Financial Modelling And Performance Of Business Entities: A Case Study

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

 

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)

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).

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)

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)

 

Figure 1: Line Chart

(Source: As created by the Author)

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. 

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.

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:

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.

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.

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.

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.

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.

Chen, F. and Hall, P., (2013). Inference for a nonstationary self-exciting point process with an application in ultra-high frequency financial data modeling.Journal of Applied Probability, 50(4), pp.1006-1024.

Chuang, S.L. and Brockett, P.L., (2014). Modeling and pricing longevity derivatives using stochastic mortality rates and the esscher transform. North American Actuarial Journal, 18(1), pp.22-37.

Crépey, S., (2013). Financial modeling. Springer Finance, DOI, 10, pp.978-3.

Huang, W. and Chen, Z., (2014). Modeling regional linkage of financial markets. Journal of Economic Behavior & Organization, 99, pp.18-31.

Jalil, M.A., Razak, D.A. and Azam, S.F., (2013). Exploring factors influencing financial planning after retirement: structural equation modeling approach.American Journal of Applied Sciences, 10(3), p.270.

Marszałek, A. and Burczyński, T., (2014). Modeling and forecasting financial time series with ordered fuzzy candlesticks. Information Sciences, 273, pp.144-155.

Nolan, J.P., (2014). Financial modeling with heavy‐tailed stable distributions.Wiley Interdisciplinary Reviews: Computational Statistics, 6(1), pp.45-55.

Schwaitzberg, S.D., (2016). Financial modeling of current surgical robotic system in outpatient laparoscopic cholecystectomy: how should we think about the expense?. Surgical endoscopy, 30(5), pp.2082-2085.

Shim, J.K., Siegel, J.G. and Shim, A.I., (2012). Financial Modeling: Tools for Budgeting and Profit Planning. Budgeting Basics and Beyond, Fourth Edition, pp.315-328.

Treasure, T., Gonzalez, R., Jameel, H., Phillips, R.B., Park, S. and Kelley, S., (2014). Integrated conversion, financial, and risk modeling of cellulosic ethanol from woody and non‐woody biomass via dilute acid pre‐treatment.Biofuels, Bioproducts and Biorefining, 8(6), pp.755-769.

Woo, T.H. and Kim, Y.I., (2014). The Financial Modeling of Nanotechnology (NT) for the Energy Industry Using Systems Thinking Decision Making.Energy Sources, Part B: Economics, Planning, and Policy, 9(2), pp.165-173.

Zafirakis, D., Chalvatzis, K.J., Baiocchi, G. and Daskalakis, G., (2013). Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy. Applied Energy, 105, pp.138-154.