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Title: Essays in factor-based investing
Author: Guo, Tengyu (James)
ISNI:       0000 0004 8501 8292
Awarding Body: London School of Economics and Political Science (LSE)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2019
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My thesis explores three questions in factor-based investing. In the first chapter, I study the correlation risk in trading stock market anomalies. I propose a simple time-series risk measure in trading stock market anomalies, CoAnomaly, the time-varying average pairwise correlation among 34 anomalies, which helps to explain both the time-series and the cross-sectional anomaly return patterns. Since correlations among underlying assets determine the portfolio variance, CoAnomaly is an important state variable for arbitrageurs who hold diversified portfolios of anomalies to boost their performance. Empirically, I show that, first, CoAnomaly is persistent and forecasts long-run aggregate volatility of the diversified anomaly portfolio. Second, CoAnomaly positively predicts future average anomaly returns in the time series. Third, in the cross-section of these 34 anomaly portfolios, CoAnomaly carries a negative price of risk. In the second chapter, instead of studying multiple anomalies in a portfolio, I focus on one specific anomaly, momentum. I find that the momentum spread negatively predicts momentum returns in the long-term, but not in the following month. I further decompose the momentum spread into the spread of young or old momentum stocks based on how long the stock has been identified as a momentum stock. I show that the negative predictability is mainly driven by the old momentum spread. As these old momentum stocks are more likely to be exploited by arbitrageurs, these findings suggest that momentum is amplified by arbitrage activity and excessive arbitrage destabilizes the asset prices and generates strong reversals. In the third chapter, I revisit the robust diversification of factor investing and study the intertemporal consideration of an anomaly investor. Motivated by Campbell et al. (2017), I use vector autoregressions (VAR) and estimate an intertemporal CAPM with stochastic volatility for market-neutral investing with the focus on a portfolio of 34 anomalies. Interestingly, based on my estimation, only the correlation-induced volatility news carries a significant risk premium, which echos my findings in the first chapter.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HB Economic Theory ; HG Finance