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Five facts about shale: it’s coming back, and coming back strong

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Five facts about shale: it’s coming back, and coming back strong

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Five facts about shale: it’s coming back, and coming back strong

  1. 1. Please see important disclaimer and disclosures at the end of the document COMMODITIES 7 February 2017 Important Notice: The circumstances in which this publication has been produced are such that it is not appropriate to characterise it as independent investment research as referred to in MiFID and that it should be treated as a marketing communication even if it contains a research recommendation. This publication is also not subject to any prohibition on dealing ahead of the dissemination of investment research. However, SG is required to have policies to manage the conflicts which may arise in the production of its research, including preventing dealing ahead of investment research. Commodity Compass Five facts about shale: it’s coming back, and coming back strong With oil prices back above the psychological $50 threshold and the OPEC/non-OPEC agreement on the table, the market is very much focused on its compliance. Attention is also inevitably drawn to the dynamics of US shale production. Will the US recovery offset OPEC cuts? Accordingly, we review some of the key dynamics of US shale production. Specifically, we highlight 5 facts about US shale production that all point to the same underlying trend: US shale is coming back, and it’s coming back strong. 1. Rig counts are increasing at an accelerating pace, and given the technological advances of the past 3 years, this should translate into significant supply. 2. Decline rates for US shale wells are still steep, but initial production levels, production profiles, and ultimate recovery volumes have increased. Going forward, higher production profiles mean stronger aggregate supply. 3. Preliminary US EIA estimates indicate that net new shale supply turned positive in December, the first time since March 2015. Net new supply recovered just 7 months after rig counts bottomed out and began to increase. 4. The increase of drilling activity comes on the back of a large stock of drilled and uncompleted wells (DUCs). The industry is also vigorously adding to this stock, which demonstrates that the shale sector is again recovering/expanding. 5. Evidence from the Bureau of Labor Statistics (BLS) is showing the oil and gas labor market is stabilizing and reversing its declining trend. Figure 1.1 – The SG NEW Overbought/Oversold Indicator. Commodities in the oversold (red) box are vulnerable to short-covering and commodities in the overbought (blue) box vulnerable to profit-taking. The SG OBOS indicator defines and identifies “oversold” (“overbought”) commodities on a weekly basis as those that are lying at the intersection of extremes in both short (long) positioning and price weakness (strength). The “oversold” (“overbought”) box is shown in red (blue) in Figure 1.1. Commodities within the “oversold” (“overbought”) box are trading in the bottom (top) 25% of their price range and have a short (long) position (calculated as the short [long] Money Manager [MM] open interest [OI] as a percentage of total OI [source: CFTC COT report]) in excess of 75% of the historical maximum. These commodities are vulnerable to short-covering (profit-taking). In Feb 207, the indicator was changed to use a rolling 1 year window for the ranges, with the range calculations also modified to align the calculation for the Positioning Component with that of the Pricing Component. This was done to increase sensitivity. Please refer to the following publication for details on the approach: Commodities Compass - Identifying “oversold” commodities – the intersection of two extremes. Source: SG Cross Asset Research/Commodities, Bloomberg. CL NG HO XB C W KW S GC SI HG SB CT KC CC LC LH CL NG HO XB C W KW S GC SI HG SB CT KC CC LCLH 0% 25% 50% 75% 100% -100% -75% -50% -25% 0% 25% 50% 75% 100% Price%ofPriceRange Short MM % Range Long MM % Range Monthly Extract from a report Cross Commodity Strategy Mark Keenan (65) 6326 7851 mark.keenan@sgcib.com Head of Commodities Research Dr. Michael Haigh (1) 212 278 6020 michael.haigh@sgcib.com Oil & Products Michael Wittner +1 212 278 6438 michael.wittner@sgcib.com Analyst David Schenck Jan 2016 commodity performance -20% 0% 20% Natural gas Gasoline Heating Oil Crude Oil Brent Soybean Oil Gasoil Feeder Cattle Lean Hogs Cocoa Live Cattle Nickel Soybeans Corn Kansas Wheat Wheat Sugar Gold Soybean Meal Cotton Aluminium Copper Coffee Silver Platinum Palladium Zinc Lead DXY This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  2. 2. Commodity Compass 7 February 2017 2 Five facts about shale: it’s coming back, and coming back strong The expression, “this time it’s different” could be the theme for 2017. Our analysis suggests that shale oil market could be very different. With oil prices back above the psychological $50 threshold for the second consecutive month after rebounding from the 12-year low in February 2016, and the OPEC/non-OPEC agreement on the table, the market is very much focused on its compliance. In addition, attention is now also inevitably drawn to the supply- side dynamics of US shale production. Perhaps the key fundamental question in the oil markets right now is “will US crude production recover quickly enough to offset OPEC/non-OPEC production cuts?” Accordingly, we review some of the key dynamics of US shale production. Specifically, we highlight 5 facts about US shale production that all point to the same underlying trend: US shale is coming back, and it’s coming back strong. 1. Rig counts are increasing at an accelerating pace, and given the technological advances of the past 3 years, this should translate into significant supply. 2. Decline rates for US shale wells are still steep, but typical production profiles have shifted upwards considerably. Going forward, higher initial production levels and production profiles (due to high-grading of well locations, technology improvements, and efficiency gains) mean stronger aggregate supply. 3. Preliminary US EIA estimates indicate that net new shale supply turned positive in December, the first time since March 2015. Net new supply recovered just 7 months after rig counts bottomed out and began to increase. 4. The increase of drilling activity comes on the back of a large stock of drilled and uncompleted wells (DUCs). The industry is also vigorously adding to this stock, which demonstrates that the shale sector is recovering and expanding once again. 5. Evidence from the Bureau of Labor Statistics (BLS) is showing the oil and gas labor market is stabilizing and reversing its declining trend. Assuming compliance with the OPEC/non-OPEC December production cut agreement is high, changes in US crude oil output will determine in large part the pace of the market rebalancing and the medium-term sustainability of the new price regime. It is within this context that the 5 facts on shale oil are evaluated. Fact #1: already more drilling and higher per-rig output In the week to 3 February 2017, the number of rigs actively drilling in the US rose to 729. Of these, 705 were operating on land and 24 were located offshore or in inland waters. The vast majority of these (583, 82%) were drilling for oil, and most rigs (596, 84%) were drilling horizontally. In assessing non-conventional shale oil supply, it is this last figure – horizontal drilling - which matters the most. Figure 1 below shows the number of active rigs drilling horizontal wells in the US, which we use as a proxy for shale oil. Since bottoming out in May 2016, drilling activity has reversed course and the number of active rigs has increased by 90%. The rig count was up for 31 of the last 37 weeks, increasing on average by 2% week-on-week. Year-on-year, the number of horizontal rigs has increased by nearly 30% (Figure 2). This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  3. 3. Commodity Compass 7 February 2017 3 Figure 1: US horizontal rig count up 84% since May 2016 low Figure 2: 3 February 2017 rig count Source: SG Cross Asset Research/Commodity In itself, this is a strong leading indicator of a rebound in US shale production. As noted by Baker Hugues, “rig count trends are governed by oil company exploration and development spending”. Stated differently, shale oil operators are investing heavily in their capacity to produce more supply. Looking at the rig count in isolation however underestimates future production, as it fails to capture the profound technological changes in the US shale industry. Since 2009, the relationship between rig count and oil output has changed considerably, and each additional rig now translates into more output than ever before. To illustrate this, we plot in Figure 3 below the number of US rigs against the 4 week average lower 48 crude production (ex Gulf of Mexico). Figure 3: US lower 48 crude production (ex GOM) vs horizontal rig count Figure 4: US horizontal rig count and crude prices (2nd nearby) Source: SG Cross Asset Research/Commodity We identify three broad trends since 2009: 1. A period of increasing drilling activity (the “ramp up”), from June 2009 to November 2011. At the peak, nearly 2,000 rigs were actively drilling every week. Lower 48 onshore crude production however remained broadly stable, ranging around 4,750 kb/d. 200 400 600 800 1000 1200 1400 Jun-14 Dec-14 Jun-15 Dec-15 Jun-16 Dec-16 US horizontal rig count count % of total %yoy Land 705 97% 30% Inland waters 2 0% 0% Offshore 22 3% -15% Gulf of Mexico 21 3% -19% Rest of US 709 97% 29% Drilling for oil 583 80% 25% Drilling for gas 145 20% 39% Horizontal (shale) 596 82% 30% Vertical 67 9% 12% Other 66 9% 25% Total US 729 100% 28% 0 200 400 600 800 1000 1200 1400 1600 3500 4500 5500 6500 7500 8500 9500 Jun-09 Jun-10 Jun-11 Jun-12 Jun-13 Jun-14 Jun-15 Jun-16 US lower 48 (ex GOM) production (Mb/d, 4w av g, lhs) Horizontal rig count (rhs) drilling ramp-up productiv ity ramp-up resilience 0 200 400 600 800 1000 1200 1400 1600 25 45 65 85 105 125 Jun-09 Dec-10 Jun-12 Dec-13 Jun-15 Dec-16 WTI 2nd nearby prices (USD/barrel, lhs) US horizontal rig count (rhs) This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  4. 4. Commodity Compass 7 February 2017 4 2. In a second period, starting in mid-2011, per-rig productivity started to increase considerably, with lower 48 onshore crude production leaping from around 5,500 kb/d to 9,160 kb/d in July 2015. The US horizontal rig count on the other hand remained broadly stable for the first two years, around 1,300 rigs. Although there is a structural lag between well drilling and production, this lag alone is not sufficient to explain the strong increase in production between the first and the second period (see below). 