This thesis studies the sources of the returns from the momentum strategy and attempts to find some hints for the heated debate on the market efficiency hypothesis over the past twenty years. By decomposing the momentum returns from a mathematical model, we investigate directly the contributors and their relative importance in generating these momentum returns.
Our empirical results support that autocorrelation of own stock returns is one of the driving forces for the momentum expected returns. The magnitude of the autocorrelation decreases as the ranking period becomes more remote. The second important source comes from the cross-sectional variation of the expected returns in the winner and loser portfolios at a given time. The third important source is the difference of the expected returns between the winner and loser portfolios. To our surprise, the cross-autocovariance does not contribute much to the momentum expected returns. Thus, the lead-lag effect can cause momentum returns, but its impact is not as significant as we had anticipated.
More importantly, by changing the weights of the winner and loser portfolios, we find that the own-autocovariance of the winner portfolio is almost negligible, compared to that of the loser portfolio. The returns of the winners are much more random than those of the losers. This asymmetric own-autocovariance found in the return decomposition provides another underlying explanation to the recent finding that the contribution of the winner and loser portfolios to the momentum returns is asymmetric, and it is the losers, rather than the winners, that drive the momentum returns.
Therefore, the market may not be as efficient as we believed before.
| Identifer | oai:union.ndltd.org:UTENN/oai:trace.tennessee.edu:utk_gradthes-1848 |
| Date | 01 December 2010 |
| Creators | Zhang, Yu |
| Publisher | Trace: Tennessee Research and Creative Exchange |
| Source Sets | University of Tennessee Libraries |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses |
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