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Income statement

FY2011

FY2012

FY2013E

FY2014F

FY2015F

FY2016F

FY2017F

FY2018F

Revenue

1,899,118

2,157,441

2,535,981

2,982,421

3,241,458

3,534093

3,665,888

3,814,298

Cost of sales

-1,439,591

-1,696,468

-2,021,760

-2,370,410

-2,571,595

-2,804,815

-2,909,134

-3,026,322

Gross profit

459,527

460,973

514,222

612,011

669,863

729,278

756,754

787,975

Other operating income

26,192

65,993

73,059

83,900

91,712

97,277

101,212

103,983

Operating expenses:

 

Distribution,costs

-170,281

-200,368

-243,044

-283,454

-306,900

-333,388

-345,317

-358,751

 

General,&administrative,expenses

-38,170

-70,128

-73,634

-77,316

-81,182

-85,241

-89,503

-93,978

Total operating expenses

-208,451

-270,496

-316,679

-360,770

-388,082

-418,629

-434,821

-452,729

Operating profit

277,268

256,470

270,602

335,141

373,493

407,925

423,146

439,229

 

Finance,costs

-42,452

-64,361

-73,384

-90,374

-99,752

-110,241

-121,647

-133,199

 

Foreign,exchange,gain,,net

1,723

5,269

-

-

-

-

-

-

 

Other,expenses/income,,net

-28,121

-3,486

-

-

-

-

-

-

Profit before income tax

208,418

193,892

197,218

244,767

273,741

297,684

301,498

306,030

Income tax benefit profit

17,629

16,890

-

-

-

-

-

-

NET PROFIT

226,047

210,782

197,218

244,767

273,741

297,684

301,498

306,030

Source: Company data, KNU estimates

Appendix 2. Kernel Historical lookup

Figure 42. Kernel Business Lifecycle: from seed to mature

Company development

Period

New segment?

Source: Company data, KNU estimates

Figure 43. Kernel Holding S.A. M&A activity

Data

Company

Segment

Value, M USD US

23 January 2008

Zaliznichni slyakhi JS

Transshipment services

2.4

14 January 2010

Allseeds Group

Oil

42

15 February 2010

Allseeds Group

Oil

28

12 October 2010

Private farming

Farming

1.60

21 March 2011

Ukrros

Sugar

142

16 August 2011

Russkie Masla Group

Oil

63.8

15 September 2011

Black Sea Industries

OIl

140

02 April 2012

Farming companies (Khmelnytsk & Poltava regions)

Farming

149.4

02 October 2012

Grain Port/Taman

Transshipment services

265

Source: Bloomberg, Company data

Figure 44. Kernel Holding S.A. M&A activity – impact on share price

Zaliznichni slyakhi JS

Allseeds Group

Private farming

Ukrros

Russkie Masla Group

Black Sea Industries

Grain Port/Taman

WIG20

KER PW

trade volumes, shares

Source: WSE, Company data

Appendix 3. DCF Building

Figure 45. DCF calculation

FY2011

FY2012

FY2013E

FY2014F

FY2015F

FY2016F

FY2017F

FY2018F

Revenue from oil, cake and meal

1,309,975

1,394,618

1,674,501

1,793,138

1,940,833

2,086,969

2,183,043

2,280,185

Revenue from grain trade

571,143

598,691

772,923

1,092,869

1,188,004

1,330,498

1,363,658

1,413,915

Revenue from farming

4,309

25,814

21,739

28,954

33,923

37,360

39,471

40,892

Revenue from grain silo services

10,521

31,256

29,176

29,087

31,401

31,852

32,181

32,149

Revenue from transshipment services

3,170

8,012

31,243

38,373

47,297

47,414

47,535

47,157

Revenue from other activities

-

99,050

6,400

Sugar plants are sold

TOTAL REVENUE

1,899,118

2,157,441

2,535,981

2,982,421

3,241,458

3,534,093

3,665,888

3,814,298

COGS

1,358,548

1,536,068

1,798,844

2,120,329

2,306,864

2,517,592

2,612,499

2,719,370

Amortization and depreciation

30,885

62,374

91,263

101,622

107,049

120,719

125,341

130,160

Payroll and payroll-related costs

24,841

49,083

44,739

49,103

51,407

53,591

54,750

56,084

Rental expenses

7,513

21,842

47,835

52,486

55,009

57,308

58,563

59,936

Other operating costs

17,804

27,101

39,078

46,871

51,265

55,605

57,981

60,772

TOTAL COSTS

1,439,591

1,696,468

2,021,760

2,370,410

2,571,595

2,804,815

2,909,134

3,026,322

GROSS PROFIT

459,527

460,973

514,222

612,011

669,863

729,278

756,754

787,975

Other operating income

26,192

65,993

73,059

83,900

91,712

97,277

101,212

103,983

Operating expenses:

