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Geraldine Weiss didn’t shout on CNBC or play poker with Wall Street insiders. She quietly beat the market with a simple, disciplined strategy rooted in dividend yields and common sense. Today, her method is still under the radar and worth a serious look.
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How Geraldine Weiss’s Method Works
Weiss believed dividends tell the truth when earnings don’t. Her core idea: a stock has a “value range” defined by its historical dividend yield. If the yield is high (price is low relative to dividends), it’s potentially undervalued. If the yield is low (price is high), it may be overvalued.
Here’s the gist:
Focus on blue-chip dividend stocks — typically with 25+ years of consistent dividends.
Find historical high and low dividend yields (e.g., 10-year range).
Buy when the current yield is near the high end (suggesting undervaluation).
Sell when the yield drops toward the low end (indicating overvaluation).
This approach forces you to buy low, sell high — without guessing where the market’s headed.
Performance That Demands Respect
Weiss’s newsletter, Investment Quality Trends, ran this strategy for decades. Between 1966 and 2002, it averaged about 11% annually, compared to the S&P 500’s 10%. But it wasn’t just the return, it was the smoother ride. Lower drawdowns. Less drama. Fewer speculative bets.
She didn’t just beat the market; she taught people how to do it rationally.
How the Bands Are Calculated
The formula to flip yield into a price is simple:
Fair Price = Dividend / Target Yield.
So, if a stock’s current dividend is $4 and:
the historical high yield is 4% → low band = $4 / 0.04 = $100
the historical low yield is 2% → high band = $4 / 0.02 = $200
That means the fair value range is $100–$200. If the stock is trading below $100, it’s likely undervalued. Above $200, it’s probably overpriced.
We can see the example of Coca Cola extrated from a web.
The price of Coca cola is in the normal range, close to be overvalued.
Python code
Here you can create your own bands:
import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
# Choose stock and download data
ticker = "JNJ"
stock = yf.Ticker(ticker)
price = stock.history(period="10y")["Close"]
dividends = stock.dividends
# Resample dividends to monthly sum, then compute TTM dividend
monthly_div = dividends.resample("M").sum()
ttm_div = monthly_div.rolling(window=12).sum()
# Align dividend and price on same monthly index
price_monthly = price.resample("M").last()
df = pd.DataFrame({
"Price": price_monthly,
"TTM_Dividend": ttm_div
}).dropna()
# Calculate dividend yield
df["Yield"] = df["TTM_Dividend"] / df["Price"]
# Compute historical yield thresholds
high_yield = df["Yield"].quantile(0.9)
low_yield = df["Yield"].quantile(0.1)
median_yield = df["Yield"].median()
# Calculate Weiss valuation bands
df["Low_Band"] = df["TTM_Dividend"] / high_yield
df["High_Band"] = df["TTM_Dividend"] / low_yield
df["Mid_Band"] = df["TTM_Dividend"] / median_yield
# Plotting
plt.figure(figsize=(14, 7))
plt.plot(df["Price"], label="Actual Price", color="black")
plt.plot(df["Low_Band"], label="Undervalued Band (High Yield)", linestyle="--", color="green")
plt.plot(df["High_Band"], label="Overvalued Band (Low Yield)", linestyle="--", color="red")
plt.plot(df["Mid_Band"], label="Median Band", linestyle="--", color="blue")
plt.title(f"Weiss Dividend Yield Valuation Bands - {ticker}")
plt.xlabel("Date")
plt.ylabel("Price ($)")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()Conclusions
I think it is a very interesting method to identify undervalued stocks. Personally, I would add or remove some of the filters that it makes.
For example, I would not take into account that the dividend should be increasing for at least 25 years. There may be many good companies that have not had a growing dividend for so many years, either because of longevity or for various reasons. I would also monitor the payout to see if the dividends are sustainable over time.
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Looking at free cash flow relative to dividends gives an idea of sustainability. In the case of Coca Cola, it can be seen that the dividend payment is much higher than the free cash flow, so it may be convenient to reduce these dividends, in order not to increase debt.
I would also comment that I would not apply this method to growth companies, such as technology companies, because they do not usually pay out dividends or pay very low dividends.
I hope you found this method interesting.
Thank you very much for your attention!