How to Invest? Let Economics, Not Wall Street, Be Your Guide
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The Economics of Saving, Insuring, and Investing? Ask a Squirrel.
We all know that starving is far worse than gorging. The former kills you. The latter gives you gas. That’s why few of us choose to go hungry or eat 76 hotdogs in ten minutes — the world record held by Joey Chestnut.
Truth is, most of us can’t manage five dogs at a single sitting. Well, maybe six if they’re smothered in cream cheese and jalapeños — the Seattle Dog, covered in red, yellow, and green goop — the Chicago Dog, filled with chili, onion, and cheese — the Coney Dog, coated in mayo, wrapped in bacon, and stuffed with beans — the Sonora Dog, or wrapped in pizza and topped with potatoes — the Italian Dog.
Each of the world’s 4 billion Squirrels works hard to achieve their daily consumption sweet spot. Their typical goal is to down 12 acorns per day, year in and year out. To get through the winters, they spend Spring, Summer, and Fall burying 10,000 acorns all across town. In so doing, squirrels are a) saving for winter when they can’t work, b) insuring against burglars, and c) diversifying against location-specific acorn rot. Evolution, it seems, has taught squirrels to make optimal personal financial decisions without paying an asset manager.
For humans as for squirrels, optimal financial moves are dictated by satiation — the complex physiological processes that makes us feel increasingly stuffed the more we pig out. This includes release of cholecystokinin and peptide, not to mention painful stomach distention, all leading our brains to order our dominant hand: Drop that Fork!
Why Satiation Is at the Heart of Personal Financial Decisions
Squirrels listen to their guts, which whispers this watchword:
Twelve acorns a day and hold the mayo.
Economists call this consumption smoothing. Twelve acorns a day beats flipping between 10 one day and 14 the next. It also beats oscillating between no acorns for three days straight followed by 48 on the fourth or rotating between … .
Consumption smoothing over time and across times — good times and bad times — is why we too save for our winters — our retirements, why we too insure against robbers, fires, massive medical bills, and other potential losses, and why we too diversify our investments.
Smooth and steady consumption connects to An apple a day keeps the doctor away. This old adage references the benefit of eating healthy. But it’s also about eating steadily and sustainably.
Bernoulli Formalized Consumption Smoothing — in 1738
Danielle Bernoulli was a member of a remarkable family of mathematical geniuses. Danielle modeled human happiness, which he dubbed utility, via a formula that depends on a person’s level of consumption. The formula has a key property called diminishing marginal utility. Consider eating hot dogs when your famished. The first dog is heavenly. The second is great. The third is good. The fourth is ok. As for the fifth? It’s meh. That’s diminishing marginal utility — smaller and smaller increases to our happiness as we consume more and more at a given sitting.
Bernoulli’s formula — his mathematical function — determines how fast we get stuffed. In economists’ standard formula — the one used in my company’s MaxiFi Planner software, the stuffing rate is a fixed parameter called the coefficient of risk aversion. The higher our risk aversion coefficient, the more we care about consumption smoothing. In fact, as our risk aversion coefficient gets really high, we try to arrange our saving, insurance, and asset allocation decisions to have exactly the same living standard — consumption per household member with adjustments for economies of shared living and the relative cost of kids — no matter our age or our luck, be it personal — We crash the car. — or general — the Fed crashes the market.
Risk aversion — the rate at which more, at the margin, is less — plays the critical role in the mathematics Bernoulli, Irving Fisher, John von Neumann and Oscar Morgenstern, and a host of other giants of finance developed in determining a household’s optimal saving, insurance, and portfolio diversification. This optimization is called expected utility maximization — making decisions that produce the highest level of happiness on average.
Expected, btw, is the statistical term for average. And, full disclosure, John von Neumann wasn’t an economist. He was a mathematician, physicist, computer scientist, and engineer who made amazing contributions to economics in his spare time — when he wasn’t developing the atomic bomb, the hydrogen bomb, the modern digital computer, and far more.
Thanks to risk aversion, financial decisions that maximize your average lifetime happiness weigh bad outcomes more heavily than good outcomes. And this extra attention to the downside leads to financial advice that’s extra cautious, whether that advice involves saving, insuring, or investing.
Modern Portfolio Theory
In 1952, Harry Markowitz developed Modern Portfolio Theory, for which he earned the Noble Prize. In his work, Markowitz assumed households would consume everything at the end of their lives. I.e., he posited a one-period model rather than a multi-period model in which households consume in each of their future years.
If you consume only at the end of your life, your consumption is just your terminal wealth. But your terminal wealth equals your initial wealth multiplied by 1 plus your portfolio return. Thus, Markowitz’s single-period assumption plus several other mathematical short cuts let him express expected utility as depending on just two things — the mean (average or expected) value of your portfolio’s return and its variance (variability).
