There is a grain of truth in what the book says but I offer four caveats for the reader to consider.
1. Basing expected returns on the US market is an egregious case of selection bias. The US market is an outlier and an unexpected outlier at that. Suggesting in 1900 to anyone that they put their net worth into the US market would have been a brave move—a ruinous civil war, rampant corruption, booms and busts, etc—Are you Joking! “Triumph of the Optimists” has some figures for other markets but not for all—many markets went to zero and never recovered. Yes you can diversify globally but this cuts the returns almost in half compared to the US market’s recent history.
People will often say they are bullish on America. This is an easier argument to dispute than it used to be—“So, nothing really serious can go wrong in a country that elected Donald Trump as president?”. But more seriously one’s feelings of confidence are a very poor prognostic indicator, as Japanese investors found post 1989.
2. Any mention of the normal distribution or the central limit theorem in relation to financial markets opens you to huge errors. This is the ludic fallacy—markets are not tame and do not comply with tractable probability distributions. The returns from one year to the next are not independent and identically distributed, and nor is the underlying distribution necessarily tame enough for the CLT to apply. I suggest to rework the numbers with more realistic distributions such as Student’s t or a power law distribution. Results may be worse than you intuitively expect.
3. There is a more subtle problem… Books advocating leverage, stocks for the long run, index and forget etc, tend to appear after a run-up in the market (as in this case after a 50% surge after the GFC slump). People tend to invest in this way also. Last year, as the market was making new highs several people advised me that they had decided on stocks for the long run because stocks always outperform bonds (until they don’t—consider the Japanese stock market, currently at 50% lower than its level in 1989). Many of these “long term holders” have since sold out, perhaps close to the bottom. My suggestion is that anyone feeling an urgent and pressing need to invest in the market for the long run may do well to trickle-feed their money into the market over a period of a few years.
4. Terrible market returns often coincide with hard times for the portfolio owner, such as unemployment, slumps in the value of other assets and other difficulties. Having a leveraged portfolio that went to zero or beyond (“losses can exceed your initial investment” as they say in the fine print) in 1932 would have been very unfortunate. Margin purchases of stocks were very popular in 1929, and in general high levels of margin lending seem to be an indicator of trouble ahead.
I do commend the study of markets to the LW community. There are so many interesting aspects to it—psychology (yours and others’), cognitive biases, subtle statistical issues and many lessons on the limitations of vanilla statistics, the subtleties of risk management in the real world, the difficulties of a system comprising intelligent adaptive agents, agency issues. And it is a way to put your insights into the nature of reality to the test.
1. Basing expected returns on the US market is an egregious case of selection bias.
FYI they redo the analysis for the FTSE and the Nikkei and they come to the same conclusion. Also, the theoretical analysis comes out the same even if returns are lower in the future than they have been in the past.
Lower expected return does mean putting a lower share into the risky asset, but expected returns would have to go very low indeed (w/o a corresponding drop in expected volatility) for the analysis not to suggest that those just starting out should use leverage. (2x leverage is way undershooting the target that the math suggests, but they suggest maxing out at 2x leverage for various practical reasons. If expected returns were a bit lower, then 2x would probably still be below the theoretical target for people at the beginning of their careers.)
2. Any mention of the normal distribution...
I am curious about this. It’s my impression that assets tend to become more correlated in a downturn. I’m not sure how much this, or the presence of fat tails, affects things, but their back test on at least three different countries’ data mitigates my concern somewhat.
3. There is a more subtle problem… Books advocating leverage, stocks for the long run, index and forget etc, tend to appear after a run-up in the market
Happily, this was at least not the case here. The book was written in 2008/2009, and published in 2010, just after the financial crisis. And we’re reading this review during the coronavirus pandemic when the S&P is still down 15% from the start of the year.
4. Terrible market returns often coincide with hard times for the portfolio owner, such as unemployment, slumps in the value of other assets and other difficulties.
This is a fair point, which I think was not addressed well enough in the book. But which was addressed well in Jess’s review! (See e.g. his discussion of disability insurance.)
I am curious about this. It’s my impression that assets tend to become more correlated in a downturn. I’m not sure how much this, or the presence of fat tails, affects things, but their back test on at least three different countries’ data mitigates my concern somewhat.
(I don’t know how it applies to this model, but...) price movements are not normally distributed, and any model that assumes they are carries a major risk of blowing up. For example: during the financial crisis Goldman Sachs chief financial officer David Viniar infamously told the Financial Times “we were seeing things that were 25-standard deviation moves, several days in a row.”
What are the chances that a 25-sigma event strikes your investment portfolio?
