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Type  Master's Thesis 
Scope  Disciplinebased scholarship 
Title  Explaining StockMarket Factor Rotation 
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Institution  University of Zurich 
Faculty  Faculty of Business, Economics and Informatics 
Number of Pages  90 
Date  April 2019 
Zusammenfassung  This thesis examines the returns of the most common equity factor strategies and tries to explain, based on the evolutionary finance model by Evstigneev et al. (2016), the timeseries variability in their risk premia. Despite the increasing interest in socalled smart beta strategies and the growing number of suggested factors, only few academics examine the impact of capital transfers to factor strategies (Harvey et al. (2016), Cong and Xu (2016), Ratcliffe et al. (2017)). Similar to prior studies by, amongst others, Arshanapalli et al. (1998), Ahmed et al. (2002), Amenc et al. (2003) and Asness et al. (2013) I show that factor returns are timevarying and thus diversifying across several factors or timing individual factor exposures could enhance portfolio returns. Furthermore, I present evidence for momentum in factor returns over the next one to twelve months which is consistent with Ehsani and Linnainmaa (2017), Arnott et al. (2018) and Gupta and Kelly (2018) who document strong and significant profitability of timeseries and crosssectional factor momentum strategies. However, most of the factor rotation strategies neglect potential offsetting effects of the capacity and crossimpact of the underlying factors (Doskov et al. (2018)). In this thesis, I present a new approach how to endogenize the population weight in the context of the evolutionary finance model by Evstigneev et al. (2016) by aggregating market valueweighted factor betas across stocks. The population weight can be regarded as a proxy of total capital or share of the total market capitalization allocated to a specific strategy. I find that population weights are timevarying but less than the corresponding factor risk premia. Furthermore, my results demonstrate that future population weight changes can be predicted by their past changes and hence suggesting the presence of momentum in population weights. Lastly, I detect a negative relationship between an increase in a factor's population weight and its corresponding risk premia. These findings are mostly consistent with Doskov et al. (2018). Finally, I demonstrate several outofsample timeseries and crosssectional multifactor strategies which are based on the findings of momentum in population weights and the impact matrix by Doskov et al. (2018). I show that the timeseries and crosssectional strategy (longonly as well as longshort) generate attractive riskadjusted returns and statistically significant alphas relative to a passive equallyweighted strategy over various holding and lookback periods. 
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