Regime Based Portfolio Optimization: A Look at the South African Asset Market - Mdluli, Nkosenhle S. (University of the Witwatersrand, Johannesburg, 2023)
Financial markets change their properties (i.e mean, volatility, correlation, and distribution) with time. However, traditional portfolio optimization strategies seek to create static, all weather portfolios oblivious to this and current economic conditions. This produces portfolios that are unable to predict events with excessive skewness and kurtosis. This research investigated the difference in portfolio percentage return, of portfolios that incorporate regimes against one that does not. HMMs, binary segmentation, and PELT algorithms were used to identify regimes in 7 macro-economic features. These regimes, with regimes identified by the SARB, were incorporated into Markowitz’s mean-variance optimization technique to optimize portfolios. The base portfolio, which did not incorporate regimes, produced the least return of 761% during the period under consideration. Portfolios using HMMs identified regimes, produced, on average, the highest returns, averaging 3211% whilst the portfolio using SARB identified regimes returned 1878% during the same period. This research, therefore, shows that incorporating regimes into portfolio optimization increases the percentage return of a portfolio. Moreover, it shows that, although HMMs, on average, produced the most profitable portfolio, portfolios using regimes based on data-driven techniques do not always out-perform portfolios using the SARB identified regimes.
A research report submitted in partial fulfillment of the requirements for the degree of Master of Science in the field of e-Science, to the Faculty of Sciences, School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2023.
Keywords: Financial markets; Economic conditions; PELT algorithms; Binary segmentation; Macro-economic features; UCTD; SDG-8: Decent work and economic growth