What it means
Large investors, such as pension funds, often try to hide the full size of an order. Showing a buy order for 2,000,000 shares would push up the price before they could finish buying, which raises their own cost.
They therefore split orders into small pieces or place them in venues that do not display them. A pilot fishing trader tries to find those hidden orders.
The trader sends a small order, often called a ping, to see whether it is filled quickly or in a pattern that suggests more volume is sitting behind it. Many pings in many venues can build a picture of where large orders are, and software can repeat the process thousands of times a second.
Once the trader believes a large buyer is present, the trader can buy first and then sell to that buyer at a slightly higher price. This is a form of trading ahead of other people's orders.
It works at high speed, so it is usually carried out by computer programs. Defenders say searching for liquidity (the ability to trade without moving the price) is a normal part of trading, and that small orders are legitimate.
Critics say it exploits investors and raises their cost of trading. The line between smart searching and unfair behaviour is debated, and rules differ between markets.
For corporate finance teams and fund managers, the practical effect is called market impact or information leakage. Even a modest rise in the price of a stock before the full order is executed increases the real cost of the trade, and the extra cost is paid in the end by the pension members or fund investors.
Investors therefore choose brokers and venues that offer protection against probing. Common protections include randomising order sizes, using minimum fill sizes, trading in venues with strong controls and timing orders to avoid patterns.
Measuring execution quality against benchmarks helps the investor see if leakage is occurring. Finance leaders do not need to master these tactics, but they should ask their trading providers how they are protected and how that protection is measured.
In practice
Real-world examples.
Example
A pension fund wants to buy 1,000,000 shares of a manufacturing company worth about $30 each. Its broker spots a pattern of tiny orders hitting several venues and splits the order across more venues with minimum sizes. The fund completes the purchase near its target price, saving a noticeable amount in costs.
Example
A high-frequency trading firm sends dozens of 100-share orders into a market and notices that each is filled instantly at a single price level. It infers a large hidden seller and positions itself accordingly. Regulators later examine whether this was fair.
Example
An asset manager compares the average price it paid with the market price at the start of the order. It finds that the price drifted up by 0.4% before most of its buying was done. The firm raises the issue with its broker and changes its trading approach.
Case study
Seen in the real world.
Quillfeather Asset Management is a fictional fund manager, and this case is illustrative. It needed to buy $12,000,000 of a mid-sized company's shares for a client portfolio.
The trading head noticed that every time the algorithm posted an order, small trades appeared in the same venue seconds earlier. Over a week, the average buying price crept 0.5% above the opening price, which on $12,000,000 meant an extra cost of about $60,000.
The firm switched to a venue with minimum order sizes and randomised its order timing. It also asked its broker for a monthly report on how prices moved around each order. The next purchase of similar size showed a drift of only 0.1%, or roughly $12,000. The illustrative lesson is that measuring execution cost can reveal leakage that is invisible in headline prices.
Watch out
Common mistakes.
- Assuming that any small order is pilot fishing, when most small orders are ordinary trades by retail investors.
- Ignoring execution costs because the fund's reported return looks acceptable.
- Believing hidden orders are completely invisible, when repeated probing with small orders can reveal them.
Questions
People also ask.
Is pilot fishing illegal?
It depends on the market and the facts, so legality is not the same everywhere, and regulators focus on manipulation and misleading conduct.
Who is most exposed to it?
Institutions trading large blocks, since their orders are big enough to move prices if detected, and funds with predictable trading patterns.
How can an investor protect against it?
By using minimum order sizes, careful venue selection, randomised timing and regular reviews of execution quality, preferably with an independent measure of costs.
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