3. The third rig-regime is characterized by a steep decline in the number of active rigs in the US. As crude oil prices began to fall in June 2014 (Figure 4), the rig count remained resilient for another 6 months – until December 2014 (blue-shaded area). Eventually, as operators reduced their new investments, the number of active rigs fell. Production proved even more resilient than the rig count: it took 35 weeks after the rig count started to decline before production began its own decline. From peak to trough, production only fell by approximately 12%. With the rig count having been steadily recovering since it bottomed out in May 2016, the analysis above shows that the relationship between rig count and production has evolved over time. If anything, the resilience of US onshore oil supply in the face of lower prices demonstrates the profound technological transformations witnessed in the shale industry. This dramatic change can be observed at both the national and the regional level. Indeed, Figures 5a to 5c plot production and rig count against each other, both at the national and state-level. Figure 5a shows US field production of crude oil, plotted against the number of active horizontal rigs. Figures 5b and 5c do so for Texas and North Dakota respectively. Darker points (bottom right) are more recent. The latest data point is printed as the orange diamond. The similarity in the scatter charts is striking: each rig is now associated with significantly more production than in 2009. Figure 5a: US crude production vs. US horizontal rig count (monthly) Figure 5b: Texas crude production (ex GOM) vs. oil directed rig count (monthly) Figure 5c: North Dakota crude production vs. oil directed rig count (monthly) Source: DOE, Baker Hughes, SG Cross Asset Research/Commodities The three charts above are consistent with the productivity gains noted in Figure 3, and show that these gains were observed in all of the major shale oil plays – the Permian and the Eagle Ford in Texasand the Bakken in North Dakota. Recognizing the profound structural transformation of the US shale oil industry is critical in being able to interpret and contextualise the weekly rig count. What caused the productivity gains? Until prices collapsed, the increases were driven by technological improvements and efficiency gains; after prices collapsed, the high-grading of well locations also became a major factor. These reasons explain in part why US production remained resilient despite a severe drop in the number of rigs. It also suggests that, given the recent uptick in the number of rigs actively 250 750 1250 4 500 6 500 8 500 10 500 UShorizontalrigcount US lower 48 (ex GOM) crude production 100 300 500 700 900 1100 200 1200 2200 3200 4200 Texasrigcount Texas onshore production (Mb/d) 0 50 100 150 200 250 0 500 1000 1500 NDrigcount N. Dakota onshore production (Mb/d) This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  5. 5. Commodity Compass 7 February 2017 5 drilling, production should expand in the US at an increasingly faster pace. Arguably, this means that a rising rig count now is more bullish for production and potentially more bearish for prices than it was in the past. Given the recent increase in the number of active rigs in the US, how fast will this new drilling activity convert to actual supply? The 2014 oil price peak and the 2016 oil price trough offers some insight into the lagged relationship between prices, rig count and production. Figure 6 below shows price, rig count and production peak and trough levels over these two periods, and the date on which these levels were recorded. Oil prices peaked in late June 2014, while rig counts reached their maximum level in late November. US field production topped in July 2015, at 9,162Mb/d. The rig count reacted 154 days after the price peak while production peaked another 8 months thereafter – in total, over 1 year after the price peak. More recently, rig counts were the lowest 126 days after prices reached their 2016 low, with US production bottoming out in September 2016, 133 days after rig count reached its minimum. Whilst simple, these observations suggest that: 1. Prices tend to drive US investment (as measured by rig activity), which in turn drive US production. 2. The time between these linkages has shorted between 2014 and 2016, a likely function of the efficiency gains. Going forward, supply should therefore be more price-elastic and we may see more production and price volatility. 3. The time lag between rig count and production decreased the most (224 days to 133 days relative to 154 days to 126 days). The advent of pad drilling (i.e., the drilling of multiple wells from one drilling site) likely contributed to this reduction, by reducing “the time it takes to move a rig from one well location to the next and by reducing the overall surface footprint”1 . 4. Referring back to Figure 3, we note that the rig-production lag is also not sufficient to explain the apparent 2-to-3 year lag between the “ramp-up” and “productivity” periods tl;dr The US horizontal rig count is increasing, evidence of new investments in shale- producing capacity. Furthermore, technological progress means each rig now produces more than before. Finally, this new supply should come online faster than before. 1 http://www.eia.gov/todayinenergy/detail.php?id=7910 Figure 6: price, rig count and prodution peak and trough Source: SG Cross Asset Research/Commodity Peak Prices Rig count Production 106.83$ 1372 rigs 9 162 MBPD 20 Jun 2014 21 Nov 2014 03 Jul 2015 +154 days +224 days Trough Prices Rig count Production 30.39$ 314 rigs 8 033 MBPD 15 Jan 2016 20 May 2016 30 Sep 2016 +126 days +133 days This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  6. 6. Commodity Compass 7 February 2017 6 Fact #2: higher initial oil production levels, production profiles, and ultimate recoverable volumes per well As production expands, so does the need to drill new wells to sustain supply at a constant level in order to offset the steep depletion profile at the early stage of shale production. A syndrome called the “Red Queen”2 , this is a consequence of the rapid decline in well productivity after the first two months, itself the result of the drop in pressure from the oil (and gas) formation as hydrocarbons are extracted. Figure 7: average oil production per well in the Permian basin (EIA) Source: EIA Figure 7 above shows the EIA’s estimated production profiles for the Permian basin, illustrating the sharp decline rates and fall in productivity after the first few months of production. For example, in 2015, 1. per-well production peaked at approximately 225 barrels per day in the first month; 2. by the second month, the average well produced only 190 barrels per day; 3. a year later, we estimate the average well to produce only 65 barrels per day. This is an important feature of shale oil production, since it implies that new wells must be consistently added to maintain production at a constant level. A visual comparison of the above production profiles for each year reveals a number of features of production factors: - The decline rates are steeper today than before – the slope is more negative. For instance, we estimate that per-well production fell by 15.5% between the first and second month in 2015 (from 225 to 190). In 2012, we estimate that the decrease was of only 10% (from 60 to 54). - The production profiles are higher today than before - the curve is shifted upwards. For instance, per-well production of oil 12 months after inception is estimated at 55 barrels per day. In 2012, this metric stood at 35 barrels per day. A key element of this is that initial production levels are also higher today than before. 2 Named after the Red Queen in Through the Looking Glass, by Lewis Carroll, who tells Alice that “it takes all the running you can do, to keep in the same place”. This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  7. 7. Commodity Compass 7 February 2017 7 The steepness of the decline rates drives the Red Queen phenomenon, but importantly , the higher initial production levels and overall upward shift in the production profiles actually more than offsets the increase in decline rates (see box 1). Box 1: Why is the height of the production profile more important than the decline rate? Decline curve analysis is an established method of evaluating the aggregate lifetime production of a well (the “expected ultimate recoverable” volume). The technique was popularized in the 1950s by American geologist J.J. Arps, who published a mathematical formula modelling a single well’s oil production over time3 . We generate a simple exponential decline curve model, where output at time t is given by: The decline rate should be calibrated using field data: For illustrative purposes, and estimating the underlying data in the EIA’s decline curve presented in Figure 7, we compute the monthly well production, the cumulative well production and the lifetime well production for the Permian: Monthly well production Cumulative well production Lifetime well production For 2015: calibration done using first and second month of production, resp. 225 and 195 barrels per day. Decline rate = 0.143 For 2012: calibration done using first and second month of production, resp. 60 and 54 barrels per day. Decline rate = 0.105 We note that the 2015 (blue) curve indeed has a steeper decline rate but also a higher output profile than the 2012 curve (brown). Over the lifetime of the well, the higher initial production level and production profile produces nearly three times as much as the lower decline curve (right chart). The extent with which each successive annual production profile is higher than the previous (Figure 7) can be determined by looking at the average daily production of newly drilled wells per rig (Figure 8). The chart is log-scaled, allowing us to assess the speed with which per-well productivity per rig increases. The four major oil basins account for over 90% of total US shale oil output. 3 http://infohost.nmt.edu/~petro/faculty/Kelly/Deline.pdf and http://www.fekete.com/san/webhelp/feketeharmony/harmony_webhelp/content/html_files/reference_material/analysis_m ethod_theory/Traditional_Decline_Theory.htm 0 50 100 150 200 250 0 3 6 9 12 15 18 21 24 27 30 2015 2012 0.0 0.5 1.0 1.5 2.0 +0m +6m +12m +18m +24m +30m Thousands 2015 2012 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 2015 2012 Thousands This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  8. 8. Commodity Compass 7 February 2017 8 Figure 8: new-well oil production per rig, rebased (2007=100), log-scale (see footnote) Log-scale allows us to better assess the speed with which changes occur. Linear lines are indicative of constant-compound rate, meaning that unit output accelerates exponentially. Source: EIA, SG Cross Asset Research/Commodity We observe that, since 2011, - per-well productivity per rig has increased at an approximately constant compound rate for the Eagle Ford basin (brown line), the Bakken basin (blue line) and the Niobrara basin (black line); - Regardless, the per-well productivity per rig of those basins already producing the greatest volume of shale oil – the Permian, the Bakken and the Eagle Ford basin – continues to increase, confounding the decline many forecasted. tl;dr US shale well decline rates are steeper than before, but technological break-throughs shifted initial production levels and production profiles higher. Initial and lifetime well- production is significantly higher than three years ago. Therefore, fewer new wells are needed to offset aging wells, and for a given supply of new wells, output will rise. Fact #3: Net new supply is turning positive The EIA’s Drilling Productivity Report now provides monthly data on the production per rig for newly drilled wells as well as estimates of the change in legacy production for the major seven production basins. Figure 9 below plots the production change of the legacy stock of wells for each month. Because of the shape of the average well’s production profile over time – the decline rates described above – the change in legacy production tends to be negative. 10 100 1000 10000 Jan-07 Jan-09 Jan-11 Jan-13 Jan-15 Jan-17 Permian Bakken Eagle Ford Niobrara This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  9. 