Dividend yield is a financial metric that provides insight into the income generated by an investment in the form of dividends, relative to its market price. Expressed as a percentage, the dividend yield is calculated by dividing the annual dividend per share by the current market price per share. This measure is particularly valuable for income-oriented investors seeking stable returns from their investments.
Reference [1] explores the connection between dividend yield, stock volatility, and expected returns. Volatility is measured as the trailing twelve-month average of the difference between monthly high and low prices. The authors pointed out,
We provide empirical evidence that excessively volatile prices drive the return predictability. Inter-temporally, dividend yields are highly profitable following periods of heightened volatility, generating approximately 1.5% per month, but these are the only periods when yield strategies generate significant returns. Similarly, in the cross-section, dividend yield predicts returns amongst volatile firms. While we find strong evidence that, during periods of high return volatility, dividend yield strategies generate significant returns, over the entirety of our sample computing dividend yield by scaling dividends by current prices generates similar returns to scaling by prices lagged one year. These results suggest that to the extent dividend yield predicts returns, this on average effect is not due to excessively volatile prices within the past year.
In short, the article showed that,
- Dividend yield serves as a good predictor of future returns, and yield strategies give substantial profits, approximately 1.5% per month following periods of heightened volatility.
- In the cross-section, dividend yield provides more accurate predictions for returns in volatile firms.
This article provides additional theoretical support for dividend investing.
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References
[1] Ahn, Seong Jin and Ham, Charles and Kaplan, Zachary and Milbourn, Todd T., Volatility, dividend yield and stock returns (2023). https://ssrn.com/abstract=4641642
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