The Upstairs Market for Large-Block Transactions: Analysis and Measurement of Price Effects

Abstract

This article develops a model of the upstairs market where order size, beliefs, and prices are determined endogenously. We test the model's predictions using unique data for 5,625 equity trades during the period 1985 to 1992 that are known to be upstairs transactions and are identified as either buyer or seller initiated. We find that price movements prior to the trade date are significantly positively related to trade size, consistent with information leakage as the block is "shopped" upstairs. Further, the temporary price impact or liquidity effect is a concave function of order size, which may result from upstairs intermediation.

Modeling Trade Direction

“…13 Keim and Madhavan (1996) Suppose a customer sends a dealer an order to sell 100,000 shares at the fourhour VWAP for 11:00am-3:00pm. ( Figure 3 shows the price trajectory.)…”

No-arbitrage, state prices and trade in thin financial markets

“…Each trader has von Neumann-Morgenstern preferences with respect to her wealth (or consumption) in the second period, with cardinal utility indices u i s : R ++ → R. We assume that each utility index is twice continuously differentiable, differentiably strictly monotonic and differentiably strictly concave, and satisfies Inada conditions. For notational simplicity, henceforth we write u i ((c i s ) S s=1 ) = s u i s (c i s ); by our assumptions, ∂u i 0, 6 while ∂ 2 u i is a diagonal, negative definite matrix.…”

“…Intuitively, an equilibrium in thin financial markets will be a triple consisting of a vector of asset prices, a profile of portfolios and a profile of price impact matrices, (P ,Θ,M ), such that: 1. all markets clear; 2. all traded portfolios are individually optimal, given the price impacts perceived by agents: for each trader i, at pricesP , tradeΘ i is optimal, given that any other trade,Θ i , would change prices toP +M i (Θ i −Θ i ); and 3. all perceived price impact matrices correctly estimate the effects of individual portfolio perturbations on the prices that would be required for the rest of the market to optimally absorb them: for each trader i, (small) perturbations to her portfolio, ∆ i , uniquely define a price at which the other traders of the market, given their price impacts, are willing to supply ∆ i to i, and the derivative of this mapping at equilibrium tradeΘ i isM i . 6 We take this vector as a column. 7 This means that the future numèraire wealth of investor i is given by e i + RΘ i , while the cost of her portfolio, if she constitutes it at pricesP , isP · Θ i , which she incurs in the first period.…”

The Price Impact of Order Book Events

“…al [3]). Alternatively, empirical studies on public data [16,18,20,28,29,43,38,39] have investigated the relation between the direction and sizes of trades and price changes and typically conclude that the price impact of trades is an increasing, concave ("square root") function of their size. This focus on trades leaves out the information in quotes, which provide a more detailed picture of price formation [15], and raises a natural question: is volume of trades truly the best explanatory variable for price movements in markets where many quote events can happen between two trades?…”