9. Commodity Compass 7 February 2017 9 Figure 9: legacy production change for 4 major shale plays Figure 10: net production change for major shale oil plays Source: SG Cross Asset Research/Commodity Log-scale allows us to better assess the speed with which changes occur. Linear lines are indicative of constant-compound rate, meaning that unit output accelerates exponentially. Source: EIA, SG Cross Asset Research/Commodity We note that: - The legacy production change was increasingly negative, even during the “fruitful” years (2009-2014). This acceleration meant that the level of new well supply required to sustain production was increasingly higher. - This trend stabilized and reversed in May 2015. This is a significant shift. A smaller share of new supply is now used to compensate the natural decline in legacy production. And a greater share of new supply is actually net new supply. Figure 10 compares the legacy production change with the new production for the four largest shale plays. Currently, the Permian and the Niobrara plays – where breakeven costs are the lowest – are generating significant new net supply (grey bars higher than blue bars), more than offsetting the losses in the other two basins. Adding changes in legacy production change with new production provides us with an estimate of net new supply. Figure 11 plots this dynamically over time. For comparison, we plot again Figure 3 next to it. We observe that it is therefore in the area of net supply that rig count and new well production per rig matters the most, as the relationship between the number of new wells (alternatively, rig activity) and the production level is tightest. -350 -300 -250 -200 -150 -100 -50 0 Jan-07 Jul-08 Jan-10 Jul-11 Jan-13 Jul-14 Jan-16 Thousandsbarrels/day Legacy production change (4 major basins) 0 20 40 60 80 100 120 140 160 180-180 -160 -140 -120 -100 -80 -60 -40 -20 0 Bakken Region Eagle Ford Region Niobrara Region Permian Region Thousandsbarrels/day Thousandsbarrels/day Legacy production change (lhs) New production (rhs) This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  10. 10. Commodity Compass 7 February 2017 10 Figure 11: net new supply and rig count from 4 major plays Figure 12: US lower 48 crude production ex GOM and horizontal rig count (same as Figure 3) Source: SG Cross Asset Research/Commodity Importantly, we see that according to preliminary US EIA estimates, net supply turned positive in December for the first time since March 2015. Shale is coming back, and it’s coming back strong. tl;dr Legacy production changes are becoming less negative, meaning less new supply is used to offset the natural decline in shale production. In addition, preliminary estimates of net supply turned positive in December for the first time since March 2015. Shale is coming back, and it’s coming back strong. Fact #4: The DUCs are quacking Referring back to Figure 11 above, we see that net supply is accelerating and even turned positive in December. Recently, rig count in the major 4 basins also increased, but the relationship between net new supply and rig count uncoupled somewhat. Could a drawdown of the number of drilled but uncompleted wells explain this? The recent drilling increase comes on the back of an increase – rather than a decrease – of the number of drilled and uncompleted wells (DUCs). This suggests DUCs have not been a major factor: the additional net supply does not come from a decrease in the number of DUCS. 0 200 400 600 800 1000 1200 -200 -150 -100 -50 0 50 100 150 Jun-09 Jun-10 Jun-11 Jun-12 Jun-13 Jun-14 Jun-15 Jun-16 Thousandsbarrels/day Net new production mom, 4 major basins (lhs) Rig count (4 major basins) 0 200 400 600 800 1000 1200 1400 1600 3500 4500 5500 6500 7500 8500 9500 Jun-09 Jun-10 Jun-11 Jun-12 Jun-13 Jun-14 Jun-15 Jun-16 US lower 48 (ex GOM) production (Mb/d, 4w av g, lhs) Horizontal rig count (rhs) What is a DUC? The EIA defines a drilled but uncompleted well as a well “whose first completion process has not been concluded”. Furthermore, “for the purpose of the EIA’s estimates, the end of the drilling process is estimated to be 20 days after drilling has commenced. The end of the first completion process is marked after the well is fracked for the first time”. Stated differently, DUCS are non- producing wells, or latent supply. This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  11. 11. Commodity Compass 7 February 2017 11 Figure 13: total (rhs) and change (lhs) of number of drilled but uncompleted wells (DUCS) Source: Bloomberg, EIA, SG Cross Asset Research/Commodities Figure 13 above shows the change in the number of DUCs by basin, as well as the total number of uncompleted wells (rhs). Months during which oil prices rose, are highlighted in green. We note: 1. The substantial increase in the number of DUCs since August 2016, 2. The substantial increase in the number of DUCs in the Permian basin (light orange bars). Together, these facts point to the industry’s general optimism, particularly in the Permian basin. Although the data is only available from the beginning of 2014 onwards, it provides interesting insights into both the industry’s financial stress and confidence going forward. - On the one hand, we note the large increase of DUCs in the second half of 2014, culminating in March 2015. This was concurrent with a prolonged period of price declines, evidence of financial stress with well operators. As drilling activity reduced from November 2014 onwards, the supply of DUCs stabilized, despite the fact that the oil market suffered another 6-month period of prices declines. “Held by drilling” leases, which mandate operators to complete wells within a specified period of time also contributed to the stabilization of DUCs, as operators preferred to complete wells rather than suffer the financial costs of not fulfilling their contracts. - From March 2016 onwards however, as prices rebounded, the stock of DUCs diminished as operators drew down on the stock of existing uncompleted wells and locked in prices. Unlike the 2014 episode however, the most recent increase must be interpreted in the light of the recent price evolutions. The uptick in DUCs is evidence of the industry’s revival and should be assessed critically. Because DUCs represent latent shale oil supply, with up to 20% of the costs already sunk, the current stock of DUCs shows the US shale industry is positioning itself for a pickup the price environment and in supply. This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  12. 12. Commodity Compass 7 February 2017 12 tl;dr The number of drilled but uncompleted wells has recently increased, most particularly in the Permian basin. Given the price-context and the rise in drilling activity, this should be interpreted as evidence of the US shale industry expanding. Fact #5: oil-labor market is stabilizing We collect data from the Bureau of Labor Statistics (BLS) to assess the employment- conditions in the oil industry in the US. We find that employment in the US oil industry has stabilized, ending a period of decline. Figure 14 plots the size of the US oil and gas workforce (seasonally-adjusted, from the BLS) against production. As would be expected, we note that employment in the oil and gas industry is a leading indicator of production. The size of the labor force started to decline from October 2014, at least 6 months before production peaked. Similarly, the labor force ceased to decline in August 2016 after 10 consecutive months of decline. Similar evidence is found with state-level statistics. Figure 15 plots the size of the workforce in the mining industry for selected states, rebased to 100 in 2009. Figure 14: US aggregate O&G employment turns stable Figure 15a and b: mining employment for selected states (rebased 2009=100) Source: SG Cross Asset Research Furthermore, we note that despite the fall in the size of the oil and gas labor force, oil production has only declined partially. As above, this implies lower extraction costs in the form of fewer man-hours per barrel, further lowering the break-even cost of US shale. tl;dr After a prolonged period of decline, the US oil and gas labor market has stabilized. This could suggest that the US industry is once again hiring and expanding capacity. 5000 6000 7000 8000 9000 10000 150 170 190 210 Jan-09 Jan-11 Jan-13 Jan-15 Thousands Oil and gas employment (SA, lhs) Production (Mb/d, rhs) 50 75 100 125 150 Jan-09 May-10 Sep-11 Jan-13 May-14 Sep-15 Colorado Kansas NewMexico Wyoming Oklahoma Texas 0 100 200 300 400 500 Jan-09 May-10 Sep-11 Jan-13 May-14 Sep-15 North Dakota This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  13. 13. Commodity Compass 7 February 2017 13 Contents 1) SG trade recommendations (selected)....................................................................................14 2) Commodity market analysis ...................................................................................................15 3) Principal Component Analysis (PCA).......................................................................................21 4) Cost curve dynamics - the SG Production Cost Model...........................................................24 5) CFTC Commitment of Trader (COT) analysis ..........................................................................26 6) Dry Powder analysis – insights into positioning .......................................................................29 7) SG price forecasts..................................................................................................................33 Commodity Compass – previous publications Dates Title Jan 2017 A new generation of RSI indicator – Measuring when the WTI curve flips Dec 2016 Drought sentiment vs. reality – a new agriculture trading model Nov 2016 Finding beta – What exactly is the “commodity market”? Oct 2016 UPDATE – Replacing the NFCI with Bloomberg FCIs in our Commodity FCI Model Oct 2016 Measuring “dry powder” in commodities – some alternative insights into positioning Sep 2016 The VIX and the VVIX – tools for extreme commodity risk management Aug 2016 Financial Conditions Indices (FCI) and long/short commodity trading signals July 2016 Trading Economic Policy Uncertainty (EPU) with commodities June 2016 Using Big Data to trade oil May 2016 The impact of rate hike probabilities on gold and other commodities April 2016 Forecasting production costs – incorporating FX, CPI and oil forecasts into the SG PCM March 2016 Introducing the SG Production Cost Model – tracking Cost Drivers in real time February 2016 “The Chinese Driving Season” - No monkey business January 2016 La Niña - The likelihood of it following El Niño and how to trade it December 2015 Commodity trading opportunities from freight markets November 2015 The impact of hedging patterns on volatility in crude oil and natural gas October 2015 The role of ETPs (ETFs & ETNs) in commodity price formation September 2015 Identifying “oversold” commodities – the intersection of two extremes August 2015 The London Metal Exchange COT report – surprisingly a very useful trading tool July 2015 ‘Rockets and feathers’ – a phenomenon for managing price retracements June 2015 Planes, trains, automobiles (and ships) – part 2. The Baltic Petroleum Trading Model May 2015 Planes, trains, automobiles (and ships) – part 1 April 2015 Devaluing EM currencies, the cost of carry and commodity spreads March 2015 Softening cost floors – EM currency depreciation and falling oil prices February 2015 The contango tango – the mechanics of contango and freight markets January 2015 The circularity of oil prices and oil burden December 2014 Commodity Put/Call Ratios – Enhancing trading and investment strategies November 2014 Supply and demand – key drivers of returns this year, as nearly all of our SD models October 2014 Interest rate increases and the influence on the commodity forward curve September 2014 Our new Oil Product Demand Indicator (OPDI) - evidence of China’s shifting economy? August 2014 The seasonality of commodity index investment flows July 2014 Evaluating the magnitude and duration of risk premia during geopolitical tensions June 2014 A new India – Copper consumption to double & gold imports to double? May 2014 The econometrics of El Niño & the impact of ECB policy changes on commodity markets April 2014 From Cocoa to Zinc – El Niño’s impact on commodities and whether it can be captured? March 2014 Eight important commodity factors to watch... February 2014 Putting recent macro events in context & analysing their impact on commodities January 2014 2013 A bad year for price performance; but a good year for the asset class This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  14. 