With its ballooning current account deficit partly a result of oil imports, Indonesia is trying to accelerate its palm-oil-based biodiesel programme. Malaysia is eager to follow suit given the depressed palm oil prices and impact on its export earnings. We believe the countries' intention to boost domestic biofuel demand will have only mild positive effects for the local palm oil industry and palm oil prices, as the enactment of ambitious mandates will prove difficult. The Indonesian biodiesel sector is likely to outperform Malaysia's given lower production costs and a larger domestic market.
Competitive Landscape
Table: Major Agribusiness Companies (USDmn)
Company Sub-Sector Revenue Fiscal Y/E Market
Capitalisation Employees
Viet Nam Dairy Products JSC
(Vinamilk) Dairy 1,472.8 12/2013 4,871.4 5,389
Kinh Do Corp
ManufacturingFood (confectionery
& snacks) 217.0 12/2013 480.1 7,069
Vinacafe Bien Hoa JSC Coffee & Food
Manufacturing 109.4 12/2013 184.2 651
Hung Vuong Seafood 525.5 12/2013 136.3 870
Societe De Bourbon Tay Ninh Sugar & Real
Estate 105.6 12/2013 81.8 778
Tuong An Vegetable Oil JSC Edible Oils &
Fats 204.2 12/2013 40.8 799
Lam Son Sugar Sugar &
Alcohol 86.4 12/2013 24.5 890
Minh Phu Seafood Seafood 528.8 12/2013 118.1 11,807
Southern Seed Crop Seeds 28.7 12/2013 36.6 441
Viet Thang Feed JSC Animal Feed 190.3 12/2013 38.0 606
Last updated June 4 2014. Source: BMI, Bloomberg
Company Profile
Global Company Strategy
Company Overview
Vietnam Dairy Products Joint Stock Company (Vinamilk) is the market leader in Vietnam's dairy industry.
It produces more than 200 dairy products for domestic sale and for export. The company was founded in 1993 as a state-owned enterprise. The state's share was recently reduced to 50% to qualify for listing on the stock market. Vinamilk controls an estimated 39% of the Vietnamese dairy market. It exports to 26 countries, including Iraq, Cambodia and the Philippines.
SWOT Analysis
Strengths
■ Leading dairy producer in Vietnam, with a dominant market share in various dairy segments (liquid milk, yoghurt, condensed milk).
■ Extensive distribution network.
■ Diverse product range and constant product innovation.
■ Soaring demand for both primary and processed dairy products in the fast-growing local economy.
■ Strong financial fundamentals (no debt, good margins).
Weaknesses
■ Reliant on international supply from New Zealand for raw materials, making the company vulnerable to international milk supply and prices as well as to foreign exchange fluctuations.
Opportunities
■ Gradual integration in the Association of Southeast Asian Nations (ASEAN) region could allow the company to grow exports and benefit from lower production costs via processing plants in countries within the region.
■ Experience in the emerging Vietnamese market is likely to increase Vinamilk's chances of success when exporting to other emerging South East Asian markets.
■ Vinamilk's recent investments in domestic capacity expansions and in New Zealand's Miraka will allow it to ease current supply shortages.
Threat
■ Growing competition in the South East Asian dairy sector coming from Western brands. Vinamilk will have to keep up its expansionary activities and develop products in order to secure its market share.
■ Given the fact that dairy products are related to customers' health and especially children's health, a food safety or product quality scare could easily harm earnings.
Reliant on Vietnam for sales, with a vast low-income rural population. Vinamilk could see its sales drop should macroeconomic headwinds appear in the country.
Good Product Diversification
Vinamilk - Revenue By Origin (LHC) & Products (RHC), 2012, % Of Total
Source: BMI, Vinamilk, Bloomberg
Company Core View
Over to next three-to-six months, we believe Vinamilk's share price will underperform the Ho Chi Minh Stock Index (VNI) given the subdued outlook for the company's margins in the coming quarters, and our constructive view for the VNI. Earnings growth will be limited by elevated input (milk powder) costs and a temporary slowdown in consumer spending in Vietnam. The company is likely to see its margins recover in the second half of 2014 owing to easing international milk prices, which will help Vinamilk's
share price head higher. In the longer term, we believe Vinamilk will strongly benefit from Vietnam's high- growth dairy market.
