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Financial Modelling: Performance Of Business Entities

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Question:

Discuss about the Case Study for Financial Modelling and Performance of Business Entities .

 

Answer:

Introduction

The financial modeling has been focused on brief observation over financial performance of business entities. It has developed brief measurement on different brands of Beer companies with the introduction of statistical financial modeling. The use of statistical tools, techniques and approach on the beer companies has been made. As per the statistical measurements, particular financial modeling, it has been included observation over the statistical financial assessment of Bia Hoi Beer company. In relation to evaluate different variables of financial attributes such as pricing, sales, growth rate and returns the statistical tools has been used for making more critical and depth analysis over the performance and positioning of the company (Nolan, 2014)

Methods

The data survey has been presented as per pricing, average pricing and shelf positioning of the 9 beer brands. The company that has been selected for the financial modeling analysis is been BIA Hoi Beer Company. Based on 30 years of sales, pricing and shelf information of BIA Hoi Beer Company, the statistical and financial modeling has been conducted tool regression analysis and linear regression analysis (Schwaitzberg, 2016). In the report, line chart tool has been used to observe the profitability and potentiality of the sales and marketing divisions of the company for making growth in the market. As per the assessment, the report also includes coefficient correlation, regression analysis, seasonal indexing for analysing trend and dummy variables (Shim et al. 2012).

 

Data findings result

Trend Analysis of BIA Hoi

 

 

Volume (in '000 litres)

4 year moving avg

2 year moving avg

Index

 

 

 

Time period

Date

Bia Hoi

 

 

 

Deseasonalized

Trends

 

1

Jan-95

10.50093

 

 

150.3

0.06986646707

160.80093

Average price of a packet of peanut

2

Feb-95

10.39675

 

 

150.9

0.068898277

171.6935

8.43

3

Mar-95

9.54165

 

 

