An independent research atlas · No.4 in Money, Examined

The Myth Ledger

Ten durable personal-finance claims, audited against economics, market evidence, and the decisions real households must make.

Start with a correction and its guardrail. Each chapter separates measured evidence from model assumptions, then offers a concrete example, a decision checklist, and sources with their limits. All ten chapters are readable without JavaScript; labs and private checkmarks are optional.

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Research method

Claims need a chain of custody.

Each chapter separates an economic identity or model from empirical measurement and from a household decision rule. Model outputs are not relabeled as facts. Counterexamples set boundaries; they do not automatically invert a claim.

FOUNDATION: An identity, framework, or equilibrium argument.
MEASURED: A result estimated from historical or survey data.
MODEL: An outcome conditional on assumptions and objective.
BOUNDARY: A condition under which the correction can fail.

Complete bibliography

Original source numbers are retained across chapters. Each finding travels with its limitation.

1
The Life-Cycle Model of Consumption and Saving ↗
Browning, M. & Crossley, T. F. (2001). Journal of Economic Perspectives 15(3), 3–22.
Finding: A clear synthesis of consumption smoothing: households trade resources across periods rather than maximize the balance at one date.
Limit: The benchmark model is not a literal instruction; borrowing limits, uncertainty, habits, and household structure matter.
2
Consumption Over the Life Cycle ↗
Gourinchas, P.-O. & Parker, J. A. (2002). Econometrica 70(1), 47–89.
Finding: Estimated household behavior shifts from precautionary saving earlier in life toward retirement saving as people age.
Limit: A structural estimate depends on assumptions about income risk, preferences, and the households represented in the data.
3
Are Americans Saving “Optimally” for Retirement? ↗
Scholz, J. K., Seshadri, A. & Khitatrakun, S. (2006). Journal of Political Economy 114(4), 607–643.
Finding: A calibrated lifecycle model found many sampled households near or above household-specific wealth targets, despite simple rules implying shortfalls.
Limit: Model-optimal is not universal; the result is sensitive to pensions, Social Security, health, earnings, family structure, and preferences.
4
Economic Growth and Equity Returns ↗
Ritter, J. R. (2005). Pacific-Basin Finance Journal 13(5), 489–503.
Finding: Across countries, faster per-capita economic growth did not translate reliably into higher shareholder returns.
Limit: Country samples are limited and results depend on start dates, market survival, valuation, dilution, and how growth is measured.
5
Economic Growth and Equity Investing ↗
Cornell, B. (2010). Financial Analysts Journal 66(1), 54–64.
Finding: Aggregate earnings, dilution, valuation, and investor ownership create a wedge between economic growth and returns earned per share.
Limit: Accounting identities discipline forecasts but do not produce a short-horizon trading signal.
6
Dividend Policy, Growth, and the Valuation of Shares ↗
Miller, M. H. & Modigliani, F. (1961). The Journal of Business 34(4), 411–433.
Finding: Under frictionless assumptions, payout policy does not create value: investment policy and cash flows determine value.
Limit: Taxes, trading frictions, signaling, agency problems, and investor constraints make real markets less frictionless.
7
Dividends, Share Repurchases, and the Substitution Hypothesis ↗
Grullon, G. & Michaely, R. (2002). The Journal of Finance 57(4), 1649–1684.
Finding: Repurchases increasingly substituted for dividends as a way to distribute corporate cash.
Limit: Repurchases can destroy value when executed at poor prices or used to offset dilution; payout form still affects taxes and governance.
8
Payout Policy in the 21st Century ↗
Brav, A., Graham, J. R., Harvey, C. R. & Michaely, R. (2005). Journal of Financial Economics 77(3), 483–527.
Finding: Manager surveys show dividends are sticky while repurchases are flexible; firms choose payout form for reasons beyond raw return creation.
Limit: Survey evidence describes corporate decisions and should not be read as proof that every repurchase or dividend is wise.
9
The Arithmetic of Active Management ↗
Sharpe, W. F. (1991). Financial Analysts Journal 47(1), 7–9.