Latest Results
Vinamilk's performance remained lacklustre in Q114 (January-March), mainly due to weaker demand and increasing input costs. Although revenue continued to record double-digit growth (15.0% in Q114 to VND7,678bn), the pace has been slowing since Q312. The company's revenue grew by 37% year-on-year (y-o-y) on average every quarter between Q109 and Q212, compared with 16% y-o-y growth between Q312 and Q114. Net income decreased y-o-y for the second consecutive quarter in Q114, by 10.1% to
VND1,573bn. Margins remained low in Q114, as global milk prices are still close to multiyear highs. Profit margins came in at 18.1%, down 4.9 percentage points y-o-y.
Lower Growth
Vinamilk - Revenue Growth, % y-o-y (LHS) & Select Income, VNDbn (RHS)
Source: BMI, Vinamilk
Domestic sales preformed better in Q114 than in recent quarters, mainly due to slightly higher selling prices, according to the company. Sales volumes in Vietnam have failed to improve, highlighting
temporarily stagnant domestic demand, which has been hurting the company's performance since Q313.
However, selling prices increased at a slower pace than input costs, as the company decided to absorb some of the elevated milk powder costs in order to maintain market share amid intense competition in Vietnam's dairy sector.
We believe Vinamilk's results will continue to disappoint in the coming quarters on the back of low consumer confidence and spending, and the government's recent decision to cap prices for children's dairy products. Vietnam's government decided to impose as of June 2014 price caps on 25 powder milk products for children younger than 6 for the top five producers and importers, including Vinamilk,
Friesland Campina Vietnam, Nestlé Vietnam, Mead Johnson Vietnam and 3A Nutrition Vietnam. The maximum retail price is determined by the wholesale price and related costs, but cannot exceed the 15% of wholesale price ceiling. As a result of this law, selling price increases for these products will most likely slow significantly in 2014 after dairy products prices increased by around 30% annually between 2009 and 2012, according to the government. Stricter regulation in Vietnam's dairy market - which has traditionally been little regulated, especially for the children's products - will limit earnings growth for Vinamilk in the coming years.
Bottoming Out?
Vinamilk - Select Margins (%)
Source: Vinamilk
Moreover, margins are likely to continue to stagnate if not fall slightly in the coming months as a result of the severe increase in milk powder prices between Q412 and Q114. Indeed, the impact on margins is usually felt with a two-quarter lag, and global milk and milk powder prices have remained elevated in Q114. We expect margins to start recovering from H214, as we see milk prices averaging significantly lower in 2014 and 2015 as dairy herds are recovering in the main producing countries (see 'Milk To Average US$19.00/cwt In 2014', March 18).
More Headwinds For Vinamilk In Near Term
Global - Whole Milk Powder Prices (USD/tonne)
Source: Global Dairy Trade
Company Strategy
We see long-term growth potential for Vinamilk given the strong growth potential for dairy consumption in Vietnam and the Asian region, the company's investment in supply chain and capacity expansion, and its strong financial position. The company is well positioned to benefit from the industry's growth, as it has a well-known brand (a recent survey by Kantar Worldpanel indicates Vinamilk's products are consumed by 94% of households in Vietnam) and a large distribution network.
We believe Vinamilk's strategy of developing mainly in the domestic market, and more specifically in value-added segments, will be to its benefit. Vinamilk boasts large market shares in key domestic markets for which we forecast strong consumption growth in the coming years. For example, Vinamilk has a 40%
market share in Vietnam's liquid milk segment, for which we forecast consumption to expand by 36.1%
between 2013 and 2018, to 272,400 tonnes, on the back of increased urbanisation, Westernisation and the ongoing spread of organised retail networks. Moreover, Vinamilk plans to scale up its production and market share in the powdered milk segment (which currently only accounts for 20% of its total sales in Vietnam), for which we believe demand will rise by 24.3% over the coming five years.
