Electronic Markets, Computing Power and the Quants: Volatility & High Frequency Trading
Electronic Markets, Computing Power and the Quants: Volatility & High Frequency Trading
Markets can be – and usually are – too active, and too volatile”
Joseph E. Stiglitz – Nobel prize-winning economist
As some of you might have noticed, the oil market is currently showing wilder fluctuations at a higher frequency than before: volatility has increased. This happened after the market enjoyed relative stability price stability during the last few years. Of course, this is partly due to U.S. shale oil production, quite high supply and lower demand due to the financial crisis aftermaths, and growing demand and supply uncertainties. However, another factor affecting volatility is the increased usage of trading indicators in combination with changes in trading practices: an increasing number of players in the financial markets tend to use algorithmic and high-frequency trading practices (HFT).
Like other derivative based markets, also the crude oil market has a wide range of market players of which many are not interested in buying physical oil. HFT traders are probably drawn towards oil futures due to the market’s volatility. Because, the greater the price swings, the greater their potential profit. HFT is not an entirely new practise, but as technology evolves it is increasingly present in today’s electronic financial markets.
These players make extensive use of computing and information technology in order to develop complex trading algorithms, which are often referred to as the “quants”. HFT trading firms try to gain advantage over other competitors which are still using mostly human intelligence and reaction times. The essence of the game is to use your algobots to get the quickest market access, fastest processing speeds, and perform the quickest calculations in order capture profits which would have otherwise been earned by someone who is processing market data slower (Salmon, 2014). At essentially the speed of light, these systems are capable of reacting to market data, transmitting thousands of order messages per second, as well as automatically cancelling and replacing orders based on shifting market conditions and capturing price discrepancies with little human intervention (Clark & Ranjan, 2012). New trading strategies are formulated by using, capturing and recombining new information with large datasets and other forms of big data available to the market. The analysis performed to derive the assumed direction of the market makes use of a bunch of indicators such as historical patterns, price behaviour, price corrections, peak-resistance and low-support levels, as well as (the moving average of) trends and counter-trends. By aggregating all this information, the databases and its (changes of) averages are usually a pretty good predictor of potential profits for HFT companies.
This information technology enabled way of trading is cheaper for the executors, but imposes great costs on workers and firms throughout the economy. Although quants provide a lot liquidity, but can also alter markets by placing more emphasis on techniques and linking electronic markets with other markets (as well information as financial linking). In most cases, non-overnight, short-term strategies are used. Thus, these traders are in the market for quick wins and use only technical analysis in order to predict market movements instead of trading based upon physical fundamentals, human intelligence or news inputs.
Although, some studies have not found direct prove that HFT can cause volatility, others concluded that HFT in certain cases can transmit disruptions almost simultaneously over markets due to its high speed in combination with the interconnectedness of markets (FT, 2011; Caivano, 2015). For example, Andrew Haldane, a top official at the Bank of England said that HFT was creating a system risks and the electronic markets may need a ‘redesign’ in future (Demos & Cohen, 2011). Further sophistication of “robot” trading at decreasing cost is expected to continue in the foreseeable future. This can impose a threat to the stability of financial markets due to amplified risks, undesired interactions, and unknown outcomes (FT, 2011). In addition, in a world with intensive HFT the acquisition of information will be discouraged as the value of information about stocks and the economy retrieved by human intelligence will be much lower due to the fact that robots now do all the work before a single human was able to process and act on the information (Salmon, 2014). For those interested in the issues of HFT in more detail, I would like to recommend the article of Felix Salmon (2014).
However, it is important to mention that not only HFT and automated systems and technicalities do cause all the volatility. Markets have known swift price swings for centuries. For example in the oil industry, geopolitical risk can cause price changes as it is an exhaustible commodity. As most people know, also human emotions can distort markets as well as terrorist actions. Even incomplete information such as tweets from Twitter and Facebook posts can cause shares to jump or plumb nowadays. As markets are becoming faster, more information is shared and systems can process and act on this information alone quickly due to (information) technological advancements, which will in turn increase volatility. Therefore, it is more important than ever that there are no flaws in market data streams, e.g. the electronic markets and its information systems need to have enough capacity to process, control, and display all the necessary information to market players in order to avoid information asymmetries.
