Prime Harvesting in Retirement

There are many theories about asset allocation in retirement. Some say that your bond percentage should be your age. Others say it should be your age minus 20. Some even say your stock percentage should rise as you age in retirement (a so-called “rising glide-path”). In his book Living Off Your Money, Michael H. McClung recommends a strategy called “Prime Harvesting” that I examine here.

The book is very technical and covers many retirement topics, but I’m going to try to be as non-technical as possible in this article and discuss only Prime Harvesting. I’ve been thinking about the best way to handle a portfolio in retirement for some time, and this book promises new ideas and a strong evidence-based approach.

First of all, “Prime Harvesting” is a great name. If you ever get into a discussion about this subject, you’ll sound like the smartest person in the room if you say, “Well, I use Prime Harvesting.” Fortunately, Prime Harvesting can be described with a few simple rules:

1. At the start of each year, if your stocks are worth more than 20% more than they were when you started retirement (adjusted for inflation), sell off 20% of the initial value of the stocks.

2. If there isn’t enough cash to make up your intended withdrawal for the year’s spending, sell bonds to make up the difference.

3. If you run out of bonds, sell stocks to make up the rest of your spending needs for the year.

4. If step 1 produced too much cash, buy bonds with the excess.

Even though these rules are simple enough, their implications aren’t immediately obvious. Let’s look at two extreme examples to get a feel for Prime Harvesting.

Booming Stocks: If stocks boom, you end up selling off excess stocks to create cash to live on, and you keep buying bonds with what’s left over.

Lagging Stocks: If stocks give below-average returns for long enough, you end up living off your bonds until they’re gone, and then you sell stocks to live.

Having stocks boom is the happy case, but let’s think about what life is like when stocks lag. Your portfolio hasn’t been performing as well as you’d like, and all your bonds are gone. Now your work experience is getting very stale and you’ve got a risky 100% stocks portfolio that is smaller than you were hoping. How well are you sleeping?

Let’s make this a little more concrete. Suppose Liz retires today with $750,000 and plans to spend $30,000 per year (rising with inflation). She begins with $300,000 in bonds and $450,000 in stocks (a 60/40 split), and uses Prime Harvesting.

Suppose that bonds lag inflation by a modest 0.5% per year for the next 9 years because interest rates rise a little. (This isn’t a prediction, but it doesn’t seem unlikely.) Suppose as well that stocks mildly disappoint by averaging 2% above inflation for the next 9 years. (Again, not a prediction, but doesn’t seem terribly unlikely.)

In this scenario, at the start of Liz’s tenth year of retirement she will sell the last of her bonds and draw partially on her stocks. She’s left with about $530,000 (adjusted for inflation) in stocks and is very nervous. Can she still afford to spend $30,000 per year?

Then the worst happens. Stocks crash 30% in year 10, are down another 10% in year 11, and are flat in year 12. She’s now got less than $250,000 (inflation adjusted) left. Her withdrawals are completely unsustainable. She needs to cut them in half. Stocks subsequently rise steadily for years but the damage has been done to Liz’s portfolio.

To be fair, I should point out that McClung also examines strategies for variable withdrawals to adapt to disappointing portfolio returns. However, these strategies would not significantly reduce Liz’s spending until after she is 100% in stocks and gets slammed by the 30% stock market crash.

How could McClung advocate a strategy so likely to leave retirees with 100% stock portfolios? The answer is that his extensive back-testing never encountered a scenario exactly like the one I described. By chance, even milder versions of this scenario haven’t occurred in the past.

McClung says that historical market data represents “known risk” and that returns outside the historical data is “speculative risk.” He would classify the scenario I described as a speculative risk. Despite the fact that McClung demonstrates a strong understanding of the risks of data mining, I believe his recommendations suffer from data mining.

To understand data mining, think of the example of trying to raise a teenager. Good strategies involve understanding what they’re going through. But if you look to the past and develop strategies taking into account drive-in movies and sock hops, then you’re guilty of data mining. You’ve over-fitted your strategy to the past and it won’t work today.

When it comes to testing financial ideas, it’s difficult to tell when you’re guilty of data mining. I can’t prove that McClung is guilty, and he can’t prove he isn’t. However, I don’t think my example of Liz’s retirement is all that unlikely. All it takes is a period of at most modest stock growth following by a crash.

If the leaves on a tree represent each historical return pattern we’ve experienced, then speculative risk comes from the possibility that your retirement will have a return pattern that is outside the tree’s boundaries. However, I believe that return patterns that cause problems for Prime Harvesting exist within the tree’s boundaries, but at places where is currently no leaf. In other words, there are return patterns that cause problems for Prime Harvesting, but are well within the character of past returns.

All this said, I’m a fan of McClung’s rigorous approach to looking for real evidence to back up retirement advice. The challenge is to define what is a reasonable range of likely future stock and bond return patterns. In my opinion, McClung is clinging too closely to historical returns. We simply have far too short a history of returns to say that we’ve seen all there is to see. Even seemingly inconsequential differences from historical returns can give big problems for Prime Harvesting.

McClung attempts to deal with the data mining problem by testing ideas on data from different countries and performing simulations where returns in 5-year groups get randomized. These efforts certainly help, but they aren’t enough. In the end, his recommendations are heavily influenced by the worst retirement period that started in 1969.

I certainly don’t know what portfolio allocation strategy is best, but I think it shouldn’t involve huge increases in portfolio risk over time. Modest adjustments to risk level could be sensible, but starting with a 60/40 allocation to stocks and bonds, and ending up 100% in stocks after a decade makes no sense to me. The fact that it has worked out reasonably well in the past brings me little comfort.