...The use of earnings forecasts in stock recommendations: Are accurate analysts more consistent?† Andreas Simon Orfalea College of Business California Polytechnic State University San Luis Obispo, CA (email: ansimon@calpoly.edu) Asher Curtis David Eccles School of Business The University of Utah Salt Lake City, UT (email: asher.curtis@business.utah.edu) This draft: September, 2010. Forthcoming, Journal of Business Finance and Accounting. ABSTRACT: We examine how analysts’ conflicting incentives to be either accurate or optimistic affect their choice to generate stock recommendations with rigorous valuation models or growth-based heuristics. Consistent with prior research the average analyst recommendation is negatively associated with rigorous valuation models and positively associated with growth-based heuristics, we document that these associations are weakest for the most accurate analysts and strongest for the least accurate analysts. We also find evidence consistent with consistency between recommendations and valuation models underlying the positive future returns from trading on the most accurate analysts’ recommendations. Our results are consistent with reputation incentives to be accurate mitigating the use of optimistic growthbased models in generating stock recommendations. Keywords: Forecast Accuracy; Fundamental Valuation; Stock Recommendations; Analyst Reputation. † Andreas Simon and Asher Curtis are, respectively, from the Orfalea College of Business...
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...in 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'...
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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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...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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...3.5 (see p. 73) would be denoted Y1 = 147.6,Y2 = 251.8, Y3 = 273.1, ... , Y52 = 281.4. Mathematical notation must also be developed for distinguishing between an actual value of time series and the forecast value. A^ (hat) will be placed above a value to indicate that it is being forecast. The forecast value for Yt is Yt^. The accuracy of a forecasting technique is frequently judged by comparing the original series Y1, Y2, ... with the series of forecast values Y^1, Y^ 2, .... Basic Forecasting Notation Basic forecasting notation is summarized as follows. Yt = value of time series at period t t = forecast value of Yt et = Yt - Yt^ = residual, or forecast error Several methods have been devised to summarize the errors generated by a particular forecasting technique. Most of these measures involve averaging some function of the difference between an actual value and its forecast value. These differences between observed values and forecast values are ofteri referred to as residuals. A residual is difference between an actual value and its forecaste Equation 3.6 is used to compute the error, or residual, for each forecast period. et = Yt - Yt^ et = forecast error in time...
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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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...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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...count: 2,662 Contents 1. Introduction 2 2. Section 1: Demand forecast 2 2.1. Moving average 2 2.2. Simple Exponential Smoothing 3 2.3. Holt’s Model 4 2.4. Winter’s Model 5 2.5. Demand forecast for XYZ 8 3. Section 2: Aggregate planning 9 3.1. Aggregate planning Question 1 10 3.2. Aggregate planning Question 2 11 4. Section 4: Pricing and revenue management 12 Appendix 15 1. Introduction This assignment is based on the market information of Pony group, an electronic manufacturer, to calculate and forecast the future development of this company. The topic is divided into three sections, the first part is to forecast the demand for next four months of Pony LCD TV screen, the second section is to identify the optimal production schedule for the cell phones market. The last section is to identify the optimal price for pony handheld consoles. 2. Section 1: Demand forecast In this section, it will provide the demand forecast for next four months based on the historical demand data. There are four forecast methods used in this part, which are moving average, simple exponential smoothing, Holt’s model, and Winter’s model respectively. Firstly, I will figure out the MAPE (mean absolute percentage error) of all these four methods, and by comparing the MAPE to find out the most accurate method, then to use the most accurate technique to give four months demand forecasts for Pony LCD TV screen. 3.1. Moving average Moving average is...
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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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...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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... 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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...20000*4+4500*11000) = 0.01005unit/$(costs of labour, raw material, energy, and capital) 2. year Quarter Sales Deseasonalized sales 2011 Q1 350 460.53 Q2 300 454.55 Q3 520 500.00 Q4 800 519.48 2012 Q1 410 539.47 Q2 380 575.76 Q3 600 576.92 Q4 960 623.38 Linear equation of deseasonalized sales: y = 23.799x + 424.17 (Using Excel) y(9) = 638.36; y(10) = 662.16; y(11) = 685.96; y(12) = 709.56 year Quarter Sales Deseasonalized sales forecast Reseasonalized forecast 2013 Q1 350 638.36 485.15 Q2 300 662.16 437.03 Q3 520 685.96 713.40 Q4 800 709.56 1092.72 3-1. Month Forecast A Actual demand e Abs (e) e square Abs (e)*100/A 1 225 200 -25 25 625 12.50 2 220 240 20 20 400 8.33 MAD 24.38 3 285 300 15 15 225 5.00 MSE 659.38 4 290 270 -20 20 400 7.41 MAPE 10.18 5 250 230 -20 20 400 8.70 6 240 260 20 20 400 7.69 7 250 210 -40 40 1600 19.05 8 240 275 35 35 1225 12.73 Sum -15 195 5275 81.40 Month Forecast B Actual demand e Abs (e) e square Abs (e)*100/A 1 210 200 -10 10 100 5.00 2 230 240 10 10 100 4.17 MAD 15.63 3 280 300 20 20 400 6.67 MSE 328.13 4 300 270 -30 30 900 11.11 MAPE 6.3018 5 200 230 30 30 900 13.04 6 250 260 10 10 100 3.85 7 220 210 -10 10 100 4.76 8 270 275 5 5 25 1.82 25 125 2625 50.41 3-2. B is more accurate given the lower values of MAD, MSE, and MAPE. 3-3. Sum of forecasting errors for Method B is 25 while sum of forecasting errors for...
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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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...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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