Cboe U.S. Equities Historical Market Volume Data 2014

Rows: 3,686
Columns: 14

Daily historical market volume, notional value, and trade counts for U.S. equities exchanges and TRFs for 2014.

AI analysis

Dataset Analysis: Cboe U.S. Equities Historical Market Volume Data 2014

1. Dataset Overview & Analytical Value

This dataset, sourced from Cboe's official data repository at cdn.cboe.com/resources/us/equities/market-statistics/historical-market-volume/markethistory2014.csv, captures a comprehensive picture of U.S. equity market microstructure across the full 2014 calendar year. It records daily trading activity — share volume, notional dollar value, and trade counts — segmented by market participant and tape classification (Tape A: NYSE-listed, Tape B: NYSE American/regional, Tape C: Nasdaq-listed), covering 252 unique trading days and 16 distinct market participants. The publisher, Cboe Global Markets, is a primary exchange operator and a highly authoritative source for exchange-level market structure data. The granularity of participant-level, tape-level breakdowns makes this dataset exceptionally valuable for studying market share concentration, intraday liquidity dynamics, and the structural relationship between trade count, share volume, and notional value — all core inputs for market microstructure research and regulatory analysis.

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2. Data Quality Observations

The dataset exhibits exceptional completeness: all 14 columns report zero null values across all 3,686 rows, which is notably clean for a financial time series of this scope. The absence of type mismatches and the logical structure of distinct counts — where Total Shares and Total Trade Count both approach the full row count in distinct values (3,686 and 3,676 respectively), confirming near-uniqueness at the aggregated level — further validate integrity. One meaningful quality flag is the minimum value of 0 appearing across all Tape A, B, and C sub-columns (shares, notional, and trade counts), which is structurally expected for participants who are absent from a specific tape on a given day but warrants confirmation that these are legitimate zeroes rather than missing data substitutions. The duplicate row count is noted as pending Phase D recomputation, which should be resolved before any aggregation analysis, particularly given the 16-participant × 252-day structure that should theoretically produce exactly 4,032 rows — the current count of 3,686 suggests some participant-days are genuinely absent rather than duplicated, which is plausible but worth auditing.

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3. Key Column Distributions & Statistical Highlights

The volume and notional columns reveal a heavily right-skewed market structure, consistent with what we'd expect when large market makers and dark pools dwarf smaller participants:

- Total Shares: Mean of ~438.5M vs. median of ~209.1M (skew = 2.01), with Q1 at ~48.2M and Q3 at ~605.7M — an enormous interquartile spread indicating extreme cross-participant heterogeneity. The 272 flagged outliers likely correspond to dominant venues on high-volume days. - Tape C Shares has the highest skewness of the three tapes (skew = 2.25, 484 outliers), reflecting Nasdaq's concentration of high-frequency, tech-heavy trading where a handful of participants handle disproportionate volume. - Tape A Trade Count is notably the least skewed metric in the dataset (skew = 1.16, only 27 outliers), suggesting that trade counts on NYSE-listed securities are more evenly distributed across participants than raw share or notional volumes — a microstructure signal worth investigating. - Total Notional: Mean of ~$17.76B vs. median of ~$8.29B (skew = 1.74) underscores that a small number of participant-days account for the bulk of dollar value transacted — consistent with the dominance of 2–3 major wholesale market makers in 2014. - The Q1-to-Q3 ratio for Tape C Notional (~$353M to ~$5.67B, a 16× spread) is the widest of any tape, pointing to Nasdaq-listed securities as the primary driver of cross-participant dispersion.

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4. Recommended Join Key Columns

The Date column is the primary recommended join key, with 252 distinct values precisely matching the standard U.S. equity trading calendar for 2014 — this aligns cleanly with any other daily financial time series. For participant-level analysis, a composite key of Date + Market Participant is essential to avoid fan-out joins, since each date has up to 16 participant rows. If joining to exchange-level datasets, Market Participant values should be mapped to standard MIC (Market Identifier Code) or MPID identifiers for interoperability. Date alone is sufficient as a join key when correlating against macro or index-level datasets where participant granularity is not available.

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5. Recommended Complementary Datasets for Correlation Analysis

| Suggested Dataset | Rationale | |---|---| | VIX Daily Close (CBOE Volatility Index, 2014) | Correlate total volume and trade count spikes against volatility regimes — classic liquidity-volatility relationship | | S&P 500 / Nasdaq-100 Daily Returns (2014) | Test whether Tape A vs. Tape C volume ratios predict or lag index-level price movements | | Federal Reserve FOMC Meeting Dates (2014) | Identify event-driven volume anomalies around policy announcements using the Date join key | | SEC Market Structure Data (MIDAS) | Cross-validate participant-level share with SEC's own tape data; check for systematic reporting gaps | | Cboe 2013 or 2015 equivalent files | Year-over-year panel construction to study HFT market share trends and post-Reg NMS evolution | | U.S. Treasury Yield Curve Daily Data (2014) | Examine whether rising rate environments correlate with shifts in Tape A (interest-rate-sensitive equities) notional values |

The combination of Cboe volume data with VIX and index return data in particular would be a high-yield starting point, as the skewed distributions and high outlier counts suggest clear event-clustering that volatility indices are designed to capture.

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)