14. Commodity Compass 7 February 2017 14 1) SG trade recommendations (selected) Commodity Type Recommendation WTI Flat price Buy Dec-17 WTI on dips The OPEC agreement to cut output by 1.2 Mb/d from January this year together with the possibility of non-OPEC cuts of some 600 kb/d should result in meaningful global oil stock draws next year (even if full compliance is unlikely). Therefore, we see scope for WTI to approach $60 by end-17. Annual global oil demand growth is forecast at a healthy 1.25 Mb/d in 2016 and 1.26 Mb/d in 2017, driven by emerging markets, especially China, India, and other non-OECD Asia. US output continues to be an important driver for non-OPEC supply as a whole. Annual US liquids production including crude and NGLs is forecast to decline by 0.48 Mb/d in 2016, but to reverse course and increase by 0.16 Mb/d in 2017. Annual US output of crude (only) is projected to contract by 0.57 Mb/d in 2016, but to decline by a smaller 0.16 Mb/d in 2017. We expect shale supply to stop declining and bottom out in 2Q17 and 3Q17, before starting to gradually grow again in 4Q17. Most shale oil full-cycle production costs are down some 30% since 4Q14 and are centred around $40-45 (WTI equivalent). WTI has averaged in the $45-50 range since 2Q16 and due to the persistent contango on the forward curve (front-month vs one-year forward timespreads have consistently been in the $3-6 range), producers have been able to lock in higher prices through hedging (selling forward). As a result, US E&P capital spending has begun to stabilise with gradual increases expected; US oil directed drilling has been gradually recovering since June. The prospect of a gradual recovery in US shale production next year is likely to slow the uptrend in oil prices. We recommend buying Dec-17 WTI on dips with a target at around $60. US natural gas Flat price Long Mar-17 US natural gas on dips December saw a record pace of storage withdrawal and winter 16/17 is now on track to be the second strongest withdrawal season on record, second only to Polar Vortex winter 13/14, and this is under a normal January through March weather scenario. The large draw is the result of lower supply and much stronger demand. Demand support will come from a combination of stronger exports (LNG and Mexico exports are already trending stronger year-on-year), industrial and R/C loads. A colder than normal winter scenario would exacerbate the situation as there is limited demand response to prices during winter months, and little ability for production to ramp within such a short period of time. It has already been proved this winter that weather can fluctuate quickly between warm and cold scenarios. January forecasts have it trending on the warmer side of the range; however, we see continued volatility as likely in anything other than a sustained mild weather scenario through February. We recommend going long the March 2017 natural gas contract on dips towards $3.0-3.2 and expect it trade up to at least $3.40-3.50 in a normal weather scenario and much higher in a colder than normal late January/February. We also see significant value in current post-March 2018 prices, which we consider to be fundamentally cheap. Palladium, platinum Relative value Buy dips in palladium spot versus a short platinum spot position Platinum and palladium prices have recently diverged significantly, with platinum prices having dropped while palladium prices have risen. We expect palladium to continue outperforming and recommend buying palladium on dips against platinum. Palladium is widely regarded as the most “industrial” metal of the two while platinum is more correlated to gold due to its higher use as jewellery. Platinum is also facing headwinds from a weaker South African rand, which makes it more profitable to produce platinum, and concerns over the health of European diesel demand (platinum is a major component in autocatalysts for diesel engines). Platinum supply concerns have also been alleviated by the successful conclusion of South African mining wage negotiations. The agreement is for a three-year wage deal, which provides union member employees with above-inflation salary increases throughout the period and thereby eliminates the likelihood of wage-related strikes across the South African PGM industry for the foreseeable future. We expect palladium to realise large annual deficits over the next two years, moderated to a degree by a rebound in autocatalyst recycling. To compare, our 2016 forecast deficit for platinum represents just 3.4% of mine supply, while the forecast palladium deficit makes up 20% of mine supply. As a result, we expect palladium prices to outperform platinum prices. The main risk to our palladium price forecasts is to the upside. Copper Flat price Buy 3-month LME copper on dips We are bullish on copper on a trend basis as the outlook for copper demand has improved due to stronger than expected Chinese demand stemming from its ongoing massive infrastructure spending and President-elect Donald Trump’s plan to launch a $1tn fiscal stimulus/infrastructure programme to stimulate economic growth. Consumption growth is expected to strengthen to 3.5% in 2017 from 1.5% last year, largely as a result of a doubling in Chinese consumption growth to 6% yoy, as the government boosts infrastructure spending to underpin growth. Forecast supply growth in 2016-2017 should see new mines starting up, bolstered by the ramping up of new projects started last year and brownfield expansions (Grasberg). However, mine disruptions could be making a comeback if events in recent weeks are anything to go by and given a series of production downgrades from major producers. We forecast that the global physical copper market will move into balance this year after an estimated surplus of 280kt last year. Average cash prices are forecast at $5,500/t in 2017. Copper, aluminium Relative value Buy dips in 3-month LME copper versus a short position in 3-month LME aluminium See above for our bullish trend view on copper. LME spot aluminium prices trended higher during most of last year. Higher prices were driven by output cutbacks by Western producers and actual closures/cuts by Chinese producers, coupled with a rebound in Chinese demand. However, further price upside appears limited as an improvement in the fundamentals does not look sustainable over the coming months. Structural oversupply in the global aluminium market has persisted for several years and shows little sign of reversing anytime soon, especially due to oversupply in China and its continued export of semi-manufactured products/fake metal to ROW markets. Thus, we recommend selling rallies in the aluminium price against copper. Source: SG Cross Asset Research/Commodities This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  15. 15. Commodity Compass 7 February 2017 15 2) Commodity market analysis This section provides a backward-looking analysis over the previous month with a focus on the main contributors to returns in the S&P, GSCI and BCOM commodity indices. Figure 2.1 – Jan sector performance Figure 2.2 – 1-Year sector performance Figure 2.3 – Jan market performance Figure 2.4 – 1-Year market performance Source: SG Cross Asset Research/Commodities, Bloomberg -4.7% -1.2% 2.4% 5.5% 5.5% 8.4% -20% -15% -10% -5% 0% 5% 10% Energy Liv estock Grains Precious Metals Sof ts Industrial Metals -9.8% -8.6% 8.5% 23.5% 26.7% 29.0% -30% -20% -10% 0% 10% 20% 30% 40% Grains Liv estock Precious Metals Energy Sof ts Industrial Metals -16.1% -8.8% -6.3% -3.3% -3.1% -2.3% -2.2% -1.9% -1.5% -1.1% -0.8% -0.8% 2.0% 2.2% 2.6% 3.1% 4.8% 4.9% 5.7% 6.1% 7.2% 8.2% 9.1% 9.7% 10.0% 10.4% 11.0% 17.8% -2.6% -40% -20% 0% 20% Natural gas Gasoline Heating Oil Crude Oil Brent Soy bean Oil Gasoil Feeder Cattle Lean Hogs Cocoa Liv e Cattle Nickel Soy beans Corn Kansas Wheat Wheat Sugar Gold Soy bean Meal Cotton Aluminium Copper Cof f ee Silv er Platinum Palladium Zinc Lead DXY -24.1% -23.4% -23.2% -13.8% -12.1% -11.3% -6.4% -5.9% 4.1% 7.0% 11.0% 12.9% 13.2% 14.4% 16.7% 16.8% 18.6% 20.8% 21.0% 21.0% 30.5% 33.1% 33.5% 35.3% 40.3% 48.8% 50.1% 72.5% -0.1% -60% -40% -20% 0% 20% 40% 60% 80% Kansas Wheat Wheat Cocoa Feeder Cattle Lean Hogs Corn Natural gas Liv e Cattle Soy bean Oil Gold Gasoline Platinum Nickel Soy beans Aluminium Cof f ee Crude Oil Silv er Soy bean Meal Cotton Copper Heating Oil Brent Lead Gasoil Sugar Palladium Zinc DXY This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  16. 16. Commodity Compass 7 February 2017 16 Energy The energy sector fell 4.7% in January making it the sector’s first monthly decline since the November OPEC deal. Energy was the weakest sector in the asset class with all six energy markets falling during the month. Within the sector, natural gas (-16.1%) fell the most and gasoil (-2.2%) the least. Over the last 12 months, the energy sector gained 23.5% with gasoil (+40.3%) being the best performing market and natural gas (-6.4%) the worst. Petroleum prices were generally volatile in January with major swings between gains and losses over the first half of the month. Whilst the deal between OPEC and non-OPEC producers last November and December to cut oil production only took effect on January 1st, markets were highly sensitive to deal related newsflow and early indications of compliance. The beginning of 2017 was met with WTI hitting $55 for the first time since July 2015 and Brent hitting an 18-month high of $58. This was driven by bullish Chinese economic data, a weak dollar, bullish US crude inventory data, and signals of output reductions from Saudi Arabia, Kuwait and Russia. , Reports of Iraq’s record-high output in December 2016, increased supply from Iran and a rise in US shale production caused a sharp decline in prices over the second week of January. In the second half of the month, a slew of bullish and bearish news affected oil prices resulting in a near-horizontal drift in prices. Positive news included strong statements from key OPEC and non-OPEC members about their adherence to production cuts, the EIA increasing its estimate for 2016 demand growth, and a weaker dollar following Donald Trump’s first press conference. Prices reversed when loading data from Iraq pointed to higher February exports, which elevated compliance concern, and reports of a decline in China’s overall exports raised concerns over the economic health of the world’s second largest consumer of oil. OPEC’s January report highlighted output reductions of 221kb/d during December 2016, with the biggest cuts coming from Saudi Arabia, the world’s largest oil exporter. Tanker tracking data from Petro-Logistics further confirmed that reductions of 900,000 bpd from OPEC were in place. However, OPEC’s efforts to reduce oil output was largely undermined by bearish US inventory data over the month , with crude oil supplies increasing by nearly 16 million barrels vs. expectations of 5 million barrels. The total oil rig count increased by 41 in January, taking the total rig count to its highest level since November 2015. US crude production increased by 191kb/d in January, the highest monthly increase since May 2015, which, along with Donald Trump’s comments on easing restrictions on oil drilling, collectively weighed on prices and sentiment. Natural gas (-6.4%) reversed its December price rally over January with prices falling to their lowest levels since November. Support for natural gas eroded on forecasts of mild temperatures replacing earlier forecast of extreme cold weather. A weaker La Niña further decreased gas-fuelled heating demand and depressed prices. Tensions between USA and Mexico and a potential “trade war” are expected to further dampen prices since Mexico is the largest importer of US natural gas, with 2016 exports exceeding 4bcf per day. Natural gas inventories declined by 15.5% (513bcf) in January, less than the 10-year average decline in January of 23%. Industrial Metals The industrial metals sector was the best performing sector both in January, (+8.4%), as well as over the past 12 months (+29%). In January, lead was the best performing metal with a This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  17. 