Bond Data Reference

The Dickerson cleaned TRACE corporate bond panel (141 columns, 2.83M rows, 1973-01 to 2025-03). Fetch via bonds-wrds-expert, stored in data/ as Parquet.

PyBondLab Column Mapping

Parquet Column	PBL Name	Description
cusip	ID	9-digit bond CUSIP
ret_vw	ret	Month-end total return (primary)
mcap_e	VW	Bond market cap at month-end
spc_rat	RATING_NUM	S&P composite rating (1-22)
permno	PERMNO	CRSP equity link (for WithinFirmSort)

Three Mapping Approaches

# Option 1: fit() params (StrategyFormation)
result = sf.fit(IDvar='cusip', RETvar='ret_vw', VWvar='mcap_e', RATINGvar='spc_rat')

# Option 2: rename upfront
data = data.rename(columns={'cusip': 'ID', 'ret_vw': 'ret', 'mcap_e': 'VW', 'spc_rat': 'RATING_NUM'})

# Option 3: Batch columns= dict (pbl_name -> user_name)
batch = pbl.BatchStrategyFormation(
    data=data,
    columns={'ID': 'cusip', 'ret': 'ret_vw', 'VW': 'mcap_e', 'RATING_NUM': 'spc_rat'},
    ...
)

Real Data Prep

data['spc_rat'] = data['spc_rat'].astype('float64')  # nullable IntegerArray breaks numba

Alternate Return Columns

Column	When to Use
ret_vw	Default. Use with MMN-adjusted signals
ret_vw_bgn	Use ONLY with noisy/unadjusted signals (_mmn suffix)
ret_vwx	Excess return (ret_vw minus duration-matched Treasury)

VW Column: mcap_e vs mcap_s

Column	Definition	When to Use
mcap_e	Market cap at end of month t	Default. Contemporary with signal
mcap_s	Market cap at end of month t-1	Lagged market cap

Rating Encoding

spc_rat: S&P composite rating (S&P first, Moody's fallback).

Numeric	Rating	Grade
1	AAA	IG
2-4	AA+/AA/AA-	IG
5-7	A+/A/A-	IG
8-10	BBB+/BBB/BBB-	IG
11-13	BB+/BB/BB-	HY
14-16	B+/B/B-	HY
17-19	CCC+/CCC/CCC-	HY
20-22	CC/C/D	HY/Default

PyBondLab filters: rating='IG' → spc_rat ≤ 10, rating='NIG' → spc_rat > 10.

Signal Clusters

141 columns in 9 clusters:

Cluster	Key Signals	Count
Identifiers & Returns	cusip, date, ret_vw, ret_type, rfret	22
Bond Characteristics	spc_rat, call, fce_val, 144a	8
Size	mcap_s, mcap_e, sze	3
Spreads & Duration	cs, md_dur, tmat, ytm, convx, age	8
Value	bbtm, val_hz, val_ipr	5
Momentum & Reversal	mom3_1, mom6_1, mom12_1, str, ltr*	21
Illiquidity	pi, ami, roll, spd_abs, spd_rel	13
Volatility & Risk	dvol, rvol, ivol_*, var_95, es_90	16
Factor Betas	b_mktb, b_dvix, b_defb, b_psb	41

Common test signals: cs, tmat, mom3_1, mom6_1, mom12_1, bbtm, md_dur, dvol, ami, var_95

Data Subsets

Subset	Filter	Rows	Use Case
Full	None	2.83M	Includes pre-TRACE (1973+)
TRACE-only	date >= '2002-08-01'	1.86M	Recommended
IG	spc_rat <= 10	~1.6M	Investment grade
HY	spc_rat > 10	~1.2M	High yield

Gotchas

1. Multiple bonds per issuer — one row per tranche per month. Aggregate by issuer_cusip or permno for firm-level.
2. cs_sprd ≠ credit spread — it's Corwin-Schultz high-low spread (liquidity). Actual credit spread is cs.
3. ar_sprd ≠ adjusted return spread — it's Abdi-Ranaldo closing price spread (liquidity).
4. No lead/lag — all variables sampled at end of month t.
5. tret NULL on recent dates — use ret_vw - rfret for excess returns instead.
6. 19% of bonds lack permno (11% in TRACE-only) — dropped in WithinFirmSort.
7. 144a column name — starts with digit, use data['144a'].
8. ret_type values — standard, default_evnt, trad_in_def. Filter ret_type == 'standard' to exclude defaults.
9. Composite ratings — spc_rat uses S&P first, Moody's fallback. Not pure S&P.
10. lib/libd = Latent Implementation Bias — NOT LIBOR.
11. Pre-TRACE era (before 2002-08) — lower quality. Use TRACE-only subset.
12. Rolling beta start dates — betas use 36-month windows, start ~2003-08.