Understand yield curve mechanics and equity factor models. Know the structural forces and documented anomalies that have driven returns — and their limitations.
This module completes the market foundations. Fixed income pricing is driven by yield curve expectations, inflation, and credit. Equity systematic research targets documented "factors" — sources of return variation that have persisted across decades and geographies. Both topics feed directly into hypothesis writing: without knowing what drives these markets, you can't explain why an edge should exist.
Bond prices move inversely to yields. A bond's yield reflects the market's expected return given maturity, credit risk, and inflation expectations. The yield curve (how yields vary by maturity) encodes the market's view of the future.
Duration measures how sensitive a bond is to interest rate changes. A bond with 5-year duration loses 5% in value for every 1% rise in yield. Higher duration = more sensitivity.
Modified duration formula:
\[ D_{\text{mod}} = \frac{D_{\text{Macaulay}}}{1 + y/m} \]Where y = yield to maturity, m = number of coupon periods per year. Price change ≈ −D_mod × Δy × Price.
Fixed income edges typically come from: (1) carry (buying yield from bonds above the repo rate), (2) curve positioning (being long or short specific parts of the curve), (3) credit selection (finding bonds that will outperform). These are harder to systematize than commodity or FX edges, which is why systematic fixed income is more challenging.
Fixed income instruments at the fund:
All futures — same clearinghouse mechanics you learned in Week 2. Daily mark-to-market, initial margin, variation margin.
Systematic research in equities often targets known "factors" — sources of return variation that persist across time and markets.
Where R_i = stock return, R_f = risk-free rate, R_m = market return, SMB = Small Minus Big (return spread between small and large caps), HML = High Minus Low (return spread between high book-to-market and low book-to-market stocks).
Factors are documented empirical phenomena, not guaranteed. Each has multi-year drawdown periods. Momentum crashed hard in 2009 (mean reversion), value underperformed 2010–2020, low-vol underperformed 2021–2022. The persistence of factors is an active research question. Some are behavioral (will compress as more people learn), others may be risk compensations (will persist).
Suppose you're looking at a stock with the following characteristics relative to peers:
This stock would score positively on momentum, value, size, and quality factors simultaneously. Multi-factor overlap can amplify returns — or expose you to a correlated factor drawdown.
| Dimension | FX | Commodities | Fixed Income | Equity Factors |
|---|---|---|---|---|
| Primary driver | Rate differentials, policy | Physical supply/demand, storage | Yield curve, inflation | Risk premia, behavioral bias |
| Main structural edge | Carry (UIP failure) | Roll yield, seasonality | Carry, curve shape | Value, momentum, quality |
| Edge persistence | High (structural risk premium) | High (physical constraints) | Moderate (rate regime dependent) | Moderate (can compress) |
| Key risk | Sudden policy shifts, carry unwinds | Roll cost in contango, delivery | Duration risk, curve inversions | Factor drawdowns, crowding |