17. Commodity Compass 7 February 2017 17 gain of 17.8% and nickel was the worst with a decline of 0.8%. Over the past 12 months, zinc has risen by 72.5% and has been the best performing market within the asset class. Metal prices in January were largely supported by the falling dollar, increased global demand and better manufacturing conditions. The dollar index (-2.64%) faced the biggest January drop in three decades. This was driven largely due to Trump’s comments about the dollar being “too strong,” as well as Fed chair Janet Yellen’s statements suggesting a more gradual rise in interest rates. Markets largely disregarded bearish news relating to the change in Indonesia’s export rules and generally rising inventories. Copper prices were up 8.2% over the month on supply concerns and increasing Chinese demand. Expectations of increased demand from the US following President Donald Trump’s proposed infrastructure spending pledge also continued to support prices. Negotiations of labour contracts at the world’s largest copper mines including Escondida in Chile and Grasberg in Indonesia are expected to cause production disruptions and potentially tighten supplies. The fall in the dollar also led to increased buying of copper from China as a safeguard against the risk of yuan depreciation. A decrease in LME inventories by 60,900mt was offset by increases in Shanghai and COMEX inventories by 66,327mt and 13,764mt respectively. The increase in aluminium (+7.2%) was supported by speculations of export barriers and supply cuts in China. The US lodged a formal complaint with the WTO against China, the world’s largest aluminium producer, alleging that subsidies by the Chinese government to its aluminium producers have created artificially low prices for the metal, contributing to the global oversupply. Concerns that this may cause trade barriers and supply disruptions elevated prices. In addition, the Chinese government is reportedly planning a halt in aluminium production during the winter in a bid to combat rising air pollution levels. Collectively, these elevated supply concerns, which were only partially offset by the increase in LME and Shanghai of 62,950mt and 24,706mt respectively. Zinc (+11.0%) prices continue to be driven higher on the back of increased demand for the metal following infrastructure investment in China and fiscal spending plans in the US. LME inventories saw the largest monthly decline since March 2016 with stocks falling by 33,400mt. Upside was restrained by a slight increase in Shanghai stocks of 9,381mt. Nickel was the only metal within the sector to fall in January. This was largely driven by the prospect of increased supply due to the revision in Indonesia’s policies to reverse the ban on unprocessed mineral exports. The increase in LME inventories of 11,010mt further dampened prices and was only partially offset by a slight drop of 4,971mt in Shanghai stockpiles. Lead prices rose following reports of a reduction in smelting in China due to low treatment charges for the concentrate and also due to a significant jump in cancelled warrants, metal earmarked for removal from LME warehouses. Recent data from the International Lead and Zinc Study Group also showed a tighter supply/demand profile going forward as production from mines declined by 7.5% in the initial 11 months of 2016 compared to the same period in 2015. Strong automobile sales figures from USA and China for December, indicative of stronger battery demand, also supported prices. LME inventories fell by 5,500mt while Shanghai inventories rose by 17,545mt. This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  18. 18. Commodity Compass 7 February 2017 18 Precious Metals The precious metals sector rose 5.5% in January, leading to an overall increase of 8.5% in the sector over the last 12 months. In January, both silver (+9.7%) and gold (+4.9%) moved higher. Gold prices increased in January, resulting in its first monthly increase since September 2016, and reaching levels last seen in November 2016. The decline in the dollar, Trumponomics, Hard Brexit, and a less hawkish Fed were the main drivers of gold in January. The general lack of clarity in Donald Trump’s economic and trade policies post his inauguration, his formal withdrawal of the US from the Trans-Pacific Partnership and renegotiation of NAFTA, reiteration of his protectionist trade policies increased gold’s safe haven appeal. Confirmation of Britain’s exit from the EU’s single market further heightened demand for the safe haven assets. Yellen’s statements, indicating a gradual rate hike, were also bullish for gold prices. Seasonal demand for the metal in China ahead of the Lunar New Year and ongoing seasonal demand in India also supported prices. Agriculture The grains sector (corn, wheat, Kansas wheat and soybeans) was up 2.4% in January with all markets higher. Wheat (+3.1%) was the best performing market. Over the last 12 months, grains declined by 9.8% making it the worst-performing sector in the asset class. The softs sector (cotton, coffee, sugar, and cocoa) increased by 5.5% in January and by 26.7% over the last 12 months. In January, coffee (+9.1%) was the best-performing market and cocoa (-1.1%) was the worst. Corn (+2.2%) prices were driven higher after USDA estimates of a reduction in corn production to 15.1bn bushels, down 78 million since last month and below expectations, but up 11.3% YoY. The USDA reported that average yields are likely to reduce to 174.6 bushels per acre due to lower harvested acres. Soybean (+2.0%) prices rose in January on reports of torrential floods in Argentina which destroyed 4 million acres of farmland. Brazil’s national crop supply agency increased estimates of production above market expectations to 104mt. This was however largely offset by the USDA report, which forecasted US production at 4.3bn bushels, down 54mn since last month and below expectations, but up 9.6% YoY. Yield and harvested area were revised downward to 52.1 bushels per acre and 82.7 million acres respectively, due to reduction in planted area by 300,000 acres. Forecasts of favourable weather in Argentina significantly slashed gains in the last week of January. Wheat prices rose as the USDA reported that winter wheat acreage fell to its lowest level in more than 100 years. This was driven primarily by the increased production in Argentina, Russia and the EU and the global supply glut. Cotton (+6.1%) prices rose on reports of increased demand from China, Indonesia and Vietnam, giving the fibre its largest monthly increase since July 2016. This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  19. 19. Commodity Compass 7 February 2017 19 Sugar (+4.8%) prices increased in January as data from the Indian Sugar Mills Association cut forecasts of sugar output to a 7-year low and data from Brazil showed that the sugar output from a key producing region declined sharply. Coffee prices rose on the back of the International Coffee Organization’s forecasts of a global shortage in supply for the year ending October 2017. Reports from Brazil’s national crop supply agency reported a fall in coffee production of 19.3% last month following forecasts of dry weather in the region. Poor crops in Indonesia and a fall in estimated production from Vietnam, the second largest producer in the world, also boosted prices. Cocoa prices touched their lowest levels in nearly four years, after the International Cocoa Organization increased estimates of global cocoa stocks above market expectations. Reports of lower cocoa processing, which is taken as a proxy for demand, further weighed on prices. Livestock The livestock sector was down 1.2% in January with all three meat markets moving lower. Over the past 12 months, the sector has fallen by 8.6%. Live cattle (-0.8%) prices edged lower following the USDA report of larger-than-expected numbers of cattle placed on feed in December. The USDA reported a placement of 1.8 million cattle in feedlots in December 2016, an increase of 18% over December 2015. Lean hogs prices declined by 1.5% following USDA estimates of higher pork production and high inventories. Prices were driven lower due to reduced demand. Sources include but are not restricted to: SG Cross Asset Research/Commodities, Bloomberg, The Financial Times, The Wall Street Journal, Reuters, Agrimoney.com and AgWeb.com. This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  20. 20. Commodity Compass 7 February 2017 20 2a) Forward curves – spread levels across the curve The top row of each square in Figure 2.5 defines the spread in terms of the month (M) and expiry year (Y) in the form ((MY)x/(MY)x+1), where x represents each successively listed contract. The second row shows the absolute level of the spread at month-end, calculated as (price (MY)x – price (MY)x+1). Figure 2.5 – Spread levels across the forward curve. Backwardation and contango shown in green and red respectively Figure 2.6 – Crude oil WTI: six-month forward curve history Figure 2.7 – Brent: six-month forward curve history Source: SG Cross Asset Research/Commodities, Bloomberg Futures contracts 1/2 2/3 3/4 4/5 5/6 6/7 7/8 8/9 9/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18 18/19 19/20 20/21 21/22 22/23 23/24 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/X7 X7/Z7 Z7/F8 F8/G8 G8/H8 H8/J8 J8/K8 K8/M 8 M 8/N8 N8/Q8 Q8/U8 U8/V8 V8/X8 X8/Z8 Z8/F9 F9/G9 -0.61 -0.52 -0.44 -0.32 -0.20 -0.15 -0.11 -0.09 -0.08 -0.05 -0.04 -0.04 -0.02 -0.01 0.00 0.04 0.03 0.02 0.01 0.00 -0.02 0.05 0.03 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/X7 X7/Z7 Z7/F8 F8/G8 G8/H8 H8/J8 J8/K8 K8/M 8 M 8/N8 N8/Q8 Q8/U8 U8/V8 V8/X8 X8/Z8 Z8/F9 F9/G9 0.12 -0.33 -0.30 -0.21 -0.12 -0.04 0.01 0.03 0.03 0.03 0.05 0.04 0.05 0.07 0.07 0.06 0.06 0.05 0.07 0.07 0.05 0.01 0.02 G7/H7 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/X7 X7/Z7 Z7/F8 F8/G8 G8/H8 H8/J8 J8/K8 K8/M 8 M 8/N8 N8/Q8 Q8/U8 U8/V8 V8/X8 X8/Z8 Z8/F9 -2.45 -21.94 -1.67 0.13 1.16 2.23 3.13 13.19 3.27 2.08 0.47 -0.89 -2.02 -19.86 -0.89 0.36 1.75 2.17 2.95 14.17 3.22 2.16 -0.05 G7/H7 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/X7 X7/Z7 Z7/F8 F8/G8 G8/H8 H8/J8 J8/K8 K8/M 8 M 8/N8 N8/Q8 Q8/U8 U8/V8 V8/X8 X8/Z8 Z8/F9 -1.91 -0.70 -0.80 -0.87 -1.12 -0.97 -0.95 -0.90 -0.90 -0.79 -0.77 -0.23 0.47 1.29 0.67 0.27 -0.16 -0.30 -0.45 -0.50 -0.45 -0.35 -0.50 G7/H7 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/X7 X7/Z7 Z7/F8 F8/G8 G8/H8 H8/J8 J8/K8 K8/M 8 M 8/N8 N8/Q8 Q8/U8 U8/V8 V8/X8 X8/Z8 Z8/F9 -4.00 -2.50 -1.75 -1.75 -2.25 -2.25 -2.25 -2.25 -0.50 0.25 -1.50 -1.25 -0.75 0.25 0.25 0.50 -0.75 -0.75 -0.75 -1.25 0.00 0.25 -1.50 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/X7 X7/Z7 Z7/F8 F8/G8 G8/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/19 19/19 -0.05 -0.04 -0.05 -0.04 0.00 0.02 -0.02 -0.05 -0.12 -0.08 0.03 0.09 0.51 0.05 -0.02 -0.02 0.00 0.02 -0.02 -0.04 -0.14 -0.10 0.03 G7/H7 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/17 17/17 17/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/19 -5.00 -3.25 -3.00 -3.00 -2.50 -2.75 -2.50 -1.25 -1.25 -1.25 0.00 0.00 0.00 1.25 1.25 1.25 1.50 1.75 2.00 2.00 2.00 2.00 2.00 G7/H7 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/17 17/17 17/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/18 18/19 -7.50 -3.50 -3.00 -2.00 -1.50 -1.75 -1.75 -1.75 -2.00 -2.00 0.00 -2.25 -2.25 -2.50 -2.50 -2.50 -2.50 -2.50 -2.50 -2.50 -2.50 -2.50 -2.00 G7/H7 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/X7 X7/Z7 Z7/F8 F8/G8 G8/H8 H8/J8 J8/K8 K8/M 8 M 8/N8 N8/Q8 Q8/U8 U8/V8 V8/X8 X8/Z8 Z8/F9 -3.25 -3.75 -2.75 -2.00 -0.50 -0.25 -0.25 1.00 2.00 2.00 6.00 6.00 8.00 10.00 10.00 10.00 18.00 18.00 18.00 18.00 19.00 19.00 17.00 G7/H7 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/X7 X7/Z7 Z7/F8 F8/G8 G8/H8 H8/J8 J8/K8 K8/M 8 M 8/N8 N8/Q8 Q8/U8 U8/V8 V8/X8 X8/Z8 Z8/F9 -17.50 -20.00 -18.00 -21.00 -18.00 -18.00 -20.00 -17.00 -17.00 -18.00 -16.00 -16.00 -16.00 -16.00 -16.00 -16.00 -16.00 -16.00 -16.00 -17.00 -17.00 -17.00 -15.00 G7/H7 H7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/U7 U7/V7 V7/X7 X7/Z7 Z7/F8 F8/G8 G8/H8 H8/J8 J8/K8 K8/M 8 M 8/N8 N8/Q8 Q8/U8 U8/V8 V8/X8 X8/Z8 Z8/F9 -1.00 -2.00 -1.50 -2.75 -2.75 -2.50 -2.50 -1.50 -1.50 -1.50 1.50 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 4.00 G7/J7 J7/M 7 M 7/Q7 Q7/V7 V7/Z7 Z7/G8 G8/J8 J8/M 8 M 8/Q8 Q8/V8 V8/Z8 Z8/M 9 M 9/Z9 Z9/M 0 M 0/Z0 Z0/M 1 M 1/Z1 -2.80 -3.10 -3.20 -3.10 -3.10 -3.30 -3.30 -3.40 -3.50 -3.60 -3.80 -12.20 -12.50 -10.50 -11.80 -12.00 -13.00 H7/K7 K7/N7 N7/U7 U7/Z7 Z7/H8 H8/K8 K8/N8 N8/U8 U8/Z8 Z8/N9 N9/Z9 Z9/N0 N0/Z0 Z0/N1 -0.07 -0.06 -0.06 -0.09 -0.09 -0.05 -0.05 -0.05 -0.06 -0.18 -0.17 -0.22 -0.20 -0.17 H7/K7 K7/N7 N7/U7 U7/Z7 Z7/H8 H8/K8 K8/N8 N8/U8 U8/Z8 Z8/H9 -7.25 -6.75 -6.75 -6.75 -7.75 -3.75 -3.25 6.00 -1.00 -7.00 H7/K7 K7/N7 N7/U7 U7/Z7 Z7/H8 H8/K8 K8/N8 N8/U8 U8/Z8 Z8/H9 -12.75 -13.75 -15.00 -17.50 -11.75 -8.25 -2.50 -10.75 -15.00 -9.25 H7/K7 K7/N7 N7/Q7 Q7/U7 U7/X7 X7/F8 F8/H8 H8/K8 K8/N8 N8/Q8 -9.75 -7.75 2.50 20.00 18.50 -3.50 1.00 -1.00 -1.00 7.00 H7/K7 K7/N7 N7/U7 U7/Z7 Z7/H8 H8/K8 K8/N8 N8/U8 U8/Z8 Z8/H9 -2.45 -2.35 -2.20 -3.05 -2.85 -1.70 -1.55 -1.45 -2.20 -2.25 H7/K7 K7/N7 N7/V7 V7/H8 H8/K8 K8/N8 N8/V8 V8/H9 H9/K9 K9/N9 Please note: Each box show s the spread betw een each listed contract dow n the curve -0.01 0.26 0.17 0.04 0.64 0.62 0.39 0.29 0.44 0.30 and w ill not necessarily cover every calender month. H7/K7 K7/N7 N7/V7 V7/Z7 Z7/H8 H8/K8 K8/N8 N8/V8 V8/Z8 Z8/H9 -0.66 -0.56 3.77 0.56 -0.32 0.18 0.22 0.55 0.33 -0.04 H7/K7 K7/N7 N7/U7 U7/Z7 Z7/H8 H8/K8 K8/N8 N8/U8 1.00 -9.00 -18.00 -26.00 -22.00 -16.00 -14.00 -17.00 G7/J7 J7/M 7 M 7/Q7 Q7/V7 V7/Z7 Z7/G8 1.43 9.97 4.23 0.27 -0.77 0.10 KEY (letters refer to expiry months, numbers to expiry years) G7/J7 J7/K7 K7/M 7 M 7/N7 N7/Q7 Q7/V7 V7/Z7 F G H J K M N Q U V X Z -0.17 -4.72 -3.63 0.10 0.22 10.28 3.95 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Cotton (CT) Cocoa (CC) Live Cattle (LC) Lean Hogs (LH) Silver (SI) Corn (C ) Wheat (W ) Soybeans (S ) Coffee (KC) Sugar (SB) Gold (GC) Crude Oil (CL) Brent (CO) Gasoline (XB) Heating Oil (HO) Gasoil (QS) Natural gas (NG) Copper (LP) Aluminium (LA) Zinc (LX) Nickel (LN) Lead (LL) 40 42 44 46 48 50 52 54 56 58 60 Jul-16 Sep-16 Nov -16 Jan-17 Mar-17 40 45 50 55 60 65 Jul-16 Sep-16 Nov -16 Jan-17 Mar-17 This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  21. 