...Financial Analysis and Forecast Report Drexel D Brown American InterContinental University Financial Management (FINA310-1602B-02) 5/8/2016 Financial Analysis and Forecast Report Introduction In the financial aspect of accounting and forecasting many mathematical computations are utilized to form data sheets that assist managers and shareholders evaluation of the firm’s current/future financial position. Financial data is also analyzed to allow internal and external comparison of the past and present performances to weigh future decisions on profitability potential against uncertainty and or risk. The financial data provided by Micro Chip Computer Corporation will be analysis to weigh past performances and determine future profitability. Financial Data Chart, Calculations, and Analysis In determining Micro Chip’s year-to-year percentage annual growth total net sales per fiscal period, we utilized the equation: (Next Year’s Net Sales – Last Year’s Net Sales) / Last Year’s Net Sales * 100. MICRO CHIP COMPUTER CORPORATION'S FINANCIAL DATA ANALYSIS | Fiscal Yrs. | FY2008 | FY2007 | FY2006 | FY2005 | FY2004 | Net Sales average | Net sales | $8,334 | $6,141 | $9,181 | $11,933 | $11,062 | $9,332 | Growth/Decline (rounded) | 36% | -33% | -23% | 8% | ---- | | Calculations: (2008 net sales of $8,334 - $6,141 of 2007 net sales) = $2,193/$6,141*100 = .357 or 36% growth in this operating period; 6,141 - 9,181/9,181 = -.3311 or -33% 2007-2006 depicts a decline...
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...European Anaiysts' Earnings Forecasts Stan Beckers, Michael Steliarcs, and Alexander Thcmson Forecasting company cnrniu^s /s ( difficult and hazardous task. In nn 7 efficient market where annly^^ts learn from ptist mistakes, there should be no persistent and systematic biases in consensus earnings accuracy. J^rcvious research has already established how some (single) individualcompany characteristics si/stematically influence forecast accuracy. So far, however, the effect on consensus eariiings biases of a comptmy's sector and country affiliatioti combined with a range of other fundameutal chanieteristics has remained largely unexplored, ilsiiig data for 19932002, this article diseiitangles and quantifies for a broad universe of European stocks how the number of analysts following a stock, the dispersion of their forecasts, the volatility of earnings, the sector and country classification of the covered conipamj, ami its nuirket capitalization influence the accuracy of the consensus earnings forecast. 5 ecurity analysts are considered to be the premier experts in the assessment of a company's prospects. Their research efforts are largely directed at producing accurate earnings forecasts, which are a key input into equity valuation models. Although the importance of financial analysis is beyond dispute, its quality has become the subject of much scrutiny and debate. Academic research has a long history of documenting systematic earnings forecast errors, but analysts' conflicts...
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...possible changes to the forecasting process in the company after several missteps of production of recent products. In this case, some readings are useful to understand the relevant theory underlying the main issues to overcome. It is clear that this case deals with the ability to manage a good forecasting tool in order to meet the demand in an uncertain world and with the key elements of the forecasting process that lead to improving forecast accuracy and operational performance. Besides, the theory about the different forecasting techniques will also have a central role in this analysis. I used 3 articles to support what I will say. The references are indicated in the footnote of this page. Who is responsible in the failures of forecast in 2002 and 2004? In 2002, the sales forecasting process was ill-designed. At Leitax, each functional group made its own forecast using its own assumptions of the future trend of the market. Each forecast was made for a single purpose: * Each director of sales made a forecast for its own scope of sales. This forecast was used as a guide for controlling operations within the supply chain and was used by the finance department in order to build a financial...
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...1. What are features common to all forecasts, elements of a good forecast, steps in forecasting, and forecasting accuracy? A: A forecast is a statement about the future value of a variable such as demand. That is, forecasts are predictions about the future. The better those predictions, the more informed decisions can be. Some forecasts are long range, covering several years or more. Long-range forecasts are especially important for decisions that will have long-term consequences for an organization or for a town, city, country, state, or nation.(Stevensons- 11e). In simple terms, forecasting is the predicting future results from the present context using forecasting tools such as statistical analysis, regression analysis, moving average, time series, etc. Larger samples and random statistical analysis would lead to better forecasting than casual or convenience method of data collection resulting in more accurate forecasting of the future trend. FEATURES COMMON TO ALL FORECASTS 1. Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future. 2. Forecasts are not perfect and their results usually differ from predicted values. Allowances should be made for forecast errors. 3. Forecasts for groups of items tend to be more accurate than forecasts for individual items because forecasting errors among items in a group usually have a canceling effect. Opportunities for grouping may arise if parts...
