Cboe U.S. Equities Historical Market Volume Data 2011

Rows: 3,780
Columns: 14

Daily historical market volume, notional value, and trade counts for U.S. equities exchanges and TRFs for 2011.

AI analysis

Dataset Analysis: Cboe U.S. Equities Historical Market Volume Data 2011

1. Dataset Overview & Value for Correlation Studies

This dataset, sourced directly from Cboe's official statistics repository at cdn.cboe.com/resources/us/equities/market-statistics/historical-market-volume/, captures daily U.S. equity market microstructure activity across 15 distinct market participants for the full 2011 trading calendar. The 252 distinct dates align precisely with the expected number of U.S. trading days in a calendar year, confirming complete annual coverage with no missing sessions. The granularity of the data — spanning three tape classifications (A: NYSE-listed, B: NYSE American/regional, C: Nasdaq-listed) across shares, notional value, and trade counts — makes it exceptionally well-suited for studying exchange competition dynamics, intraday liquidity patterns, and the relationship between trading activity and broader market events such as the August 2011 U.S. credit downgrade and European debt crisis volatility. The Cboe publisher origin also lends high credibility to the figures, as this is primary-source exchange data rather than a derived or aggregated feed.

2. Data Quality Observations

The dataset exhibits near-pristine quality across its 3,780 rows and 14 columns. Total null cells across all columns is zero, which is exceptional for a financial time-series dataset of this complexity and strongly suggests the source pipeline enforces completeness constraints before publication. All numeric share, notional, and trade count columns carry min=0 values for individual tape-level metrics, which is expected and legitimate — not all 15 market participants trade on all three tapes every day, so zero-volume observations are structurally valid rather than indicative of missing data. The Total Shares and Total Trade Count columns have distinct=3,780 — matching the full row count exactly — confirming every row is uniquely identifiable by its aggregate totals, and implying zero exact duplicate rows across the dataset. No type mismatches are flagged, with decimals and integers appropriately assigned to volume/notional and trade count columns respectively. The one area warranting downstream attention is the high outlier counts on tape-level columns: Tape C Shares logs 491 outliers and Tape A Notional logs 298 outliers, suggesting a small number of market participants or days with extreme activity that could disproportionately influence regression models if not treated.

3. Key Column Distributions & Statistical Highlights

The most analytically rich columns are the total and tape-level metrics, all of which exhibit pronounced right skew — a hallmark of financial volume data where most participants trade modestly but a few dominate on high-volatility days. Total Shares (mean: ~522M, median: ~193M, skew: 1.68) shows a median less than 40% of the mean, indicating heavy upper-tail distortion; the IQR spans from ~50M to ~855M, a 17x range reflecting enormous cross-participant disparity. Tape C Shares is the most skewed of the tape columns (skew: 2.06), with a median of ~43M but a maximum of ~1.83B — likely driven by high-frequency Nasdaq activity on volatile market days. Tape B Trade Count carries the highest skewness in the trade count family (skew: 2.18), with a median of ~99,737 trades but a maximum exceeding 3.3M, suggesting episodic burst activity from specific participants. Notably, Total Notional has the most compressed skew (1.47) among totals and only 77 outliers, implying that large share volumes don't always translate proportionally to large dollar values — a signal that price-per-share heterogeneity across tapes materially dampens notional concentration. Log-transforming all volume and notional columns is strongly recommended before any regression or correlation work.

4. Recommended Join Key Columns

The Date column is the primary join key, with 252 distinct values mapping cleanly to U.S. trading days — it should serve as the anchor for any time-series merge with external datasets. The Market Participant column (15 distinct string values) functions as a secondary categorical join key, enabling participant-level panel data construction when combined with Date. Together, (Date, Market Participant) forms a composite natural key that uniquely identifies each of the 3,780 rows (252 days × 15 participants = 3,780 exactly). For cross-dataset joins, Date should be standardized to ISO 8601 format and verified against trading calendar datasets to exclude weekends/holidays before merging. The tape-level columns (A/B/C) are not join candidates but are strong candidates as dependent or independent variables in multivariate models.

5. Recommended Pairing Datasets for Correlation Discovery

Several external datasets would pair powerfully with this data. VIX daily closing values (2011) — also published by Cboe — would directly test whether market-wide volatility predicts total share volume or trade count spikes, with the August 2011 volatility episode providing a natural experiment. S&P 500 or Nasdaq Composite daily returns would allow testing of the well-documented volume-volatility relationship and whether Tape C activity leads or lags index movements. Federal Reserve or economic calendar data (FOMC meeting dates, NFP release days) could reveal how scheduled macro events concentrate trading in specific tapes or among specific participant classes. Historical exchange market share data from FINRA or SRO filings would enrich the Market Participant dimension, enabling competitive dynamics analysis — for instance, whether dark pool TRF volumes grew at the expense of lit exchange tapes during 2011. Finally, sector ETF volume data (e.g., XLF, XLE) mapped to Tape A vs. Tape C would help decompose whether notional-to-share divergence is sector-driven, since financial and energy stocks often trade at higher per-share prices than tech names.

Columns

Date (date)
Market Participant (string)
Tape A Shares (decimal)
Tape B Shares (decimal)
Tape C Shares (decimal)
Total Shares (decimal)
Tape A Notional (decimal)
Tape B Notional (decimal)
Tape C Notional (decimal)
Total Notional (decimal)
Tape A Trade Count (integer)
Tape B Trade Count (integer)
Tape C Trade Count (integer)
Total Trade Count (integer)