21. Commodity Compass 7 February 2017 21 3) Principal Component Analysis (PCA) Please see the end of this section for an explanation of PCA and the Economic Policy Uncertainty Indices (EPU) The average level of systemic risk (the macro, dollar and liquidity factors combined) across the complex continued to fall significantly in January. The energy sector had the biggest decline, with the sector becoming increasingly more vulnerable to idiosyncratic risk (fundamentals). There is now very little system risk in the sector with the average level of idiosyncratic risk over 93%, its highest level since Nov 2014. This shift started following the OPEC/non-OPEC deals at the end of 2016 and looks to have continued last month with the markets’ focus on compliance data. The potential US supply (shale) response is also playing an increasingly important role. Both these dynamics are highly fundamental, explaining the profile. The dollar factor rose for gold to over 15%, and also across most of the agriculture sector. Unusually, the agricultural sector was more vulnerable to systemic risk than the energy sector. Changes in Economic Policy Uncertainty (EPU) were largely uneventful, but remained elevated. This was surprising, especially in the US with the birth of Trumponomics. In general, an increase in the EPU profile suggests that systemic risk, or more specifically, negatively orientated systemic risk will likely increase across the asset class. Figure 3.1 – PCA profile for copper Figure 3.2 – PCA profile for Brent Figure 3.3 – PCA profile for gold Figure 3.4 – PCA profile for the BCOM Figures 3.1 to 3.4 show the change in explanatory power of the major factors (as determined by the PCA model) over the past 12 months for copper, Brent, gold and the BCOM commodity index. The underlying prices used in the analysis are the daily settlement prices of the BCOM F3 excess return index and the daily settlement prices of the respective BCOM F3 excess return component indices for the individual markets. Source: SG Cross Asset Research/Commodities 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Feb-16 May -16 Aug-16 Nov -16 Macro Dollar Liquidity 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Feb-16 May -16 Aug-16 Nov -16 Macro Dollar Liquidity 0% 10% 20% 30% 40% 50% 60% Feb-16 May -16 Aug-16 Nov -16 Macro Dollar Liquidity 0% 5% 10% 15% 20% 25% 30% 35% 40% Feb-16 May -16 Aug-16 Nov -16 Macro Dollar Liquidity This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  22. 22. Commodity Compass 7 February 2017 22 Figure 3.5 – PCA analysis: change in profile over the month Figure 3.5 shows the change in explanatory power for each of the major factors across 22 major commodity markets and the BCOM commodity index over the month. The underlying prices used in the analysis are the daily settlement prices of the BCOM F3 excess return index and the daily settlement prices of the respective BCOM F3 excess return component indices for the individual markets. The average shown at the bottom of the table is the equally-weighted arithmetic mean of the 22 markets. The average value will differ from the BCOM value due to weighting and correlation effects. Source: SG Cross Asset Research/Commodities Figure 3.6 – Historical PCA profile of the BCOM commodity index since 2008 Source: SG Cross Asset Research/Commodities PCA explained: Principal Component Analysis (PCA) is a statistical tool that allows us to break down commodity price returns and isolate the major explanatory variables. SG has developed a PCA model, specifically for commodity markets, that uses 23 different non-fundamental variables. These include measures of inflation, currency changes, credit spreads, implied volatility, equity and changes in equity indices. These variables are simplified into three principal components through the PCA process. Each component is a linear combination of the original 23 variables that can be mapped to a “real world” factor by examining and interpreting the underlying weightings of these variables. The first factor is defined as a macro-related factor, the second a currency factor, and the third an interest rate or liquidity factor. Each of the three factors is linearly regressed against each commodity to determine the explanatory power each factor has on the variance of that commodity. The residual, or that which is not explained by the regression process, is attributed to fundamentals (specific commodity supply & demand dynamics). Macro Dollar Liquidity Fundamental Macro Dollar Liquidity Fundamental Aluminium 4.07% 0.00% 0.83% 95.09% 2.99% 1.77% 4.47% 90.76% Copper 15.64% 1.27% 9.47% 73.58% 9.43% 3.08% 15.54% 71.94% Lead 1.69% 0.96% 4.09% 93.24% 2.86% 4.03% 5.86% 87.25% Nickel 10.96% 4.66% 1.65% 82.76% 16.28% 6.50% 6.09% 71.14% Zinc 2.44% 0.25% 12.09% 85.22% 4.77% 3.37% 10.86% 81.00% Gold 18.33% 15.08% 1.80% 64.79% 19.32% 5.13% 5.69% 69.85% Silver 3.32% 11.46% 1.93% 83.29% 4.20% 14.51% 0.52% 80.78% Crude Oil (WTI) 2.25% 7.32% 0.30% 90.12% 11.88% 9.07% 0.15% 78.90% Brent 1.85% 7.20% 0.41% 90.53% 10.94% 9.41% 0.24% 79.41% Heating Oil 0.31% 4.75% 0.51% 94.42% 6.60% 12.16% 0.02% 81.23% Gasoline 0.04% 4.90% 0.41% 94.64% 3.07% 8.73% 0.11% 88.08% Gasoil 0.08% 6.27% 0.39% 93.25% 3.58% 15.99% 0.42% 80.00% Natural Gas 1.18% 0.69% 1.67% 96.47% 0.37% 0.00% 0.02% 99.61% Corn 1.48% 14.27% 0.04% 84.22% 0.60% 6.07% 0.01% 93.32% Wheat 3.17% 9.70% 0.05% 87.08% 2.19% 0.59% 2.69% 94.54% Soybeans 0.29% 9.53% 0.19% 89.98% 0.39% 4.93% 0.02% 94.66% Cotton 0.22% 2.78% 1.14% 95.86% 0.01% 0.88% 0.00% 99.10% Sugar 3.04% 4.68% 3.89% 88.40% 1.99% 0.04% 2.84% 95.13% Coffee 0.54% 7.87% 0.16% 91.44% 0.18% 0.22% 3.66% 95.94% Cocoa 5.38% 2.82% 0.83% 90.96% 11.25% 0.42% 0.19% 88.15% Live Cattle 0.20% 0.35% 0.01% 99.45% 0.35% 0.13% 0.10% 99.42% Lean Hogs 3.68% 6.32% 0.81% 89.16% 1.85% 1.38% 0.00% 96.77% BCOM 0.42% 15.53% 0.13% 83.89% 6.75% 15.98% 0.06% 77.20% AVERAGE 3.64% 5.60% 1.94% 88.82% 5.23% 4.93% 2.70% 87.13% January-2017 December-2016 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2008 2009 2010 2011 2012 2013 2014 2015 2016 Macro Dollar Liquidity Fundamentals This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  23. 23. Commodity Compass 7 February 2017 23 3a) Economic Policy Uncertainty (EPU) Indices Economic Policy Uncertainty (EPU) is a class of economic risk where the future path of government policy is uncertain. Policy uncertainty may refer to uncertainty about monetary or fiscal policy, the tax or regulatory regime, or uncertainty over electoral outcomes that will influence political leadership. In the context of our PCA analysis (Section 3), changes in EPU can lead to changes in systemic risk. Specifically, increases in EPU are associated with negatively orientated systemic risk. In order to track these changes, we use the EPU indices, developed by Economic Policy Uncertainty4 Figure 3.7 – Monthly EPU indices for the US, EU and China. Increases (decreases) in the indices show rising (declining) EPU Source: SG Cross Asset Research/Commodities Figure 3.8 – Monthly changes in the US, EU and China EPU Index. Black line illustrates standard deviation (12m rolling) threshold. Changes in EPU in excess of this threshold are significant (red bars) and are associated with commodity price weakness Source: SG Cross Asset Research/Commodities, Bloomberg Economic Policy Indices: The Baker, Bloom and Davis news-based index of economic policy uncertainty for the US is based on the frequency of newspaper references to policy uncertainty. 10 large newspapers are used. The European Policy Index is constructed based on newspaper articles regarding policy uncertainty. The China Index is constructed using a scaled frequency count of articles about policy-related economic uncertainty in the South China Morning Post (SCMP), Hong Kong's leading English-language newspaper. The method follows our news-based indices of economic policy uncertainty for the US and other countries. We only consider the news-flow EPU Indices. Source: www.policyuncertainty.com. 4 Baker, Scott; Bloom, Nick; Davis, Steven (2011). "Measuring Policy Uncertainty?" (PDF). Stanford Mimeo. 0 100 200 300 400 500 600 700 800 Jan-07 Jan-09 Jan-11 Jan-13 Jan-15 Jan-17 US Europe China -100% -50% 0% 50% 100% 150% 200% Feb-12 Feb-13 Feb-14 Feb-15 Feb-16 US -100% -50% 0% 50% 100% 150% Feb-12 Feb-13 Feb-14 Feb-15 Feb-16 Europe -100% -50% 0% 50% 100% 150% 200% Feb-12 Feb-13 Feb-14 Feb-15 Feb-16 China This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  24. 24. Commodity Compass 7 February 2017 24 4) Cost curve dynamics - the SG Production Cost Model Changes in currencies, inflation, energy costs and metal prices can have a profound impact on mining costs. Understanding the impact of these changes is critical in assessing how individual mining costs and, by extension, broader industry cost curves are evolving. The model provides a more accurate and timely assessment of the cost curve, allowing the current level of price support and the likelihood of cuts in mine supply to be more accurately addressed. The model is described in the SG Production Cost Model (SG PCM) publication. Figure 4.1 – Changes in the Cost Floor (90th Percentile) – Using the Spot Price (SPM) Methodology. The implied cost floor is shown in blue, the published cost floor in brown Source: SG Cross Asset Research/Commodities, SNL Metals & Mining , Bloomberg Figure 4.2 – Copper – Industry costs vs SG PCM implied costs. Implied costs derived from 199 mines (SPM) Figure 4.3 – Zinc – Industry costs vs SG PCM implied costs. Implied costs derived from 71 mines (SPM) Figure 4.4 – Lead – Industry costs vs SG PCM implied costs. Implied costs derived from 56 mines (SPM) Figure 4.5 – Nickel – Industry costs vs SG PCM implied costs. Implied costs derived from 67 mines (SPM) SPM = Spot Price Methodology, “Industry” refers to the 90th percentile total cash costs (SNL Metals & Mining) Source: SG Cross Asset Research/Commodities, SNL Metals & Mining, Bloomberg Metal Price 90th Percentile Implied Cost (PCM) Change in Cost Floor ($) Change in Cost Floor (%) Price - Cost Floor ($) Price - Cost Floor (%) Copper $5,536 $3,929 $4,057 $127 3.24% $1,479 36.45% Zinc $2,576 $1,548 $1,481 -$67 -4.34% $1,095 73.98% Lead $2,017 $1,390 $1,427 $37 2.63% $590 41.33% Nickel $10,020 $11,478 $10,698 -$780 -6.80% -$678 -6.33% * Total Cash Costs (SNL M etals & M ining) SPOT PRICEMETHODOLOGY 3500 4000 4500 5000 5500 6000 6500 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Price Industry Implied (SPM) 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Price Industry Implied (SPM) 1200 1400 1600 1800 2000 2200 2400 2600 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Price Industry Implied (SPM) 7000 7500 8000 8500 9000 9500 10000 10500 11000 11500 12000 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Price Industry Implied (SPM) This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  25. 