Distribution costs

-170,281

-200,368

-243,044

-283,454

-306,900

-333,388

-345,317

-358,751

General and administrative expenses

-38,170

-70,128

-73,634

-77,316

-81,182

-85,241

-89,503

-93,978

Operating profit

277,268

256,470

270,602

335,141

373,493

407,925

423,146

439,229

EBITDA

308,153

318,844

361,865

436,763

480,543

528,644

548,486

569,389

Finance cost

-42,452

-64,361

-73,384

-90,374

-99,752

-110,241

-121,647

-133,199

Foreign exchange (loss)/gain, net

1,723

5,269

-

-

-

-

-

-

Other expenses/income, net

-28,121

-3,486

-

-

-

-

-

-

Profit before income tax

208,418

193,892

197,218

244,767

273,741

297,684

301,498

306,030

tax rate

23%

21%

19%

16%

16%

16%

16%

16%

Profit before income tax from continuing operations

208,418

193,892

197,218

244,767

273,741

297,684

301,498

306,030

Tax at statutory tax rate

-47,936

-42,824

-

-

-

-

-

-

Deferred tax income

10,589

4,823

-

-

-

-

-

-

Tax effect of expenses, net

54,976

54,891

-

-

-

-

-

-

Tax income benefit

17,629

16,890

-

-

-

-

-

-

NET PROFIT

226,047

210,782

197,218

244,767

273,741

297,684

301,498

306,030

Interest paid(=Total Interest * (1-T))

31,834

46,607

55,630

72,620

81,998

92,487

103,893

115,445

NOPAT

257,881

257,389

252,848

317,387

355,739

390,171

405,392

421,475

Amortization

3,626

12,081

21,729

26,505

29,834

32,156

33,774

34,903

Depreciation

28,735

53,424

69,534

75,117

77,215

88,563

91,567

95,258

Total D&A

32,361

65,505

91,263

101,622

107,049

120,719

125,341

130,160

Increase in trade accounts receivable

-52,080

-9,281

-14,256

-30,089

-17,459

-19,723

-8,883

-10,003

Increase in prepayments and other current assets

18,925

1,181

-22,299

-19,364

-11,235

-12,693

-5,716

-6,437

Increase in restricted cash balance

1,720

5,716

-

-

-

-

-

-

Increase in taxes recoverable & prepaid

-1,371

-10,247

-

-

-

-

-

-

Increase in biological assets

3,292

11,585

-6,730

-25,719

-18,693

-13,391

-9,507

-6,710

Increase in inventories

-24,889

-206,817

-3,948

-79,719

-46,737

-53,185

-24,079

-27,203

Increase in trade accounts payable

4,830

-23,220

6,260

5,970

3,464

3,914

1,763

1,985

Increase in advances from customers and other current liabilities

-130,536

-11,051

14,934

32,361

18,777

21,212

9,554

10,758

Total change in WC

-180,109

-242,134

-26,038

-116,559

-71,883

-73,866

-36,868

-37,610

Tangible Assets

Additions, Tangible Assets

40,743

95,702

40,000

40,000

40,000

30,410

31,441

32,709

Acquisitions, Tangible Assets

112,407

186,316

159,266

107,318

107,927

96,621

99,898

103,925

Intangible Assets

Acquisitions (lease rights)

37,496

37,498

37,500

37,500

37,500

37,500

37,500

37,500

Total CapEx

190,646

319,516

236,766

184,818

185,427

164,531

168,839

174,134

FFCF

81,307

117,632

205,478

272,493

325,025

339,892

Period

1

2

3

4

5

6

Discount,ratio

0.8785

0.7717

0.6780

0.5956

0.5232

0.4597

PV FFCF

71,428

90,783

139,310

162,296

170,062

156,232

USES OF FCF

Interest payment

55,630

72,620

81,998

92,487

103,893

115,445

New Borrowings

79,001

98,049

109,655

119,246

120,774

122,589

FCFF for dividends and cash

104,679

143,061

233,135

299,252

341,906

347,036

Cash

45,513

69,631

151,013

209,947

251,456

255,227

Dividends

59,166

73,430

82,122

89,305

90,450

91,809

Dividends per share, USD

0.75

0.93

1.04

1.13

1.14

1.16

Dividends per share, PLN

2.36

2.93

3.28

3.57

3.61

3.67

Source: Company data, KNU estimates

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

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

FY2012

EV/S

FY2012

EV/EBITDA 2012

EV/LB

FY2012

Bunge

US

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

UA

MAYA GR

523.5

n/a

2.29

5.67

3.30

Astarta

UA

AST PW

377.57

3.33

1.59

4.19

2.53

MHP

UA

MHPC LI

1155.13

4.75

1.63

4.98

7.15

Agroton

UA

AGT PW

117.06

4.76

1.52

5.98

0.89

Industrial Milk Company

UA

IMC PW

71.00

4.02

3.11

17.41

1.51

KSG Agro

UA

KSG PW

88.87

2.60

3.73

9.91

2.19

Kernel Holding S.A.

UA

KER PW

2098.62

6.80

0.97

6.47

8.43

Median

x

x

x

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

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

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

n/a

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

Ihh = 3464

1st place – 36%

Bottled oil Ihh = 3078

1st place – 32%

Grain

Ihh = 1498

3rd 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)