Learning theory and equity valuation: an empirical analysis.
| Autor | Zoratto Sanvicente, Antonio |
| Cargo | Report |
-
Introduction and Objectives
Predictability and excess volatility involving stock returns are topics of major concern in the finance literature. These empirical anomalies contradict the efficient market and rational investor hypotheses that are basic to several pricing models. The learning literature attributes the occurrence of such phenomena to parameter revision by rational agents in their dividend forecasting models. Pástor and Veronesi (2003) have developed a model that relates learning concepts to equity valuation. One of the model's implications is that the market-to-book (M/B) ratio is positively related to the uncertainty about a firm's future profitability. Since that uncertainty declines over time, thanks to the learning effect, the model predicts that a younger firm should have a higher M/B ratio than an identical, albeit older firm.
The authors tested that implication using the Fama and MacBeth (1973) procedure with annual data for listed companies in the U.S. market, covering the 1962-2000 period. The results confirmed the model's predictions.
The objective of the present article is to test the implication of the Pástor and Veronesi (2003) model for the effect of age on the M/B ratio using data for companies listed at the BOVESPA. However, a different econometric procedure is adopted. Panel data models are used, in such a way as to incorporate firm-specific effects and time-series effects on all sample data. Additional conjectures are proposed and tested regarding the learning process.
The article is structured as follows: in the next section, a review of the literature is presented, and this is followed by a discussion of the econometric methods used. Section 4 presents the data and their descriptive statistics. Section 5 presents the test results. Section 6 extends the empirical analysis to conjectures on the learning process, while section 7 contains the article's conclusions.
-
Literature Review
According to Fama (1970), an efficient market is that in which current prices reflect all available information. This implies that, whatever expected return model is used, the information available at that moment is fully utilized in the determination of equilibrium returns. A market in which (i) there are no transactions costs, (ii) all agents have costless access to complete information, and (iii) all agents agree as to the implications of such information for the prices of all assets, is certainly an efficient market. These conditions, while sufficient, are not necessary for market efficiency, i. e., their absence does not automatically lead to the market inefficiency, but may be a cause of inefficiency. (1)
The most significant implication of the efficient market hypothesis is the impossibility, on the basis of currently available information, of setting up an investment strategy that will produce above-equilibrium returns.
A complementary hypothesis is that of rational expectations. According to Copeland et al. (2003), the rational expectations hypothesis predicts says that asset prices are determined by their expected cash flows. Thus, a market with rational expectations is an efficient market, since prices will reflect all existing information.
The most widely known expect return model, both among academics and market practitioners, is the Capital Asset Pricing Model (CAPM), developed by Sharpe (1964), Lintner (1965) and Black (1972). For the purposes of this paper, the main predictions are: (i) an asset's expected return is linearly related to its beta (its measure of non-diversifiable risk), (ii) beta is sufficient for explaining contemporaneous return differences in a sample of assets.
However, there is ample literature providing empirical evidence of return predictability and excess volatility, that is, facts that contradict the efficient market and rational investor hypotheses, as well as CAPM implications.
For example, Fama and French (1992) obtained evidence, for the 1963-1990 period, indicating that returns for U.S. stocks are strongly correlated with size, as measured by market capitalization (M) and with the book-to-market ratio (B/M), and weakly correlated with beta Fama and French (1993, 1996) suggest that the anomalies not explained by the CAPM can be captured by a three-factor model including: (i) the excess return on the market portfolio relative to the return on the risk-free asset; (ii) the difference between the returns on a low market value stock portfolio and the returns on a high market value stock portfolio ("size effect"); and (iii) the difference between the returns on a high book-to-market ratio stock portfolio and the returns on a low book-to-market ratio stock portfolio ("value effect"). Jegadeesh and T. (1993) present evidence for the U.S. market, using a 1965-1989 sample, indicating that strategies long on winners and short on losers in the 3-12 preceding months provide abnormal returns for one year after the construction of the corresponding portfolios.
LeRoy and Porter (1981) and Shiller (1981) argue that stock price volatility is much higher than would be justified by changes in expectations regarding future dividends. Shiller computed the upper bound for stock return volatility given by the efficient market hypothesis. He found evidence indicating that the volatilities of the Standard and Poor's 500 index and the Dow Jones Industrial Average were more than five times above that upper bound. This comparison covered the 19871-1979 period for the Standard e Poor's 500 index, and the 1928-1979 period for the Dow Jones Industrial Average. The author argues that the difference is too large to be explained by measurement errors, index composition problems, or tax legislation changes.
According to Brav and Heaton (2002), two major theoretical approaches have emerged as explanations for such anomalies. The first one uses behavioral factors to relax the assumption that investors process information in a fully rational manner. In behavioral theories, investors suffer from cognitive biases and, although they know the economy's fundamental structure, they act irrationally.
The second theoretical approach, the so-called structural rational uncertainty approach, preserves the rationality assumption, but relaxes the assumption that agents have complete knowledge of the economy's fundamental structure. This approach explores the distinction between rational expectations and rational investors. In a rational expectations environment, rational investors make statistically optimal decisions in a world in which they possess all relevant structural information. Outside the realm of rational expectations, investors still make statistically optimal decisions, but do not have full knowledge of the economy's structure.
For example, even when an investor knows that a firm's profitability follows a mean reversion process, he/she will not have access the true value of the reversion parameter, and his/her decisions will be based on estimates. If the economy's parameters were constant overtime, the learning process would eliminate financial anomalies.
Brav and Heaton (2002) compare the two theoretical approaches emphasizing deviations relative to the rational expectations hypothesis. They examined simple models in which representative investors must estimate unknown relevant parameters in order to determine an asset's intrinsic value. In the behavioral approach, investors display a conservative bias or give large weight to the more recent data. In the structural rational uncertainty approach, investors adopt bayesian techniques when estimating parameters. The focus of their analysis is in the overreaction and underreaction phenomena. They conclude that, even though different hypotheses had been relaxed, the mathematical and predictive similarities of the two models make it impossible to distinguish one from the other.
Timmermann (1993) uses the structural rational uncertainty and learning approach to present the intuition for the occurrence of excess volatility and return predictability.
Consider a rational agent using least square techniques to estimate dividend growth rates, and that the agent obtains an average growth estimated below the true growth rate. Therefore, the stock price will be lower than its intrinsic value, since the first is equal to the present value of expected dividends. The subsequent dividend payment will produce a high rate of return for the investor, for two reasons: (i) the dividend yield (dividend/stock price) will be high due to the low stock price; and (ii) the revision of the average growth rate of future dividends will result in the stock's appreciation. This dynamics will result in positive correlation between dividend yield and stock returns.
As to the effect on volatility, Timmermann (1993) considers a dividend shock in order to assess the implications of a rational expectations model against those of a model with learning. In the rational expectations model, stock prices are proportional to dividends and, hence, the dividend shock will be transmitted as a proportional price shock. Learning implies an additional effect on stock price, since the estimate of the average dividend growth rate is also influenced by the dividend shock. Using simulation, Timmerman finds evidence for the generation of statistically significant correlations between dividend yields and future returns by the learning effects. In the meantime, excess volatilities are observed only in small samples, since, as sample size increases, the estimated parameter converges it its true value. This ends up reducing the effect of learning on volatility.
Lewellen and Shanken (2002) argue that tests of market efficiency are unable to distinguish between a market with learning and an irrational market. They demonstrate that the empirical properties of returns may diverge significantly from those perceived by investors, even when the efficient market and rational investor hypotheses...
Para continuar a ler
PEÇA SUA AVALIAÇÃOCOPYRIGHT GALE, Cengage Learning. All rights reserved.
