Cboe U.S. Equities Historical Market Volume Data 2022

Rows: 4,769
Columns: 14

Daily historical market volume, notional value, and trade counts for U.S. equities exchanges and TRFs for 2022.

AI analysis

Dataset Analysis: Cboe U.S. Equities Historical Market Volume Data 2022

1. Dataset Overview & Analytical Value

This dataset, sourced directly from Cboe's official statistics portal (cdn.cboe.com/resources/us/equities/market-statistics/historical-market-volume), captures daily U.S. equity market activity across 19 distinct market participants — exchanges and Trade Reporting Facilities (TRFs) — for the full calendar year 2022. The data spans 251 trading days, and the 4,769 rows reflect the panel structure: one record per market participant per trading day. The three-tape architecture (Tape A for NYSE-listed, Tape B for NYSE American/regional, Tape C for Nasdaq-listed) makes this particularly valuable for studying market fragmentation, liquidity concentration, and inter-venue competition. For correlation studies, the combination of shares traded, notional dollar values, and trade counts across tapes enables rich multi-dimensional analysis of how volume migrates between venues during periods of volatility, policy changes, or macro events throughout 2022 — a year marked by aggressive Fed rate hikes, a broad equity bear market, and elevated volatility.

2. Data Quality Observations

The dataset demonstrates exceptionally clean quality across all 14 columns. Total null cells equal zero across all 4,769 rows — a rare characteristic in financial market data that typically suffers from late reporting or venue outages. All columns are appropriately typed (Date, String, Integer, Decimal), with no apparent type mismatches noted. The duplicate row count is flagged as pending recomputation against the Parquet phase, which warrants attention before finalizing any aggregation analysis — given the panel structure (251 days × 19 participants = 4,769 expected rows), the row count aligns perfectly, strongly suggesting zero duplicates, but this should be confirmed. One subtle quality note: Tape B and Tape C Shares both show a minimum of 0, as does Tape B Notional, which is expected for venues or TRFs that may not report activity across all tapes on every day, but these zero values should be distinguished from true nulls in downstream analysis to avoid distorting averages or correlation coefficients.

3. Key Column Distributions & Statistical Highlights

The volume and notional columns reveal a highly right-skewed distribution profile consistent with what you'd expect from market microstructure data — a handful of dominant venues (e.g., Nasdaq, NYSE) drive disproportionately large observations while dozens of smaller venues cluster near zero. Total Shares has a mean of ~625M but a median of only ~184M (skew=2.71), confirming that large-cap, high-volume days or dominant participants pull the mean far right. The 375 outliers in Total Shares are worth flagging for venue-level analysis. Tape C Notional shows the most extreme spread: mean of ~$12.6B vs. a median of ~$2.8B (skew=2.68, σ=~$22.8B, 520 outliers), reflecting Nasdaq's dominance in high-value tech trading. Trade count columns are notably better behaved — Total Trade Count has a skew of 1.94 (the lowest among volume metrics), and the Q1/Q3 interquartile range of ~709K to ~5.2M provides a reasonable working range for "typical" days, with 504 outliers likely corresponding to options expiration dates, FOMC announcement days, or the June/September 2022 volatility spikes. Tape A Trade Count shows the most compressed skew at 1.65, suggesting NYSE-listed securities had relatively more consistent participation across venues throughout the year.

4. Recommended Join Key Columns

The Date column is the primary and most powerful join key, already flagged as such, with 251 distinct values perfectly matching the U.S. equity trading calendar for 2022. For cross-dataset joins, Date should be formatted consistently as ISO-8601 (YYYY-MM-DD) to avoid merge failures with external sources. Market Participant (19 distinct string values) serves as the secondary key for any venue-level or exchange-level panel joins — however, normalization against a reference table of exchange identifiers (e.g., MIC codes like XCBO, XNAS, XNYS) would significantly improve interoperability with other financial datasets that use standard identifiers. A composite key of Date + Market Participant uniquely identifies every row and is the recommended approach for any many-to-many join scenario involving multiple datasets.

5. Suggested Paired Datasets for Correlation Discovery

Several dataset categories would pair powerfully with this data. VIX/CBOE Volatility Index daily data is the most natural companion — also a Cboe product — to test whether elevated implied volatility correlates with volume spikes, venue fragmentation shifts, or increased trade counts on specific tapes. Federal Reserve FOMC meeting dates and rate decision magnitudes would enable event-study analysis around the 2022 hiking cycle's impact on notional values and trade velocity. S&P 500 / Nasdaq Composite daily returns and price data (e.g., from Yahoo Finance or CRSP) would allow correlation of notional value against index-level price movements to study whether high-notional days precede or follow large price moves. Short interest data (FINRA publishes bi-weekly aggregate short volume by venue) could reveal whether specific TRFs show elevated short-side activity on high-volume Tape C days. Finally, earnings announcement calendars (e.g., from Compustat or Bloomberg) would help decompose whether trade count outliers cluster around earnings seasons, adding fundamental-driven context to the venue-level microstructure patterns visible in this dataset.

Columns

Date (date)
Market Participant (string)
Tape A Shares (integer)
Tape B Shares (integer)
Tape C Shares (integer)
Total Shares (integer)
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)