LMarques and dos Santos (2016) studied how political information affect stock returns using webscrapping methodology to get news from important media websites.
The answer from accounting literature were laid down by \citet*{Beaver_1968} and \citet*{Ball_1968} which found evidence that accounting data has informational value to change investors' expectations. Both approaches 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.
\citet*{Kothari_2016} surveyed the literature on presented models of expected returns in valuation framework (dynamic models) and in asset pricing framework (static models) that deals with . 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 ones. The second problem concerns the fact 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. To overcome the first problem \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.
This project deals with the second problem and proposes an empirical investigation into the impact of analysts' forecasts on expected and actual stock returns in a dynamic setting. 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).
Therefore this research aims to contribute to better understanding of the role of accounting information in stock returns. We will also investigate whether risk aversion and discount rates might be a channel through which accounting may impact on stock returns. In addition, we will test the relation between analysts’ forecasts and expected returns in a dynamic asset pricing framework.
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 also substitutes for Ohlson's model for Lucas setting, but we will explore the fact that, 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.