Estimating Long-Term Market Impact of Large Equity Trades
Summary
The document asks how large institutional stock sales affect prices over weeks, months, or years, including the lasting losses that other large holders might face. It raises questions about whether permanent impact relates to peak temporary impact, how impacts from multiple traders combine, and whether trade size, execution duration, and correlated order signs change the outcome.
The responses offer measurement ideas rather than a settled estimate. One suggests studying ticket data and measuring multiday VWAP impact by aggregating impact across volume bins, while cautioning that volume surprises and changing execution practices complicate estimates. It also notes that crowding and order imbalance can widen spreads and costs, and suggests signed-volume measures as possible indicators. Another response proposes comparing overnight returns with intraday returns to look for mean reversion. The document provides no quantitative findings or method for estimating losses to remaining holders; its suggestions are exploratory and depend on available data.
Key ideas
- Large institutional trades may have both temporary and lasting price effects, but the document gives no estimate of their relative size.
- Multiday VWAP orders can be studied by aggregating price impact across volume bins.
- Changing execution methods and unpredictable volume make historical impact estimates difficult to generalize.
- Crowded order flow may widen spreads and trading costs, and signed-volume measures can help identify it.
- Comparing overnight and intraday returns is suggested as a way to examine mean reversion.
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Full text
# Permanent or long-term (months) market impact of large trades in stocks / equities # Permanent or long-term (months) market impact of large trades in stocks / equities I need an estimate of the "permanent" long-term price impact of large institutional trades. When an investor makes a large trade, there will be a price impact due to the trade. Institutional portfolio managers make very large trade decisions, sometimes choosing to sell \$10M, \$100M, or even \$1B at a time. I am interested in estimating the price impact of "large" trades (greater than a typical day's dollar volume), over time-spans of the "few weeks / few months / few years". The idea that I'm pursuing is "herding risk": if a few "holders" each have large positions in an illiquid asset, if one or more of them sells their position, the remaining holders will experience long-term losses (losses that won't improve with time). I'm trying to estimate what these long-term losses might be. For instance, a stock might have a \$1B market cap, a \$10M daily dollar volume, and a 2% daily volatility. Stock holders Alice, Bob, and Charley each own \$100M of the stock. Alice and Bob both sell fraction $f \in [0,1]$ of their positions. What is the long-term loss incurred by Charley? Some details to give some more specificity / character / color to the question (thanks to suggestions here and arxiv searches): - Temporary vs permanent Is permanent impact some fraction of peak temporary impact? Or is permanent impact completely independent of temporary impact? Somewhere in between? - Concave or linear some theory suggests concave impact allows for arbitrage (free lunch), but empirical results suggest concave (in the order size) impact is reality. - Multiple simultaneous how does one "sum" market impacts from Alice and Bob (multiple managers)? Do you add dollar volume or add impacts or somewhere in between? - Duration If a manager sells $1B dollars, surely they will spread the orders over a large amount of time to minimize impact. Does duration affect permanent impact, or just temporary impact? - Correlation How does correlation between signs (sell vs buy) of multiple trades affect impact? - Is permanent impact "predestined"/exogenous or "arbitrary"/endogenous When a stock price falls permanently after a large sell order, did it fall because it was going to fall anyways, or did it fall because of the large sell order? ## Answer by Mike (score 1) https://quant.stackexchange.com/a/44677 IMHO there is a general shift toward algorithmic execution for institutionz over the last 5 to 10 years, and depending on your method of execution the price impact can vary, so I am not certain whether you will get meaningful results using long histories. Some older papers look at tickets data and aggregate impact of large tickets as proxy for institutional trades, but effect size have diminished on replication. I have measured impact of multiday VWAP orders as the integral of impact of each volume bin, but because of the magnitude and stochastic nature of volume surprise, it's difficult to measure a priori. Moreover, you see spreads and cost widen if the trade is crowded (defined here as trade order imbalance), which can be determined post hoc using various signed volume measure (e.g. VPIN). If you have access to tick level data this might be a better approach than trying to find functional forms of market impact costs. ## Answer by michaelcarniol (score 0) https://quant.stackexchange.com/a/44676 Look for mean reversion in overnight returns as compared to the intraday (open to close) return. I believe AQR has a paper using this measure on SSRN and some AB Bernstein researchers used this measure in a paper in Quantitative Finance.
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