This thesis includes three essays on topics related to the predictability of market returns. I investigate i) the predictability of market returns from an adjusted version of cay ratio (cayadj), ii) the explanatory power of a conditional version of the consumption-CAPM which uses predictor variables to scale the pricing kernel, and iii) whether information about future market returns can be extracted from a large set of commodity data. The first essay studies the predictive ability of cayadj . In Campbell and Mankiw (1989), the consumption-wealth ratio is represented as a linear function of expected market returns and consumption growth. Lettau and Ludvigson (2001) build their study on Campbell and Mankiw (1989) and estimate the ratio cay as a proxy for the consumption-wealth ratio, assuming that the fluctuation in expected consumption growth is constant. I argue that the variation in expected consumption growth should be taken into consideration and propose adjusting the cay ratio by the estimates of expected consumption growth. After making the adjustment, I find that the predictabilities of market returns, particularly at annual, bi-annual, and tri-annual horizons, are greatly improved. The significant predictive ability of cayadj still holds in out-of-sample forecasts. The second essay examines the performance of a conditional version of the consumption-CAPM, where conditioning variables are used to scale the pricing kernel. I find that incorporating the conditioning information into the standard consumption-CAPM greatly improves the performance in asset pricing tests, particularly when using cayadj as the conditioning variable. Moreover, the performance of conditional consumption-CAPM is as good as the ultimate consumption risk model (Parker and Julliard, 2005) which measures the consumption risk over several quarters. Further tests show that the factors of conditional consumption-CAPM drive out the consumption risk measured over several quarters. The third essay evaluates the ability of lagged commodity returns to forecast market returns. In order to exploit the predictive information from a relatively large amount of commodity returns, I apply the partial-least-squares (PLS) method pioneered by Kelly and Pruitt (2013). I find that the commodity returns measured over previous twelve months show strong predictive power in monthly and three-month forecasts, in-sample and out-of-sample. The findings are robust to controlling for risk factors such as momentum, Fama-French three factors and industry returns previously identified to be significant predictors of market returns (Hong, Torous and Valkanov, 2007).
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:680035 |
| Date | January 2016 |
| Creators | Wang, Jingya |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/empirical-studies-on-stock-return-predictability(682e59f0-6a33-4c0e-b0ca-a7f1128b1f7d).html |
Page generated in 0.002 seconds