Efficient Portfolio Selection

Merak believed that an efficient frontier analysis method that combined the robustness of the Monte Carlo approach with the confidence of the Markowitz approach would be a very powerful tool for any industry. However, it soon became clear that there are other ways to address the problem that do not require a Monte Carlo component. Three subgroups were formed, and each developed a different approach for solving the problem. These were the Portfolio Selection Algorithm Approach, the Statistical Inference Approach, and the Integer Programming Approach

Finite Horizon Portfolio Selection

We study the problem of maximising expected utility of terminal wealth over a nite horizon, with one risky and one riskless asset available, and with trades in the risky asset subject to proportional transaction costs. In a discrete time setting, using a utility function with hyperbolic risk aversion, we prove that the optimal trading strategy is characterised by a function of time (t), which represents the ratio of wealth held in the risky asset to that held in the riskless asset. There is a time varying no transaction region with boundaries b(t) < s(t), such that the portfo- lio is only rebalanced when (t) is outside this region. The results are consistent with similar studies of the in nite horizon problem with in- termediate consumption, where the no transaction region has a similar, but time independent, characterisation. We solve the problem numerically and compute the boundaries of the no transaction region for typical model parameters. We show how the results can be used to implement option pricing models with transaction costs based on utility maximisation over a nite horizo

Optimal portfolio selection for cash-flows with bounded capital at risk.

Optimal; Optimal portfolio selection; Portfolio; Selection; Cash flow; Capital at risk; Risk;

Pricing exotic options under local volatility.

Optimal; Optimal portfolio selection; Portfolio; Selection; Cash flow; Capital at risk; Risk; Pricing; Options;

Portfolio selection using neural networks

In this paper we apply a heuristic method based on artificial neural networks in order to trace out the efficient frontier associated to the portfolio selection problem. We consider a generalization of the standard Markowitz mean-variance model which includes cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount of capital to be invested in each asset. We present some experimental results obtained with the neural network heuristic and we compare them to those obtained with three previous heuristic methods.Comment: 12 pages; submitted to "Computers & Operations Research

Defensive online portfolio selection

The class of defensive online portfolio selection algorithms,designed for fi nite investment horizon, is introduced. The Game Constantly Rebalanced Portfolio and the Worst Case Game Constantly Rebalanced Portfolio, are presented and theoretically analyzed. The analysis exploits the rich set of mathematical tools available by means of the connection between Universal Portfolios and the Game- Theoretic framework. The empirical performance of the Worst Case Game Constantly Rebalanced Portfolio algorithm is analyzed through numerical experiments concerning the FTSE 100, Nikkei 225, Nasdaq 100 and S&P500 stock markets for the time interval, from January 2007 to December 2009, which includes the credit crunch crisis from September 2008 to March 2009. The results emphasize the relevance of the proposed online investment algorithm which signi fi cantly outperformed the market index and the minimum variance Sharpe-Markowitz’s portfolio.on-line portfolio selection; universal portfolio; defensive strategy

Age dependent portfolio selection

This paper addresses the issue of portfolio risk exposure as a function of age, and it focuses the debate by presenting detailed cross-sectional evidence about individual portfolios. It provides new empirical results that characterized the relationship between age and the risk exposure of individual portfolios. The evidence from cross-sectional data suggests that individuals do not follow behavior proscribed by economic theory or by Wall Street advisors, rather the results of this paper suggest that current body of theoretical literature does not adequately describe the behavior of individuals. It implies that a satisfactory model ofindividual behavior needs to focus on factors not linearly correlated with age.Demography ; Saving and investment

Unified Framework of Mean-Field Formulations for Optimal Multi-period Mean-Variance Portfolio Selection

The classical dynamic programming-based optimal stochastic control methods fail to cope with nonseparable dynamic optimization problems as the principle of optimality no longer applies in such situations. Among these notorious nonseparable problems, the dynamic mean-variance portfolio selection formulation had posted a great challenge to our research community until recently. A few solution methods, including the embedding scheme, have been developed in the last decade to solve the dynamic mean-variance portfolio selection formulation successfully. We propose in this paper a novel mean-field framework that offers a more efficient modeling tool and a more accurate solution scheme in tackling directly the issue of nonseparability and deriving the optimal policies analytically for the multi-period mean-variance-type portfolio selection problems