(Don't) Take a Fill
Vol #91: July 29th, 2022
Venture capitalists like to play serious, talking about how well a team can “execute”. Traders and reapers alike know that execution is a far more grave matter.
Trade execution is one of the most thankless jobs in finance. The romantic notion of traders putting on big positions and riding them all the way to the bank misses the two most delicate and nuanced parts of that operation - putting the trade on, and taking it off.
This state transition can quickly turn a big winner into an average trade. When your position is losing money, the problems become doubly worse.
Stop losses are a tempting way to protect your capital if the market moves. This past week on Twitter, professional traders resoundingly dispelled this myth. I’d call it a debate, but as far as my bubble goes it was mostly criticism of a straw man.
The goal of a stop loss order is to do exactly what it sounds like - close a losing position before it gets worse. If you buy 100 shares of AAPL and pay $150, a stop loss at $140 means that if the price of AAPL drops below $140 you’ll automatically sell the shares and cap your loss on the trade to $1000.
When the broker sees the price of AAPL at or below $140, they simply send a closing order to sell 100 shares. It’s notable that this order type is at the broker level - exchange matching engines are anonymous with regards to your position, and only care about price and size.
At first blush, stop losses sound like a very useful risk management technique. It’s a predetermined exit point, conscientiously calibrated outside the heat of battle. A computer that’s watching every tick and responding faster than a human can will preserve your capital during a rout.
What’s wrong with stop losses is not the risk management, it’s the implementation. The difficulty of trade execution lies in the ever important question of liquidity, and preserving optionality.
Execution is not a problem for the average buyer (or seller) of 100 or even 1000 shares of AAPL. Unless you’re moving significant inventory in the top 100 names, execution is clicking a button and there’s no need for nuance. When liquidity is deep, execution is a cake walk. Fire and you’ll be filled.
When the order size outstrips the liquidity, when the market is particularly fragile, execution needs to be more nuanced. You’re threading the needle through an imbalance of supply and demand, and this takes time to manage. As prices and liquidity shift, it takes more than an if/then statement to resolve this.
The more complex trading algorithms are managing hundreds of different “points of light” to determine how to post their buy and sell orders. Depth of book, fee structure, relative pricing, dark pools, and trade history are among the various factors that “Sniper”, “Guerilla”, or “BestX” might take into consideration.
Rather than just “Sell 1000 AAPL”, they will chop that order up over time, post across exchanges, and try to get the best average price given the current market dynamics. Silent pings go out to trading desks across the world, and liquidity is sliced away like a Jenga game on a sailboat.
With these stakes, being average is winning. Most transaction cost analysis compares to a benchmark of the average price during the trade window. Beating the VWAP (volume weighted average price) is considered a very good trade.
If it takes thousands of lines of code and a PhD in fluid dynamics to be average, what hope does a knee jerk reaction have?
Stop losses almost always get triggered at the worst possible time. The very fact that you’re being forced to close the trade, dramatically increases the odds it will be a rough exit.
Even the most liquid markets are prone to air pockets, drop outs, and long wicks (the lines on a candlestick chart). Since most liquidity is provided algorithmically, it can vaporize in picoseconds, causing a market to temporarily free fall.
False rumors, misinterpreted press releases, or a macro-sneeze can cause a sharp drop in AAPL’s price, causing your stop loss to be triggered. If AAPL briefly touches $140 but then rebounds back, you’ve already locked into buying high and selling low.
When those jitters happen, it’s very improbable that you get a good fill. If a stock is dropping rapidly, it’s because there’s a supply imbalance, and your stop loss sell order is just piling on to the wrong side of the trade.
Stop losses demand liquidity when it is most expensive. You’ve shown your hand with a price insensitive order (“GET ME OUT!”) and are going to pay both liquidity providers and exchange fees for this privilege. If you can’t handle the intraday volatility of a position, your sizing is probably wrong in the first place.
Liquidity providers are in the business of managing inventory across buyers and sellers, not taking directional positions. A simple stop loss order looks no different than the deluge of flow pushing price down, and will be priced accordingly.
All of the problems with stop loss execution should not however obviate the conceptual importance of setting position guidelines and internal risk management “stop losses”.
There is no trader in the world that doesn’t live under risk limits. They might be Bill Hwang loose, but even the most supplicant banker only has a limited amount of capital to offer. Deftly managing your exposure within these limits is what turns some money into more money.
If you liked AAPL at $150, and the market starts to prove you wrong, perhaps it’s time to take a second look when stock is priced at $140. Maybe it’s another buying opportunity, that’s worth reallocating other capital towards, but perhaps it’s simply time to take an “L” and move on. Good traders are always reevaluating their books.
Stop losses are deceiving, because while it seems appealing to cap your losses on any given trade, doing this in an overly systematic way is detrimental to long term PnL. Not only do you consistently lock in losers, but you’re doing so when it is most expensive.
Don’t set programmatic stop losses to manage your capital; this is paying someone else (dearly) to manage your risk. Instead, set up predefined internal limits that force you to reevaluate your positions at given intervals or price points.