PRICE-EARNINGS RATIO, DIVIDEND YIELD, AND MARKET-TO-BOOK RATIO TO PREDICT RETURN ON STOCK MARKET: EVIDENCE FROM THE EMERGING MARKETS
Notice bibliographique
Résumé
ABSTRACT This study examines the predicability of stock returns in the 12 emerging stock markets by using price-earnings ratio, dividend yield, and market-to-book ratio as predictive variables during the period of 1997-2003. We analyse the predicability by deriving a new index value using the proper combination of these predictive variables and multi-regression models. We find that the predictability of stock returns in the emerging markets is variant. The market-to-book ratio stands to reveal significant results in terms of predicting stock returns for a one-year period among others for most of the emerging market countries. The dividend yield comes in at second place. The results of the validation tests also show that the forecasting power of the estimated model is fairly good. Finally, these evidences support the individual predictive power of some, but not all financial variables in the stock market. This causes much discussion on the effectiveness of the stock market. INTRODUCTION Economists and financial practitioners have sought to identify variables that predict stock returns in the market for the last two decades. They have looked at various metrics as future predictors of share price performance. They have found size, interest rate, beta, market-to-book ratio (MtB), price-earnings ratio (PfE), and dividend yield (DY) to be rather good indicators among many others. The aim of this study is to examine the ability of the MtB, P/E, and DY to forecast market returns in an integrated form. The use of MtB is motivated by the findings of Fama and French (1992) that shows the MtB ratio of individual stocks has the ability to explain cross-sectional variation in stock returns. We have also included a dividend and earning yield measure as independent variables in our estimation. Intuitively, P/E ratio, MtB ratio, and the DY could be viewed as a multiple that the market attaches to earnings, book value and dividend respectively. In determining market prices and earnings, book value and dividend would be useful for predicting the behaviour of future stock prices or returns. Thus, one would be able to construct profitable trading strategies based on earnings, book value, and dividend. According to the mispricing view, P/E effect states that stocks with low P/E ratios (or high EfP ratios) earn significantly higher returns than stocks with high P/E ratios (or low E/P ratios). Thus, there is an inverse relation between the P/E ratio and portfolio returns, and an investor could achieve systematically higher returns by investing the right stock having low P/E ratio. Then, the P/E could be a suitable fundamental variable to explain the cross-section of returns in the stock market. Similarly, the MtB effect states that securities with high ratios between its market value and its book or equity value, persistently obtain lower returns than those securities with low MtB ratios. One explanation for the market-to-book ratio's ability to predict returns is that book value proxy for future cash flows. Dividing current market price by a cash flow proxy produces a variable that is correlated with future returns. This correlation is greater when better-expected cash flow proxies be used. Coming to the DY, a high DY is a signal that the market is undervalued. The predictive power of DY stems from the role of dividends in capturing the permanent component of prices, whereas the P/E ratio is predictive due to earnings being a good measure of business conditions, which captures counter-cyclical risk aversion. On the other hand the DY, in some studies, is defined to be somewhat better at explaining variation in real returns on the value-weighted index. The slightly better ability of DY to explain value-weighted index returns may be related to the fact that a large fraction of the firms in the market do not pay dividends in the last years. However, in finite samples, using only dividends may be problematic because they are often manipulated, smoothed, or set to zero, making them potentially poor indicators of the true value-relevant cash flows in the future. …
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,052 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».