25. Commodity Compass 7 February 2017 25 4a) Individual mine analysis The following charts illustrate how the most recently reported total cash costs - defined as the sum of all minesite costs (mining + milling), transport and offsite costs, smelting and refining costs and royalties - have been impacted by changes in the underlying cost drivers (currency movements, inflation, oil (fuel) prices, treatment and refining charges and the metal prices themselves). Each bubble represents a mine – the area of the bubble is a function of its most recently reported total annual production (scaled across all metals). The position on the x-axis is driven by the most recently reported total cash cost (sourced from SNL, Metals & Mining5 ) and the position on the y-axis is the extent by which the total cash cost has been shifted by recent changes in the Cost Drivers using our Spot Price Methodology. Please refer to the SG Production Cost Model (SG PCM) publication for full details. Figure 4.6 – Copper – Change in total cash costs for all mines in the top ten largest producing countries Figure 4.7 – Zinc – Change in total cash costs for all mines in the top ten largest producing countries Figure 4.8 – Lead – Change in total cash costs for all mines in the top ten largest producing countries Figure 4.9 – Nickel – Change in total cash costs for all mines in the top ten largest producing countries Source: SG Cross Asset Research/Commodities, Bloomberg Country labels at the bottom of each chart run from the smallest producer (top left) to the largest producer (bottom right) 5 The following document produced by SNL Metals & Mining methodology and definitions provides a detailed overview of SNL’s principals and methodologies. -20% -15% -10% -5% 0% 5% 10% 15% 500 1500 2500 3500 4500 5500 6500 Russia Poland Mexico Indonesia Canada Zambia Australia Peru USA Chile -20% -15% -10% -5% 0% 5% 700 900 1100 1300 1500 1700 1900 2100 Namibia Boliv ia Ireland Sweden Canada Mexico India USA Peru Australia -20% -15% -10% -5% 0% 5% 10% 400 900 1400 1900 Morocco Ireland South Af rica Boliv ia Sweden India Peru Mexico USA Australia -20% -15% -10% -5% 0% 5% 10% 15% -5000 0 5000 10000 15000 20000 25000 Philippines South Af rica Cuba Japan New Caledonia Brazil Indonesia Canada Australia Russia This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  26. 26. Commodity Compass 7 February 2017 26 5) CFTC Commitment of Trader (COT) analysis The following charts show the trends and extremes in the Money Manager (MM) and producer/merchant/processor/user (PMPU) sub-categories of the COT report for the major commodity markets. In Section 5a, we show the new (since 2014) LME COT data for copper, aluminium, zinc, lead and nickel. Large MM long and short positions indicate that speculative can be vulnerable to sudden price reversals if positions unwind quickly. Trends in the PMPU positioning are often indicative of future price dynamics, as it is widely accepted that the PMPU category possesses an informational edge. By plotting changes in the long and short positions independently, the drivers of net positioning become clearer. Below are a few key highlights and observations from the charts below: Both long PMPU (consumer) and short PMPU (producer) hedging in WTI continued to increase. Producers, as a % of OI, are currently the most hedged they have been since mid 2007. Short PMPU (producer) hedging in Natural gas dramatically increased, with the market currently as a % of OI, the most hedged it has even been. Long MM liquidation in gold continued, but the short selling ceased. The long MM position in copper and aluminium remained near record highs with the short positions remaining small and unchanged. Figure 5.1 – Crude oil MM positions - % of total open interest Figure 5.2 – Crude oil PMPU positions - % of total open interest Figure 5.3 – Natural gas MM positions - % of total open interest Figure 5.4 – Nat. gas PMPU positions - % of total open interest Data is as of the most recent COT report with graphs extending back to the inception of the disaggregated data (June 2006). The shaded regions in each chart show the historical range of the individual long (light blue) and short (light grey) positions. The lines on each chart show the actual historical long (dark blue) and short (dark grey) positions, with the net position shown in orange. All positions are expressed as a percentage of open interest. Data includes only futures. Producer/merchant/processor/user is an entity that predominantly engages in the production, processing, packing or handling of a physical commodity and uses the futures markets to manage or hedge risks associated with those activities. Money manager for the purpose of this report is a registered commodity trading advisor (CTA); a registered commodity pool operator (CPO); or an unregistered fund identified by CFTC. These traders are engaged in managing and conducting organised futures trading on behalf of clients. Source: SG Cross Asset Research/Commodities, Bloomberg, www.cftc.gov -15% -10% -5% 0% 5% 10% 15% 20% 25% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position -50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position -50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position -30% -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  27. 27. Commodity Compass 7 February 2017 27 Figure 5.5 – Gold MM positions- % of total open interest Figure 5.6 – Gold PMPU positions- % of total open interest Figure 5.7 – Corn MM positions- % of total open interest Figure 5.8 – Wheat MM positions- % of total open interest Figure 5.9 – Soybean MM positions- % of total open interest Figure 5.10 – Cotton MM positions- % of total open interest Data is as of the most recent COT report with graphs extending back to the inception of the disaggregated data (June 2006). The shaded regions in each chart show the historical range of the individual long (light blue) and short (light grey) positions. The lines on each chart show the actual historical long (dark blue) and short (dark grey) positions, with the net position shown in orange. All positions are expressed as a percentage of open interest. Data includes only futures. Producer/merchant/processor/user is an entity that predominantly engages in the production, processing, packing or handling of a physical commodity and uses the futures markets to manage or hedge risks associated with those activities. Money manager for the purpose of this report is a registered commodity trading advisor (CTA); a registered commodity pool operator (CPO); or an unregistered fund identified by CFTC. These traders are engaged in managing and conducting organised futures trading on behalf of clients. Source: SG Cross Asset Research/Commodities, Bloomberg, www.cftc.gov -40% -30% -20% -10% 0% 10% 20% 30% 40% 50% 60% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position -70% -60% -50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position -40% -30% -20% -10% 0% 10% 20% 30% 40% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position -60% -50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position -30% -20% -10% 0% 10% 20% 30% 40% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position -40% -30% -20% -10% 0% 10% 20% 30% 40% 50% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Long Range Short Range Long Position Short Position Net Position This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  28. 28. Commodity Compass 7 February 2017 28 5a) London Metal Exchange (LME) COT Analysis The following LME COT data is available since July 2014. Figure 5.11 – Copper MM positions- % of total open interest Figure 5.12 – Copper PMPU positions- % of total open interest Figure 5.13 – Aluminium MM positions- % of total open interest Figure 5.14 – Zinc MM positions- % of total open interest Figure 5.15 – Lead MM positions- % of total open interest Figure 5.16 – Nickel MM positions- % of total open interest Data is as of the most recent COT report with graphs extending back to the inception of the data (July 2014). The shaded regions in each chart show the historical range of the individual long (light red) and short (light brown) positions. The lines on each chart show the actual historical long (dark red) and short (dark brown) positions, with the net position shown in blue. All positions are expressed as a percentage of open interest. Data includes only futures. Producer/merchant/processor/user Entities that are predominantly engaged in production, processing, packaging or handling of metal and that use the LME to manage or hedge risks associated with those activities. Money manager Entities that are engaged in managing and conducting LME contracts on behalf of underlying Clients such as investment fund firms. Source: SG Cross Asset Research/Commodities, Bloomberg, www.lme.com -30% -20% -10% 0% 10% 20% 30% 40% Jul-14 Jan-15 Jul-15 Jan-16 Jul-16 Long Range Short Range Long Position Short Position Net Position -50% -40% -30% -20% -10% 0% 10% 20% 30% 40% Jul-14 Jan-15 Jul-15 Jan-16 Jul-16 Long Range Short Range Long Position Short Position Net Position -40% -30% -20% -10% 0% 10% 20% 30% 40% Jul-14 Jan-15 Jul-15 Jan-16 Jul-16 Long Range Short Range Long Position Short Position Net Position -30% -20% -10% 0% 10% 20% 30% 40% 50% Jul-14 Jan-15 Jul-15 Jan-16 Jul-16 Long Range Short Range Long Position Short Position Net Position -50% -40% -30% -20% -10% 0% 10% 20% 30% 40% Jul-14 Jan-15 Jul-15 Jan-16 Jul-16 Long Range Short Range Long Position Short Position Net Position -40% -30% -20% -10% 0% 10% 20% 30% 40% Jul-14 Jan-15 Jul-15 Jan-16 Jul-16 Long Range Short Range Long Position Short Position Net Position This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  29. 29. Commodity Compass 7 February 2017 29 6) Dry Powder analysis – insights into positioning In the October 2016 Commodities Compass - Measuring “dry powder” in commodities – some alternative insights into positioning, we looked at alternative ways of analysing commodity COT Money Manager (MM) positioning data. We provided additional insights into gauging the likelihood of further investment flows based on historical allocation patterns, the number of entities currently in the market, the current position limits and of course current prices. This approach essentially allows us to determine the amount of “dry powder” there is in each commodity to support a potential move. The approach also provides additional insight into market positioning and significant value in assessing trade conviction and identifying new trading opportunities. A key benefit of this approach is that it helps us answer some of the following types of questions: Are there many entities left to buy/sell the market? How much capital could be potentially allocated to a position at current prices? How close are the current positions to position limits? How vulnerable are current positions to profit-taking (long liquidation or short covering)? How strong could the potential price retracements and rallies be? Here we provide updates of charts for some of the key commodities. Below we highlight a few interesting observations from the charts below: WTI As described in our previous Compass, the likelihood of the long futures position in WTI (Figure 6.1) Managers (MMs) increasing was discussed. With the futures position near record highs, but the number of long traders still relatively low, the most obvious way for this to happen would be for new long traders to enter the market. Interestingly however, the current mismatch between the size of the futures position and the dollar position (due to low oil prices) provided an alternative way. Due to the low dollar exposure, the existing long position, which even though is near record size in terms of OI, could easily be increased without necessarily breeching any exposure limits. Both these scenarios were discussed. Last month the long position rose to record high, as the dollar exposure on the existing position was in fact increased. This is now at normal levels (on the trend line) given the number of long traders, which suggests that going forward, for the position to expand further, new traders will likely need to enter the market. In terms of the stability of the current position, the size (in lots) of both the long and short positions suggests the market is vulnerable to significant long liquidation/ short covering. Cotton The long and short futures and dollar positions (Figures 6.9 & 6.10) continue to be at opposite extremes, with the longs at historical maxima (and having expanded slightly last month) and the shorts near historical minima. This suggests that MM driven upside is largely limited (due to simple absence of any dry powder), whereas the downside risk via long liquidation and fresh shorts could be considerable. Natural gas As discussed in our previous Compass, the downside risk via long liquidation and fresh shorts was considerable; this has now reduced after the 16% fall in price last month. This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  30. 