New on FinoLib · Volume III: Finance Encyclopedia
My latest article is now live on FinoLib, the free finance library I write: High-Frequency Trading and Algorithmic Execution Explained, from Volume III: Finance Encyclopedia. Read the summary below for the substance of the topic, then continue to FinoLib for the complete treatment.
5.5 Algorithmic Execution Strategies
The idea in plain words
Execution algorithms split a large order into many small pieces so that buying or selling doesn’t push the price against you, which is called market impact. The right way to measure the cost is implementation shortfall: the gap between a paper portfolio traded at the decision price and the real one. Which algorithm to use depends on how fast you expect the price to drift against you while you trade.
Key points
■Implementation shortfall = delay cost + execution cost + opportunity cost + fees.
■In the worked $10 million order, shortfall was $54,400 (54.4 bps) even though the trader beat VWAP by $11,200.
■Trading slowly cuts impact but adds drift exposure and risk; the Almgren–Chriss framework formalizes the trade-off.
A closer look
A large institutional order — say, a pension fund needing to buy 200,000 shares, or $10 million, of a single stock — cannot simply be sent to the market at once without severely moving the price against the buyer (known as market impact). Execution algorithms exist specifically to break large orders into many smaller pieces, traded strategically over time to minimize this impact.
A worked example
Worked Example — Compare the Scenarios: How Fast to Trade
The same 200,000-share buy is 10% of the stock’s average daily volume; daily volatility is 2%. Impact costs are illustrative assumptions in line with the idea that faster trading pays more impact. Let d be the expected adverse drift in bps per day. With a steady schedule over T days, the order is on average half unfilled, so expected drift cost = d × T ÷ 2, and the standard deviation of the cost is about volatility × √(T ÷ 3).
Decision rule
Measure every order against the price at the moment of decision (implementation shortfall), not VWAP. Then set the pace from expected drift: below about 8 bps a day of adverse drift, spread the order over several days; between about 8 and 60 bps a day, use a full-day schedule; above about 60 bps a day, trade within hours. The break-evens rest on the illustrative impact figures above; recompute them with your own impact model, and skip the exercise for orders under about 1% of daily volume.
The costliest mistake
Judging execution by VWAP alone. In the worked example, the trader beat VWAP by $11,200 while the order cost $54,400 against the decision price, $24,000 of it from shares never bought. A desk paid to beat VWAP is rewarded for trading passively and leaving orders unfinished, the one cost VWAP cannot see. Record the decision time and price for every order and report all four shortfall components.
5.6 How High-Frequency Trading Actually Captures Its Edge
The idea in plain words
High-frequency traders make money from speed. Some act as electronic market makers, quoting prices on both sides. Others use latency arbitrage: they spot a stale quote, one that hasn’t yet caught up with fresh news, and trade against it before it updates. The evidence shows HFT narrowed spreads compared with human market making, yet the races over speed still impose a measurable tax on liquidity.
Key points
■Latency arbitrage is adverse selection at machine speed: the fastest firm trades against quotes before their owners can cancel.
■London data for 2015 show about 71,000 races a day, roughly 20% of volume, and a tax of about half a basis point.
■Hendershott, Jones and Menkveld (2011) found that algorithmic trading narrowed spreads for large stocks.
Decision rule
A reading rule for HFT claims: ask which activity is meant. For electronic market making, the evidence to check is spreads and depth; for latency arbitrage, it is the race tax on resting quotes. For anyone running an algorithm: if a strategy can send orders, it needs hard pre-trade limits on size, position and loss, and a kill switch a human can use in seconds. Monitoring alone is not enough; Knight received internal alert emails before the open.
The costliest mistake
Deploying trading code without tested pre-trade limits and a kill switch. Knight Capital lost over $460 million in 45 minutes, about $10.2 million a minute (460 ÷ 45). A limit that blocked new orders once gross exposure exceeded a set multiple of normal would likely have capped the losses within minutes. SEC Rule 15c3-5 requires such controls; having them on paper is not the same as testing them on every release.
What the full article covers
In the full article I take you through these sections:
■5.5 Algorithmic Execution Strategies
■5.6 How High-Frequency Trading Actually Captures Its Edge
It includes worked examples, a clear decision rule, the one mistake that costs people the most, a recap, a reveal-on-click quiz and the official sources behind every figure.
Volume III: Finance Encyclopedia
Read the full article on FinoLib
The complete article, High-Frequency Trading and Algorithmic Execution Explained, with every worked example, diagram, quiz and source, is free to read in Volume III, Finance Encyclopedia on finolib.com.
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