We should expect a 4σ event to happen twice in our lifetime. A 5σ event occurs about every 5000 years, or once since the beginning of recorded history. A 6σ event might have happened roughly twice in the millions of years since homo sapiens branched off from the other apes. A 7σ event comes along every billion years or so, or four times since our planet coalesced out of a cloud of interstellar dust. We pass the Big Bang somewhere around the 8σ mark. At 20σ, the number of years we’d have to wait is ~10x higher than the number of particles in the universe, etc.
(which is to say, Goldman and friends’ models were disastrously, absurdly, cosmologically wrong.)
AFAIK Benoit Mandelbrot was the first to start warning people about this, and his PhD student Eugene Fama wrote his thesis on it...back in 1965! Which gives you a sense of how crazy it is that people would still try to apply normal distributions to financial markets.
Mandelbrot’s book The Misbehaviour of Markets is worth a read. I’ve also written a summary of his ideas here, in the context of stress-testing the assumptions of the ‘early retirement’ movement.
I endorse ESRogs’ replies. I’ll just add some minor points.
1. Nothing in this book or the lifecycle strategy rests on anything specific to the US stock market. As I said in my review
The fact that, when young, you are buying stocks on margin makes it tempting to interpret this strategy is only good when one is not very risk averse or when the stock market has a good century. But for any time-homogeneous view you have on what stocks will do in the future, there is a version of this strategy that is better than a conventional strategy. (A large fraction of casual critics seem to miss this point.)
If you are bearish on stocks as a whole, this is incorporated by you choosing a lower equity premium and hence lower overall stock allocation. This choice is independent of the central theoretical idea of the book.
2. Yours is a criticism of all modeling and is not specific to the lifecycle strategy.
3. As ESRogs mentioned, neither this book nor my review has the timing you suggest, so the psychoanalysis of proponents of this strategy appears inconsistent.
4. I acknowledged this sort of argument in my review, and indeed argued that the best approaches hinges on such correlations. But consider: even in the extreme case where I believes my future income is highly correlated with the stock market and is just as volatile, the lifecycle strategy recommends that my equity exposure should start low when I’m young and then increase with age, in opposition to conventional strategies! So even if you take a different set of starting assumptions from the authors, you still get a deep insight from their basic framework.
Or, to put your comment more succinctly, the book talks about several variables as if though they are independent (e.g. market mid-term ROI, personal income, amount of leverage most brokers provide), but historically speaking these variables have always been heavily correlated.
the book talks about several variables as if though they are independent (e.g. market mid-term ROI, personal income, amount of leverage most brokers provide)
FWIW, the book does discuss the correlation of one’s income with stock market returns. They cite a study on this (from early in the 90s I believe) suggesting that most people’s income correlations with the market are between 0 and 20% (with many retail workers even having negative correlation with the market!). I was surprised how low those numbers where when I read that. I’d be curious to look into this more.
Your DCA recommendation in 3 is found by several studies to be riskier and lower return than lump sum investing. Do you believe those are similarly flawed?
There is a grain of truth in what the book says but I offer four caveats for the reader to consider.
1. Basing expected returns on the US market is an egregious case of selection bias. The US market is an outlier and an unexpected outlier at that. Suggesting in 1900 to anyone that they put their net worth into the US market would have been a brave move—a ruinous civil war, rampant corruption, booms and busts, etc—Are you Joking! “Triumph of the Optimists” has some figures for other markets but not for all—many markets went to zero and never recovered. Yes you can diversify globally but this cuts the returns almost in half compared to the US market’s recent history.
People will often say they are bullish on America. This is an easier argument to dispute than it used to be—“So, nothing really serious can go wrong in a country that elected Donald Trump as president?”. But more seriously one’s feelings of confidence are a very poor prognostic indicator, as Japanese investors found post 1989.
2. Any mention of the normal distribution or the central limit theorem in relation to financial markets opens you to huge errors. This is the ludic fallacy—markets are not tame and do not comply with tractable probability distributions. The returns from one year to the next are not independent and identically distributed, and nor is the underlying distribution necessarily tame enough for the CLT to apply. I suggest to rework the numbers with more realistic distributions such as Student’s t or a power law distribution. Results may be worse than you intuitively expect.
3. There is a more subtle problem… Books advocating leverage, stocks for the long run, index and forget etc, tend to appear after a run-up in the market (as in this case after a 50% surge after the GFC slump). People tend to invest in this way also. Last year, as the market was making new highs several people advised me that they had decided on stocks for the long run because stocks always outperform bonds (until they don’t—consider the Japanese stock market, currently at 50% lower than its level in 1989). Many of these “long term holders” have since sold out, perhaps close to the bottom. My suggestion is that anyone feeling an urgent and pressing need to invest in the market for the long run may do well to trickle-feed their money into the market over a period of a few years.