Vinamilk On Top
Select Companies - Operating (LHC) & Profit (RHC) Margins, %
Source: Bloomberg
With new downstream projects coming online soon (a second powdered milk factory and a liquid milk factory was completed in FY13), the company is now heavily investing in upstream capacity and plans to build three new dairy farms in 2014 and 2015. Vinamilk, which sources 25% of its raw milk from small- scale farms in Vietnam, is ramping up its cow farming business and aims to source 60-70% of its raw milk needs from internally owned farms by 2024.
We also highlight Vinamilk's export growth potential. Exports (mainly to Iraq, Cambodia, the Philippines, Thailand and Australia) accounted for 14% of total revenue as of FY13, compared with 10% in FY07.
Exports are likely to see sustained growth in the coming years, as they will benefit from access to new
markets such as the US, and the full implementation of the ASEAN Economic Community in the coming years. Although the current 2015 timeline for integration looks unlikely, we do expect closer commercial and financial ties with lower import tariffs across the region in the coming years. Vinamilk is trying to capitalise on looser investment regulations in the region. It announced in January 2014 the creation of Angkor Dairy in Cambodia, and plans to start building a factory in the country in FY14 in order to save costs.
Positive Picture
Vinamilk - Free Cash Flow (VNDmn)
Source: Vinamilk
Strong Margins And Low Debt Levels
Vinamilk enjoys the strongest margins relative to its peers (despite the recent decline in margins), thanks to its ability to control expenses and maintain a very low level of debt. Operating and profit margins have been on an uptrend lately at a time when global milk prices have been historically high. Profit margins reached 21.1% in FY13, significantly higher than the Asia dairy industry average of 7.7%. Moreover, Vinamilk's liquidity, efficiency and solvency ratios are generally higher than its peers. Vinamilk also regularly records positive and growing free cash flows despite having invested heavily in FY13. This bodes well for the
company's expansion plans. We therefore believe Vinamilk will remain an outperformer in the industry over a multi-quarter horizon owing to its efficiency, cost control and emphasis on high-growth demand markets.
Improved Performance Over Longer Term
Select Companies & Ho Chi Minh Index (VNI), Rebased
Note: January 2 2014 = 100. Source: BMI, Bloomberg
Valuation
We believe Vinamilk has now relatively low valuations relative to historical levels and to peers. Vinamilk's 12-month trailing price/earnings ratio (PE) decreased significantly since November 2013 and is now standing at a 15 month low of 15.7x. This is close to its three-year average of 14.8x and to the current PE of the VNI of 13.2x. It is significantly below its competitors' average of 34.5x (the average is pushed higher by Bright Dairy and Nestlé India).
Eventually Heading Up
Vinamilk - Share Price, VND (daily chart)
Source: BMI, Bloomberg
Share Price Analysis
We believe Vinamilk's share price has now bottomed out after recording weakness since May 2014. The share price is likely to recover in the coming months and will break above short-term resistance coming at VND120,000, heading towards VND140,000. The share price is unlikely to go beyond that level given the lacklustre outlook for margins in Q214.
Table: Vinamilk's Financial Highlights, 2008-2013
2008 2009 2010 2011 2013
Revenues* 8,209 10,614 15,753 21,627 30,949
Revenues Growth 25.6 29.3 48.4 37.3 16.5
Operating Income* 1,248 2,340 3,347 4,317 7,295
Operating Margin 15.2 22 21.2 20 23.6
Net Income* 1,250 2,376 3,616 4,218 6,534
Profit Margin 15.2 22.4 23 19.5 21.1
Vinamilk's Financial Highlights, 2008-2013 - Continued
2008 2009 2010 2011 2013
Net Debt/EBITDA -0.4 -1.1 -0.5 -0.8 -0.8
EPS (VNDmn) 1,559 4,632 4,556 5,145 7,839
* In VNDbn; margins in %; na = not available. Sources: BMI, Bloomberg
Demographic Forecast
Demographic analysis is a key pillar of BMI's macroeconomic and industry forecasting model. Not only is the total population of a country a key variable in consumer demand, but an understanding of the
demographic profile is key to understanding issues ranging from future population trends to productivity growth and government spending requirements.
