This work also characterizes analytically the dynamic properties of asset prices in the presence of learning effects. A large literature in finance shows that stock returns is, to some extent, predictable by means of forecasting variables such as dividend yields, interest rates and macroeconomic variables with a clear business cycle component. However, standard forecasting models essentially assume that the same forecasting specification stays in effect throughout the entire sample. If stock prices are predictable, simple notions of market efficiency would suggest that they should not be predictable for very long periods of time. The challenge is to identify the 'pockets in time' where predictability prevails.
Predictability is likely to be linked to the state of the economy.
In periods with abundant liquidity investors will exploit any predictable patterns, while in periods with low liquidity, such as during economic recessions, predictable patterns may still be present in stock prices. Part of this research focuses on forecasting models that separate the data on stock prices into two states, namely a recession state and an expansion state. I find that the best forecasting model is very different during recessions and expansions. Forecasting variables such as interest rate shocks and default premia have a large impact on stock prices during recessions and a much smaller impact during expansions. This research also finds that stock returns can be predicted only during the very short periods around turning points of the economic cycle.