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(2021) Bay, JoshuaThesisThis paper explores extensive asset allocation possibilities and asset pricing tests shedding light into the cross-sectional and time-varying nature of combining multi-asset alternative risk premia. Existing literature in the multi-asset risk premia space is limited in terms of allocation studies as most research on combining factor exposures are only in the single-stock equity space. The literary gap is further exacerbated over the last decade with the explosion of new factors discovered. To address this, key asset allocation techniques commonly used in allocating across long-only traditional asset classes and equity factors are applied to multi-asset risk premia. The results seem to suggest the key assumptions of expected returns, followed by expected risks, higher moments and then lastly correlations in this order of importance are associated with building portfolios with higher risk-reward. To the best of my knowledge, this is one of the first papers that provide a comprehensive and practical study of a wide array of portfolio implementation approaches to multi-asset risk premia. This paper serves as an annex for investors to better understand the interaction and concentration of multi-asset risk premia exposures to meet their desired investment profiles.
(2021) Besley, MichaelThesisBoth industry and academic research document the sustained outperformance of Australian small capitalisation (cap) managers with regard to market benchmarks and standard academic models. In contrast to both their large company peers and overseas fund manager returns, the high relative returns generated by these small company managers have continued despite increased competition from new managers. This paper confirms the persistence of these anomalous returns and explores the sources of alpha generation by Australian small cap managers. The commonly used Carhart factor model does not explain the persistence of this alpha. Carhart alpha averages 0.3% per month for the group, with 22 out of 46 funds having statistically significant alphas. By adding a combination of factors to the standard Carhart model approximately two thirds of this alpha can be explained. These factors include betting against beta, avoidance of stocks with lottery characteristics, a preference for stocks with strong profitability and strong balance sheets while avoiding ‘junk’ stocks. After controlling for all these factors, average alpha declines to 0.08% per month with only four funds still having statistically significant alpha. While most managers avoid high beta and lottery stocks, the better performing funds demonstrate higher loadings away from lottery and distressed stocks and towards profitability factors than their poorer performing peers.