Working Paper: CEPR ID: DP6714
Authors: Robert P. Flood; Andrew K. Rose
Abstract: This paper applies the Meese-Rogoff (1983a) methodology to the stock market. We compare the out-of-sample forecasting accuracy of various time-series and fundamentals-based models of aggregate stock prices. We stick as close as possible to the original Meese-Rogoff sample and methodology. Just as Meese and Rogoff found for the case of exchange rates, we find that a random walk model of stock prices performs as well as any estimated model at one to twelve month horizons, even though we base forecasts on actual future fundamentals of dividends and earnings. Using this metric and for this sample period, aggregate stock prices seem to be as difficult to model empirically as exchange rates.
Keywords: aggregate; dividend; earning; exchange; forecast; fundamental; growth; model; rate
JEL Codes: F37; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| dividends and earnings (G35) | stock prices (G12) |
| present value of expected future dividends (G35) | stock price (P_t) (G19) |
| present value of expected future earnings (J17) | stock price (P_t) (G19) |
| fundamentals (Y20) | predictive accuracy of stock price models (G17) |
| stock prices (G12) | random walk model performance (C52) |