Cost-efficient factor investing in emerging equity markets

When factor investing is applied to emerging equity markets, due to the universe’s illiquid structure, the market friction must be considered. Risk-adjusted on-paper returns of such strategies look particularly appealing, but significant implementation hurdles stand in their path. While factor investing has been well-examined in literature, research gaps remain. This dissertation undertakes three comprehensive studies to resolve existing research gaps concerning portfolio cost-efficiency regarding the trade-off between return and implementation costs in emerging equity markets. Various approaches for further improvement of this trade-off extend the research. The first study demonstrates a factor-based strategy in emerging markets and provides a better understanding of the above trade-off. Multiple sensitivity analyses present the benefits of a first cost-mitigation approach. The second study further seeks to understand equity portfolios’ return and cost dynamics in a macroeconomic context. Leading indicators from developed and emerging markets are utilized to forecast the near-term factor regime. This prediction is adaptively implemented into the portfolios, adds a timing component, and highly increases the cost-efficiency. The third study extends the efficacy of the researched cost-mitigation strategy by implementing the benefits of a stock liquidity prediction based on state-of-the-art machine learning models.