30. Commodity Compass 7 February 2017 30 Figure 6.1 – WTI - long (blue) and short (red) MM position in lots (y-axis) versus number of MMs (x-axis). Spot month position limit (dotted line) – 3000 lots Figure 6.2 – WTI - long (blue) and short (red) MM exposure in $bn (y-axis) versus number of MMs (x-axis) Figure 6.3 – NG - long (blue) and short (red) MM position in lots (y-axis) versus number of MMs (x-axis). Spot month position limit (dotted line) – 1000 lots Figure 6.4 – NG - long (blue) and short (red) MM exposure in $bn (y-axis) versus number of MMs (x-axis) Figure 6.5 – Corn - long (blue) and short (red) MM position in lots (y-axis) versus number of MMs (x-axis). Spot month position limit (dotted line) – 600 lots Figure 6.6 – Corn - long (blue) and short (red) MM exposure in $bn (y-axis) versus number of MMs (x-axis) Data is as of the most recent COT report with graphs extending back to the inception of the disaggregated data (June 2006). Data includes only futures. The large blue and red dots indicate the most recent week. The dotted lines indicate the spot month position limit per trader. Money manager (MM) for the purpose of this report is a registered commodity trading advisor (CTA); a registered commodity pool operator (CPO); or an unregistered fund identified by CFTC. These traders are engaged in managing and conducting organised futures trading on behalf of clients. Source: SG Cross Asset Research/Commodities, Bloomberg, www.cftc.gov y = 3545x y = -1808.1x -400,000 -300,000 -200,000 -100,000 0 100,000 200,000 300,000 400,000 500,000 0 20 40 60 80 100 120 y = 0.2844x y = -0.1219x -$20 -$10 $0 $10 $20 $30 $40 $50 0 20 40 60 80 100 120 y = 2891.6x y = -3887.7x -500,000 -400,000 -300,000 -200,000 -100,000 0 100,000 200,000 300,000 400,000 0 20 40 60 80 100 120 140 y = 0.1171x y = -0.1426x -$50 -$40 -$30 -$20 -$10 $0 $10 $20 $30 0 20 40 60 80 100 120 140 y = 2865.1x y = -2540.9x -500,000 -400,000 -300,000 -200,000 -100,000 0 100,000 200,000 300,000 400,000 500,000 0 50 100 150 200 y = 0.0742x y = -0.0518x -$10 -$5 $0 $5 $10 $15 $20 0 50 100 150 200 This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  31. 31. Commodity Compass 7 February 2017 31 Figure 6.7 – Soybeans - long (blue) and short (red) MM position in lots (y-axis) versus number of MMs (x-axis). Spot month position limit (dotted line) – 600 lots Figure 6.8 – Soybeans - long (blue) and short (red) MM exposure in $bn (y-axis) versus number of MMs (x-axis) Figure 6.9 – Cotton - long (blue) and short (red) MM position in lots (y-axis) versus number of MMs (x-axis). Spot month position limit (dotted line) – 300 lots Figure 6.10 – Cotton - long (blue) and short (red) MM exposure in $bn (y-axis) versus number of MMs (x-axis) Figure 6.11 – Sugar - long (blue) and short (red) MM position in lots (y-axis) versus number of MMs (x-axis). Spot month position limit (dotted line) – 5000 lots Figure 6.12 – Sugar - long (blue) and short (red) MM exposure in $bn (y-axis) versus number of MMs (x-axis) Data is as of the most recent COT report with graphs extending back to the inception of the disaggregated data (June 2006). Data includes only futures. The large blue and red dots indicate the most recent week. The dotted lines indicate the spot month position limit per trader. Money manager for the purpose of this report is a registered commodity trading advisor (CTA); a registered commodity pool operator (CPO); or an unregistered fund identified by CFTC. These traders are engaged in managing and conducting organised futures trading on behalf of clients. Source: SG Cross Asset Research/Commodities, Bloomberg, www.cftc.gov y = 1477x y = -1115.6x -200,000 -150,000 -100,000 -50,000 0 50,000 100,000 150,000 200,000 250,000 300,000 0 50 100 150 200 y = 0.0912x y = -0.0556x -$10 -$5 $0 $5 $10 $15 $20 $25 0 50 100 150 200 y = 724.21x y = -638.45x -80,000 -60,000 -40,000 -20,000 0 20,000 40,000 60,000 80,000 100,000 120,000 0 20 40 60 80 100 120 y = 0.0284x y = -0.0205x -$3 -$2 -$1 $0 $1 $2 $3 $4 $5 0 20 40 60 80 100 120 y = 3416.8x y = -3212.6x -500,000 -400,000 -300,000 -200,000 -100,000 0 100,000 200,000 300,000 400,000 500,000 0 20 40 60 80 100 y = 0.0704x y = -0.0535x -$6 -$4 -$2 $0 $2 $4 $6 $8 0 20 40 60 80 100 This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  32. 32. Commodity Compass 7 February 2017 32 Figure 6.13 – Gold - long (blue) and short (red) MM position in lots (y-axis) versus number of MMs (x-axis). Spot month position limit (dotted line) – 3000 lots Figure 6.14 – Gold - long (blue) and short (red) MM exposure in $bn (y-axis) versus number of MMs (x-axis) Figure 6.15 – Silver - long (blue) and short (red) MM position in lots (y-axis) versus number of MMs (x-axis). Spot month position limit (dotted line) – 1500 lots Figure 6.16 – Silver - long (blue) and short (red) MM exposure in $bn (y-axis) versus number of MMs (x-axis) Figure 6.17 – Live cattle - long (blue) and short (red) MM position in lots (y-axis) versus number of MMs (x-axis). Spot month position limit (dotted line) – 450 lots Figure 6.18 – Live cattle - long (blue) and short (red) MM exposure in $bn (y-axis) versus number of MMs (x-axis) Data is as of the most recent COT report with graphs extending back to the inception of the disaggregated data (June 2006). Data includes only futures. The large blue and red dots indicate the most recent week. The dotted lines indicate the spot month position limit per trader. Money manager for the purpose of this report is a registered commodity trading advisor (CTA); a registered commodity pool operator (CPO); or an unregistered fund identified by CFTC. These traders are engaged in managing and conducting organised futures trading on behalf of clients. Source: SG Cross Asset Research/Commodities, Bloomberg, www.cftc.gov y = 1902.7x y = -1271.9x -500,000 -400,000 -300,000 -200,000 -100,000 0 100,000 200,000 300,000 400,000 500,000 0 20 40 60 80 100 120 140 y = 0.2354x y = -0.153x -$20 -$10 $0 $10 $20 $30 $40 $50 0 20 40 60 80 100 120 140 y = 964.01x y = -809.03x -150,000 -100,000 -50,000 0 50,000 100,000 150,000 0 20 40 60 80 y = 0.0942x y = -0.0734x -$6 -$4 -$2 $0 $2 $4 $6 $8 $10 $12 0 20 40 60 80 y = 1144.6x y = -727.5x -100,000 -50,000 0 50,000 100,000 150,000 200,000 0 20 40 60 80 100 120 140 y = 0.0543x y = -0.0324x -$6 -$4 -$2 $0 $2 $4 $6 $8 $10 0 20 40 60 80 100 120 140 This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  33. 33. Commodity Compass 7 February 2017 33 7) SG price forecasts The following charts show the average annual SG price forecasts over the next three years. The current year is based on the average of the forthcoming quarterly forecasts. For detailed forecasts, please refer to our quarterly Commodities Review publications. Forecast years are indicated by the following colours: Figure 7.1 – Crude oil (WTI) Figure 7.2 – Brent Figure 7.3 – Natural gas (NYMEX) Figure 7.4 – Copper (LME) Figure 7.5 – Aluminium Figure 7.6 – Zinc Price history (grey line) is based on the front month futures contract (unadjusted). Source: SG Cross Asset Research/Commodities, Bloomberg 2017 2018 2019 20 30 40 50 60 70 80 90 100 110 120 2014 2015 2016 2017 2018 2019 Forward Curv e 54.75 62.50 67.00 20 30 40 50 60 70 80 90 100 110 120 2014 2015 2016 2017 2018 2019 Forward Curv e 56.25 65.00 70.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 2014 2015 2016 2017 2018 2019 Forward Curv e 3.47 3.86 3.52 4000 4500 5000 5500 6000 6500 7000 7500 2014 2015 2016 2017 2018 2019 Forward Curv e 5499 5750 6000 1400 1500 1600 1700 1800 1900 2000 2100 2200 2014 2015 2016 2017 2018 2019 Forward Curv e 1750 1750 1800 1400 1600 1800 2000 2200 2400 2600 2800 3000 2014 2015 2016 2017 2018 2019 Forward Curv e 2650 2800 2700 This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  34. 34. Commodity Compass 7 February 2017 34 Forecast years are indicated by the following colours: Figure 7.7 – Lead (LME) Figure 7.8 – Nickel (LME) Figure 7.9 – Corn Figure 7.10 – Wheat Figure 7.11 – Soybeans Figure 7.12 – Cotton Price history (grey line) is based on the front month futures contract (unadjusted). Source: SG Cross Asset Research/Commodities, Bloomberg 2017 2018 2019 1400 1600 1800 2000 2200 2400 2600 2014 2015 2016 2017 2018 2019 Forward Curv e 2225 2300 2400 7000 9000 11000 13000 15000 17000 19000 21000 2014 2015 2016 2017 2018 2019 Forward Curv e 12000 13000 14000 300 350 400 450 500 550 2014 2015 2016 2017 2018 2019 Forward Curv e 361.50 375.00 380.00 300 350 400 450 500 550 600 650 700 750 800 2014 2015 2016 2017 2018 2019 Forward Curv e 426.25 431.00 435.00 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 2014 2015 2016 2017 2018 2019 Forward Curv e 955.00 975.00 950.00 50 55 60 65 70 75 80 85 90 95 100 2014 2015 2016 2017 2018 2019 Forward Curv e 70.10 72.20 73.60 This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  35. 35. Commodity Compass 7 February 2017 35 Forecast years are indicated by the following colours: Figure 7.13 – Sugar Figure 7.14 – Coffee Figure 7.15– Live cattle Figure 7.16– Lean hogs Figure 7.17 – Gold Figure 7.18 – Silver Price history (grey line) is based on the front month futures contract (unadjusted). Source: SG Cross Asset Research/Commodities, Bloomberg 2017 2018 2019 10 12 14 16 18 20 22 24 26 2014 2015 2016 2017 2018 2019 Forward Curv e 20.55 19.70 19.30 50 70 90 110 130 150 170 190 210 230 2014 2015 2016 2017 2018 2019 Forward Curv e 170.75 165.00 170.00 90 100 110 120 130 140 150 160 170 180 2014 2015 2016 2017 2018 2019 Forward Curv e 111.75 123.00 129.00 30 50 70 90 110 130 150 2014 2015 2016 2017 2018 2019 Forward Curv e 61.75 65.10 68.30 1000 1050 1100 1150 1200 1250 1300 1350 1400 2014 2015 2016 2017 2018 2019 Forward Curv e 1299 1275 1250 10 12 14 16 18 20 22 24 2014 2015 2016 2017 2018 2019 Forward Curv e 19.00 19.00 19.00 This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)
  36. 36. Commodity Compass 7 February 2017 36 Global Head of Research Brigitte Richard-Hidden (33) 1 42 13 78 46 brigitte.richard-hidden@sgcib.com CROSS ASSET RESEARCH – COMMODITIES GROUP Head of Commodities Research Dr. Michael Haigh (1) 212 278 6020 michael.haigh@sgcib.com New York Natural Gas Oil & Products Breanne Dougherty Michael Wittner (1) 212 278 7113 (1) 212 278 6438 breanne.dougherty@sgcib.com michael.wittner@sgcib.com London Coordinator Global Technicals Metals Cross Commodity Strategy Stephanie Aymes Robin Bhar Jesper Dannesboe (44) 207 762 5898 (44) 207 762 53 84 (44) 207 762 5603 stephanie.aymes@sgcib.com robin.bhar@sgcib.com jesper.dannesboe@sgcib.com Singapore Cross Commodity Strategy Mark Keenan (65) 6326 7851 mark.keenan@sgcib.com Bangalore Agriculture Rajesh Singla (91) 80 6731 8882 rajesh.singla@sgcib.com This document, published on 7-Feb-2017 at 9:41 AM CET, is being provided for the exclusive use of MICHAEL KINGSLEY (INTERMARKET S.A.)

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