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Pro Forma Analysis Agribusiness Finance LESE 306 Fall 2009

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PASTFUTURE PRESENT Historical analysis Comparative analysis Historical price and yield trends Pro forma analysis Forming expectations about future prices, costs and productivity Ad hoc extrapolations Projections based upon available outlook data Projections based upon econometric analysis

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2009 2010 2011 2012 2013 2014 2015 Timeline Required for Capital Budgeting… Assume it is the year 2009 and John Deere wants to project farm machinery and equipment sales over the next six years to determine if plant expansion is necessary.

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2009 2010 2011 2012 2013 2014 2015 Timeline Required for Capital Budgeting… Assume it is the year 2009 and John Deere wants to project farm machinery and equipment sales over the next six years to determine if plant expansion is necessary. Capital budgeting models of investment decisions require projections of the annual revenue and cost values over the entire 2010 to 2015 time period.

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Ad Hoc Modeling Approaches Naïve model – using last year’s prices, costs and yields Simple linear trend extrapolation of historical prices, costs and yields Using assumptions made by others

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Econometric Model Approach Capturing future supply/demand impacts on prices and unit costs Linkages to commodity policy Linkages to domestic economy Linkages to the global economy

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Historical Data on Fixed Input Sales to Farmers

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Econometric Analysis Based on Time Trend Extrapolation I t = f(Year t )

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poor job A linear time trend projection of future farm machinery and equipment sales therefore does a poor job of predicting future sales activity.

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Econometric Analysis Based on Investment Theory Econometric Analysis Based on Investment Theory I t = f{[E(P t )×E(Q t )]/E(c t )}

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muchbetter job An econometric model based on investment theory does a much better job of predicting future sales activity.

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Concept of Derived Demand for Farm Machinery The demand for farm machinery is driven by the expected net economic benefit from use of the machine….

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Crop Market Equilibrium D S D S Quantity Price PePe QeQe D S Demand consists of: -Industrial use -Feed use -Exports -Ending stocks Demand consists of: -Industrial use -Feed use -Exports -Ending stocks Supply consists of: -Beginning stocks -Production -Imports Supply consists of: -Beginning stocks -Production -Imports

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Forecasting Future Commodity Price Trends D S $4 10 $1 $7 D = a – bP + cYD + eX Own price Own price Disposable income Disposable income Other factors Other factors

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D S $4 10 $1 $7 S = n + mP – rC + sZ Own price Own price Input costs Input costs Forecasting Future Commodity Price Trends Other factors Other factors

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Projecting Commodity Price D = S D S $4 10 $1 $7 D = 10 – 6P +.3YD + 1.2X S = 2 + 4P –.2C + 1.02Z Substitute the demand and supply equations into the the equilibrium condition and solve for price Substitute the demand and supply equations into the the equilibrium condition and solve for price

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PEPE QEQE Assumes perfect knowledge of outcomes in all 5 areas!!!! Point Forecast Assumptions

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Supply-side risk for a given price… QLQEQHQLQEQH PEPE Structural Pro Forma Analysis

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Demand and supply- side risk and potential price variability… QLQEQHQLQEQH PHPEPLPHPEPL Structural Pro Forma Analysis

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Estimating the Annual Supply and Use of Wheat

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Income elasticity Cross price elasticity Econometric Analysis – Food Use Own price elasticity

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Observed and Predicted Values For Wheat Food Use

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Remaining Steps to Forecasting the Price of Commodity Develop similar econometric equations for feed use, exports and ending stock demand. Develop econometric equations for production and import supply. (Q D =Q S ) excess demand equals zero Substitute the estimated equations into the market equilibrium definition (Q D =Q S ) and solve for the price where excess demand equals zero.

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The Market Model Demand equations: Q d,i = a 0 - a 1 (Price) + a i (demand shifters) Supply equation: Q s,i = b 0 +b 1 (price) + b i (supply shifters) Market equilibrium: ΣQ d,i = ΣQ s,i

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Conclusions Econometric models preferred over naïve models and linear time trend models. Much more accurate. elasticities Provide much more information (e.g., elasticities). sensitivity analysis potential variability Allow for sensitivity analysis with independent (exogenous) variables when evaluating potential variability about expected trends.

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