Mutual fund industries in emerging markets : evidence from China. (2019)
Type of ContentElectronic Thesis or Dissertation
Degree NameDoctor of Philosophy
PublisherUniversity of Canterbury
AuthorsZhan, Hannah Cheng Juanshow all
There are three aims in this thesis: the first is to test, in the context of the Fama and French Five-Factor-Model, what combination or combinations of fac- tors best explain the stock return variations in China. The second is to test, through two sensitivity analyses, whether the factors’ explanatory power can be increased significantly by redefining their cutting points or by adopting differ- ent factor construction methods. And finally, to test whether analysing mutual fund performance using the different models produces fundamentally different conclusions about funds’ performance.
To serve these purposes, we set up five chapters. Chapter 1 gives an intro- duction and reviews literature on the material discussed in chapters 2 through 5. Chapter 2 tests various combinations of factors specified in the Fama and French Five factor model and investigates what combinations of factors best explain the stock return variation in our study period. Chapter 3 contains two sensi- tivity analyses which test whether 1) by redefining cut-points for the size factor produces a factor with significantly different explanatory power; 2) by adopt- ing different factor construction methods, whether significantly different size and value factors can be produced. In chapter 4, we use various traditional capital asset pricing models, in particular, the CAPM model, our optimal model found in chapter 2, Fama and French Three factor model and the Fama and French Five factor model, to examine the Chinese equity mutual fund performance. Finally, in chapter 5, we give our conclusions and limitations of this research.
We decided, based on chapter 2, that the model consisting a market and a size factor fares best in the Chinese A-Share stock market. This decision was made based on a number of considerations including the adjusted R-Squared, the trade-off between the adjusted R-Squared and the multicollinearity problems, the GRS test and the data credibilities. Chapter 3 indicates that various cutting points and different methods of factor constructions do not influence the factors’ explanatory power significantly. More interesting are the findings in chapter 4: using our optimal model containing a market and a size factor, the risk-adjusted return is much lower than the other factor models. This result was robust when we split our study period into two sub-periods.