Appendix 4. Crushing capacities

Figure 46: Kernel capacities and expected sales

	FY2011	FY2012	FY2013E	FY2014F	FY2015F	FY2016F	FY2017F
Crushing capacities, Ths. tons	1808	2318	2978	2978	3278	3578	3578
Bulk oil sales, Ths. tons	821.0	828.4	1169.6	1212.2	1302.9	1391.0	1438.7
y-o-y	124.3%	0.9%	41.2%	3.6%	7.5%	6.8%	3.4%
Bottled oil sales Ths. liters	118.0	131.9	141.0	146.7	149.5	152.1	153.5
Bottled oil sales, Ths. tones	108.5	121.3	129.8	134.9	137.5	140.0	141.2
y-o-y	5%	12%	7%	4%	2%	2%	1%
Grain trading, Ths. tones	1810.0	2123.2	2788.5	3897.3	4123.8	4661.8	4784.2
y-o-y	-19%	17%	31%	40%	6%	13%	3%
External terminal throughput, Ths. tons	2121.4	1809.3	3309.3	3809.3	4309.3	4309.3	4309.3

Source: Company data, KNU estimates

Appendix 5. Prices

Figure 47: Discounted Fapri prices using UkrArgoCOnsult indices / USD per metric ton

	2011	2012	2013	2014	2015	2016	2017
Sunfloweroil FOB	1264.29	1165.97	1099.84	1136.10	1162.78	1188.67	1213.13
Wheat FOB	270.44	247.89	261.78	268.17	271.58	272.42	272.78
Barley FOB	165.62	204.75	168.62	193.43	185.87	192.73	190.38
Corn FOB	183.17	197.26	196.90	204.77	202.80	203.31	203.60

Source: FAPRI, KNU estimates

Appendix 6. Peers Valuation of Kernel Holding S.A.

Figure 48. Multiples of company’ peers

Competitor

Country

Ticker

MarkCap, USD M

P/E

FY 2012

EV/S

FY 2012

EV/EBITDA 2012

EV/LB FY 2012

Bunge

DG US

8218.31

9.8

0.2

7.4

n/a

Rus Agro

RUS

AGRO LI

792.00

2.05

0.89

8.16

2.64

Pava

RUS

AKHA RM

24.85

27.40

0.45

18.11

n/a

Mriya

MAYA GR

523.5

n/a

2.29

5.67

3.30

Astarta

AST PW

377.57

3.33

1.59

4.19

2.53

MHP

MHPC LI

1155.13

4.75

1.63

4.98

7.15

Agroton

AGT PW

117.06

4.76

1.52

5.98

0.89

Industrial Milk Company

IMC PW

71.00

4.02

3.11

17.41

1.51

KSG Agro

KSG PW

88.87

2.60

3.73

9.91

2.19

Kernel Holding S.A.

KER PW

2098.62

6.80

0.97

6.47

8.43

Median

4.4

1.6

7.4

2.5

Premium/Discount

55.09%

-39.01%

-12.11%

232.68%

Weights*

51%

11%

38%

Valuation

19,30%

X**

Source: Bloomberg, KNU estimates

*weights were calculated based on frequency using each multiples in investment analysis;

**using the production-based multiples, like EV/landbank, don’t give the adequate valuation result cause of inapplicable it to some company – competitor & partial using of landbank in their activity by others.

Figure 49. Key peers financial ratio

Operation ratios 2011/12

Market ratios 2011/12

EBITDA Margin,%

Net Margin,%

ROE,

ROIC,

Effective Tax Rate

Dvd Payout Ratio

Net Debt/EBIT

EBITDA/

Interest Expense

Debt/

Equity

P/Book

P/Cash Flow

MC/

Sales

Bunge

8.05

7.18

2.90

1.60

4.47

6.78

2.76

5.63

33.80

0.75

3.21

0.14

Rus Agro

11.11

4.88

10.94

5.54

7.90

0.00

4.86

6.49

0.95

1.34

0.54

Pava

0.53

n/a

2.48

0.52

n/a

0.53

27.84

1.12

0.14

N/A

0.13

Mriya

29.42

22.05

40.33

35.23

0.40

29.42

2.00

3.39

64.92

n/a

Astarta

33.98

22.38

37.98

32.23

28.84

n/a

1.95

7.57

68.40

0.95

31.97

0.89

MHP

31.97

19.16

32.68

19.80

1.05

31.97

2.50

6.09

97.03

1.31

5.84

0.94

Agroton

-1.76

12.74

25.39

-2.12

5.59

0.00

1.63

13.70

43.20

0.98

n/a

1.17

IMC

19.96

0.09

17.85

60.66

0.40

0.00

181.52

2.76

20.21

0.66

n/a

2.44

KSG Agro

67.51

n/a

37.67

80.55

n/a

0.00

1.92

4.53

25.65

1.23

9.76

2.56

Source: Bloomberg

Appendix 7. Concentration of markets

Figure 50. Market concentration by sectors & domestic market position

	<1000 - competitive market	100-1800 – competitive/ monopoly	≥ 1800 monopoly market
Bulk oil I_hh = 3464			1^st place – 36%
Bottled oil Ihh = 3078			1^st place – 32%
Grain Ihh = 1498		3^rd place – 8%

Source: Bloomberg, KNU estimates

Appendix 8. Supply chain disruption risk

Figure 51. VAR model for evaluation the influence of supply chain disruption risk

Vector autoregression model is the generalization of the autoregression models in case of many variables. Their advantage is the fact that they can be used not only as the way of forecasting, but they also allow to examine the dynamic interconnection between the variables. The utility for our segment analysis is that this instrument allows us to find the function of impulse reaction and variance decomposition of segmental EBIT margin. Function of the impulse reaction represents the trajectory the dependant variable follows in case of unit impulse of a random variable occurs in the equation that denotes another variable. Moving to our segments, they enable to evaluate the influence of random event happened in a particular segment on all the other segments. The decomposition of the variance allows analyzing the origins of the variance of forecast error. It also the indicator of amount of information each variable contributes to the other variables in the autoregression. Therefore, it could be used to determine which segment is dependent on which. The greater percent of the variance is explained by other variable, the greater causation effect of that variable. Frequency: quarterly data Period: 2009Q3-2013Q1 Lags: 2 (based on Schwarz information criterion)

Variance of EBIT margin of different segments explained by EBIT margin from other segments

Source: Bloomberg, KNU estimates

Appendix 9. Kernel`s Structure

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