# 1% Dividend Yield Rise Predicts 7% Higher Stock Returns

Cite as: Gorak, R. (2026). 1% Dividend Yield Rise Predicts 7% Higher Stock Returns. Tradicted. https://www.tradicted.com/research/hodrick-dividend-1992/
Paper: Robert J. Hodrick — *Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement*
Published in: The Review of Financial Studies (1992)
Original: https://doi.org/10.1093/rfs/5.3.351
DOI: 10.1093/rfs/5.3.351

Key finding: The VAR-implied slope coefficients indicate that a one percent increase in the dividend yield implies a seven percent per annum increase in the expected return on stocks over the next year and a four percent per annum increase over the next four years.

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Dividend yield predictability is the finding that a stock's dividend-to-price ratio forecasts its future returns. In "Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement," Hodrick (1992) used CRSP data from 1926 to 1987. The 744-observation sample showed a one percent dividend-yield rise implied seven percent higher annual returns at one year and four percent at four years. A VAR-implied R² for four-year returns reached 37.3% in the 1952–1987 subsample, nearly triple the 12.7% full-sample estimate.

## What the Study Found

The joint chi-square(3) test statistic for lagged returns, dividend yields, and T-bill rates equals 22.765 in the 1952–1987 subsample. Its confidence level exceeds .999, versus 9.867 and a .980 confidence level in the full 1927–1987 sample. A test using lagged returns alone has an 80% Type II error rate in Monte Carlo simulations. The joint test using all three variables cuts that error rate to just 2.5%. VAR-implied coefficients show a one percent dividend-yield increase raises annualized returns by seven percent at one year and four percent at four years.

> "Since the vector autoregressive alternative has correct size and supplies long-horizon statistics that appear to be unbiased measurements, it emerges as the preferred technique."
>
> Hodrick (1992), *Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement*, p. 29.

## Methodology

The data come from CRSP monthly series on NYSE value-weighted returns, dividend yields, and one-month Treasury bill rates. The full sample has 744 monthly observations from January 1926 to December 1987. Three sub-periods are used: Sample A (730 observations, 1927:2–1987:11), Sample B (431 observations, 1952:1–1987:11), and Sample C (299 observations, 1927:2–1951:12). The VAR controls for lagged returns, lagged dividend yields, and the relative Treasury bill rate, with term and default premiums as additional controls.

## Key Statistics

| Metric | Finding | Context |
|---|---|---|
| Joint χ²(3) test statistic | 22.765 | Sample B: 1952:1–1987:11, confidence level >.999 |
| Implied VAR slope coefficient, β(12)/12 | 7.147 | Sample B, one-year horizon |
| Implied VAR slope coefficient, β(48)/48 | 4.424 | Sample B, four-year horizon |
| Implied R²₂(48) | .373 | Sample B, four-year horizon |
| VAR-implied long-horizon slope | β(k) = e1'[C(1)+...+C(k)]e2 / [e2'C(0)e2] | Derived from VAR autocovariances, eq. (16) |
| Type II error rate, joint test | 2.5% | Monte Carlo, .05 critical value, alternative hypothesis |

## Full Sample vs Post-1951 Subsample

| Measure | Sample A (1927:2–1987:11) | Sample B (1952:1–1987:11) |
|---|---|---|
| Joint χ²(3) test statistic | 9.867 | 22.765 |
| Confidence level of joint test | .980 | >.999 |
| Implied R²₂(48), four-year horizon | .127 | .373 |

## Why This Matters

Investors timing equity allocations with dividend yields face a real methodological choice in how the predictability is tested. Standard OLS long-horizon regressions can overstate statistical significance unless standard errors account for overlapping-return bias. Hodrick's Monte Carlo evidence shows a vector autoregression avoids this bias by deriving long-horizon statistics from a well-behaved one-step-ahead model. Quantitative managers building valuation-based timing signals should note that the inference procedure chosen can change whether a signal looks statistically reliable.

## FAQ

### Does a higher dividend yield predict higher stock returns?

A one percentage point rise in the dividend yield implied a seven percent per annum return increase over the next year, per Hodrick (1992). The estimate comes from a vector autoregression fit to 1952–1987 CRSP data on NYSE returns, dividend yields, and Treasury bill rates.

### How much of long-horizon stock return variance do dividend yields explain?

The VAR-implied R² for dividend yields rose from 6% at one month to 37.3% at four years in the 1952–1987 subsample. The full 1927–1987 sample showed weaker explanatory power, reaching only 12.7% at four years. Both estimates come from the paper's vector autoregression of returns, dividend yields, and Treasury bill rates.

### Why do standard OLS regressions overstate the predictability of long-horizon stock returns?

Standard OLS long-horizon regressions inflate test statistics, with critical values rising from 1.966 at one month to 3.825 at 48 months. Hodrick (1992) attributes this bias to summing many overlapping autocovariance terms in the Hansen and Hodrick (1980) standard error.

### What is the vector autoregression (VAR) approach to testing return predictability?

The VAR approach models returns, dividend yields, and Treasury bill rates jointly, achieving a 2.5% Type II error rate for its joint predictability test. It derives long-horizon forecasting statistics by iterating one-step-ahead predictions instead of directly regressing overlapping returns. Hodrick (1992) identifies this VAR method as the preferred technique for measuring long-horizon return predictability.

## Source

Hodrick, R. J. (1992). Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement. The Review of Financial Studies, 5(3), 357–386.

[Read the full paper →](https://doi.org/10.1093/rfs/5.3.351)