the aim of this study is to evaluate the effect of information delay on theperformance of joint investment funds. in order to achieve the aim of thisstudy sample consisted of twenty funds in the tehran stock exchange from2010 to 2014 the systematic elimination method has been adopted. in thisstudy, the linear regression test has been used in order to evaluate the researchhypothesis. data analys...

The purpose of this paper is to analyze the effect of either booms or disasters on the Sharpe Ratio. We provide a closed form expression of the Sharpe Ratio of an index whose log-return follows an arbitrary distribution. That is, besides variance, we allow for skewness, kurtosis and higher cumulants of the log-return to be non-zero. Our article has two main contributions. First, the Sharpe Rati...

The purpose of this study is two-fold. First, it is to invert an insurance industry index(s) from a linearly independent factor structure derived from the application of the Ross (1976) arbitrage-pricing model (APT) on a sample of insurance industry returns. The second objective is to identify the effect this index has on the performance of the Sharpe multi-index model in the formation of the m...

While many trading strategies are based on price prediction, traders in financial markets are typically interested in optimizing risk-adjusted performance such as the Sharpe Ratio, rather than the price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output ...

If we exclude the assumption of normality in return distributions, the classical risk–reward Sharpe Ratio becomes a questionable tool for ranking risky projects. In line with Sharpe thinking, a general risk–reward ratio suitable to compare skewed returns with respect to a benchmark is introduced. The index includes asymmetrical information on: (1) ‘‘good’’ volatility (above the benchmark) and ‘...

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...

Traditional risk-adjusted performance measures, such as the Sharpe ratio, the Treynor index or Jensen’s alpha, based on the mean-variance framework, are widely used to rank mutual funds. However, performance measures that consider risk by taking into account only losses, such as Value-at-Risk (VaR), would be more appropriate. Standard VaR assumes that returns are normally distributed, though th...

We propose to train trading systems and portfolios by optimizing objective functions that directly measure trading and investment performance. Rather than basing a trading system on forecasts or training via a supervised learning algorithm using labelled trading data, we train our systems using recurrent reinforcement learning (RRL) algorithms. The performance functions that we consider for rei...