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Algorithms & Commodities - A Perilous Duo

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Undertaking mundane daily activities, such as doing groceries or paying electricity bills, how many of us actually wonder what really drives prices we are going to pay? Major events, either local or global, or a recent economic news about inflation for example might cross one’s mind. However very few would probably think of sophisticated algorithms incessantly running in server rooms across the globe. This discovery, worrisome yet intriguing, could elucidate those unacquainted with concepts of volatility arbitrage and the like, on how far development of financial technology has wandered.

The rise of high-frequency trading (HFT) has provoked an entire spectrum of reactions, ranging from pure excitement of quantitative (quant) types to grave concerns of market conservatives. Before moving to more profound aspects of the phenomenon, let’s first remind what that esoteric form of trading actually is. While algorithmic trading simply refers to employment of computer algorithms for execution of large trades with aim to minimize market impact of those transactions, the “high-frequency” prefix takes it to a whole new level. It leverages tremendous computing power and strategic location close to exchanges (an artifice called collocation), to arbitrage away the most infinitesimal price discrepancies that only exist over the infinitesimal time horizons.

Ostensibly, high-frequency traders have an informational edge that enables them leap-frog other investors and thus have their orders matched on trading venue at more favorable prices. First high-frequency trades date back to at least 1998, when the U.S. Securities and Exchange Commission authorized electronic exchanges. Since then, huge progress has been made in terms of HFT latency – execution time has diminished from several seconds to mili- or even microseconds by 2010. The major U.S. trading shops and quantitative hedge funds that have contributed to those developments included, inter alia, GETCO, Citadel LLC and foremost Renaissance Technologies, a New York based investment management company founded by James Simons, a meritorious mathematician and former Cold War code breaker. As of 2016, high-frequency trading accounts for approximately 50 percent of daily equity market volume in the U.S.

But the question remains as to what these arcane trading practices have to do with purchasing decisions we face every day? The plain, yet somewhat enigmatic answer is – commoditization of commodities markets. Commodity trading, historically relying upon proprietary information and thus favoring those directly engaged in respective business operations and preferably being a part of supply chain, has switched towards the era of algorithm-driven hyper-liquid markets, from academic point of view their ultimate achievable state in terms of efficiency and transparency. According to a report by the Boston Consulting Group, gold, copper and electric power have already began their transition into the hyper-liquid level, with other commodities, including food ones, lagging not far behind. This trend, fueled by developments in HFT space, arouses a lot of controversy, among both market practitioners and solicitous observers.

The rise of sophisticated algorithms, delegating trading decisions to thousands lines of source code rather than old-fashioned, Oxford-educated guys sitting in front of eight Bloomberg screens, to some extent has certainly helped ordinary investors. Influx of liquidity dramatically diminished bid-ask spreads and thus reduced transaction costs faced by market participants. Actually, it has been found that Canadian bid-ask spreads increased by 9 percent in 2012 as result of fees imposed by the government that effectively reduced limited high-frequency trading activity. Moreover, deepened liquidity enables a broad spectrum of investors to more precisely craft and implement their risk intentions. Narrowing our area of interest to commodities, it might be also argued that newly created market depth might serve as advantage for producers being now able to manage volatility more efficiently and take advantage of lower transaction costs.

At first glance, those brilliant pieces of code leave little to be desired by investors and indirectly the wider population. Deep and liquid markets seem like an ultimate goal achieved by financial visionaries. Nonetheless, serious concerns have been raised regarding potentially adverse effects of high-frequency trading volume growth. Spectrum of core arguments encompasses market distortions, systematic risk amplification or social fruitlessness and all of these have received profound coverage by academics or finance professionals. In fact, some of essential benefits and risks associated with HFT have been addressed in a previous BESA article(add hyperlink here). However, the issue that only recently got closer attention of public opinion, is the potential harmful impact the employment of trading algorithms might have on commodities markets, which are undoubtedly interwoven with access to goods we consume every day.

