Financial Analyse For Business Report

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Financial Analyse For Business Report

Introduction
The aim of this report is to present a financial analysis of the Leeds Food hall. This is one of the Britain’s Food halls that offer a broad range of fresh foods at economical and affordable price. The underlying objective of this Food hall is to provide its customers with fresh and high quality food all the time. Given the instrumental role played by Leeds Food hall in fresh food market, this report will seek to identify the sustainability and continuity of the Leeds Food hall business based on its financial performance. In essence, the report will look at net present value of the forecasted performance of the Food hall, evaluated the sensitivity of the forecasted financial performance to the changes in various economical parameters and issue a recommendation of the strategic policy and decisions that should be made by the Leeds Food hall’s management to ensure its financial sustainability. The report starts with e4xplanation of the methodology, then looks at the findings before concluding with recommendation and summary of the main ideas in the report.
2.0 Methodology
The financial data about the Leeds Food hall for the years 2011 through 2015 was collected. At first, the historical sales of the Leeds Food hall (for the years 2011 through 2015) was collected. The sales revenue collected was for the four quarters of each of the five years. Given that the data have seasonality, they were forecasted by de seasonalize the data first. In de seasonalize, the seasonal behaviour of the data was removed. The quarterly revenue was presented in graphical format to illustrate the seasonality (Tong, 2012). The graph for the seasonal and de seasonalize revenues was obtained for the historical data, which helped to get the series for the data, which was made a series by obtaining line of the best fit (Borenstein, & Notsund, 2015). In total there were 20 quarters for the entire historical period. The historical forecasts were used to get the future forecast for the years 2016 through 2018. The forecasted period has 12 quarters making the total historical and forecasted quarters into 32 as illustrated in Appendix 2.
The forecast of the data was carried out using the standard time-series technique. Theories used to elucidate the way in which the forecasting analysis was carried out were obtained from electronic sources that include institution’s website and Google scholars. After carrying out the forecasting the net present value of the expected future cash flows was carried out (Nerlove, Grether & Carvalho, 2014). The NPV was obtained by discounting the future cash flow into the beginning of the year 2016 which is considered a base year (Chawla et al., 2015). The NPV was used to evaluate the viability of refurbishing the Leeds Food hall. A positive NPV is a pointer to the suitability of refurbishment to the business while the negative NPV would indicates its infeasibility (Lyle & Wang, 2015). Various techniques were employed to obtain trend prediction in terms of profits and costs which were expected to generated and incurred for the forecast year 2016 through 2018. The additive time series model was used to carry out the costs and sales analysis effect of seasonality adjustment in all the three quarters. In overall the study employed different analytical techniques to forecast and evaluate refurbishment project for the Leeds Food hall.
3.0 Results
3.1 Initial Findings
The findings of the analysis as illustrated in Appendix 1.0 shows that the revenue of Leeds Food hall increased variably from 2012 to 2015. The sales of the Food hall fluctuated between the seasons (that is between the quarters) where there were more sales in the last quarter, followed by the first quarter, and second quarter and then the third quarter. Similarly the growth in sales for the period under analysis indicates that there was growth fluctuation. For example, the sales for the first quarter increased from years one to two before decreasing in year three and then increasing significantly between years three to year five. After de seasonalising the data the forecasting was carried out as illustrated in the Appendix 2.0. The forecasting table indicates that Average Sum of seasonal variation among the four quarters was 479 pounds while the adjusted average sum was 478 pounds and as the adjustment continue to take place the average adjusted sum of seasonal was found to add up to zero as indicated in appendix 2.0.