I ask chatGPT to tell me about the 4% withdrawal rule.

[Time2]

I think chatGPT has a good answer, that covers it pretty well. Is there anything you would add or delete?

Can you tell me about the 4% withdrawal rate rule.

chatGPT responded,

The 4% withdrawal rate rule is a popular guideline used in retirement planning to determine how much you can withdraw annually from your retirement savings without running out of money over the course of your retirement. Here's an overview of the rule:

Background
Origin: The 4% rule was first introduced by financial planner William Bengen in a 1994 study. He analyzed historical data on stock and bond returns in the U.S. and found that a 4% withdrawal rate allowed retirees to have a steady income stream while minimizing the risk of depleting their savings over a 30-year retirement period.

How It Works
- Starting Point: In the first year of retirement, you withdraw 4% of your total retirement savings.
- Subsequent Years: In each following year, you adjust that initial withdrawal amount for inflation. For example, if you started by withdrawing $40,000 in the first year, and inflation is 2%, you would withdraw $40,800 in the second year.

Assumptions
- Portfolio Composition: The rule assumes a balanced portfolio, typically with a mix of stocks and bonds (often suggested to be around 50-60% stocks and 40-50% bonds).
- Time Horizon: The rule is designed for a retirement period of 30 years. If your retirement is expected to last longer, adjustments may be necessary.
- **Historical Market Performance**: The 4% rule is based on historical data, and future market conditions could differ, potentially making the rule less reliable.

Criticism and Considerations
- Market Volatility: The 4% rule doesn’t account for the sequence of returns risk, which is the risk of experiencing poor investment returns early in retirement.
- Low-Interest Rates: In today's low-interest-rate environment, some experts argue that a 4% withdrawal rate may be too aggressive and suggest using a lower rate, such as 3% or 3.5%.
- Flexibility: The rule assumes a fixed withdrawal rate, but in reality, many retirees adjust their spending based on market conditions, health needs, or lifestyle changes.

Alternative Approaches
- Dynamic Withdrawal Strategies: Some strategies adjust withdrawal rates based on market performance, allowing for more flexibility.
- Bucketing Strategy: This involves dividing your retirement savings into different "buckets" based on time horizons and risk tolerance.

Conclusion
The 4% withdrawal rate rule is a useful starting point for retirement planning, but it’s important to consider your specific financial situation, risk tolerance, and retirement goals. Many financial advisors recommend using the 4% rule as a guideline rather than a strict rule and adjusting it based on personal circumstances.

 

[Koolau]

Pretty much word for word the way I recall it being presented in these pages many times before.

 

[sakowitzm]

Related: Is anyone aware of a rule of thumb; a calculator; an article that talks about adjusting the 4% rule for a shorter retirement? Given my family history, it's unlikely that I'll live more than 20 more years. I would not rely on that, but it would be interesting to consider what %%% would be appropriate for a 20-year retirement.

 

[Midpack]

^^ FIRECalc: A different kind of retirement calculator requires you specify spending, portfolio $ and years in retirement at a minimum. Plus many more variables if you choose. You can do it yourself, or…

Using the Investigate tab, using past history and assuming a 95% success rate (same as SWR) - where 4% is the SWR for 30 years, 4.85% is the SWR for 20 years.

 

[donheff]

What I find interesting is that the AI can't reach out to find that answer from any external source (e.g. DB, Web sites, etc.). It relies on the weighting assigned to tokens (word fragments) and their connection in its neural network, and on the context provided in the prompts when questioned, to essentially pull the whole response out of thin air in a manner analogous to us answering the question extemporaneously. The model was trained on billions of texts, but it doesn't store those texts, it simply weights the tokens and their connection strengths. the fact that it can generate such detailed, accurate responses based on that array of associations is as spooky as quantum mechanics.

 

[Dtail]

General agreement. However concerning the market volatility comment, the 4% guidance DOES in fact take into account sequence of returns risk. That is effectively the reason why it is a 4% guidance instead of a ~6.5% guidance rule.

 

[SecondCor521]

- Market Volatility: The 4% rule doesn’t account for the sequence of returns risk, which is the risk of experiencing poor investment returns early in retirement.

ChatGPT's answer is mostly good except for the above. The 4% rule definitely accounts for sequence of returns risk. That's why it's the 4% rule and not higher; one needs to withdraw at a lower rate than the long term portfolio return in order to allow for the portfolio volatility. (ETA: Crossposted with @Dtail above.)

Although it's true that the 4% rule doesn't account for it completely - if followed slavishly, there are some historical start years that fail, and others that come close to failure. The shorthand for this is that the 4% rule is about 95% safe.

