QUALITATIVE FORECASTING METHODS Qualitative forecasting methods are based on educated opinions of appropriate persons 1. Delphi method: forecast is developed by a panel of experts who anonymously answer a series of questions; responses are fed back to panel members who then may change their original responses a- very time consuming and expensive b- new groupware makes this process much more feasible 2. Market research: panels‚ questionnaires‚ test markets‚ surveys‚ etc. 3. Product life-cycle
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Business Forecasting Coursework Introduction The data of this coursework are business investment in the quarterly series in the manufacturing sector from 1994 to the second quarter of 2008 in UK. In the coursework‚ firstly analyze the former 50 data to forecast the latter 8 ones and then compare with the real data to see if the forecasting model is a good fit or not. As adopting two different approaches to make the forecasting work‚ including regression with Dummy Variables method and Box-Jenkins
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THE IMPACT OF EXCHANGE RATE FLUCTUATION ON MACROECONOMIC PERFORMANCE IN NIGERIA CHAPTER ONE INTRODUCTION 1.1 BACKGROUND OF THE STUDY This study is designed to examine the causes of exchange rate fluctuations and their impact on the Nigerian economy since there is scarcely any country that lives in absolute autarky in this globalised world. The economies of all the countries of the world are linked directly or indirectly through asset or/and goods markets. This linkage is made possible through
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Nike Inc. Case 1. What is the WACC and why is it important to estimate a firm’s cost of capital? WACC is weighted average cost of capital‚ which is the expected rate of return on average from all the company’s existing debts and securities. It takes into account all different types of financing in the company’s capital structure. The reason it is important to estimate WACC is because it measures what it costs the firm to take on a project based on its current Debt and Equity mix. When the
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To manage exchange rate risk activity‚ Tiffany’s objectives should be to minimize foreign exchange rate risk and lower counterparty risks. We want to minimize these risks because Tiffany & Co. is selling goods that are denominated in US dollars‚ but sold for yen in the Japanese market. The objective of this program is to prevent the depreciation of the yen against the US dollar by hedging the currency. The expected Japanese sales of Tiffany & Co. should be actively managed by purchasing hedging contracts
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Financial Forecasting Beverly Clarkson March 7‚ 2014 FIN/200 Week 3 Checkpoint Financial Forecasting Checkpoint Financial forecasting is one of the most important developing series of projecting a financial statement. With a projection statement‚ a firm can
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By 1991‚ LL. Bean Inc was in the catalog business. LL Bean was a major cataloger manufacturer and retailer in the outdoor sporting specialty field. Their golden rule was: "Sell good merchandise at a reasonable profit‚ treat your customers like human beings‚ and they will always come back for more". They had six million active customers‚ and by 1991‚ twenty-two different catalogs ("books") were mailed. 80% of all their orders came in by telephone. They mainly reached the client using direct marketing
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Hw #M10: Holt p. 58 #3‚ 5; p. 64 # 1‚5 A car accelerates uniformly in a strait line from rest at the rate of 2.3m/s^2. a. What is the speed of the car after 55m? Vf^2= Vi^2+2ad Vf= 2(2.3m/s^2)(55m)= 253m‚ √253m= 16m/s b. How long does it take the car to travel 55m? t= (2∆d)/ (Vi+Vf) t= 2(55m)/ (0+16m/s)= 6.9sec An aircraft has a liftoff speed of 20km/h. What minimum uniform acceleration does this require if the aircraft is to be airborne after a takeoff
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1) Raw data‚ not seasonalized 2) Seasonal Adjustment used: Census II X-12 multiplicative (MASA): Used because of the presence of seasonal variations that are increasing with the level of my series. Increasing degree of variability overtime… TX non seasonalized and seasonalized 3) Combined seasonally adjusted with non-seasonally adjusted De-seasonalizing the data helped with the removal of seasonal component that creates higher volatility in model. Now‚ variations
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Second difference 13 Forecast based on ARIMA (0‚ 1‚ 4) model 13 Return the seasonal factors for forecasting 14 Part 4. Discussion of different methods and the results 15 Comparison of different methods in terms of time series plot 15 Comparison of different models in terms of error 17 Assumptions and the discussion on the sensitivity of assumptions 18 Conclusion 18 Business Forecasting Coursework Introduction The data of this coursework were drawn from the UK national statistics.
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