Mutual fund selection is a notoriously difficult task, because past performance poor predictor of future performance. We propose measure that incorporates simple idea: shrinkage, in the sense Bayes-James-Stein, should be applied to gross return parameters, but not fees, which are known. The proposed Shrinkage Adjusted Sharpe ratio (SAS) substantially improves prediction out-of-sample relative e...

This paper examines the fundamental building blocks of Sharpe ratio to debate over economic interpretation this well-known tool used measure risk-adjusted performance various financial portfolios and funds. It focuses on expected return an investment versus a benchmark portfolio (or index) return. By leveraging set statements assumptions, I isolate information content as expression from alpha. ...

The Sharpe ratio function is a commonly used risk/return measure in financial econometrics. To estimate this function, most existing methods take two-step procedure that first estimates the mean and volatility functions separately then applies plug-in method. In paper, we propose direct method via local maximum likelihood to simultaneously negative log-volatility as well their derivatives. We e...

The Sharpe ratio is a measure based on the theory of mean variance, it performance portfolio when risk can be measured through standard deviation. This paper suggests Sharpe-ratio solution using second order cone programming (SOCP). We use penalty-regularized method to represent nonlinear problem. present computationally tractable way determining portfolio. A Markov chain structure employed und...

We develop a theory for valuing non-diversifiable mortality risk in an incomplete market. We do this by assuming that the company issuing a mortality-contingent claim requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. We apply our method to value life annuities. One result of our paper is that the value of the life annuity is identical to the upper go...

In this thesis we will use Random Forests to define a trading strategy. Using this powerful machine learning technique, we will try to predict the daily price changes of financial products that move similarly over the long term, so-called cointegrated pairs. We propose a way to adjust our portfolio based on these prediction, while limiting our risk. Firstly, we test our strategy on data generat...

We propose to train trading systems by optimizing financial objective functions via reinforcement learning. The performance functions that we consider as value functions are profit or wealth, the Sharpe ratio and our recently proposed differential Sharpe ratio for online learning. In Moody & Wu (1997), we presented empirical results in controlled experiments that demonstrated the advantages of ...

This paper examines the impact of hedge fund redemption restrictions such as lockup period, notice period, and redemption period on fund flow, risk, and performance. We first examine the effects of redemption restrictions conditional on past poor performance. We then examine the differential impact of redemption restrictions under different market conditions. We find that during normal periods,...

Our objective in this article is to study analytically the effect of borrowing constraints on asset returns. We explicitly characterize the equilibrium for an exchange economy with two agents who differ in their risk aversion and are prohibited from borrowing. In a representative-agent economy with CRRA preferences, the Sharpe ratio of equity returns and the riskfree rate are linked by the risk...