دانلود مقاله ISI انگلیسی شماره 22454
عنوان فارسی مقاله

پیش بینی سود سهام در مدل ارزش فعلی ورود خطی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
22454 2012 21 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Predicting dividends in log-linear present value models
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Pacific-Basin Finance Journal, Volume 20, Issue 1, January 2012, Pages 151–171

کلمات کلیدی
پیش بینی - عملکرد سود - جریان نقدی - رشد سود سهام
پیش نمایش مقاله
پیش نمایش مقاله پیش بینی سود سهام در مدل ارزش فعلی ورود خطی

چکیده انگلیسی

In a present value model, high dividend yields imply that either future dividend growth must be low, or future discount rates must be high, or both. While previous studies have largely focused on the predictability of future returns from dividend yields, dividend yields also strongly predict future dividends, and the predictability of dividend growth is much stronger than the predictability of returns at a one-year horizon. Inference from annual regressions over the 1927–2000 sample imputes over 85% of the variation of log dividend yields to variations in dividend growth. Point estimates of the predictability of both dividend growth and discount rates are stronger when the 1990–2000 decade is omitted.

مقدمه انگلیسی

In a present value model, the market price-dividend ratio is the present value of future expected dividend growth, discounted at the required rate of return of the market. If the dividend yield, the inverse of the price-dividend ratio, is high, then future expected dividend growth must be low, or future discount rates must be high, or both. While there is a very large body of research focusing on the predictability of future returns by the dividend yield, the forecasting power of dividend yields for future dividend growth has been largely ignored. In fact, Cochrane's (2011) presidential address to the American Finance Association overlooks totally the predictive ability of the dividend yield to forecast future cashflows and concentrates entirely on the dividend yield's ability to forecast future returns.1 In this paper, I highlight the evidence of predictability of dividend growth by the dividend yield, and estimate the relative importance of future dividends for explaining the variation of the dividend yield. I begin by standard simple regressions of long-horizon dividend growth and long-horizon total returns (which include both capital gain and dividend income). To characterize the predictability of dividend growth and expected returns, I work with the log-linear dividend yield model of Campbell and Shiller (1988b). Although this setup only approximates the true non-linear dividend yield process, this approach maps the one-period regression coefficients directly to the variance decompositions.2 However, since long-horizon regression coefficients can be very different from one-period regression coefficients, I also run weighted long-horizon regressions following Cochrane (1992) to compute variance decompositions. Here, future dividend growth or returns are geometrically downweighted by a constant, which is determined from the log-linear approximation. In my analysis, I am careful to use robust t-statistics and account for small sample biases (see Nelson and Kim, 1993). Using a log-linear Vector Autoregression (VAR) as a data generating process, I show that Newey and West (1987) and robust Hansen and Hodrick (1980) t-statistics have large size distortions (see also Hodrick, 1992 and Ang and Bekaert, 2007). On the other hand, Hodrick (1992) t-statistics are well-behaved and have negligible size distortions. Simulating under the alternative hypothesis of dividend growth or return predictability by log dividend yields, I find that Hodrick (1992) t-statistics are also the most powerful among these three t-statistics. Whereas using Wald tests to determine the significance of variance decompositions produces severe small sample distortions, testing the variance decompositions from regression coefficients has much better small sample behavior. Further, if log dividend yields are used as predictive instruments rather than dividend yields in levels, the Stambaugh (1999) bias resulting from a correlated regressor variable is negligible. The first striking result is that using data from 1927 to 2000 on the CRSP value-weighted market index, dividend growth is strongly predictable by log dividend yields. A 1% increase in the log dividend yield, lowers next year's forecast of future dividend growth by 0.13%. Dividend growth predictability is much stronger at short horizons (one-year) than at long horizons. In contrast, returns are not forecastable by log dividend yields at any horizon, unless the returns during the 1990s are excluded. Second, if the 1990s are omitted, the evidence of both dividend growth predictability and return predictability becomes stronger.3 From 1927 to 1990, the magnitude and significance of the predictability coefficient of dividend growth still dominates, by a factor of two, the predictability coefficient of returns at an annual horizon. Without the 1990s, dividend growth predictability is significant at longer horizons (up to four years) with data at a monthly frequency. Third, using one-period regressions (restricted VARs) to infer the variance decomposition of dividend yields assigns over 85% of the variance of the log dividend yield to dividend growth over the full sample. This is because, at one-year horizons, the magnitude of the predictability coefficient of dividend growth is much larger than the predictability coefficient of returns. While it is hard to make any statistically significant statements about the variance decompositions using the asymptotic critical values from Wald tests, I can attribute a major portion of the variance of the log dividend yield to dividend growth, and this attribution is highly significant once I account for the size distortions of the small sample distributions. Finally, inference from weighted long-horizon regressions to compute the variance decomposition is treacherous because of the serious size distortions induced by the use of overlapping data. Use of Newey and West (1987) or robust Hansen and Hodrick (1980) standard errors leads to incorrect inference that attributes most of the variation in log dividend yields to expected returns. With robust t-statistics, no statistically significant statement can be made about the variance decompositions. However, the point estimates show that the predictability of expected returns, although small at short horizons, increases at long horizons, as found by Shiller (1981) and others. In contrast, while dividend yields strongly predict dividend growth at short horizons, the point estimates of the long-horizon predictability of dividend growth are insignificant and smaller. Why has the predictability of dividend growth been over-looked in the literature relative to the predictability of returns?4 Previous studies concentrate on the predictive regressions with expected total or excess returns and do not consider the predictability of dividend growth. For example, while Fama and French, 1988 and Hodrick, 1992 consider putting long-horizon expected (excess) returns on the LHS of a regression, they do not forecast long-horizon dividend growth with dividend yields. In Campbell and Shiller's (1989) VAR tests of the dividend discount model, dividend growth does not have its own separate forecasting equation by log dividend yields. In Campbell and Ammer (1993), no cashflows appear directly in the VARs even though past cashflows are observed variables. Instead, Campbell and Ammer specify the process for returns and only indirectly infer news about dividend growth from the VAR as a remainder term. In contrast to these studies, I explicitly run regressions with dividend growth on the LHS, and include dividend growth as a separate variable with its own law of motion in the overall data-generating process. Chen and Zhao (2009) also show that not including direct measures of cashflows and discount rates leads to incorrect inference about dividend growth predictability; all the VAR data-generating processes I consider include both returns and dividend growth. The rest of the article proceeds as follows. Section 2 describes the construction of dividend yields, growth rates and returns from the CRSP market index. Section 3 motivates the empirical work using Campbell and Shiller's (1988b) log-linear relation. Section 4 outlines the regression framework and compares the size and power of various robust t-statistics. I decompose the variance of the log-dividend yield in Section 5, imputed by one-period regressions and Cochrane (1992) long-horizon weighted-regressions. Section 6 concludes.