Finding: Before costs, the aggregate active dollar must equal the market; after higher costs, the aggregate active dollar must lag it.
Limit: The identity applies to a properly defined market and aggregate holdings, not to a claim that no active investor can outperform.
10
Do Stocks Outperform Treasury Bills? ↗
Bessembinder, H. (2018). Journal of Financial Economics 129(3), 440–457.
Finding: Long-run wealth creation in U.S. equities is extremely concentrated in a small minority of stocks.
Limit: The comparison is buy-and-hold to bills and the result does not mean most stocks always fall or that concentration can be forecast in advance.
11
Luck versus Skill in the Cross-Section of Mutual Fund Returns ↗
Fama, E. F. & French, K. R. (2010). The Journal of Finance 65(5), 1915–1947.
Finding: The distribution of mutual-fund performance is largely consistent with insufficient net alpha after costs, with few extreme outcomes beyond chance.
Limit: Factor models are imperfect and the study does not say skilled managers cannot exist; it says identifying them ex ante is hard.
12
SPIVA U.S. Scorecard, Year-End 2025 ↗
S&P Dow Jones Indices (2026). S&P Indices Versus Active scorecard.
Finding: The recurring scorecard compares active funds with category benchmarks across horizons and reports survivorship and style consistency.
Limit: Results vary by category and period; benchmark choice, taxes, and investor-specific constraints can change the relevant comparison.
13
Stock Prices, Earnings, and Expected Dividends ↗
Campbell, J. Y. & Shiller, R. J. (1988). The Journal of Finance 43(3), 661–676.
Finding: Valuation ratios contain information about long-horizon returns, consistent with prices varying relative to fundamentals.
Limit: Long-horizon predictability is not short-horizon timing precision, and overlapping observations make inference difficult.
14
A Comprehensive Look at the Empirical Performance of Equity Premium Prediction ↗
Goyal, A. & Welch, I. (2008). The Review of Financial Studies 21(4), 1455–1508.
Finding: Many celebrated return predictors performed poorly out of sample relative to a simple historical-average forecast.
Limit: Forecast evaluation is period- and specification-dependent; weak predictability may still matter for long-range planning.
15
Long-Horizon Predictability: A Cautionary Tale ↗
Boudoukh, J., Israel, R. & Richardson, M. (2019). Financial Analysts Journal 75(2), 17–30.
Finding: Overlapping long-horizon returns can make statistical relationships look more certain than the independent information supports.
Limit: The critique concerns inference and confidence, not a claim that valuations contain zero information.
16
Buffett’s Alpha ↗
Frazzini, A., Kabiller, D. & Pedersen, L. H. (2018). Financial Analysts Journal 74(4), 35–55.
Finding: Berkshire’s record combined exposure to safe, high-quality, inexpensive stocks with unusually stable leverage and disciplined implementation.
Limit: A factor description is not an easy replication recipe; insurance float, financing stability, governance, taxes, scale, and temperament are distinctive.
17
Berkshire Hathaway 2016 Shareholder Letter ↗
Buffett, W. E. (2017). Berkshire Hathaway.
Finding: Buffett argues that both large and small investors should generally use low-cost index funds rather than enrich high-fee intermediaries.
Limit: A shareholder letter is primary commentary, not a controlled test, and personal allocation still depends on risk capacity and liabilities.
18
Beyond the Status Quo: A Critical Assessment of Lifecycle Investment Advice ↗
Anarkulova, A., Cederburg, S. & O’Doherty, M. S. (2025). SSRN working paper, revision dated 2025.
Finding: Historical block-bootstrap simulations across developed markets favor globally diversified all-equity allocations in the authors’ modeled lifecycle outcomes.
Limit: This is a working paper, not a universal prescription; results depend on historical resampling, utility and ruin definitions, adherence, and omitted real-world frictions.
19
Stocks for the Long Run? Evidence from a Broad Sample of Developed Markets ↗
Anarkulova, A., Cederburg, S. & O’Doherty, M. S. (2022). Journal of Financial Economics 143(1), 409–433.
Finding: International history reveals meaningful long-horizon loss risk that is obscured by focusing only on successful U.S. market history.
