This article studies optimal portfolio decisions with (long-term) liabilities for small-open economy based investors, including the optimality of currency hedging (Walker (2008a)). The representative investor is based in Chile, but results are likely to hold more generally. The problem is set up...

When should you harvest a tax loss? Tax-loss harvesting is a strategy used to improve the net of tax returns of a portfolio, where an investor deliberately sells assets that have incurred a loss in order to use these losses to offset current or future capital gains. However, an important...

In financial markets, the closing price serves as an important benchmark. We introduce a market model to analyze the stability of the closing price with presence of three types of volume: distorting volume, volume that targets the closing price, and volume that is unrelated to the closing price....

This thesis investigates models of market risk assessment based on genetic algorithms, with specific reference to asset portfolio choice under volatile market conditions. It does so by developing computational simulations of asset portfolios, which are then subjected to stressful price events. A...

We develop a novel Mean-Max Drawdown portfolio optimization approach using buy-and-hold portfolios. The optimization is performed utilizing a multi-objective evolutionary algorithm on a sample of S&P 100 constituents. Our optimization procedure provides portfolios with better Mean-Max Drawdown...

This paper finds the optimal consumption and asset allocation strategy for an Australian retiree who aims to maintain a level of minimum consumption. I use a discrete dynamic programming algorithm, with historical stock return distribution and a regime switching investment model, while taking...

We explore reinforcement learning methods for finding the optimal policy in the linear quadratic regulator (LQR) problem. In particular we consider the convergence of policy gradient methods in the setting of known and unknown parameters. We are able to produce a global linear convergence...