The accompanying charts detail Vietnam's population pyramid for 2013, the change in the structure of the population between 2013 and 2050 and the total population between 1990 and 2050, as well as life expectancy. The tables show key datapoints from all of these charts, in addition to important metrics including the dependency ratio and the urban/rural split.
Population Pyramid
2013 (LHS) And 2013 Versus 2050 (RHS)
Source: World Bank, UN, BMI
Population Indicators
Population (mn, LHS) And Life Expectancy (years, RHS), 1990-2050
Source: World Bank, UN, BMI
Table: Vietnam's Population By Age Group, 1990-2020 ('000)
1990 1995 2000 2005 2010 2013e 2015f 2020f
Total 68,910 76,020 80,888 84,948 89,047 91,680 93,387 97,057
0-4 years 9,315 9,323 7,128 6,898 7,229 7,152 7,012 6,575
5-9 years 8,606 9,212 9,253 7,023 6,791 7,052 7,181 6,968
10-14 years 7,857 8,541 9,162 9,117 6,899 6,619 6,757 7,147
15-19 years 7,359 7,788 8,492 9,050 9,011 7,686 6,866 6,726
20-24 years 6,644 7,222 7,673 8,333 8,874 9,148 8,936 6,802
25-29 years 6,006 6,470 7,065 7,471 8,112 8,528 8,772 8,837
30-34 years 5,138 5,890 6,352 6,910 7,286 7,703 8,022 8,680
35-39 years 3,888 5,065 5,803 6,242 6,763 7,011 7,208 7,940
40-44 years 2,463 3,826 4,994 5,719 6,147 6,472 6,685 7,127
45-49 years 2,017 2,409 3,753 4,935 5,648 5,894 6,054 6,589
50-54 years 1,968 1,959 2,346 3,700 4,855 5,306 5,521 5,926
55-59 years 2,046 1,891 1,885 2,237 3,542 4,278 4,677 5,330
60-64 years 1,669 1,934 1,790 1,734 2,068 2,795 3,352 4,444
65-69 years 1,412 1,522 1,771 1,610 1,562 1,673 1,906 3,104
70-74 years 1,028 1,216 1,322 1,530 1,399 1,360 1,379 1,695
Vietnam's Population By Age Group, 1990-2020 ('000) - Continued
1990 1995 2000 2005 2010 2013e 2015f 2020f
75-79 years 752 819 984 1,080 1,263 1,219 1,167 1,160
80-84 years 430 536 597 732 815 919 964 900
85-89 years 224 261 336 385 483 517 546 654
90-94 years 71 108 132 177 210 245 268 306
95-99 years 16 25 41 53 74 83 89 115
100+ years 2 4 7 12 17 21 24 30
e/f = BMI estimate/forecast. Source: World Bank, UN, BMI
Table: Vietnam's Population By Age Group, 1990-2020 (% of total)
1990 1995 2000 2005 2010 2013e 2015f 2020f
0-4 years 13.52 12.26 8.81 8.12 8.12 7.80 7.51 6.77
5-9 years 12.49 12.12 11.44 8.27 7.63 7.69 7.69 7.18
10-14 years 11.40 11.23 11.33 10.73 7.75 7.22 7.24 7.36
15-19 years 10.68 10.25 10.50 10.65 10.12 8.38 7.35 6.93
20-24 years 9.64 9.50 9.49 9.81 9.97 9.98 9.57 7.01
25-29 years 8.72 8.51 8.73 8.79 9.11 9.30 9.39 9.11
30-34 years 7.46 7.75 7.85 8.13 8.18 8.40 8.59 8.94
35-39 years 5.64 6.66 7.17 7.35 7.60 7.65 7.72 8.18
40-44 years 3.57 5.03 6.17 6.73 6.90 7.06 7.16 7.34
45-49 years 2.93 3.17 4.64 5.81 6.34 6.43 6.48 6.79
50-54 years 2.86 2.58 2.90 4.36 5.45 5.79 5.91 6.11
55-59 years 2.97 2.49 2.33 2.63 3.98 4.67 5.01 5.49
60-64 years 2.42 2.54 2.21 2.04 2.32 3.05 3.59 4.58
65-69 years 2.05 2.00 2.19 1.89 1.75 1.83 2.04 3.20
70-74 years 1.49 1.60 1.63 1.80 1.57 1.48 1.48 1.75
75-79 years 1.09 1.08 1.22 1.27 1.42 1.33 1.25 1.19