151.4

0.06302278732

180.02495

8.75

4

Apr-95

10.42048

10.2149525

10.22304

151.9

0.06860092166

193.58192

8.25

5

May-95

10.56563

10.2311275

10.15376375

152.2

0.06941938239

205.02815

8.18

6

Jun-95

9.77784

10.0764

10.14293625

153.2

0.06382402089

211.86704

7.33

7

Jul-95

10.07394

10.2094725

10.15880625

153.7

0.06554287573

224.21758

8.34

8

Aug-95

10.01515

10.10814

10.02802625

153.6

0.06520279948

233.7212

8.64

9

Sep-95

9.92472

9.9479125

10.03885

153.5

0.06465615635

242.82248

7.05

10

Oct-95

10.50534

10.1297875

10.143845

153.5

0.06843869707

258.5534

8.62

11

Nov-95

10.1864

10.1579025

10.30436875

152.4

0.06683989501

264.4504

7.44

12

Dec-95

11.18688

10.450835

10.53377

152.4

0.07340472441

286.64256

9

13

Jan-96

10.5882

10.616705

10.594315

154.9

0.06835506779

292.5466

8.18

14

Feb-96

10.32622

10.571925

10.63187125

155.7

0.06632125883

300.26708

8.9

15

Mar-96

10.66597

10.6918175

10.61794125

156.3

0.06824037108

316.28955

7.42

16

Apr-96

10.59587

10.544065

10.537125

156.6

0.06766200511

326.13392

8.37

17

May-96

10.53268

10.530185

10.52397125

156.8

0.06717270408

335.85556

7.27

18

Jun-96

10.27651

10.5177575

10.4629125

156.9

0.06549719567

341.87718

7.98

19

Jul-96

10.22721

10.4080675

10.4112725

157.1

0.0651

351.41699

7.51

20

Aug-96

10.62151

10.4144775

10.3087925

157.2

0.06756685751

369.6302

8.11

21

Sep-96

9.6872

10.2031075

10.176695

157.25

0.06160381558

360.6812

7.71

22

Oct-96

10.06521

10.1502825

10.22527375

154.257

0.0652496159

375.69162

7.1

23

Nov-96

10.82714

10.300265

10.36343

157.5

0.06874374603

406.52422

8.91

24

Dec-96

11.12683

10.426595

10.49193875

157.65

0.07057932128

424.69392

7.04

25

Jan-97

10.20995

10.5572825

10.58265125

157.8

0.06470183777

413.04875

8.43

26

Feb-97

10.26816

10.60802

10.61395

157.9

0.06502951235

424.87216

8.15

27

Mar-97

10.87458

10.61988

10.50572125

158.1

0.0687829222

451.71366

8.56

28

Apr-97

10.21356

10.3915625

10.46991375

158.2

0.06456106195

444.17968

8.48

29

May-97

10.83676

10.548265

10.58216625

159

0.06815572327

473.26604

7.36

30

Jun-97

10.53937

10.6160675

10.46264875

159.311

0.06615594654

475.4921

7.16

31

Jul-97

9.64723

10.30923

10.31862875

160.1

0.06025752655

459.16413

8.47

32

Aug-97

10.28875

10.3280275

10.21143625

170.32

0.06040834899

499.56

7.93

33

Sep-97

9.90403

10.094845

9.97813375

170.6

0.05805410317

497.43299

8.65

34

Oct-97

9.60568

9.8614225

9.997555

171.233

0.05609713081

497.82612

8.23

35

Nov-97

10.73629

10.1336875

10.2231075

171.5

0.06260227405

547.27015

7.18

36

Dec-97

11.00411

10.3125275

10.39150125

171.6

0.06412651515

567.74796

8.59

37

Jan-98

10.53582

10.470475

10.53726125

172

0.06125476744

561.82534

8.71

38

Feb-98

10.13997

10.6040475

10.496085

172.3

0.05885066744

557.61886

8.92

39

Mar-98

9.87259

10.3881225

10.35258

172.6

0.05719924681

557.63101

7.37

40

Apr-98

10.71977

10.3170375

10.2339625

172.6

0.0621075898

601.3908

8.89

41

May-98

9.87122

10.1508875

10.1701975

172.9

0.05709207634

577.62002

7.87

42

Jun-98

10.29445

10.1895075

10.20647

172.99

0.05950893115

605.3569

8.52

43

Jul-98

10.00829

10.2234325

10.13165125

173

0.05785138728

603.35647

7.85

44

Aug-98

9.98552

10.03987

10.14196625

173.1

0.05768642403

612.46288

8.99

45

Sep-98

10.68799

10.2440625

10.25198125

173.2

0.06170894919

654.15955

8.82

46

Oct-98

10.3578

10.2599

10.2741925

173.3

0.05976803231

649.7588

8.79

47

Nov-98

10.12263

10.288485

10.52593

173.4

0.05837733564

649.16361

8.3

48

Dec-98

11.88508

10.763375

10.67081

173.6

0.0684624424

744.08384

7.73

49

Jan-99

9.94747

10.578245

10.61110375

174.3

0.05707096959

661.72603

7.38

50

Feb-99

10.62067

10.6439625

10.649825

175.25

0.06060296719

706.2835

7.67

51

Mar-99

10.16953

10.6556875

10.5344

175.6

0.057913041

694.24603

7.97

52

Apr-99

10.91478

10.4131125

10.52572

177.62

0.06145017453

745.18856

8.73

53

May-99

10.84833

10.6383275

10.64260125

177.92

0.06097307779

752.88149

8.08

54

Jun-99

10.65486

10.646875

10.621815

178.1

0.05982515441

753.46244

8.73

55

Jul-99

9.96905

10.596755

10.53134

178.2

0.05594304153

726.49775

8.56

56

Aug-99

10.39146

10.465925

10.4267725

178.35

0.05826442389

760.27176

8.55

57

Sep-99

10.53511

10.38762

10.36035125

178.5

0.05902022409

779.00127

7.26

58

Oct-99

10.43671

10.3330825

10.3692125

178.6

0.0584362262

783.92918

7.51

59

Nov-99

10.25809

10.4053425

10.5605475

178.8

0.05737186801

784.02731

7.11

60

Dec-99

11.6331

10.7157525

10.73707375

179

0.06498938547

876.986

7.09

61

Jan-00

10.70568

10.758395

10.77886125

179.2

0.05974151786

832.24648

8.2

62

Feb-00

10.60044

10.7993275

10.90197125

179.3

0.0591212493

836.52728

8.08

63

Mar-00

11.07924

11.004615

10.86774375

179.4

0.06175719064

877.39212

7.54

64

Apr-00

10.53813

10.7308725

10.6998825

180

0.05854516667

854.44032

8.34

65

May-00

10.45776

10.6688925

10.67955125

181.2

0.05771390728

860.9544

7.73

66

Jun-00

10.68571

10.69021

10.62050875

182

0.05871269231

887.25686

8.26

67

Jul-00

10.52163

10.5508075

10.54736375

182.1

0.05777940692

887.04921

8.61

68

Aug-00

10.51058

10.54392

10.6072875

182.3

0.05765540318

897.01944

8.63

69

Sep-00

10.9647

10.670655

10.57218375

182.5

0.06008054795

939.0643

7.28

70

Oct-00

9.89794

10.4737125

10.43835375

182.6

0.05420558598

875.4558

8.9

71

Nov-00

10.23876

10.402995

10.491985

182.7

0.05604137931

909.65196

8.44

Table 1: calculation of trends and Index price

(Source: Created by Author)

