The Future of Commodity Markets Part 2: Technology and Innovation

Book: Commodities: Markets, Performance, and Strategies
Editors: H. Kent Baker, Greg Filbeck, Jeffrey H. Harris
Publisher: Oxford University Press, 2018
ISBN: 9780190656010

Regulation Is Reshaping Who Trades Commodities

This is Part 2 of Chapter 28 by Hunter Holzhauer. Part 1 covered market growth and oversupply. This part focuses on two major forces reshaping the industry: the regulatory environment and the rise of algorithmic trading.

The Regulation Wave

Increased regulation is adding costs and limiting resources for commodity traders. In a KPMG (2016) survey, one-third of commodity traders expected regulation to have a “medium” impact on their operations. Only 13 percent expected “significant” impact. But here is the interesting number: 31 percent said the impact was “unclear.” Traders literally could not figure out how regulations would affect them because there were so many to track.

The chapter lists the top regulations by perceived impact: MiFID II, EMIR, Dodd-Frank, Corporate Tax Reform III, Base Erosion and Profit Sharing (BEPS), Financial Markets Infrastructure Act, REMIT, Securities and Futures Act, Basel IV, and the Extractive Industries Transparency Initiative. That is a lot of acronyms and a lot of compliance work.

The main regulatory concerns? Complexity (21 percent of traders) and increased compliance costs (18 percent). Those costs come from consulting external advisors, hiring legal and compliance staff, reallocating internal resources, and upgrading reporting systems. One-third of traders reported additional financing costs due to fewer funding options.

The regulation most likely to be a massive burden is changes in capital requirements. While the intent was to address “too big to fail” firms, smaller traders who lack dedicated compliance departments may be hit hardest.

The Bank Exodus

The biggest story in commodity trading over the past decade is the mass departure of investment banks. Two specific regulations played key roles: the Dodd-Frank Act in the U.S. and Basel III/CRD IV in the EU. These regulations, combined with collapsing price volatility, created a double hit on bank profitability.

The numbers are striking. Assets at the top 10 commodity hedge funds dropped from over $50 billion in 2008 to less than $10 billion by 2015. That is an 80 percent decline. Bank revenues from commodities fell 18 percent in 2015 alone, and the first half of 2016 was even worse, with revenues down 25 percent compared to the same period in 2015.

Banks are not just losing money. They are losing people. Top commodity specialists are leaving for trading houses like Glencore, Cargill, Vitol, and Trafigura, which offer less red tape. According to one employment specialist, banks are losing 10 to 20 percent of their commodity specialists to trading houses each year. Others are starting their own firms. David Silbert, who built Deutsche Bank’s commodities business, left to create TrailStone Group. Ben Freeman left Goldman Sachs to found HudsonField LLC.

Some banks are not even keeping their commodity divisions. Morgan Stanley tried to sell its oil division three times, first to Qatar’s sovereign wealth fund, then to Russia’s Rosneft, before finally selling to Castleton Commodities International in 2015. JPMorgan sold a substantial portion of its physical commodities business to Mercuria. Nine of the 10 largest Western banks in physical commodity trading either left completely or substantially reduced activity.

Who Is Filling the Gap?

As banks exit, trading houses and non-bank energy companies are picking up market share. Companies like Axpo Trading and BP have performed well in physical markets, especially electricity and natural gas where bank departures have been most prominent.

Three banks have stayed in the game: Goldman Sachs, JPMorgan, and Citi, listed in order of 2015 commodity revenue. The bright spot is that no new surprise exits happened in 2015, and some banks even reported revenue growth. Citi has been particularly aggressive, winning multiple “House of the Year” awards for executing long-dated transactions and structuring complex deals that other banks would not touch.

The banks that survive will likely do so by bundling commodity trading with other services like M&A consulting and capital acquisition. Goldman Sachs, for example, has been financing investments in hydraulic fracturing, horizontal drilling, and renewable energy.

Market Maturation and Shrinking Margins

Increased regulation and transparency are squeezing intermediary traders. As recently as 2009, traders using long-term fixed-price contracts for thermal coal could earn margins of $3 to $5 per ton. Now that thermal coal trades on transparent open markets, those margins have fallen to $1 to $3 per ton. Natural gas may face similar margin compression as liquefied natural gas becomes more accessible.

Larger firms with more financing options are better positioned for this environment. They can negotiate better prices, create their own financing sources, and take advantage of economies of scale. Smaller firms are being squeezed out. According to KPMG, 88 percent of commodity traders feel “some” or “strong” pressure on trading margins. Nearly 25 percent plan to revise strategies, and 22 percent plan to exit certain business activities entirely.

New Performance Metrics May Cause Problems

Historically, commodity traders were evaluated on returns on equity. Now they are increasingly evaluated on returns from total capital employed (equity plus debt). This shift is driven by the increase in bond issuances by traders looking to secure long-term capital for asset acquisitions.

Here is the unintended consequence: the new metrics effectively punish traders who hold large inventories to smooth out supply and demand imbalances. If holding inventory becomes less profitable, traders will hold less of it. Less inventory means less liquidity buffer. And less liquidity means a higher probability of price spikes when supply gets disrupted.

The Rise of Algorithms

Nearly three-fourths of traders surveyed by Algofxsolution (2016) said algorithmic trading is affecting the future of their industry. Algorithms are already faster and more accurate than most human traders at optimizing market conditions.

The impact will likely split the industry. A large percentage of traders and analysts will work for a handful of large firms that can afford to build and maintain proprietary algorithms. The remaining traders will focus on nuanced strategies in specific niches, especially emerging markets or complex deals where algorithms are less effective.

The numbers on adoption are interesting. Only 29 percent of traders plan to use algorithms in the future. Nearly half (45 percent) have no plans to use them. And 27 percent are undecided. Amateur traders are the most polarized: 41 percent think algorithms will hurt retail trading profitability, while 45 percent think they will improve it.

Professional traders are more measured. Only 32 percent see algorithms as harmful and 38 percent see them as beneficial. The remaining 30 percent expect neutral effects. This suggests that professional traders have a better sense of how algorithms will complement rather than replace their work.

My Take

The bank exodus is the most consequential trend in this chapter. When nine of 10 major banks leave an industry, that is not a minor adjustment. It is a structural transformation. The question is whether trading houses can provide the same depth of liquidity and breadth of services that banks offered. The evidence so far suggests they provide more short-term, front-of-curve liquidity but lack the balance sheets for long-dated hedges and the relationship services (credit lines, portfolio margining) that commercial end-users relied on.

The algorithm story is still early. Most commodity traders have not made up their minds about algorithmic trading, which tells me the technology is not yet as dominant in commodities as it is in equities. Commodity markets have more idiosyncratic factors (weather, politics, physical logistics) that are harder for algorithms to model.


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