From a technical perspective, there are two pivotal phenomena which are induced by automated trading systems and might lead to severe spillover effect into the real economy. The first one concerns HFT’s potential to exacerbate volatility. It is crucial to note that in principle the market participants relying on high-frequency strategies do not necessarily cause volatility themselves. Massive and unbridled price swings have always been a pivotal feature of many commodities markets, particularly those heavily dependent upon geopolitical risks, such as oil or energy. The HFT’s contribution to unexpected price moves comes from the nature of some sophisticated strategy classes.

For instance, liquidity-detection strategies exploit detected large institutional-sized orders and trade ahead of them. This results in amplifying price swings and thereby boosting volatility. The second well-documented trend is an increased correlation between the price of commodities and other asset classes. That issue has been thoroughly examined by researchers David Bicchetti and Nicolas Maystre. In their study “The synchronized and long-lasting structural change on commodity markets: evidence from high frequency data”, presented at the United Nations Conference on Trade and Development they have found “striking” spikes of correlation between commodities, such as oil, soybeans and corn and the U.S. equity market over very short intraday time horizons. “Before 2008, high-frequency co-movements between commodity and equity markets did not usually differ from zero over a long-lasting period at such high frequencies” they said. But “in the course of 2008, these correlations departed from zero and became strongly positive after the collapse of Lehman Brothers. (…) We believe a conjunction of factors made that change possible. First, financial technical innovation spurred HFT through the gradual introduction of full electronic trading on exchange platforms since 2005. Second, investors moved away from passive strategies and opted for active ones.”

While structural changes of this kind are not exactly a new thing for the markets, it is important to ask ourselves what might be their impact on the society. The ongoing financialization of commodities markets has certainly some positive effects on involved parties, however it goes without saying that food prices being driven by speculative financial strategies seem like a debatable issue. In 2011, in a letter to the ICE Futures US exchange, the chairman of the World Sugar Committee said the activity of high-frequency and algorithmic-based speculative funds “only serves to enrich themselves at the expense of the traditional market users”.

This issue has been also addressed by Kaitlin Cordes from Columbia Center on Sustainable Investment. While marking that further research on food price volatility needs to be done, Ms. Cordes expressed her concerns regarding HFT ability to “impede the realization of the right to food by introducing greater volatility in commodity markets and significantly affecting real food prices, with detrimental impacts on peoples’ access to food.” She also pointed at potential impact of price volatility on those with low income (who spend a majority of their income on food) and threat that price swings may pose to local food producers forced to employ suboptimal temporary coping mechanisms (e.g. sale of assets).

It is no surprise that U.S. regulators have taken steps to bring some HFT under closer scrutiny by proposing somewhat ambiguous measures. US Commodity Futures Trading Commission launched a plan to create a more precisely defined trading regime for all automatically traded futures contracts on US exchanges. In addition to obligatory registration with CFTC of all market participants trading directly on an exchange, the proposed rule would also enable CFTC to gain access to trader’s source code without subpoena. This proposal met with fierce opposition from traders, for whom disclosing their algorithms equals to being left defenseless against the rest of the market.

Unrestrained access to state-of-art technology and talent pool, mingled with deeply rooted speculative instinct of mankind have drew a clear path for financial industry to follow. When vanilla strategies proved partly fallible, hedge funds and proprietary trading shops turned to academic mathematicians and computer scientists asking for a hand in establishing competitive advantage. While this marked the beginning of financial revolution, it also created room for an enormous scope of potential risks. Many of them have already been addressed by both researchers and market regulators, however one needs to place high-frequency trading in the grand scheme of things in order to spot where more underestimated threats lie. Market participants and financially versed observers certainly need to carefully weight both economic benefits of technology advancement as well as its perhaps indiscernible, yet detrimental implications in order to truly understand the ambiguous nature of contemporary financial world and its ties with real economy.




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