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...Business Proposal Objectives: You will apply economic principles presented in Weeks One through Three in this week's assignment. Your assignment will be reviewed by your peers and by your facilitator in week five and should be revised as necessary based on feedback as the first part of the final assignment in week six. Select a new, realistic good or service for an existing industry. Write the economic analysis section of a business proposal. This will include statements about the market structure and the elasticity of demand for the good or service, based on text book principles. You need to create hypothetical data, based on similar real world products to estimate fixed and variable costs. Required Elements: * Identify market structure * Identify elasticity of the product * Include rationale for the following questions: * How will pricing relate to elasticity of your product? * How will changes in the quantity supplied as a result of your pricing decisions affect marginal cost and marginal revenue? * Besides your pricing decisions, what are your suggested nonpricing strategies? What nonpricing strategies will you use to increase barriers to entry? * How could changes in your business operations alter the mix of fixed and variable costs in line with your strategy? * No more than 1400 words * Your proposal is consistent with APA guidelines Business Proposal - Thomas Money Service, Inc. Scenario The following pages...
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...FINAL PROJECT: Chapter 1: Page: 23 Case Study: National Air Express 1. Is the productivity measure of shipments per day per truck still useful? Are there alternatives that might be effective? The productivity can be measured by the number of stops covered by each driver. I think measuring shipments per day per truck is still useful. It helps to keep on track with the amount of services that can be offer on every day basis and with the area covered by each driver. By doing that, the company can tell how many areas have been covered by a driver per day; therefore productivity can be assessed in measurable way. 2. What, if anything, can be done to reduce the daily variability in pickup call-ins? Can the driver be expected to be at several locations at once at 5pm? To reduce daily variability in pickup call-ins, the company should avoid call-ins during peak time; what I mean is that the company can offer to its customer’s deals to call at maybe nights when the network is not that busy so people for important calls only can use and benefit from the network. By offering deals to clients that call at night times the driver might be able to make to several place before peak time which is 5pm. This strategy can be apply in order to reduce the business during the day time so the driver can easily and correctly do their job. 3. How should package pickup performance be measured? Are standards useful in an environment that is affected by weather, traffic, and other random variables...
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...3) HP-150 has less variety (17 types of keyboards due (languages) while CPU is the same), but for HP 120 there were an average of 6 options per product. Less variety, again, provides fewer inventories and more flexibility to the process. To reduce complexity Question 2: How serious is the forecasting problem? In other words, does success with JIT depend on good forecast? Forecasting problem seems to be severe at the plant. Case says that “…manufacturing does a lot of “second guessing” because the forecasts are terrible”. However, good forecast for JIT systems is crucial. As JIT significantly reduces the amount of raw materials, WIP inventories and finished goods on hand, it greatly relies on accurate information, i.e. on the timely delivery of exactly the right raw materials in the right place in the right amount. This leaves little room for forecast errors. In case of JIT forecast is easier (less time between orders). On the beginning of JIT implementation accurate forecast is more crucial. Once relationship with suppliers gets closer, it may create more flexibility and responsiveness even hedging the forecast error. Question 3: What is the...
