The Research Problem and its Importance.

The role of information in determining the dynamics of stock returns is one of the cornerstone problems in financial economics since \citet*{Bachelier_1900}.  His results on the irrelevance of past information (including accounting one) to forecast stock returns were more widely known as efficient market hypothesis after \citet*{Fama_1965} and \citet*{Fama_1970}. Actually, that means any information is incorporated instantaneously in stock prices so that no information set can help forecasting stock prices. For instance, \citet*{marquespolitical} questioned whether political news could affect stock returns in Brazil and, after performing a thorough webscrapping to get news from most important media websites, found nothing but Presidential election itself seemed to have influenced stock returns despite they had even a death of one of the top Presidential candidates in the middle of campaign and Presidential impeachment process in their sample.
In accounting literature, though, the answer to Fama's challenging propositions came soon as \citet*{Beaver_1968} and \citet*{Ball_1968} found evidence that informational content of accounting data can to change investors' expectations and, then, stock returns.  Both approaches - informational content and market efficiency - has brought on an extensive literature as shown by \citet*{Malkiel_2003} and \citet*{Kothari_2001}.  These conflicting viewpoints nevertheless seem to have been converging as \citet*{Merton_1973} and \citet*{Lucas_1978} established the importance of agents' information set on describing martingale properties of stock prices.  In addition, \citet*{Hansen_1987} showed the conditioning information has a role in estimating stock returns and \citet*{Ross_1976} provided the theoretical fundamentals on which \citet*{Fama_1993}  built an empirical model which found evidence balance sheet numbers help pricing securities. Also,  \citet*{OHLSON_1995} includes accounting variables in a classical expected discounted dividend model to develop his valuation model. Finally, \citet{EASLEY_1996}\citet*{Easley_1997}\citet*{Easley_2002} and \citet*{Easley_2004} have given an theoretical explanation  of the importance of accounting numbers to correctly evaluate stock returns and also provided evidence of it allows us to unite both approaches. Recently, dealing with Brazilian data \citet*{linhares2017} applied panel data and vector auto regressions methods to investigate impact of current ratio, earnings per share and book value per share on Brazilian stock returns and found no evidence of it.  
 In particular, \citet*{Kothari_2016} surveyed the literature on analysts' forecast and asset pricing and presented  models which intended to link analysts' forecasts and expected returns in valuation framework (dynamic models) and in asset pricing framework (static models).  The dynamic models they presented are derived from  \citet{Ohlson_1995} while the static ones descend from \citet*{Easley_2004} and Sharpe (1964).   Notwithstanding \citet*{Kothari_2016} argue models in valuation framework are more successful in providing estimations for expected returns proxies than asset price ones, as it can be seen, for instance, in \citet*{Gebhardt_2001} and \citet*{Pastor2008}, those approaches have two main problems. 
The first one is that they are not akin to be easily statistically evaluated, because they use simulation methods instead of statistical inference.  The second problem concerns the fact that 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 main objective of this project is to investigate empirically the impact of analysts' forecasts on expected and actual stock returns and a secondary aim is to evaluate the information content of Fama and French factors to forecast stock returns all in a generalized dynamic set up where risk preferences and individual discount rates might have a role.  Actually, we follow the lead of \citet*{Kothari_2016} who 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).  

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).
Recently, \citet*{Ghosh_2016}  incorporated \citet*{Fama_1993} into  \citet*{Lucas_1978} dynamic model of asset pricing in order to factorize the stochastic discount factor in business cycle and Fama and French factors leaving room for macroeconomic and accounting factors to affect asset returns. Our approach to solve the problem we posed is to substitute for Ohlson's model for Lucas setting which means the pricing equation now is the following:
\(p_{t}= E_{t}\left\{ \beta. \frac{u'(c_{t+1})}{u'(c_{t})}.\left(p_{t+1} + d_{t+1}\right) | I_{t} \right\}\)
where \(p_{t}\) is stock price, \(\beta\) is individual inter-temporal discount, \(d_{t+1}\) is dividend paid at period  t+1 and \(I_{t}\) is the information set available to agents at period t. The whole equation means actual prices are the expected value of future cash flows discounted by the marginal rate of inter-temporal substitution given the information set available.