Regression analysis

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

 

 

 

 

Multiple R

0.7490463781

 

 

 

 

 

 

 

R Square

0.5610704766

 

 

 

 

 

 

 

Adjusted R Square

0.5593147585

 

 

 

 

 

 

 

Standard Error

0.493727656

 

 

 

 

 

 

 

Observations

252

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

 

 

 

df

SS

MS

F

Significance F

 

 

 

Regression

1

77.90001504

77.90001504

319.5675198

0

 

 

 

Residual

250

60.94174957

0.2437669983

 

 

 

 

 

Total

251

138.8417646

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95%

Upper 95%

Intercept

10.21156172

0.06005310596

170.042191

0

10.09328722

10.32983621

10.09328722

10.32983621

Trends

0.0006347898175

0.00003550983348

17.87645154

0

0.0005648532595

0.0007047263755

0.0005648532595

0.0007047263755

Table 2: calculation of Regression Analysis

(Source: As created by Author)

Line Chart 

Figure 1: Line Chart

(Source: As created by the Author)

Analysis and discussion

Discussion on Line chart

A 20 years line chart has been developed on BIA Hoi Beer Company. This line chart helps company to analyse sales volume and price of the product. A line chart represented by numerical and quantitative data. Bia Hoi Beer company’s 20 years line chart ease to understanding large statistical and quantitative data. With the implication of line graph, the sales and pricing fluctuations over 20 years has been analysed. Managers of the company analyse the data from the line chart and take the business decisions.    

According to Treasure et al. (2014) a line chart of a company has been developed between the two or more variables. Independent variables are drawn on horizontal axis and dependent variable on vertical axis. Two variables of BIA Hoi Beers Company are Sales volume and the price. This two variables are directly correlated two each other’s. As, if sales volume of a product goes up then the price of the product also increase (Pignataro, 2013). Similarly, sales volume of a product decreases than price of the product also decreased. In January 1996, Bia Hoi Beer Company’s sales volume has been evaluated 10.39 (in 000 liter) and price per unit has been analysed 2.96. In February 1996, the sales volume of Bia Hoi has been decreased from 10.39 to 9.54 (in 000 liter). This has reduced the price of per unit beer cost that has affected market price of the beer. Thus, the price margin of beer has declined to increase its demand and push its sales volume. In the statistical observation, it has been stated that the company decrease in sales has dictated the increase in its expenses and cost of production. Thus, the beer company can develop more strategic approaches to make development and improvement in the financial field.

 

Estimating a model of volume of sales using trend analysis

Four years moving average is an indicator of current trends. Once determine the result it is plotted into a chart. Trend analysis has helped the company to identify the strengths and the weaknesses of the company. When the trend of a particulars month is, lower that indicates that company is not performing well. On the other hand, a positive and high trend indicates that the company is making goods sales (Woo and Kim, 2014, p.780). The trends analysis of a company also depends on the market index number. Therefore, if the index number of a particular period is in a high position then the company’s price per unit also increases. In this study, the production on March 1995 was 9.54 (000 liters).

It was increased to 10.42 (000 liters) to April, because of the higher index price in April 1995. This indicates that there is a correlation between the sales volume, index price and the price of a product. The entire variables are statistically significant as they are positively correlated (MarszaÅ‚ek and Burczynski, 2014, p.78). In July 2005 to Jun 2007, a rapid growth of sales volume has been measured and identified by the company.  A regression analysis has been developed between sales volume and trend of BIA Hoi. The coefficient interception of trends is 10.21. This indicates that there is a positive relationship between sales and trends. Multiple R indicates that this regression is statistically fit. P value of this regression is less than 0.05. Therefore, null hypothesis will be rejected. 

Discussion on adding a seasonal index

Another regression analysis has been calculated among the sales, trend and seasonal index. The dependent variable of this regression was sales. On the other hand, trend and seasonal index were two independent variables. Coefficient intercept at 12.87, which indicates a positive relationship among three variables. This multiple regression is statistically fit, as multiple R is higher value.   

It is important to identify the risk involved in the business. The risk in a business was mainly two type, systematic risk and the unsystematic risk. Systematic risk involvement is identified with beta calculation. Coefficient of the regression indicates that this two variables is positively correlated to each others. The Multiple R of this regression is 0.50 (Bielecki and Rutkowski, 2013, p.97). That means it is higher than the 0.05. Therefore, the null hypothesis will be rejected in this regression. R square value of the regression indicates that the regression is statistically fit.

