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  Stock market time-varying expected returns: A global and historical perspective


   College of Arts & Social Sciences

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Prof A Black, Dr O Klinkowska  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

This project studies stock market time-varying expected returns (also known as discount rates or risk premium*) using international data over the time period 1900 – 2011 (and sub-periods). The objective is to understand the reasons why stock market prices change and to see if common sources across countries drive stock market movements. This work is predominantly empirical using theoretical developments in the time-varying returns literature to motivate the direction of study and choice of appropriate econometric techniques. In so doing, the study seeks to contribute a deeper understanding to key asset pricing questions such as:

1) Why do stock market returns change over time?

2) If returns are changing then stock prices are changing – how can we explain these changes?

3) Do investors, on average, care more about expected future cash flows or risk when decisions are made to buy and sell stocks? Can we find this out from ratios that relate fundamentals to price?

4) Are time-varying expected returns related to recessions and other economic activity and can we capture this from a global and historical data set (1900 – 2011)?

Using data on stock market returns, bond market returns, exchange rates, consumption and gross domestic product from seventeen countries (Australia, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, Norway, South Africa, Spain, Sweden, Switzerland, U.K., and the U.S.) this study will provide a careful empirical analysis using time series and panel data econometrics. The econometric applications are motivated by theoretical developments including inter-temporal asset pricing models and concepts such as the Fama-French three factor model.

Econometric techniques include: Vector autoregression, co-integration, forecasting, and panel and pooled data techniques. Depending upon the results it is also possible that ARCH/GARCH plus state space models and Kalman filtering will be utilised.

In summary, the study would help flesh out the reasons why stock prices change by focussing on expected return variation over time and across countries and offering deep explanations for the findings.

 About the Project