This paper discusses the role that Genetic Algorithms (GA) can have in determining asset allocation for multi sector funds. We present an asset allocation model where the investors' utility function departs from the quadratic utility function assumed by the standard Mean-Variance optimisation....

We propose a robust portfolio optimization approach based on Value-at-Risk (VaR) adjusted Sharpe ratios. Traditional Sharpe ratio estimates based on limited historical return data are subject to estimation errors. Portfolio optimization based on traditional Sharpe ratios ignores this uncertainty...

We consider a portfolio execution problem where a possibly risk-averse agent needs to trade a fixed number of shares in multiple stocks over a short time horizon. Our price dynamics can capture linear but stochastic temporary and permanent price impacts as well as stochastic volatility. In...

The Black-Litterman model aims to enhance asset allocation decisions by overcoming the problems of mean-variance portfolio optimization. We propose a sample based version of the Black-Litterman model and implement it on a multi-asset portfolio consisting of global stocks, bonds, and commodity...

We investigate trade execution strategies that maximize expected exponential utility. In contrast to existing literature our strategy dynamically executes both market and limit orders. We derive a Hamilton-Jacobi-Bellman equation for the optimal combination of these order types and solve it...

Maximizing the expected logarithmic utility, or equivalently the geometric mean, of a portfolio is a well-known yet controversially discussed objective. Nonetheless, it is an often used objective function for computing real-world portfolios and in particular it met a great amount of sympathy in...

For the problem of continuous time optimal portfolio selection, we found that the optimal strategies for investors with different performance criterions can be constructed using a limited number of fixed processes (mutual funds), for a incomplete market with a larger number of available risky...

Unknown model parameters, like expected returns, cannot be accurately estimated from short samples. Respective estimation error most likely leads to the portfolio, inconsistent with its target risk/return profile. We investigate the ways of reducing the impact of estimation error on portfolio...

In this paper, we use risk-sensitive control methods to solve a jump-diffusion Asset-Liability Management (ALM) problem. We show that the ALM problem admits a unique classical solution under two different sets of assumptions