Capital Market Expectations: Building the Inputs Everything Else Consumes

Learning objectives

You can:

1. State the seven-step capital-market-expectations process, and explain why a forecast is a distribution with a stated horizon rather than a point prediction.
2. Name the seven defects of a historical estimate: non-stationarity, regime shift, ex-post risk read as ex-ante risk, data mining, time-period bias, survivorship, asynchronous pricing, and say which a longer sample makes worse.
3. Build a bond's expected return from blocks, reconcile the sum to a quoted yield, and subtract expected credit loss to convert a promised yield into an expected return.
4. Compute rolldown return on a stated curve, cross-check it against the modified-duration shortcut, and quantify a short-rate path from a Taylor-type rule with the error band its own inputs imply.
5. Compute a Grinold-Kroner equity return decomposed into income yield, net share change, inflation, real earnings growth and repricing, including the case where buybacks make the share-change term negative.
6. Reconcile a decomposed equity return against a risk-premium-based required return for the same index, attributing the gap to specific components rather than splitting the difference.
7. Apply the Singer-Terhaar adjustment, state and defend an integration weight, and quantify how far an emerging-market assumption moves across the full range of that weight.
8. Build a real-asset expected return from cap rate, net-operating-income growth and cap-rate change, and correct an appraisal-based volatility and correlation for smoothing before either reaches a covariance matrix.
9. Forecast a currency from relative purchasing power parity, relative economic strength, capital flows and savings-investment balances, and say which dominates at which horizon.
10. Assemble, shrink and stress-test a full assumption table: the positive-semi-definite check, a shrinkage estimator, the sanity ranges, a component-level comparison against two published sets, and a written falsification test.

The duality, stated once. The gated skill is the by-hand build: a bond return decomposed and reconciled, a Grinold-Kroner sum, a Singer-Terhaar blend, a de-smoothed volatility. The productivity payoff is running the whole table in fifty lines of Python and re-running it monthly. The risk in between is worth naming now. An assistant asked for "long-term capital market assumptions for Indian equities" returns a fluent, plausible, correctly formatted number with no decomposition behind it, and a number with no decomposition cannot be argued with, corrected or falsified. The verification is the reconciliation: does the sum of the blocks tie to a quoted yield, does the table survive its own sanity ranges, and is the correlation matrix's smallest eigenvalue non-negative.

Prerequisites & connections

Builds on. M3.02 supplies the equity risk premium three ways, historical, implied and country-adjusted; the implied-ERP number used as a cross-check below is that module's output, pulled from its ERP card, not rebuilt. M3.03 supplies the clean risk-free construction, including the subtraction of a sovereign default spread from an Indian G-sec yield, and the currency rule deciding which risk-free rate belongs in which forecast. M7.03 owns inflation measurement, the curve's shape and the term premium; the maturity-premium block below is that term premium, cited rather than re-derived. M7.02 owns the policy rate and the transmission mechanism, and the Taylor rule appearing here as a forecasting device is that module's diagnostic tool put to a different use. M7.05 owns cycle dating and the growth-inflation regime grid. M7.06 owns exchange-rate determination, covered interest parity and the balance of payments. QM1.01 supplies sampling distributions and standard errors, QM1.04 the multiple-testing discipline that decides how much of a backtested premium to believe, and M6.04 the behavioural catalogue the forecasting errors below are mapped to.

Feeds forward. AA1.01 is the direct consumer: it takes this table, runs the frontier, the global minimum-variance portfolio and the tangency portfolio, and owns that machinery including the constraint arithmetic and the input-sensitivity demonstration. AA1.02 blends these return assumptions with market-equilibrium returns under Black-Litterman and budgets risk when a return forecast is too fragile to size a position at all. AA1.05 derives a policy portfolio from the table and an institution's constraints. AA1.03 measures what that portfolio delivered, and a sloppy assumption set here becomes a sloppy benchmark there. AL1.02 and PW1.03 consume the real-asset lines, and the quant-code region turns the pipeline into production software.