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...L.L. Bean has adopted a two stage ordering process for products with “one-shot” commitments (i.e. products that they get to order only once because of long supplier lead times). First they determine a forecast for an item and then they have a process for converting that forecast into an order quantity. Questions 1. How significant (quantitatively) of a problem is the mismatch between supply and demand for L.L. Bean? From the first page of the case we have an estimate of $11 million cost of lost sales and backorders and $10 million associated with having too much of the wrong inventory. These costs are stated as being a conservative estimate. 2. On the course website is an Excel file that contains demand and forecast data for a collection of items. Suppose those are the data L.L. Bean will use to plan their next season. Consider an item that retails for $45 dollars and costs L.L. Bean $25 dollars. The liquidation price for this item will be $15. The sales forecast for this item is 12,000. What order quantity would L.L. Bean choose for this item? Using L.L. Bean’s current methodology, our first step is to understand the frequency distribution of past forecast errors. We compute the error by dividing the actual by the forecast such that a number above 1 represents that the item was under forecasted. The plot below provides a histogram of these errors. As an example, from this plot, 50% of the errors are between 0.49 and 1.04 or 66.6% of the errors are between 0.49 and...
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... HP-150 has less variety (17 types of keyboards due (languages) while CPU is the same), but for HP 120 there were an average of 6 options per product. Less variety, again, provides fewer inventories and more flexibility to the process. To reduce complexity Question 2: How serious is the forecasting problem? In other words, does success with JIT depend on good forecast? Forecasting problem seems to be severe at the plant. Case says that “…manufacturing does a lot of “second guessing” because the forecasts are terrible”. However, good forecast for JIT systems is crucial. As JIT significantly reduces the amount of raw materials, WIP inventories and finished goods on hand, it greatly relies on accurate information, i.e. on the timely delivery of exactly the right raw materials in the right place in the right amount. This leaves little room for forecast errors. In case of JIT forecast is easier (less time between orders). On the beginning of JIT implementation accurate forecast is more crucial. Once relationship with suppliers gets closer, it may create more flexibility and responsiveness even hedging the forecast error. Question 3: What is the natural explanation for the plunge in sales in the...
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...Executive Summary The Auto Parts manufacturer for automobile spare parts, Director of Marketing Research Ted Ralley is currently working on the sales forecast for next year. Ted has previously noticed forecast errors are not inexpensive and must determine the sales forecast for 2008 based on the sales from the previous four years (2003-2007) as precisely as possible. After running the following methods: Holtz-Winters Additive Model, Regression with Times Series, Regression with Economic Factors, and Holtz-Winters Multiplicative Model. Built on the data and error the most appropriate method of forecasting is Regression with Economic Factors. According to this model, sales for the year 2008 will decrease significantly, which may be result in recession. Consequently, it is best that Auto Parts plan efficiently with the available resources to prevent any possible loss of revenue. Background Forecasting is a preparation disposition that aids organizations in its endeavors to survive the uncertainty of the future, relying primarily on data from the past and present and study of trends. Most companies essentially strategize their budget by analyzing the information provided to their organization’s forecast. Companies that prepare for the future by using the forecasting method are less likely to encounter losses even though forecasts are not always 100% accurate. But they still provide management with a greater indication of what can be avoided or used more/less based on previous...
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...and analyst forecast accuracy Ting Luo Department of Accounting, School of Economics and Management, Tsinghua University, Beijing, People’s Republic of China, and Analyst forecast accuracy 257 Wenjuan Xie Department of Accounting and Finance, Whittemore School of Business and Economics, University of New Hampshire, Durham, New Hampshire, USA Abstract Purpose – The purpose of this study is to examine the impact of unidentifiable individual differences among financial analysts on the cross section of their earnings forecast accuracy. Design/methodology/approach – The paper employs the concept of analyst fixed effects to control for unidentifiable individual differences. Various psychological factors, such as decision style and personality traits, are documented to impact individuals’ decision making. However, analysts’ individual differences in such psychological factors are not captured by identifiable personal attributes employed in finance literature, such as years of experience. The methodology used addresses this issue and presents a more comprehensive study of analyst forecast accuracy. Findings – The paper documents that unidentifiable analyst-specific effects are significant, and that controlling for them improves model fitting and changes the explanatory power of some of the traditionally used independent variables in the literature. The paper confirms that the analyst’s firm-specific experience, the intensity of following that a firm receives, and the forecast horizon are...