نتیجه گیری انگلیسی

High dividend yields significantly predict low future dividend growth. The predictability of dividend growth at short horizons (1–2 years) dominates the estimates of predictability of expected returns from dividend yields. Dividend growth predictability is even stronger when the 1990s are excluded from the sample. The strong short horizon predictability of dividend growth means that variance decompositions implied by one-period regressions assign over 85% of the variance of log dividend yields to dividend growth. This high variance decomposition to dividends is highly significant using inference from regression coefficients computed with robust standard errors and Wald tests with critical values from small sample distributions. Long-horizon regressions, and weighted-long horizon regressions, suggest that dividend growth predictability is strongest at short horizons (1–3 years) and is weak at long horizons. Over 1927–1990 there is evidence that expected return predictability is stronger at long horizons (3–5 years) and is insignificant at short horizons. Over the whole sample from 1927 to 2000, there is no evidence of the predictability of expected returns at any horizon. While the point estimates of the variance decompositions from weighted-long horizon regressions suggest that expected returns drive most of the long-term variation in log dividend yields, with robust covariance estimates the standard errors are so large that no significant attribution is possible. However, at short horizons, movements in dividend growth account for over twice the amount of variation in log dividend yields than movements in discount rates, and this short-term attribution to dividend growth is highly significant. Although this study has focused only on U.S. data, the findings indicate that examining the importance of dividend growth and discount rate components in explaining the dynamics of dividend yields could be even more relevant for Asian-Pacific markets. Table 8 reports the capital gain and income components of total returns in local currencies, where the income yield is given in Eq. (1) over a recent sample from January 1993 to June 2011. Income returns account for 21% of the U.S. total return over this period. This is toward the low end; in this basket of Asian-Pacific markets, Australia, China, Hong Kong, Japan, New Zealand, Singapore, and Thailand all have income returns accounting for more than one quarter of total returns. In Japan, total returns have entirely been driven by dividends. Given the even larger importance of dividends as a proportion of total returns in these other Asian-Pacific markets, examining how dividend yields are affected by future expectations of dividend growth versus returns is a fruitful area for future research.

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