the overall hedge fund market return is below or above its sample median. After adjusting for risk and f...

We introduce an extension of hedge automata called bidimensional context-free hedge automata. The class of unranked ordered tree languages they recognize is shown to be preserved by rewrite closure with inverse-monadic rules. We also extend the parameterized rewriting rules used for modeling the W3C XQuery Update Facility in previous works, by the possibility to insert a new parent node above a...

We study the economic importance of accounting information as defined by the value that sophisticated investors can extract from financial statements when maximizing their expected utility from holding a portfolio of U.S. equities. Our approach applies the elegant parametric portfolio policy (PPP) method of Brandt, Santa-Clara and Valkanov (2009) that models portfolio weights as a linear functi...