80-84 years 0.62 0.70 0.74 0.86 0.91 1.00 1.03 0.93
85-89 years 0.32 0.34 0.42 0.45 0.54 0.56 0.58 0.67
90-94 years 0.10 0.14 0.16 0.21 0.24 0.27 0.29 0.32
95-99 years 0.02 0.03 0.05 0.06 0.08 0.09 0.10 0.12
Vietnam's Population By Age Group, 1990-2020 (% of total) - Continued
1990 1995 2000 2005 2010 2013e 2015f 2020f
100+ years 0.00 0.00 0.01 0.01 0.02 0.02 0.03 0.03
e/f = BMI estimate/forecast. Source: World Bank, UN, BMI
Table: Vietnam's Key Population Ratios, 1990-2020
1990 1995 2000 2005 2010 2013e 2015f 2020f Dependent ratio, % of total working age 75.8 71.0 61.3 50.8 42.9 41.4 41.3 41.9 Dependent population, total, '000 29,712 31,567 30,734 28,617 26,741 26,860 27,293 28,655
Active population, % of total 56.9 58.5 62.0 66.3 70.0 70.7 70.8 70.5
Active population, total, '000 39,198 44,453 50,154 56,331 62,306 64,820 66,094 68,402 Youth population, % of total working age 65.8 60.9 50.9 40.9 33.6 32.1 31.7 30.2 Youth population, total, '000 25,778 27,076 25,544 23,038 20,918 20,822 20,950 20,690 Pensionable population, % of total working age 10.0 10.1 10.3 9.9 9.3 9.3 9.6 11.6 Pensionable population, total, '000 3,934 4,491 5,190 5,579 5,823 6,037 6,343 7,965
e/f = BMI estimate/forecast. Source: World Bank, UN, BMI
Table: Vietnam's Rural And Urban Population, 1990-2020
1990 1995 2000 2005 2010 2013e 2015f 2020f
Urban population, % of total 20.3 22.2 24.4 27.3 30.4 32.3 33.6 36.9
Rural population, % of total 79.7 77.8 75.6 72.7 69.6 67.7 66.4 63.1
Urban population, total, '000 13,958 16,867 19,716 23,175 27,064 29,632 31,384 35,771 Rural population, total, '000 54,952 59,153 61,172 61,773 61,983 62,048 62,003 61,286
e/f = BMI estimate/forecast. Source: World Bank, UN, BMI
Methodology
Industry Forecast Methodology
BMI's industry forecasts are generated using the best-practice techniques of time-series modelling and causal/econometric modelling. The precise form of model we use varies from industry to industry, in each case being determined, as per standard practice, by the prevailing features of the industry data being examined.
Common to our analysis of every industry is the use of vector autoregressions. Vector autoregressions allow us to forecast a variable using more than the variable's own history as explanatory information. For
example, when forecasting oil prices, we can include information about oil consumption, supply and capacity.
When forecasting for some of our industry sub-component variables, however, using a variable's own history is often the most desirable method of analysis. Such single-variable analysis is called univariate modelling. We use the most common and versatile form of univariate models: the autoregressive moving average model (ARMA).
In some cases, ARMA techniques are inappropriate because there is insufficient historic data or data quality is poor. In such cases, we use either traditional decomposition methods or smoothing methods as a basis for analysis and forecasting.
