Trade size

Not all trades carry the same information. A $50 trade and a$50,000 trade on the same instrument represent fundamentally different market participants with different motives, horizons, and information sets. Separating them is one of the most powerful decompositions in empirical microstructure.

The academic foundation

Barclay & Warner (1993) introduced the stealth trading hypothesis: informed traders preferentially use medium-sized orders to conceal their activity. Their analysis of NYSE data showed that medium-sized trades (500–9,999 shares) accounted for a disproportionate share of cumulative price changes — far more than their volume share would predict. Small trades were noise; large trades were visible and quickly faded by market makers. The information edge concentrated in the middle.

Chakravarty (2001) extended this using audit trail data that identified institutional origins, confirming that institutions were responsible for the bulk of medium-sized trades and their associated price impact. Easley, Kiefer & O'Hara (1997) formalized the idea that trade size carries information within their sequential trade model, showing that larger trades shift market maker beliefs more.

Our segmentation

We segment trades into three buckets by notional value:

• Small: < $100 — predominantly retail, influenced by UI defaults and round-number psychology
• Medium: $100–$1,000 — mixed; includes algorithmic execution slicing larger orders (the Barclay-Warner stealth zone)
• Large: ≥ $1,000 — institutional, hedgers, arbitrageurs, or market makers rebalancing

For each segment, we compute volume, trade count, and directional ratios independently.

Why the disaggregation matters

When large-trade buy ratio diverges from small-trade buy ratio, you have a disaggregated flow signal. Large buyers accumulating while retail sells is the microstructure signature of informed accumulation — exactly the pattern Barclay & Warner documented. Both sides aligned is a crowded consensus.

Key patterns:

• Large trades dominating volume share during low-count periods → concentrated activity, potential informed flow. This maps to Easley & O'Hara's (1987) finding that information events increase trade arrival rates for specific size categories.
• Small-trade count spike without proportional volume → retail activity, typically non-predictive noise
• Medium-trade count surge → algorithmic execution splitting orders, systematic participants active — the stealth trading regime

The coefficient of variation (std/mean) of trade size captures distributional concentration: high CV means a few large trades dominate while most are tiny. This matters for interpreting any aggregate metric — a VWAP dominated by one block trade is qualitatively different from one shaped by thousands of small fills.

Distribution shape (skewness, kurtosis) of trade amounts completes the picture. In crypto markets, the extreme right tail of the size distribution is often more informative than the mean — consistent with the heavy-tailed trade size distributions documented by Gopikrishnan et al. (2000) across equity markets.

References:

• Barclay, M.J. & Warner, J.B. (1993). "Stealth Trading and Volatility." Journal of Financial Economics, 34(3), 281–305.
• Chakravarty, S. (2001). "Stealth-Trading: Which Traders' Trades Move Stock Prices?" Journal of Financial Economics, 61(2), 289–307.
• Easley, D., Kiefer, N.M. & O'Hara, M. (1997). "One Day in the Life of a Very Common Stock." Review of Financial Studies, 10(3), 805–835.
• Easley, D. & O'Hara, M. (1987). "Price, Trade Size, and Information in Securities Markets." Journal of Financial Economics, 19(1), 69–90.
• Gopikrishnan, P., Plerou, V., Gabaix, X. & Stanley, H.E. (2000). "Statistical Properties of Share Volume Traded in Financial Markets." Physical Review E, 62(4), R4493.