Beware the Alpha Sharks! High-Frequency Trading and Its Impact on Markets
Maureen O’Hara, professor of finance at Cornell University, and member of the Systemic Risk Council has a warning to traditional investment managers, i.e., low frequency traders (LFT): High-frequency trading (HFT) is now the norm, and it is not going away. Continuing to pretend that HFT has no effect on your investments and on your alpha is at best naïve and at worst ignorant. Need proof?
New Laws, Including Breaking the Speed of Light
Both the Chicago Mercantile Exchange and NASDAQ are building a new faster link between their data centers in Chicago and New Jersey to “beat” the speed of light. Wait, I thought the speed of light was a universal maximum? It is, but you see by constructing two 60-foot towers the additional height travels a shorter distance than if the connection were at ground level due to the curvature of the earth. Doing this earns each transaction a faster execution time of — wait for it — 4 milliseconds!
Exchange attention is directly proportional to the amount of trades executed on their exchange. Tall towers will result in more orders and thus, more profits. So HFT is not going away, and this is why you have to understand it.
Importantly, as O’Hara pointed out at the 2013 Financial Analysts Seminar, “getting HFT” is not just about obsessing over speed, as the media so often does. No, she argues markets are forever different, yes, due to the speed of trading, but also the nature of market making, and even basic concepts like liquidity are dramatically different in an HFT world. O’Hara emphatically states that this does not make markets worse, just different than before.
One important advantage discussed infrequently is that HFT bases decisions on a large amount of information. This processing speed of Big Data is leading to the development of new fundamental information, and combined with the lightning-quick execution, you are at a permanent disadvantage. You might ask, “How weird could it be, really?”
Your Clock is Wrong
Since the dawn of time, humans have based their time measurements in chronology. That is, for people, time is a measurement system used to sequence events. But machines think in terms of cycles and are therefore event based, not time based. Machines complete a cycle at various rates depending on the amount of information involved in a particular instruction. So machines are volume based. Their world is measured in jobs done, not in the regular rhythms tapped out by time.
As it happens, HFT relies on machines, and thus it relies on measuring time in terms of events. This volume-time is challenging for us humans. But for a “silicon,” it is the natural way to process information. Why is this important?
If you use a volume clock as opposed to a time clock to measure returns — say look at trades every 1,000 shares — return distributions are much more well behaved. Goodbye fat tails. Goodbye the leptokurtic middle. Goodbye skew. Because HFT machines are looking at the world through a volume-clock lens, your actions are predictable; the distributions allow for extremely accurate predictions.
Ever lose money because a savvy trader anticipated your actions? Thought so. Now imagine an army of machines all looking at the world through their lens and programmed to take advantage of your naïveté. In other words, machines can see your clumsy human trades from hundreds of milliseconds away. Goodbye alpha!
We are all swimming in silicon waters, whether we want to or not. Now there are “predatory algorithms” or “algos” looking to feed. These algos are a special type of informed trader. Rather than possessing exogenous information yet to be incorporated in the market price, they know that their endogenous actions are likely to trigger microstructure mechanisms in either your trader or your algorithms, and all with a highly predictable and foreseeable outcome.
Examples of these alpha sharks include:
- Quote stuffers: Here the algo overwhelms an exchange with messages with the sole intention of slowing down competing algos in order to gain a slight trading advantage.
- Quote danglers: These algos send dozens of quotes that force traders trying to complete an order quickly to chase a price against their interests.
- Pack hunters: These are algos designed to find other predator algos that are each hunting independently. Once these fellow algos are identified they form a pack in order to maximize the chances of triggering a cascading effect.
So what can a low-frequency trader do?
- Stop putting in round lot orders as it signals to algos that you are a GUI (graphical user interface) trader.
- Stop pretending that you don’t need to understand HFTs and that it has no effect on you and your investors.
- Develop statistics (e.g., VPIN) to monitor HFTs activity and take advantage of their weaknesses.
- Use “smart brokers” that are specialized in searching for liquidity and avoiding a footprint. LFTs must become invisible to repel the alpha sharks.
- Do not target a participation rate. This is a trade order in which you instruct the broker to “only buy 1% of the volume today.” Instead, determine the optimal execution that fits the prevalent market conditions.
- Trade in exchanges that incorporate smart circuit breakers and matching engines.
O’Hara predicts a time in the very near future in which firms will employ “silicon analysts.” These experts will be highly proficient traders trained to understand how algos work and to help you get about your business without losing portfolio limbs to the alpha sharks.
This year, the 2015 Financial Analysts Seminar will be held in Chicago on 20–23 July. Learn more about the agenda and speaker details on our website.
Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.
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