Mark Masuoka is a successful money manager based in Newport Beach, California. His firm, Fast Forward Forecasting is best known for using superior macro-economic forecasting to time the stock market. Currently, he is bearish on the stock market and is invested 50% invested in the S&P and 50% invested in T-bills and high-grade commercial paper. He is concerned that the U.S. market is overvalued at current levels, but he does not want to be out of the market entirely.
Mark claims that dividend yields are a strong indicator of whether the market is over-valued, however he has no specific quantitative model that measures this relationship. Mark hired Dick Cassama as an intern for the summer. He gave him what he believed to be a straightforward task: to test whether dividend yields have predicted stock market movements in the past, and to identify key thresholds that should indicate when to move out of stocks into T-bills. Masuoka also asked Dick to provide some estimates of the reliability of the prediction model, and some measure of how much better a client will perform if they use his management services.
Dick decided to use historical data, and some statistical methods in order to address Masuoka's request. In addition, Mark wondered if their might be additional factors that could be used to time the stock market. He decided to build his own tactical asset allocation model based on past data.
Prepare an analysis of dividend yields as predictors of the S&P 500. In addition, you have the option of developing your own tactical asset allocation model, and presenting the results. In your analysis of Masuoka's and your own tactical allocation strategy, you should address the following issues:
1) How significant is the prediction power for short-horizon returns (one-year) and for long-horizon (four to ten year) returns?
2) How would you exploit this strategy to make money?
3) Is the effect confined to any specific time periods? Is it only useful when yields are high or low?
4) How can you reasonably estimate trading profits?
5) What is an appropriate benchmark against which to measure the portfolio performance? How can you describe the systematic risk of the strategy?
6) How can you use your model in a mean-variance framework to improve the inputs?
7) Based upon your model, what do you expect for today's market? How reliable is your prediction?