Next, Markowitz showed how to find efficient portfolios — portfolios that had the highest mean return for a given variance. Which efficient portfolio to chose then boiled down to the one that maximized expected utility. And this boiled down to a household’s risk aversion. More (less) risk averse households would hold a larger (smaller) share of their wealth in bonds and other safe assets and a smaller (larger) share of their wealth in stock and other risky assets. Thus was born mean-variance portfolio optimization, which Markowitz called Modern Portfolio Theory.
Modern Portfolio Theory (MPT) is Now Ancient Portfolio Theory
MPT’s recommendations connected to economic theory and were, thus, modern relative to what passed for portfolio advice at the time. But people don’t live for just one period. They live for many years and face a host of factors that Markowitz’s framework entirely ignores.
The list includes marital status, the presence and ages of children, economies of shared living, the relative cost of children, career, housing, and retirement decisions, federal and state income taxes, payroll taxes, Social Security benefits, cash-flow constraints, college support for kids, Roth and non-Roth tax-favored retirement accounts, life insurance needs and premium payments, Medicare IRMAA premiums, Roth conversion strategies, RMDs, QCDs, annuitization, and, well, don’t get me started.
Each of these factors enters into expected lifetime utility maximization. And each can materially impact your optimal, potentially time-varying investment strategy. Expected lifetime, not one-period utility maximization is economics’ gold standard. This, not one-period optimization, is what we teach grad students in finance and economics in business schools and economics departments. Markowitz focused on one period because one period made his mathematics simple and there were no computers available in 1952 to solve complex mathematical problems computationally rather than analytically.
Expected Lifetime Utility Maximization — Practice Meets Theory
“Ok, I get it. I’d like to make investment decisions that take into account everything MPT leaves out, that maximize my average lifetime utility, and that don’t require my metamorphosing into a squirrel.”
How do I do it?
Simple. Run my company’s MaxiFi Planner financial planning software. MaxiFi does Living Standard Monte Carlo® analysis in maximizing your expected lifetime utility. I.e., it considers, via Monte Carlo simulations, all the annual living-standard paths you’ll potentially experience if you invest more or less aggressively through time. To be precise, MaxiFi simulates the range of potential annual living-standard paths for your current investment strategy X, calculates your lifetime utility arising under each path, averages these lifetime utilities and then compares this average with the average arising under safer and riskier alternative investment strategies, Y and Z. By trying different X, Y, and Z strategies, you quickly hone in on the one that maximizes your lifetime utility.
Running MaxiFi is easy as pie. Don’t take my word for it. Read these scores of household testimonials. Or check out this article in Bankrate, which names MaxiFi “Best financial Planning Software of 2025.”
Tens of thousands of households use MaxiFi entirely on their own, with my help or that of my colleagues, or with a financial advisor who runs MaxiFi for their clients. MaxiFi handles the entire gamut of financial issues. This is what you’d expect from economics-based planning — one tool that handles all financial issues for the simple reason that all financial issues are interconnected.
As Bankrate attests, MaxiFi sets the commercial gold standard for financial planning software. But it also meets the academic gold standard. Again, don’t take my word for it. MIT’s Robert Merton, who won the Nobel Prize for co-developing the Black-Scholes-Merton option pricing formula, doesn’t endorse products. But he did send me this statement to post on our website.
I assign MaxiFi Planner in my asset management course at MIT’s Sloan School of Management as an outstanding science-based lifecycle and retirement management platform.
Before he passed, the economics wizard, Nobel Laureate Paul Samuelson, routinely referred to Robert Merton as “the Issac Newton of Finance.” Samuelson was not exaggerating. Merton has made enormous contributions to finance, in general, and to personal finance, in particular. Merton’s description of MaxiFi as “outstanding” and our collection of user testimonials are the best reward I could have hoped for in my 32-year quest to deliver state-of-the-art economics-based financial planning to the public. MPT was state-of-the-art 73 years ago. It’s not today.
Is your Investment Portfolio Based on MPT?
MPT, with some tweaks, remains Wall Street’s standard means of providing portfolio advice. According to Perplexity:AI,
Modern portfolio theory, developed by Harry Markowitz, guides much of Wall Street’s advice by emphasizing risk-reward trade-offs, diversification, and efficient portfolio construction. Tools based on MPT, such as mean–variance optimization, remain central to how investment firms assess risk and allocate assets—for example, by finding the asset mix that offers maximum return for a given level of risk.
This is troubling news if you or your financial advisor are using Wall Street’s conventional financial planning software to choose your portfolio. Factors that MPT ignores can make a huge difference to your optimal portfolio allocation. Here are four examples. They show that the amount of your Social Security benefits, your housing plans, the taxable nature (Roth vs non-Roth) of your retirement accounts, and your off-the-top obligations can make a day-and-night difference to your optimal holdings of stocks versus bonds.
How Dangerous Is Conventional, MPT-Based Financial Advice? Four Examples
Consider hypothetical single Alice, age 63, who lives in Montana. Alice earns $75K. She plans to retire at 67 and start Social Security at 70, when she’ll start collecting $57,732 annually in today’s dollars. Alice rents for $2400 a month, has $2 mil in an IRA, which she’s smoothly withdrawing in real terms starting this year. She also has $300K in her savings account.
