Dividend Aristocrats are stocks from the S&P 500 that have managed to increase their dividends for 25 consecutive years or more. They are typically large, blue-chip companies with a market cap of at least $3 billion.
One example of a Dividend Aristocrat is Johnson & Johnson (JNJ) which has 57 years of consecutive dividend increases.
Investors like investing in Dividend Aristocrats for their lower volatility and reliable returns. But Dividend Aristocrats are not always good investments.
Some aristocrats can experience slow growth and some may be in declining industries.
So let’s take a look at how Dividend Aristocrats have performed over the last 25 years. And let’s answer the question – are you better off investing in Dividend Aristocrats or in the S&P 500?
To see whether Dividend Aristocrats are worth investing in I am going to run three tests. And instead of comparing the usual buy-and-hold returns I am going to look at the returns from a regular, monthly strategy (i.e. dollar cost averaging).
In test one, we will invest $1000 into the S&P 500 ETF (SPY) every month.
In test two, we will invest $1000 into one random stock from the S&P 500 index each month.
In test three, we will invest $1000 into one random stock from the Dividend Aristocrats index each month.
We are going to run this simulation using trading commissions of $0.005 per share and we will run our backtest between 1/1994 – 1/2019, that’s 25 years of data.
This monthly investing approach is similar to the one we talk about in our course Zero To One Million.
A Quick Note About The Data
In order to make sure this is a fair test, there are also a few issues we need to take care of first.
Naturally, we need the data to be clean and adjusted for capital actions including dividends.
Second, we need a list of all historical members of the S&P 500 Dividend Aristocrats index.
(If we were to only backtest on stocks incorporated in the index today then we would run the risk of survivorship-bias).
Fortunately, our data provider, has recently built such a list so that we can backtest the aristocrats with confidence.
Now those details are out of the way, let’s get on with running our tests and comparing our results.
Test One – Investing In SPY
In this first test we will invest $1000 into the S&P 500 ETF SPY every month between 1/1994 to 1/2019.
As you can see from the following statistics and equity curve this strategy produced an 8.97% annualised return over 25 years. Our final equity finished just over $900,000. That’s a decent return and better than most investors realised during that period.
- Annualised Return: 8.97%
- Maximum Drawdown: -52.03%
- Win Rate: 95.35%
Test Two – Investing In S&P 500 Stocks
In this test we will invest $1000 into one random stock from the S&P 500 each month. As mentioned already, we will use a list that includes historical members to avoid survivorship-bias.
We will also allow multiple positions in the same stock so that if we get a (random) signal to buy the same stock, we will simply invest an additional $1000.
Also, we will sometimes have left-over cash after purchasing a stock (due to no fractional shares) so this will be used to invest in another (random) position.
We will run the simulation 100 times and average our results in order to make sure our results are not affected by luck.
As you can see from the following statistics and equity curve, investing $1000 into one random stock from the S&P 500 index produced an annualised return of 10.19% over 25 years. Our final equity finished over $1.1 million.
- Annualised Return: 10.19%
- Maximum Drawdown: -53.03%
- Win Rate: 77.42%
Test Three – Investing In Dividend Aristocrats
In this test we will invest $1000 each month into one random member of the S&P 500 Dividend Aristocrat index.
Like before, we will allow multiple positions per stock (this is especially necessary seeing there are only 57 Dividend Aristocrats available to us).
We will also invest any leftover cash into an additional position (also selected at random).
As with test two, we will run the simulation 100 times and take an average of our results to reduce the impact of randomness.
As you can see from the following statistics and equity curve, investing $1000 into one random stock from the S&P 500 dividend aristocrats index produced an annualised return of 10.63% over 25 years. Our final equity ended at over $1.25 million.
- Annualised Return: 10.63%
- Maximum Drawdown: -41.80%
- Win Rate: 86.42%
Conclusions & Observations
In this article we tested a simple investing strategy known as dollar cost averaging. The aim was to see whether Dividend Aristocrats might make better monthly investments than other S&P 500 stocks.
We found that investing monthly into S&P 500 Dividend Aristocrats produced a better annual return than investing in the SPY ETF and a slightly better annual return than investing in S&P 500 stocks.
Monthly investing in Dividend Aristocrats also produced a lower equity drawdown and a higher win rate over the 25 year simulation.
Overall, it seems that regular investing into dividend aristocrats is a sound strategy for investors to consider.
Thank You For Reading
Joe Marwood is an independent trader and the founder of Decoding Markets. He worked as a professional futures trader and has a passion for investing and building mechanical trading strategies. If you are interested in more quantitative trading strategies, investing ideas and tutorials make sure to check out our program Marwood Research.
This post expresses the opinions of the writer and is for information or entertainment purposes only. It is not a recommendation or personalised investment advice. Joe Marwood is not a registered financial advisor or certified analyst. The reader agrees to assume all risk resulting from the application of any of the information provided. Past performance, historical or simulated results are not a reliable indicator of future returns and may not account for real world settings. Financial trading is full of risk and margin trading can lead to financial losses totalling more than what is in your investment account. We take care to present accurate analysis but mistakes in backtesting and presenting of analysis regularly occur. Please read the Full disclaimer.
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