Limit: Historical country data have measurement challenges and cannot enumerate every future political or market regime.
20
Who Should Buy Long-Term Bonds? ↗
Campbell, J. Y. & Viceira, L. M. (2001). American Economic Review 91(1), 99–127.
Finding: Bond risk depends on the investor’s horizon and inflation exposure; inflation-indexed and nominal bonds hedge different liabilities.
Limit: The model abstracts from taxes, default, transaction costs, and behavioral responses to losses.
21
The Safe Withdrawal Rate: Evidence from a Broad Sample of Developed Markets ↗
Anarkulova, A., Cederburg, S., O’Doherty, M. S. & Sias, R. (2025). Journal of Pension Economics & Finance 24(3), 464–500.
Finding: A 38-country sample produces more severe retirement-spending risk than conventional U.S.-only backtests.
Limit: A constant real withdrawal and a binary ruin threshold are only one retirement objective; flexible spending, annuities, pensions, taxes, and bequests alter the problem.
22
The Golden Dilemma ↗
Erb, C. B. & Harvey, C. R. (2013). Financial Analysts Journal 69(4), 10–42.
Finding: Gold may preserve purchasing power over extremely long spans, but its real price is highly variable over ordinary investment horizons.
Limit: Gold’s history is regime-dependent and estimated “fair value” relationships are too imprecise for confident timing.
23
Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold ↗
Baur, D. G. & Lucey, B. M. (2010). The Financial Review 45(2), 217–229.
Finding: Gold can behave as a hedge or short-lived safe haven in some market stress episodes, which is different from tracking consumer prices.
Limit: Correlations are time-varying, market-specific, and sensitive to the chosen crisis window.
24
Time and Place Where Gold Acts as an Inflation Hedge ↗
Wang, K.-M., Lee, Y.-M. & Thi, T.-B. N. (2011). Economic Modelling 28(3), 806–819.
Finding: Gold’s inflation-hedging relationship differs across countries, regimes, and short versus long horizons.
Limit: Threshold estimates are sample-specific and do not guarantee future hedge effectiveness.
25
Assessing High House Prices: Bubbles, Fundamentals and Misperceptions ↗
Himmelberg, C., Mayer, C. & Sinai, T. (2005). Journal of Economic Perspectives 19(4), 67–92.
Finding: Rent-versus-buy comparisons require the annual user cost of housing, including financing, taxes, maintenance, risk, and expected appreciation—not price-to-rent alone.
Limit: Expected appreciation and risk premia are unobservable; small assumption changes can reverse a local conclusion.
26
Owner-Occupied Housing as a Hedge Against Rent Risk ↗
Sinai, T. & Souleles, N. S. (2005). The Quarterly Journal of Economics 120(2), 763–789.
Finding: Owning can hedge long-horizon exposure to local rents, so housing is both an asset and a stream of housing services.
Limit: The hedge is location-specific and comes with concentration, mobility, financing, and maintenance risks.
27
Owner-Occupied Housing and the Composition of the Household Portfolio ↗
Flavin, M. & Yamashita, T. (2002). American Economic Review 92(1), 345–362.
Finding: A home’s size relative to net worth can dominate household portfolio risk, especially for younger leveraged owners.
Limit: The model simplifies moving, labor-income correlation, taxes, and heterogeneous local markets.
28
Diversification Across Time ↗
Ayres, I. & Nalebuff, B. (2013). The Journal of Portfolio Management 39(2), 73–86.
Finding: The authors model modest early-life leverage that spreads equity exposure more evenly across time and can improve modeled retirement outcomes.
Limit: Leverage adds financing, margin-call, behavioral, labor-income, and path risks; the strategy is unsuitable for many households and accounts.
29
Optimal Portfolio Choice for Long-Horizon Investors with Nontradable Labor Income ↗
Viceira, L. M. (2001). The Journal of Finance 56(2), 433–470.
Finding: Labor-income stability and its correlation with markets affect how much financial risk a household can rationally bear.
Limit: Human capital is neither a traded bond nor known with certainty; job loss can coincide with market stress.
30
Household Finance ↗
Campbell, J. Y. (2006). The Journal of Finance 61(4), 1553–1604.
Finding: Households face participation, borrowing, refinancing, diversification, and advice frictions that make textbook optimization difficult.
Limit: Normative models must be translated through actual products, taxes, institutions, and household behavior.