Analyzing Alice’s Asset Allocation Using Lifetime Expected Utility Maximization
The chart below compares Alice’s Comfort Index — MaxiFi’s index of expected lifetime utility — from investing for the rest of her days in three different ways. Her current strategy is investing 50%-50% in stocks and bonds. The Safe Strategy entails investing 20% in stock and 80% in bonds. The “Risky Strategy” is 80% in stock and 20% in bonds. (MaxiFi can consider time-varying asset allocations, e.g., life-cycle funds. But I’m taking a simple example.)
As the chart shows, Alice, who has moderate risk tolerance, i.e., she has a moderate-sized risk aversion coefficient, does best with the 50-50 stock-bond strategy. The safe (risky) strategy is 9 (1) percent worse in the following sense. It generates the same expected lifetime utility as would arise were Alice to invest on a 50-50 basis, but consume 9 (1) percent each year along each living standard path that the 50-50 strategy produces. Were Alice able to tolerate almost no risk — the case of a high risk aversion coefficient, the risky strategy would be 7 percent worse. At the other extreme — if Alice can tolerate lots of risk, the risky strategy is 18 percent better.
Example 1: Alice Has More Assets and No Social Security
Let’s now change Alice’s inputs and assume she has an extra $1 million more in regular assets, but no claim whatsoever to Social Security. This leaves Alice with the same spending power as in the base case were she to invest solely in inflation-indexed bonds.
Would MPT’s portfolio advice change if Alice had more assets, but no future Social Security benefits? No. It’s focused on the mean and variance of Alice’s final assets, not on the mean and variance of her final living standard let alone the level and variability of her living standard over her remaining 37 potential years of life.
Regardless of MPT’s assessment, Alice is now at far greater risk from investing in stock. The reason is obvious to a squirrel, but not to MPT. Social Security was providing a floor to Alice’s income. It effectively represented a major holding in bonds, specifically inflation-indexed bonds. As a result, moderately risk tolerant Alice was essentially indifferent between a 50-50 and an 80-20 stock-bond portfolio.
But absent her implicit bond holdings in the form of Social Security, an 80-20 stock-bond split of her financial assets entails a much larger effective share of resources invested in stock. As a result, as the next chart shows, the 80-20 portfolio is 51 percent worse, not 1 percent worse, than the 50-50 portfolio! In the case Alice can tolerate almost no risk, the risky strategy is 70 percent worse, not 7 percent worse as was previously the case!
Example 2: Alice Plans to Purchase a House at Retirement
As a second example of the Wall Street’s dangerously outmoded investment advice, suppose Alice intends to make a cash purchase, at retirement, of a $300K house with $20K in annual expenses. In this case, the 80-20 risky strategy is simply too risky. Nine of MaxiFi’s five hundred Monte Carlo fail. In these cases, Alice’s investments do so poorly that she can’t swing the purchase. As the next chart shows, the safe 20-80 strategy is now seven times better than the 50-50 strategy if Alice is moderately risk averse. If she’s highly risk averse, it’s 20 times better. Again, Alice’s future house purchase is not something MPT will pick up. Hence, largely investing Alice in stock, as Wall Street’s software would surely do, places Alice at major risk.
Example 3: Alice IRA Is a Roth IRA, Not a Traditional (Taxable) IRA
This example simply switches Alice $2 mil IRA from a taxable IRA to a Roth. Compare the chart below with the first chart. If Alice has moderate risk tolerance, holding an 80-20 stock portfolio is 33 percent worse if her IRA is a Roth. As the top chart shows, it’s only 1 percent worse if her IRA is taxable. The reason is that Alice effectively has larger asset holdings because no taxes are due on Roth withdrawals. Another way to explain this is that the government shares Alice’s investment risk when her IRA is taxable. How so? Because high returns mean higher taxes and low returns mean lower taxes. In short, the government shares the upside as well as the downside of taxable IRA returns. Would MPT capture this? No way.
Example 4: Alice Provides $30,000 in Annual Support to her Disabled Niece
Now suppose that Alice has a beloved 20-year-old, disabled niece who she is committed to support on an ongoing basis via monthly payments totaling $30,000, annually, in today’s dollars. As the final chart below shows, this commitment, which effectively represents a negative bond holding, makes holding stock far riskier. Assuming that Alice is moderately risk averse, the 80-20 stock-bond portfolio is now 14 percent worse, not 1 percent worse, than the 50-50 portfolio. In short, one can’t provide fiduciarily responsible portfolio recommendations without knowing all the financial details of the household. Factors that never show up on MPT’s radar can make huge differences to proper investment advice.
Conclusion
Conventional financial planning, including conventional investment advice, can be dangerous to your financial health. And being asked to pay for this advice adds insult to injury.
Economics Matters — Blog/Podcast/Financial Riddler/MaxiFi Puzzler
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