This thesis studies the opportunities arising from algorithmic trading that is gradually becoming available to private investors. For this purpose three portfolio selection models are crossed with three rebalancing strategies. The result is eight individual algorithms that build partly on observed practical application and partly on theoretical portfolio optimisation. The first category of algorithms is non-rebalancing and features a naïve portfolio and a Markowitz portfolio. The second category is fixed period rebalancing algorithms featuring a naïve, a Markowitz and a Gârleanu-Pedersen portfolio model. The third category, are the same three portfolio models with dynamic rebalancing, conditional on increased expected utility. These eight combinations are formulated in matrix algebra for algorithmic application. Subject to transaction costs and predicted returns, the eight algorithms are run in a back-testing simulation. The simulation is run on daily price observation from a portfolio of 58 stocks from the S&P500 index and the U.S. 10 year treasury yield from 1994-2014. In addition, historical price data from 1980-1994 is applied in ARIMA models for long term and short term price forecasting. Four quantitative result measures are drawn from the simulation to be used in a comparative evaluation of the eight algorithms. It is found that automatically rebalanced portfolios can yield a superior utility and that the dynamic algorithms are significantly outperforming other algorithms on transaction costs. It is also shown that the Markowitz portfolio and especially the Gârleanu-Pedersen model are significantly more prone estimation errors from poor return predictability.
|Educations||MSc in Applied Economics and Finance, (Graduate Programme) Final Thesis|
|Number of pages||119|