But I do know all the Cool Kids are all agog about SORR and it's an allegedly new shiny toy to play with and analyze. Whatever.

What I find interesting is that the AI can't reach out to find that answer from any external source (e.g. DB, Web sites, etc.). It relies on the weighting assigned to tokens (word fragments) and their connection in its neural network, and on the context provided in the prompts when questioned, to essentially pull the whole response out of thin air in a manner analogous to us answering the question extemporaneously. The model was trained on billions of texts, but it doesn't store those texts, it simply weights the tokens and their connection strengths. the fact that it can generate such detailed, accurate responses based on that array of associations is as spooky as quantum mechanics.

I'm fairly sure that the training data for the AIs includes the entire content of the web, which obviously includes a lot of discussion of the 4% rule, SORR, etc.

 

[donheff]

I'm fairly sure that the training data for the AIs includes the entire content of the web, which obviously includes a lot of discussion of the 4% rule, SORR, etc.

Correct, the training data covers all of this. The important factor that makes it spooky to me is that the training data is not stored somewhere to be retrieved later and is not accessible through external connections. The training data allows the model to weight tokens and their interconnections so it can later "remember" the most likely next word as it generates an answer. We don't know how a human stores such connections in our brains or how we pull them back together when we "remember" something we learned. And data scientists do not know how these large language models do it either. They do know that there is no literal store of the textual material trained upon, just a vast array of tokens and associations.

 

[GrayHare]

Humans might be doing the same thing, that is, creating a string of likely words.

 

[Koolau]

Humans might be doing the same thing, that is, creating a string of likely words.

And then there are the infinite number of chimps banging away at typewriters recreating the w*rks of Shakespeare - given enough time.

 

[mrfeh]

I think chatGPT has a good answer, that covers it pretty well. Is there anything you would add or delete?


Criticism and Considerations
- Market Volatility: The 4% rule doesn’t account for the sequence of returns risk, which is the risk of experiencing poor investment returns early in retirement.
- Low-Interest Rates: In today's low-interest-rate environment, some experts argue that a 4% withdrawal rate may be too aggressive and suggest using a lower rate, such as 3% or 3.5%.

I think it's very good, with the exception of the 2 items above.

• it does take into account SORR implicitly, by including years of past performance where SORR occurred
• we are no longer in an environment of historically low interest rates

 

[Aerides]

ChatGPT and all the others really are just scraping all these other places that have talked about it endlessly. I don't get the fascination with it at this point.

One thing that's often overlooked, and missing above, is the original 4%/30 year SWR assumes that year zero has no withdrawals.

 

[sakowitzm]

^^ FIRECalc: A different kind of retirement calculator requires you specify spending, portfolio $ and years in retirement at a minimum. Plus many more variables if you choose. You can do it yourself, or…

Using the Investigate tab, using past history and assuming a 95% success rate (same as SWR) - where 4% is the SWR for 30 years, 4.85% is the SWR for 20 years.

Thanks Midpack. I kinda sorta knew that, but my 66-year old memory falters.

 

[pb4uski]

... The 4% rule doesn’t account for the sequence of returns risk, which is the risk of experiencing poor investment returns early in retirement. ...

I don't agree with this part. The 4% rule DOES account for SORR, that is why it is 4% rather than the average investment earnings rate... the impact of SORR is baked into the 4% SWR.

 

[pb4uski]

ChatGPT and all the others really are just scraping all these other places that have talked about it endlessly. I don't get the fascination with it at this point. ...

I think the attraction is the convenience of having the AI do the searching and summarizing the search results. I use Perpleity, but find it handy. Perplexity also provides links to the relevant web pages that it used in its response, which is handy to look at the underlying web pages.

 

[pb4uski]

...The important factor that makes it spooky to me is that the training data is not stored somewhere to be retrieved later and is not accessible through external connections.

Perplexity provides links to the webpages that it is relying on in it's response, which I find useful if I want to dig in to the details.

 

[Ballhawker]

What I find interesting is that the AI can't reach out to find that answer from any external source (e.g. DB, Web sites, etc.). It relies on the weighting assigned to tokens (word fragments) and their connection in its neural network, and on the context provided in the prompts when questioned, to essentially pull the whole response out of thin air in a manner analogous to us answering the question extemporaneously. The model was trained on billions of texts, but it doesn't store those texts, it simply weights the tokens and their connection strengths. the fact that it can generate such detailed, accurate responses based on that array of associations is as spooky as quantum mechanics.

Yes! And this one reason why we have no idea how this world will look in a decade or two following the full unleashing of AI. This change is likely to make the pre- and post "world wide web", say between 1990 and the early 2000's changes look rather pedestrian.