Impact of dummy activity

As per the financial modeling of BIA Hoi, it can be seen that a disease namely “killer yeast strain” affected the beer brewing method and it had a severe effect. The virus of this disease is known as “Gastroenteritis”. Hence, the fear of the virus has been quickly spread throughout the world due to avoidance of the consumption of beer as well as yeast, which is related to the beer products. As a result, it can be concluded that there is an overall effect of the harmful diseases among the two periods (Charnes, 2012). Bia Hoi sales were affected due to virus. Therefore, two dummy variables have been added to the beer production. Coefficient of first dummy (D1) was 3.07 and for (D2) it was 0.05. First dummy has a positive impact on the sales of the company, as their coefficient is positive. On the other hand, the impact of second dummy is very negative as the coefficient was 0.

 

Competitors pricing strategy

The pricing strategy of a company depends on the competitors pricing policy. If the competitor's allow the customers to buy a product with low price then the cost of the goods of the company needs to be lower. On the other hand, a regression analysis has been developed between the BIA Hoi cost pricing and competitors pricing. Regression analysis has shows that San Migual, Angkor, Tiger, Chang companies intercepts at 13.00. This indicates a positive correlation. On the other hand, a negative impact has been identified among BeerLao, Klang, Bintang, Bia Saigon. All the correlation value is in negative.        

Complementary goods

According to Girault and Valk (2013) the sales of a particulars product may get hamper for complementary goods. Customers may opt for new product with their existing price range. There are 8 more premium competitors available in Malaysian market. People of Malaysia will compare the pricing strategy and then decide to choose a particulars brand. Therefore, it is important for Bia Hoi to introduce complementary foods in order to stay in the market.Peanut is a complementary goods of beers.  A regression analysis has been developed on a dependent variable and the independent variable. A strong relationship has been developed in this regression.       

Merits of shelf position:

According to Finnerty (2013), in case of lower pricing, each of the time, the products are available in lower pricing rate. This leads to the benefit of the clarity. However, it does not give to the retailers along with higher and new advertising policy. Therefore, the shoppers predict to get the products at a lower pricing rate. On the contrary, in case of the promotional sales techniques, a retailer may have ongoing facilities to acquire shoppers’ concentration into the shops. Moreover, Benth and Benth (2013) mentioned that sales products can be stimulating the advertising with the help of the communication.

On the other hand, lower or the shelf position technique has a tendency to fix the low costs as the overall strategy and technique is able to construct and develop the infrastructure and the effectiveness of the supply chain. Moreover, advertising is assumed less costly with the help of the lower rate of pricing strategy. The reason can be discussed, as it is not needed to the retailers to promote and sale each of the items.

In the points of Huang and Chen (2014), lower pricing approach is simple for the customers to understand.  As a result, this marketing policy will easy to be appealed to the clients to save money in case of appropriate purpose. Nevertheless, Jalil et al. (2013) stated that middle shelf positioning is a kind of practices of to set a price, which is greater than the marketable price. In this context, the anticipation of the consumers is seemed to the higher quality. In addition, the quality of the products, however, the seller has been ventured highly in the marketplace, which is required to provide the impression in terms of greater quality.

In order to establish the relationship among different shelf positioning and sales, Crepey (2013) cited that there is a negative relationship among the sales and the lower pricing approach. Therefore, it can conclude that lower the pricing techniques, higher will be the sale. On the other hand, higher the pricing approach, lower will be the sale. As per the financial modeling of beer company, the researcher has been discussed a relevant model in the following:

Leveraged Buyouts model:

As per the statement of this model, it can be stated that in case of the transaction of the single asset, a combination of the equity from the borrowed money, can be framed in such a way that the cash flow of the assets are assumed as the collateral. According to Chuang and Brockett (2014), the cost of the debt has lower rate of the cost of capital. On the other hand, return from the equity is raised with the rise for money. Hence, debt effectively provides as a lever to raise the returns on the investment. In the words of Chen and Hall (2013), the leveraged buyout model can employ when the financial sponsor obtains a company. On the contrary, most of the corporate transactions can be partially funded in terms of the banking fund.

