At present, HFT algorithms use newsfeed to determine the direction of the markets. The news articles are parsed with keywords where its effects are determined by comparing with a database of outcomes. But this method is not intelligent enough to distribute buys and sells calls so that the algorithm will not cause the players to go in at the same time holding the same positions and cause a flash crash like what had happen before. Other than a profit centred approach by the algorithms, a better model will be a goal oriented approach where you specify the investment, timeline, and risks you are willing to take. It is better for a trader to get regular income than high commissions for trading for your customers and the FCC must encourage it. If you are able to control the algorithms and nobody goes it at the same time with the same position, the market will be in equilibrium and the risks of a Flash crash will be taken away. There will be a move towards intelligence in software and cpu processing where even quantum computers will be used before 2029 when there will be great strides in data transmission where it will be possible to link every exchanges in the world. HFT using Super AI will take off soon before 2029.
‘People no longer are responsible for what happens in the market, because computers make all the decisions,’ – Michael Lewis, author of Flash Boys.
More than half of all equities traded in the US is done not by humans but by super computers capable of placing millions of orders each day and gaining advantage through moving milliseconds before the competition. This high-frequency trading has seen market makers and the largest players use algorithms and data to make money from placing vast amounts of orders to earn wafer thin margins.
But these margins have become even slimmer and the opportunity has dwindled: revenue last year was around 86% lower than it was when high-frequency trading was at its peak less than a decade ago. With pressure continuing to grow consolidation has started to take hold of the sector as high-frequency traders look to fend off tougher conditions.
We have a look at what high-frequency trading is and why it has declined.
Spreads and liquidity go hand-in-hand. Markets with high activity levels offer smaller spreads while those with lower trading volumes tend to offer larger spreads: with the spread being the difference in price between the buy (offer) and sell (bid) prices quoted for an asset.
The foreign exchange market, for example, sees over $5 trillion of currency traded each and every day to hold the title as the most liquid market in the world and it is this staggering volume that creates small spreads that only offer material profit opportunities if they are traded in large volumes. It is this reason why many choose to use leverage in markets with high liquidity such as forex, so volumes are maximised in order to take more substantial positions that otherwise might not be worthwhile. For less liquid markets such as small-cap stocks the spreads on offer are typically much larger.
Giving large institutions an advantage over smaller organisations and retail investors raises obvious questions about the ethics and fairness of high-frequency trading, and rightly raises the argument that it doesn’t help promote a level playing field. As well as competing with one another retail investors have to compete with an algorithm that is far superior than human trading.
Considering the importance of data for high-frequency trading and the fact the cost of such data is rising the role of dark pools is significant. These are private exchanges where institutional investors trade large volumes with one another without having to disclose the details of the deal to the wider market. This also means the transactions conducted in dark pools bypasses the servers feeding the data used by the algorithms established by high-frequency traders.
Dark pools are controversial. On one hand there is an argument in favour for them as the biggest players can trade large volumes without upsetting or disturbing the wider financial markets. On the other is the argument that they provide a way for corporate giants to deal amongst themselves while leaving everyone else in the dark.
These private exchanges are nothing new. Dark pools have been around since the 1960s and although data from these exchanges is slim it is thought the volume being traded has grown while the level of high-frequency trading on public markets has fallen. This is because the ability to trade large volumes on dark pools without causing severe price movements in the market means high-frequency traders have less opportunity to conduct larger trades on public markets, which in turn has put more attention on lower-volume deals which high-frequency trading is not designed for. Previous ‘flash crashes’ or sharp price movements caused by high-frequency trading has only glistened the appeal of dark pools.
The most substantial piece of regulation considered to have spurred on high-frequency trading from 2005 onwards was the introduction of the Regulation National Market System (Reg NMS) in the US. This regulation is what gave traders the insight into the strategies of other investors in the hope that, in times of crisis or during downturns, trading would continue rather than result in non-communicative brokers avoiding taking sell orders as they had done in the 1987 crash.
The International Financial Law Review highlights one rather notable aspect of Reg NMS that meant all orders that were placed had to be executed at the best price regardless of what exchange it is on, thus allowing high-frequency traders to spot trends in one exchange before rushing to capitalise by placing orders on another exchange before the effect has had a chance to ripple through. While it was meant to provide a more transparent and level playing field between the largest players in the financial market, everyone else was put at a disadvantage.
Things have been tightened since, with MIFID II in Europe and FINRA in the US both including rules on algorithm trading. The London School of Economics and Political Science states a major problem with regulating high-frequency trading is defining exactly what it is. While there are generally accepted characteristics there is no universally accepted definition.
Still, MIFID II implemented new rules requiring high-frequency traders to gain authorisation from market authorities and required better record-keeping as part of wider attempts to stamp out any abuse. However, there is widespread acceptance that there is much further to go in regulating the sector.
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