A recent story in Advanced Trading goes after some of the minutae of High Frequency Trading and provides a glimpse of the total value that HFT may provide to behemoth PT powerhouses such as Goldman Sachs. The article presents a very valuable perspective on just why HFT is so critical these days, especially when cash traders go for 6 hour Starbucks breaks between 10 am and 3:30 pm: "high frequency trading firms, which represent approximately 2% of the 20,000 or so trading firms operating in the US markets today, account for 73% of all US equity trading volume. These companies include proprietary trading desks for a small number of major investment banks, less than 100 of the most sophisticated hedge funds and hundreds of the most secretive prop shops, all of which operate with one thing in mind—capture profit opportunities by being smarter and faster than the closest competition." And as the market keeps going up day in and day out, regardless of the deteriorating economic conditions, it is just these HFT's that determine the overall market direction, usually without fundamental or technical reason. And based on a few lines of code, retail investors get suckered into a rising market that has nothing to do with green shoots or some Chinese firms buying a few hundred extra Intel servers: HFTs are merely perpetuating the same ponzi market mythology last seen in the Madoff case, but on a massively larger scale. When it all blows up, the question is whether the SEC will go after the perpetrators of this pyramid with the same zeal that it pursued Madoff himself. We think not.
The reason for this, as the AT article points out, is that HFT has become the biggest cash cow for Wall Street: "The incredible capabilities offered by technology have given meteoric rise to a relatively few high frequency proprietary trading firms that now wield far greater influence on the markets today than most people recognize." How big of a cash cow:
"Proprietary trading takes in a number of unique strategies, including market making, arbitrage (ETFs, futures, options), pairs trading and others based on the linked trading of more than one asset class, e.g., futures index and cash equities. In fact, TABB Group estimates that annual aggregate profits of low latency arbitrage strategies exceed $21 billion, spread out among the few hundred firms that deploy them."
The $21 billion estimate is smack in the middle of the FIXProtocol estimated $15-$25 billion in revenue that HFT generates. So let's do a back of the envelope calculation: Goldman controls roughly 50-60% of principal program trading on the NYSE, which in turn accounts for 30% of all global program trading. Throw in Goldman's domination of dark pool trading through Sigma X, and one can come up to quite a sizable number - It would not be a stretch to conclude that, through various conduits, Goldman is directly responsible for over 20% of global HFT trading. 20% of $21 billion is over $4 billion a year. As margins on HFT are sky high (it doesn't cost all that much to tweak a few hundred lines of code - and if Sergey Aleynikov is any indication, $400,000/year for VPs in the program is peanuts for a firm like Goldman), this $4 billion likely drops to the bottom line almost dollar for dollar. Let's recall that Goldman's Q2 earnings were $3.44 billion. Does this mean that HFT/PT accounts for roughly 25% of earnings for the firm that is a hedge fund in all but FDIC backing? Zero Hedge would in fact take the over, especially in this environment where M&A fees are a distant memory. We leave this question open, but even if we are off, it would not be by order of magnitude, and would explain why Goldman has thrown the kitchen sink into dominating such NYSE programs as the SLP, and is expending so much energy to dominate dark pools as well.
Going back to the AT article, which provides some additional critical observations, especially with regard to the Aleynikov arrest and his ludicrous $750,000 bail which surpasses that of indicted Ponzier Sir Allen Stanford:
First, strategies that optimize the value of high frequency algorithmic trading are highly dependent on ultra-low latency. The right decisions are based on flowing information into your algorithm microseconds sooner than your competitors. To realize any real benefit from implementing these strategies, a trading firm must have a real-time, colocated, high-frequency trading platform—one where data is collected, and orders are created and routed to execution venues in sub-millisecond times.
Next, since many of these strategies require transacting in more than one asset class and across multiple exchanges often located hundreds of miles apart, i.e., NY to Chicago, that infrastructure will often require roundtrip long haul connectivity between the data centers. [TD:Any real estate professionals out there who can determine just how easy it is to set up a colocated station within millisecond distance of the NYSE, and whether or not Goldman has any rights of first refusal on this real estate optionality? Nothing like a little derivative monopoly to keep potential SLP vendors at bay]
Lastly and most importantly, this code has a limited shelf life, whose competitive advantage is diluted with each second it is outstanding. While a prop desk's high level trading strategy may be consistent over time, the micro-level strategies are constantly altered—growing stale after a few days if not sooner—for two important reasons. Firstly, because high frequency trading depends on ridiculously precise interaction of markets and mathematical correlations between securities, traders need to regularly adjust code—sometimes slightly, sometimes more—to reflect the subtle changes in the dynamic market. The speed and volatility of today's markets is such that the relationships forming the core of our algorithm strategies often change within seconds of our ability to implement the very strategies that exploit them. Secondly, competitive intelligence is so good across all rival trading firms that each is exposed to the increasing susceptibility of their strategies being reverse engineered, turning their most profitable ideas into their most risky. As a result, any firm acquiring the "stolen" code would gain benefit from it for no more than a few days before that firm would need to adjust the code to the dynamic conditions. Since these changes build on themselves, in a matter of weeks that code would look quite different from that which was originally "stolen."
And the conclusion:
There's no doubt that Goldman Sachs, or any other proprietary trading firm, could indeed lose tens of millions of dollars from its proprietary trading if their strategies are stolen—and that is very serious. The competitors that obtain access to these trading secrets could (and would) use it to front run or trade against it, ruining even the most well-planned tactics. This news story contains many very important sub-plots: trading espionage, the necessity for a trading firm to have sophisticated security systems built around its technology, the requirements for risk management, and even the potential for proprietary trading software to be targeted on a wider scale for terrorist activity; but more than anything else it highlights the critical role played by high frequency prop trading in this new market.
This is indeed, a conclusion that Zero Hedge has been pounding the table on for months. It is imperative that Wall Street firms shed much more light into this astronomically profitable yet highly misunderstood and under the radar concept. In the absence of more information, the likelihood that Wall Street firms who dominate order flow and have material unfair advantages over virtually everyone else, should be isolated from trading up to the point where they provide sufficient information to make the market a fair and equal playing field for all investors. Until that moment, investing, trading and speculating is doomed to have the same outcome for the majority of market participants as playing roulette with 35 instances of 00, a much lower fun coefficient and no ability to be comped for your room in light of significant trading losses.Sphere: Related Content Print this post