To overcome that problem \citet*{linhares2017} applied panel data and vector autoregressions methods to investigate impact of current ratio, earnings per share and book value per share on Brazilian stock returns. In particular, \citet*{Ghosh_2016} showed an approach on how to incorporate \citet*{Fama_1993} into \citet*{Lucas_1978} dynamic model of asset pricing.
In addition, the validity of Ohlson's model - as any other discounted cash flow model - rests upon the hypothesis individuals are risk neutral which forbids us to inquire the role of individual risk preferences in expected returns.
This project proposes an empirical investigation into the impact of analysts' forecasts on expected and actual stock returns following the lead of \citet*{Kothari_2016}, who performed a widely literature survey over the impact of analysts' forecasts and asset pricing and concluded "[...] the current state of literature presents a promising opportunity for future research." (p. 209), that "although the implications of analysts’ forecasts to cash flows is clear and the empirical evidence is vast, the links between analysts’ forecasts and expected returns are less established." and later go further saying "Evidence on the link between analysts’ forecasts and expected returns is relatively scarce" (p. 212). We aim to contribute to fill this gap.
Therefore our planned contribution is not only finding the relation between analysts’ forecasts and expected returns but also to deal with this issue in a asset pricing framework where we can investigate the role of risk aversion and individual discount rates in this relation.
Theoretical Background and Main Empirical Hypotheses
From a theoretical viewpoint, in equilibrium agents' expectations collapse into actual prices as clearly posed in \citet{Lucas_1978} and \citet{Breeden_1979}. They say nothing though about the role of accounting numbers in the formation of those expectations. Actually, they are compatible with \citet*{Fama_1970} semi-strong market efficiency hypothesis where all public information is somehow already into market prices, which doesn't leave room for any kind of forecast based on accounting information. On the other side, the empirical literature derived from \citet*{FAMA_1992} and \citet*{Fama_1993} finds price effects of accounting indices in expectation of returns while \citet{OHLSON_1995} develops a model where accounting data matters in a, as the author says, "neoclassical framework" (p. 662), which means in his terms that "value equals the present value of expected dividends" (p. 662).
Indeed, in Lucas setting, we can can have a twofold interpretation of analysts' forecasts. The first one is to suppose analysts' forecasts are in agents' information set while the second interpretation rests on the fact all agents are equal and then the analysts are themselves the agents who is solving Lucas problem. The first interpretation implies strong correlation and even causality in the sense of \citet{Granger_1969} between forecasts and actual prices while the second interpretation makes actual prices converge in distribution to analysts' forecasts.
Also Lucas model implies marginal rate of inter-temporal substitution, risk aversion and discount factor may be estimated from macroeconomic data since the model has a representative agent. Besides, this approach allows to rationalize stock analysts forecasts, in particular, when it comes to fundamentalist analysis.