As a result, it is assumed that the representation of leveraged buyout model is very efficient. Benth and Benth (2013) mentioned that LBO is mostly observed in the private organisations. On the other hand, with the rise in the financial sponsors, it is expected that there is a greater return from the leverage. More precisely, it can be stated as the greater ratio of debt to the shares of equity. As a result, it can conclude that the financial sponsors have incentive to recruit the suitable amount of debt to finance the acquisition. Barndorff-Nielse et al. (2012) opined that LBO model has followed some important characteristics such as the stability of the cash inflows, the quantity of the supply of equity by the financial sponsor and the total economic atmosphere.

 

Significance of LBO model:

The leveraged buyout model allows the companies to make the purchase easier. Burgin, M. and Meissner (2012) cited that as asset combining with the equity as well as the debt capital has a significance of debt in case of the overall capital. The ranges of the total capital have been lies 70% to 80% of the total share of the capital. This is the reason why leveraged buyout model is able to leverages itself with the help of the borrowed funds. In addition, the main objective of leveraged buyout model may be differentiated as it has a dependency on the purpose of purchasing an organization. In this occasion, Brauchart et al. (2015) stated that if an organization require to increasing the present operations or can raise the scale of the business. As a result, this type of strategic leveraged buyout can be achieved by the way of the mergers as well as the acquisitions.

Limitations of LBO model:

Bohn (2015) stated that the major risk of the LBO model could be discussed in terms of the financial distress. In case of the private equity organization with the higher debt, always want to enhance their amount of returns. In addition, the general value creation of the LBO model is subject to increase the flow of cash. In most of the situation, these outcomes derive from the reduction has a greater impact sometimes. On the other hand, Baaquie (2013, p.1666) supported that LBO model is against the willingness to the target. More critically, it can be argued that most of the organisation will exit by taking of the cash out of the organisation.

Conclusion and Recommendation

A regression analysis has been developed to identify the dependent and the independent variable. It gives the company to know the relationship between the trends, sales volume, pricing and dummy variable. It is recommended for the company to decrease the cost structure and operating expenses. On the other hand, a ranking has been developed in this financial modeling to identify the current market situation.

As recommendation, it can be said the company can develop market analysis and assessment, to acknowledge the competitive environment. Property to the analysis, the management of the van includes strategic approaches to boost its sales volume. On the other hand, the diversification technique can also used. With the diversification, the beer company can make investment in other business fields to generate more revenue from the market.

 

References list:

Books

Barndorff-Nielsen, O.E., Mikosch, T. and Resnick, S.I. eds., (2012). Lévy processes: theory and applications. Springer Science & Business Media.

Benth, F.E. and Benth, J.S., (2013). Modeling and pricing in financial markets for weather derivatives (Vol. 17). World Scientific.

Benth, F.E. and Benth, J.S., (2013). Modeling and pricing in financial markets for weather derivatives (Vol. 17). World Scientific.

Bielecki, T.R. and Rutkowski, M., (2013). Credit risk: modeling, valuation and hedging. Springer Science & Business Media.

Bingham, N.H. and Kiesel, R., (2013). Risk-neutral valuation: Pricing and hedging of financial derivatives. Springer Science & Business Media.

Charnes, J., (2012). Financial modeling with crystal ball and excel. John Wiley & Sons.

D'Ecclesia, R.L. and Zenios, S.A. eds., (2012). Operations Research Models in Quantitative Finance: Proceedings of the XIII Meeting EURO Working Group for Financial Modeling University of Cyprus, Nicosia, Cyprus. Springer Science & Business Media.

Finnerty, J.D., (2013). Project financing: Asset-based financial engineering. John Wiley & Sons.

Girault, C. and Valk, R., (2013). Petri nets for systems engineering: a guide to modeling, verification, and applications. Springer Science & Business Media.

Pignataro, P., (2013). Financial modeling and valuation: A practical guide to investment banking and private equity. John Wiley & Sons.

Schultz, G.M., (2016). Investing in Mortgage-Backed and Asset-Backed Securities,+ Website: Financial Modeling with R and Open Source Analytics. John Wiley & Sons.

Varian, H.R. ed., (2013). Economic and financial modeling with Mathematica®. Springer.

Journals

Baaquie, B.E., (2013). Financial modeling and quantum mathematics.Computers & Mathematics with Applications, 65(10), pp.1665-1673.

Bohn, J., (2015). Financial Modeling, Actuarial Valuation and Solvency in Insurance. Quantitative Finance, 15(5), pp.735-740.

Brauchart, J.S., Dick, J. and Fang, L., (2015). Spatial low-discrepancy sequences, spherical cone discrepancy, and applications in financial modeling. Journal of Computational and Applied Mathematics, 286, pp.28-53.

Burgin, M. and Meissner, G., (2012). Negative probabilities in financial modeling. Wilmott, (2012)(58), pp.60-65.

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