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...shipped. It is an important tool for managing a sustainable business, whether it comes from customer surveys, general predictions, market trends, or in-depth economical analysis (Hartman, 2015). Demand forecasts are necessary since the basic operations process, moving from the suppliers' raw materials to finished goods in the customers' hands, takes time. There are many advantages that come with demand forecasting, if done accurately, such as having adequate supply – business has to make sure it has enough supply of a product/service to meet the demands. If there is not enough supply, it can lead to lost of sales as customers buy from a competitor and if there is more supply than demand then the business’ revenue will be effected because of the expenses for labour, production, and shipping (Hartman, 2015). Also, managing human resources – this allows the business to manage human resources more efficiently by looking at information that gives managers an idea of how many workers they will need, and where their labour needs will be the highest (Hartman, 2015). For example, retail stores higher more staff in September to December because of Black Friday and Christmas/Boxing Day. However, there can be many challenges that businesses may overcome when doing forecasting. A few obstacles that may affect the forecast accuracy performance can be: * Economic pressure – when a society or country is facing undesirable economic times, such as during a recession. Many customers will...
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...LAB’S MODULE: FORECASTING In this exercise, you will learn how to create forecasts for a product group through SAP. SAP provides a complete set of forecasting tools that can be used in a number of sales and operations areas. The most flexible set of forecasting tools are provided in the Sales and Operations Planning (SOP) transaction. The flow of the forecasting flowchart can be seen below: [pic] 1: Create Product Group |Purpose of Exercise | |To assist with Sales Order Planning you firstly need to create a Product Group. Product groups (product families) support high-level | |planning. A product group combines other product groups and materials. | |Menu Path |Logistics ( Production ( SOP ( Product Group ( Create | |Trans Code | | The Create Product Group: Initial Screen appears. 1. Type KidBikeGrp### in the Product group field. 2. Type ### Sport & Fun Kid Bike Prod Grp in the Description field. 3. Type DL00 (Plant Dallas) in the Plant field. 4. Type ST (items) in the Base Unit field. 5. Check that Materials radio button has been selected. ...
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...Forecasting Methods and Forecast Modeling Sources of Forecasting Errors Although larger samples improve forecasting precision, samples may be limited if older data are unavailable or not comparable. Data collected more frequently increases sample size but may not add much information. Because forecast inferences cannot be based on future data, extrapolation of past relationships results in an unknown amount of bias, especially if (1) explanatory variables move outside their historical range (2) some variables no longer are important while others become important (3) variable coefficients change substantially or switch signs Conditional forecasting occurs when one or more explanatory variables must be guessed because their values not known with certainty for the period forecast. Unconditional forecasting occurs when the future values of all explanatory variables are known with certainty. In conditional forecasting, errors may be huge because we first must forecast values of the explanatory variables. Only unconditional forecasts are free of these errors. Contingency forecasting involves generating several forecasts, one for each alternative set of circumstances, or "scenario," that is likely to arise. The estimation period is the time series data used to fit a forecasting model. Ex post forecasting involves "forecasting" the most recent observations after withholding them from the estimation period. By contrast, ex ante forecasting uses an estimation period...
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...ride over the past few years, but they mostly got ignored, or were proven wrong by events. And, to a certain extent, if you cry doom long enough you'll always get proved right, given the laws of entropy. So this is a bit 'faux' controversy... This is the introductory note taking exercise for a Pestle analysis of China, drawn on conventional internet sources such as Wikipedia, the CIA World Factbook and Nationmaster. China is the most populous country in the world, with 1.34 billion people. It has the third largest GDP, with $4.84 trillion, behind Japan and the U.S. Like India, the currency and conditions make it useful to look at some statistics using Purchasing Power Parity, which bumps up China's GDP to $7.8 trillion, which would move it ahead of Japan. It also is the second in the world in annual military spending, although that needs a bit of context, as the world's number two spends about 15% of what the world number one (USA) spends. But with PPP, that looks like more money, and insofar as it is used to pay salaries, rather than buy Israeli rocket parts, PPP is valid in this context too. China is badly governed by the Communist Party, and in my five-year Pestle forecast I will be making the case that misgovernance will prove to be the root cause of a downward spiral that will cost China dearly. It will mark the end of an unprecedented run of growth and opportunity that began in 1978, and for the past 25 years it has averaged 10% growth per year. The record stops this...
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