In my opinion, HFT is strengthened by the current state of computing technology and cost reductions of computing power now enable the execution of highly complex algorithms in a split-second. As prices go down and speed goes up, these systems will become more and more attractive as they outperform human intelligence. This can potentially form an issue in the future: volatility might increase and it is this volatility that provides many opportunities for traders, but not the necessary stability for producers and consumers which are more long-term focussed.
Therefore, in the future action is necessary to restrict, or at least reduce, HFT. Examples might be big data collection by regulators to monitor risk and predict future flash crash or volatility events. Another option can be the introduction of a “minimum resting period” for trading. So traders have to hold on to their equity or trade for a pre-specified time before selling it on, reducing the frequency and thus volatility. Also, widening spreads will help as it makes quick selling and buying more costly and thus HFT less attractive.
Given that the financial market’s watchdogs currently have difficulties with regulating automated trading. Some HFT firms have enjoyed enormous profits from their trading strategies (Jump trading, Tower Research capital, DRW). For example also during the last turmoil of August this year, a couple of HFT firms earned a lot of money (Hope, 2015). Due to these successes, new players enter the market and competition is growing. As speed is essential (even milliseconds matter) HFT firms try to place their servers physically near the exchanges (such as the NYSE), so they can increase their advantage. The HFT firms are expected to stay in the market, ultimately resulting in more price volatility (Hope, 2015).
What do you think, how far should we let our technology intervene with the financial markets? Do we really need to allow algobot’s or similar automated trading systems to influence our financial markets as they can perform the human job faster, fact-based and at a lower cost? Or should the financial markets be always human intelligence based, which might be ultimately better for the economy as a whole and also provides a richer knowledge base of the real world economy (as it this information remains valuable and numbers do not always say everything)?
In case you are interested in this dilemma, I can also recommend reading Stiglitz’ speech at the Federal Reserve Bank of Atlanta in 2014.
Author: Glenn de Jong, 357570gj
Hope, B. (2015) Historic Profits for High-Frequency Trading Firm, Wall Street Journal, Retrieved from: http://www.wsj.com/articles/historic-profits-for-high-frequency-trading-firm-today-1440446251
Grant, J. and Stafford, P., “Studies say no link between HFT and volatility” Retrieved from: http://on.ft.com/nEdpJF
Zhang, F. (2010). High-frequency trading, stock volatility, and price discovery. Available at SSRN 1691679.
Kirilenko, A. A., Kyle, A. S., Samadi, M., & Tuzun, T. (2014). The flash crash: The impact of high frequency trading on an electronic market. Available at SSRN 1686004.
Coates, J. M., Gurnell, M., & Rustichini, A. (2009). Second-to-fourth digit ratio predicts success among high-frequency financial traders. Proceedings of the National Academy of Sciences, 106(2), 623-628.
Salmon, F. (2014), The problems of HFT, Reuters Blog, Retrieved from: http://blogs.reuters.com/felix-salmon/2014/04/15/the-problems-of-hft-joe-stiglitz-edition
Demos, T., Cohen, N, (2011) High-frequency trading adding risk, Haldane says, Financial Times, 8 of July 2011, Retrieved from: http://on.ft.com/o0FDdH
Clark, C., Ranjan, R., (2012) How Do Broker-Dealers/Futures Commission Merchants Control the Risks of High Speed Trading? Federal Reserve Bank of Chicago, 13/06/2012, Retrieved from: https://www.chicagofed.org/publications/policy-discussion-papers/2012/pdp-3
Caivano, V. (2015), The Impact of High-Frequency Trading on Volatility. Evidence from the Italian Market (March 2, 2015). CONSOB Working Papers No. 80. Retrieved from: http://dx.doi.org/10.2139/ssrn.2573677