BMI mainly uses ordinary least squares estimators. In order to avoid relying on subjective views and encourage the use of objective views, we use a 'general-to-specific' method. BMI mainly uses a linear model, but simple non-linear models, such as the log-linear model, are used when necessary. During periods of 'industry shock', for example, if poor weather conditions impede agricultural output, dummy variables are used to determine the level of impact.
Effective forecasting depends on appropriately selected regression models. We select the best model according to various different criteria and tests, including but not exclusive to:
■ R2 tests explanatory power; adjusted R2 takes degree of freedom into account;
■ Testing the directional movement and magnitude of coefficients;
■ Hypothesis testing to ensure coefficients are significant (normally t-test and/or P-value);
■ All results are assessed to alleviate issues related to auto-correlation and multicollinearity;
Human intervention plays a necessary and desirable role in all or our industry forecasting. Experience, expertise and knowledge of industry data and trends ensure analysts spot structural breaks, anomalous data, turning points and seasonal features where a purely mechanical forecasting process would not.
Sector-Specific Methodology
Within the Agribusiness industry, issues that might result in human intervention could include but are not exclusive to:
■ Technology development that might influence future output levels (for example greater use of biotechnology);
■ Dramatic changes in local production levels due to public or private sector investment;
■ The regulatory environment and specific areas of legislation, such as import and export tariffs and farm subsidies;
■ Changes in lifestyles and general societal trends;
■ The formation of bilateral and multilateral trading agreements, and political factors.
The following two examples show the demand (consumption) and the supply (production) of rice. Note that the explanatory variables for both are quite similar, but the underlying economic theory is different.
Example Of Rice Consumption Model
(Rice consumption)t = β0 + β1*(real private consumption per capita)t + β2*(inflation)t + β3*(real lending rate)t + β4*(population)t + β5*(government expenditure)t + β6*(food consumption)t-1 + εt
Where:
■ β are parameters for this function.
■ Real private consumption per capita has a positive relationship with rice consumption, if rice is a normal good in a particular country. If rice is an inferior good in a country, the relationship is negative. So the sign of β1 is determined by a specific product within a specific country.
■ When inflation is high, people with rational expectations will consume today rather than wait for tomorrow's high price to come. Higher rice demand in year t due to higher inflation in that year leads to an assumed positive sign of β2.
■ The relationship between real lending rate and rice consumption is expected to be negative. When real lending rates increase, disposable incomes, especially for those with mortgage burdens, etc, will decrease.
So the sign of β3 is expected to be negative.
■ Of course, other things being equal, growth in rice consumption can also be caused by growth in population. Consequently, positive sign of β4 is expected.
■ Government expenditure typically causes total disposable incomes to rise. So the sign of β5 is expected to be positive.
■ Human behaviour has a trend: A high level of food consumption in previous years means there is very likely to be a high level of food consumption the next year. So the positive sign of β6 is expected.
■ ε is the error/residual term.
Example Of Rice Production Model
(Rice production)t = β0 + β1*(real GDP per capita)t + β2*(inflation)t + β3*(real lending rate)t + β4*(rural population)t + β5*(government expenditure)t + β6*(food production)t-1 + εt
Where:
■ The same as above: the relationship between real GDP per capita and rice production depends on whether rice is normal or inferior good in that country.
■ If high inflation is caused by food prices increasing, farmers will be more profitable. Then they will supply more agricultural product (eg rice) to increase their marginal (extra) profit, although this is tempered by the rising cost of other inputs in line with inflation.
■ There is a global move towards corporate farming, away from small holdings, in order to achieve greater agricultural productivity. Corporate farming means more investment in the modes of production, ie agricultural machinery. Higher real lending rates discourage investment, which in turn reduce production.
■ BMI assumes that only the rural population has a positive effect on agricultural product supply.
■ With supportive government policy, other things being equal, rice production is expected to go up. Government expenditure is likely to play some role in supporting agribusiness.
■ Again, previous food production positively affects this year's prediction.