High Frequency Trading. Its effects on stock markets and how to control them

Not long ago the worlds’ stock markets looked like this: Big rooms with walls covered by monitors showing continuously changing graphs, numbers, and columns. In the room, on the floor, a nervous crowd of traders and brokers shouting in a muddle, trying to buy and sell stocks. This picture has changed- we are still able to observe the room and the monitors, just the hectic crowd of traders has shrunken together. Quietly working computers have taken over its place. (World Economy, 2014) The computers process information faster, never take time off and in fact, rarely need somebody to control them. (Seith, 2010) Some of these high- frequency trading (HFT) computers are stocked with customized trading programs. These programs known as algorithms are based on mathematical formulas. They are able to independently evaluate stock market rates and to pursue complex strategies at a clearly faster pace than human traders ever could. (World Economy, 2014) “The high-frequency traders’ basic strategy is very simple: trade as much and as quickly as possible. The profit margin on each transaction is frequently quite small, but these small amounts add up to billions due to mass and velocity.” (World Economy, 2014) This strategy only pays off, if the trading programs are always a split second faster than everyone else. Therefore, the distance to the stock market plays a key role, since it’s about millionth seconds. The shorter the cable, the faster the fluency of information. (Bonometti, 2014) While high frequency-traders represent only a small part of all traders, approximately 60% of all US stock trades and 40% of all EU stock trades can be accredited to them. (Seith, 2010) Even though many market researchers state that they increase the market’s liquidity and decrease volatility, the incredible speed, the scale and the non-transparent way in which the high-speed traders operate raise skepticism among market-observers worldwide. The fear of market manipulation and conventional controlling systems losing grasp has grown. Those fears found confirmation on “Black Thursday 2.0” (Seith, 2010) when in May 2010 the first so-called flash crash, caused by an uncontrolled rampage of several, interacting algorithms, made Dow Jones collapse out of thin air. (Buchter, 2014) First regulatory steps have been implemented to prevent such incidents in future. HFT might bring some benefits for stock markets such as higher liquidity and the reduction of volatility, but there are dangerous risks such as the systematic bypassing of conventional traders, market manipulation and technical errors. Hence it is essential to implement further regulatory measurements to control HFT for the sake of equity, integrity and stability of global stock markets.

At latest when the 1st flash crash happened at Wall Street back in 2010, when a market value of almost $1 trillion disappeared within only minutes (Heismann, 2013), the advantages and disadvantages of HFT are being discussed forcefully and make the whole topic strongly controversial. Proponents claim that “HFT [...][ is not] harmful [...],but [...] creates additional liquidity [...][for the market]” (Reimer, 2012). If a market has a high level of liquidity that means that a recipient can be found for every offer made. (World Economy, 2014). Proponents claim that this is especially important now, that many traders move away from official trading platforms to alternative non-transparent private platforms, so-called Dark Pools. (Heismann, 2013) Attached to this circumstance is the fear of decreasing liquidity on official trading platforms. However, “the liquidity created by HFT is extremely fleeting” says Hans Burg, professor of banking business at the University of Hohenheim. Further, high-frequency traders work especially efficient in markets that are highly liquid already, since they are dependent on purchasers for their high volume deals. High-frequency traders also see themselves accused that they retreat in case of crises when liquidity is needed most. (Nagel, 2016) ETH Zürich researchers declared in a study from 2012: “We question in particular the argument that HFT provides liquidity and suggest that the welfare gains derived from HFT are minimal and perhaps even largely negative on a long-term investment horizon.“ (Henn, 2013) Another claim made is that HFT is decreasing the volatility on conventional stock markets. (O’Brien, 2014) This means that the fluctuation of stock prices between different locations and in general decreases, what improves price quality and fairness (Francioni, 2012). The assertion is highly controversial though since there is dissent, on how to measure the effect (Neill, 2012) because some trading algorithms have the opposite effect. (deutsche-boerse.com, 2013).

Opponents fear tremendous disadvantages and an increased disparity for traders who cannot afford the programming of an HFT algorithm or its installation. (Nagel, 2016)The programming of an HFT algorithm is quite costly which makes it inaccessible for traders who lack the required purchasing power. Same counts for the computers needed. Successful high-frequency traders need the fastest computers available on the market since those machines need to place millions of orders per day. Experts assume that only during the rush hour at Wall Street between 3 pm and 4 pm each one of those machines trades volumes up to 80 million securities. (Welchering, 2012) For this endeavor a machine is needed that is at least ranked in the top 20 of the fastest computers worldwide. The purchasing price for these high-performance computers is $50 to $60 million. On top, there are estimated running costs of $7.5 million, annually. (Welchering, 2012). Since it is all about speed, it is necessary to place the machines close to the stock market. Many high-frequency traders take advantage of so-called co-location services. (Bonometti, 2014) These are rental services offered by the stock markets themselves, which allow high-frequency traders to place their computers literally in the same building as the stock markets servers, in exchange for a costly fee.

(Picardo, 2016) These special machine placements enable high-frequency traders to directly tap the main artery of financial information and to process them in the fastest way possible. (Buchter, 2014) This obvious financial barrier excludes normal traders from accessing the technology and significantly improves the competitive position of those can access it. (Bonometti, 2014) The advantage high-frequency traders get for their investment is a clear information edge and the resulting ability to react in a speed which normal traders cannot match. (Buchter, 2014) In an interview in December 2012, Andrew Brooks described high-frequency traders as: “[Gamblers who] can see the end of a horse race and then place bets on the winning horse.” (Henn, 2013) There are two main tactics high-frequency traders can pursue to make use of their information edge. The first tactic is the so-called “rebate arbitrage” (Picardo, 2016) They can spot price trends before anybody else does. Hence, they are enabled to buy stocks of an in value increasing commodity and resell it to slower traders, milliseconds later, before the price trend has ended, with a minimal profit per share. Similar to black market dealers, they buy the good tickets before anybody else can buy them and then sell them more expensively later. (Buchter, 2014) This has the effect that slower traders do not only pay an explicit exchange fee, “but also an implicit fee for high-frequency trading, because this type of trading is interposed between most stock market transactions” (World Economy, 2014) The second tactic is the so- called “front running”. (Nagel, 2016) This tactic allows high-frequency traders to make profits with almost no risks. A big institutional trader (T1), for instance, sends out an order for 10,000 shares of a commodity (C1) offering $X per share.

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