An algorithmic approach to investment
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Date
2019
Authors
Paskarammorthy, AB
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Abstract
The objective of this research is to develop a framework for an online low-frequency investment
algorithm and compare its performance against the equally-weighted and market-cap weighted
benchmark portfolios. The research presents an algorithmic framework for a control system that
automates quantitative active portfolio management. The control system sequentially constructs
a strategic benchmark portfolio to earn systematic risk premiums, and an active tactical portfolio
to exploit temporary opportunities to earn excess returns.
Portfolio controls are determined through mean-variance optimisation. The online property of
the control system is achieved through recursively estimating the mean-variance inputs with an
adaptive algorithm. Inputs for the systematic benchmark portfolio are determined using a
rational asset pricing model. Active returns are determined with an ad-hoc predictive model. Risk
factors are selected by the investor and excess returns are defined against these factors. Tactical
bet size is determined by the relative realised predictive accuracy between systematic and active
expected return predictions. Portfolios are robust to estimation errors through offline
regularisation of the mean and covariance estimates.
A specific configuration of the framework is implemented and tested out-of-sample using
approximately ten years of weekly data from the JSE. The out-of-sample results did not
demonstrate statistically significant out performance over the benchmarks on a gross and net
basis. Sharpe-Ratio point estimates over the out-of-sample period indicated benchmark
out performance on a gross basis, but benchmark under performance after incorporating
transaction costs.
Description
dissertation submitted in fulfillment of the requirements for the degree of Master of Science
in the
School of Computer Science and Applied Mathematics,
University of the Witwatersrand, Johannesburg.
February , 2019