Tuesday 11 August 2015

Wilmar International

UOBKayhian on 11 Aug 2015

2H15 to deliver a good set of numbers on higher sales volume with overall lower PBT margins. We trim our earnings forecast by 6% to factor in our revised view on biodiesel volume and sugar margins.

FY15F PE (x): 11.4
FY16F PE (x): 10.4

Key takeaways from briefing point to a better 2H15. Wilmar International’s (Wilmar) management is confident of delivering better earnings in 2H15 (UOBKH estimate: 2H15 accounts for about 60-65% of full-year earnings) to be driven by much higher sales volume with stable or slightly lower PBT margins as the mid-stream processing operations for palm oil, soybean and sugar are currently enjoying lower commodity prices. Overall, sales volume overall will be much higher in 2H15 as sugar sales kicks in, and as Wilmar gears up for higher sales volume from downstream palm processing and consumer packs thanks to the year-end festive demand in China, India and Indonesia. Lower earnings estimates for 2015-17. We have lowered our earnings estimates by 6.2%, 5.6% and 6.0% for 2015, 2016 and 2017 respectively. This is to reflect our adjustment to our assumptions which include: a) lower sales volume for its Tropical Oil Manufacturing to 5-6% from previous 6-10% on lesser biodiesel sales volume for 2015 due to the delay in biodiesel blending in Indonesia and higher competition expected in 2016-17, and b) lower sugar PBT margins with our forecasts trimmed to US$12- 13.70/tonne in 2015-17 from US$17-19/tonne previously as margins were affected by the decline in sugar prices. Maintain BUY with a lower target price of S$3.60 (previously: S$3.65) to reflect the lower earnings estimates. Our target price is derived from SOTP valuation and translates into a blended 2015F PE of 13.3x. Wilmar is a clear beneficiary of the low commodity prices and the Indonesia’s new regulation which favours downstream players. Its earnings are also relatively more resilient in low commodity price environment vs its peers due to its most integrated business model.

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