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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)   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. Chen, F. and Hall, P., (2013). Inference for a nonstationary self-exciting point process with an

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