4. Terrible market returns often coincide with hard times for the portfolio owner, such as unemployment, slumps in the value of other assets and other difficulties. Having a leveraged portfolio that went to zero or beyond (“losses can exceed your initial investment” as they say in the fine print) in 1932 would have been very unfortunate. Margin purchases of stocks were very popular in 1929, and in general high levels of margin lending seem to be an indicator of trouble ahead.
I do commend the study of markets to the LW community. There are so many interesting aspects to it—psychology (yours and others’), cognitive biases, subtle statistical issues and many lessons on the limitations of vanilla statistics, the subtleties of risk management in the real world, the difficulties of a system comprising intelligent adaptive agents, agency issues. And it is a way to put your insights into the nature of reality to the test.
FYI they redo the analysis for the FTSE and the Nikkei and they come to the same conclusion. Also, the theoretical analysis comes out the same even if returns are lower in the future than they have been in the past.
Lower expected return does mean putting a lower share into the risky asset, but expected returns would have to go very low indeed (w/o a corresponding drop in expected volatility) for the analysis not to suggest that those just starting out should use leverage. (2x leverage is way undershooting the target that the math suggests, but they suggest maxing out at 2x leverage for various practical reasons. If expected returns were a bit lower, then 2x would probably still be below the theoretical target for people at the beginning of their careers.)
I am curious about this. It’s my impression that assets tend to become more correlated in a downturn. I’m not sure how much this, or the presence of fat tails, affects things, but their back test on at least three different countries’ data mitigates my concern somewhat.
Happily, this was at least not the case here. The book was written in 2008/2009, and published in 2010, just after the financial crisis. And we’re reading this review during the coronavirus pandemic when the S&P is still down 15% from the start of the year.
This is a fair point, which I think was not addressed well enough in the book. But which was addressed well in Jess’s review! (See e.g. his discussion of disability insurance.)
(I don’t know how it applies to this model, but...) price movements are not normally distributed, and any model that assumes they are carries a major risk of blowing up. For example: during the financial crisis Goldman Sachs chief financial officer David Viniar infamously told the Financial Times “we were seeing things that were 25-standard deviation moves, several days in a row.”
What are the chances that a 25-sigma event strikes your investment portfolio?
We should expect a 4σ event to happen twice in our lifetime. A 5σ event occurs about every 5000 years, or once since the beginning of recorded history. A 6σ event might have happened roughly twice in the millions of years since homo sapiens branched off from the other apes. A 7σ event comes along every billion years or so, or four times since our planet coalesced out of a cloud of interstellar dust. We pass the Big Bang somewhere around the 8σ mark. At 20σ, the number of years we’d have to wait is ~10x higher than the number of particles in the universe, etc.
(which is to say, Goldman and friends’ models were disastrously, absurdly, cosmologically wrong.)
AFAIK Benoit Mandelbrot was the first to start warning people about this, and his PhD student Eugene Fama wrote his thesis on it...back in 1965! Which gives you a sense of how crazy it is that people would still try to apply normal distributions to financial markets.
Mandelbrot’s book The Misbehaviour of Markets is worth a read. I’ve also written a summary of his ideas here, in the context of stress-testing the assumptions of the ‘early retirement’ movement.
I endorse ESRogs’ replies. I’ll just add some minor points.
1. Nothing in this book or the lifecycle strategy rests on anything specific to the US stock market. As I said in my review
If you are bearish on stocks as a whole, this is incorporated by you choosing a lower equity premium and hence lower overall stock allocation. This choice is independent of the central theoretical idea of the book.
2. Yours is a criticism of all modeling and is not specific to the lifecycle strategy.
3. As ESRogs mentioned, neither this book nor my review has the timing you suggest, so the psychoanalysis of proponents of this strategy appears inconsistent.
4. I acknowledged this sort of argument in my review, and indeed argued that the best approaches hinges on such correlations. But consider: even in the extreme case where I believes my future income is highly correlated with the stock market and is just as volatile, the lifecycle strategy recommends that my equity exposure should start low when I’m young and then increase with age, in opposition to conventional strategies! So even if you take a different set of starting assumptions from the authors, you still get a deep insight from their basic framework.
Or, to put your comment more succinctly, the book talks about several variables as if though they are independent (e.g. market mid-term ROI, personal income, amount of leverage most brokers provide), but historically speaking these variables have always been heavily correlated.
FWIW, the book does discuss the correlation of one’s income with stock market returns. They cite a study on this (from early in the 90s I believe) suggesting that most people’s income correlations with the market are between 0 and 20% (with many retail workers even having negative correlation with the market!). I was surprised how low those numbers where when I read that. I’d be curious to look into this more.
Your DCA recommendation in 3 is found by several studies to be riskier and lower return than lump sum investing. Do you believe those are similarly flawed?