Transcript Forecasting • • • • • OBJECTIVES 1. Show value of forecasting in business planning. 2. Produce some basic forecasting procedures. 3.

Forecasting
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• OBJECTIVES
1. Show value of forecasting in business planning.
2. Produce some basic forecasting procedures.
3. Show the limits of a forecast.
4. Illustrate how to use forecasts in business
planning.
• I. Introduction
• Firms need forecasting to stay abreast of changes in
customer demand & market condition for inputs &
outputs.
• In the era of economic dependence, competition &
uncertainty (e.g., droughts, embargoes, price
freezes) the ability to forecast future business
conditions accurately is important to agribusinesses.
• Forecasting reduces the uncertainty on business
decision making & shd lead to increased profits –
e.g. firms wld not expand pdn if an economic
downturn is forecasted for the next year.
• 11. The Basics
• A. Need to forecast basic economic variables (GDP,
interest rates, etc.) & relate these to pricing, pdn,
sales, inventory decisions for agribusiness firms.
• 1. Forecast Frequently: Forecasts explains past &
predicts future customer buying habits, & anything
affecting mkt conditions. Failure to forecasts mkt
changes can lead business failure. Thus its important
to forecast frequently on anything influencing firms
• 2. Use Appropriate Forecasting Techniques:
Forecasts is made in many ways, from throwing
darts, to large, complex mathematical computer
models. However, businesses have to use the
forecasting techniques appropriate to their situation.
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• B. The Five Factors of Forecasting
The selection of a forecasting procedure is dependent on the
ff factors:
(1) accuracy desired,
(2) time permitted to develop the forecast,
(3) the complexity of the situation to be explained,
(4) the time period to be projected, &
(5) the amount of money available to carry out forecast.
All these factors are inter-related – e.g. if time used in
forecasting is short, accuracy of the projection cld suffer or
if time used is long, accuracy increases, but so will the cost.
The key is to determine the proper mix of the 5 factors.
As a general rule, the best forecasts are those using the most
straightforward, uncomplicated methods.
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• C. Sources of Forecasts
Forecasting can be obtained from private & public sources.
1. Private Firms: Private forecasting firms use sophisticated
computer models to make their projections & sell them to
other firms for a fee. Some large agribusiness firms operate
their own in-house forecasting units
2. Trade Associations: Small firms that cannot buy or
forecast on their own cos of cost can & do get forecasts
thro their trade associations (e.g. Cattleman's Association)
3. Government:Some forecasting data (e.g. town, retail
sales, popn, age, income, # of households) is available, free
of charge, from federal, state, & local gov’t agencies – e.g.
Census of Retail Trade published by US Deptof Commerce.
4. Business Publications:Include business publications like
Fortune, Business Week, Wall Street Journal, Farm Journal,
Grain Miller's News, Poultry Times, Supermarket News, etc
• D. Types of Data
• 1. Cross-sectional Data:Used to determine economic forces
that influence other variables at a time or place– e.g. qty
meat eaten in US, where data is qty of beef eaten in each
state last year. Paired with other cross-sectional data - e.g.
prices of beef, & barbecue sauce, & income – beef
processors can see impact of these data on US beef cnsptn
as well as forecast changes of these on future beef sales.
• 2. Time Series data: Frequently used in forecasting &
involves many observations of same variable over many
time periods at the same location -e.g. av. monthly farmlevel cattle price from Jan 1990 to now. It makes it possible
to identify reoccurring patterns in cattle prices that cld help
predict prices in future months & thus assist beef processors
to lower costs by indicating when to buy beef at lowest
price in coming months.
• Simple Time Series Analysis
• Many economic data – e.g agric pdt prices, company sales
etc. exhibit repeating patterns of behavior or regularity
when plotted over time.
• The simplest approach to examining such data assumes that
each observation comprises 4 components:
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–
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(1) trend,
(2) seasonal effect,
(3) cyclical effect, and
(4) an irregular effect or error term.
• Y=TxSxCxI
• where Y sales, price or other variable under analysis
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T
the trend component
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S
the seasonal component
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C
the cyclical component I/
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I
the it-regular component or error term
• Trend
Season
Cyclical
Irregular
• Trend: Is the broad long-term growth or decline of the
industry or firm.
• Season: represents annually recurring forces that affect sales
or prices.
• Cyclical: Is a measure of forces that act as broad irregular
waves. Such cycles may be due to demographic changes,
general business cycles, etc.
• Irregular: Is simply an error term and represents variations
that cannot be attributed to trend,season or cycle.
• E. Forecasting Procedures
• There are a many forecasting methods that can assist
a firm with its planning
• 1. Extrapolation: Simplest procedure & assumes that
whatever happened in the past will continue to
happen in the future – e.g. if corn price rose 10%
last year, one expects it to rise by 10% again this
year.
– Disadvantage: Method has no economic base, & this
limits its long-run use & may cause a manager to miss
quick changes in economic environment.
– Advantage:Effective forecasting method in short run (1
yr or less), & ease of application makes it an attractive
"first-cut" forecasting procedure as many economic
variables are often slow to change.
• 2. Graphical Analysis: Extrapolation can be a good
forecaster when combined with graphical analysis. Here, the
time-series data are plotted on a graph to permit forecasters
to "see" changes to the variable over time covered. A trend
line can be fitted to the graph & its slope gives expected
future or long-run direction of the change.
• Trend Line ___ A rise in
Price
trend
price from $2 in yr 9 to $6
6
in yr 13 i.e. av. Price rise
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of $1 per yr. If things stay
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the same we can forecast
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an increase in price of $1
2
for next yr
1
• Caution: Method shd be used 1 2 3 4 5 6 7 8 9 10 11 12 13 time
for short-run forecasting as it has no economic foundation
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• Adjusting for Trend in Time Series Data
1. Adjusting for Inflation
Graphical analysis does not provide a realistic picture of the
data or impact of inflation – e.g. does trend in graphical
analysis show a real increase in price of the pdt or shows a
rise in the general level of all prices due to inflation?
Deflation: To overcome this it is necessary to deflate or
remove from these prices the effects of inflation – i.e. by
dividing the price series by an appropriate index of price
levels calculated for the same period, such as the Index of
Prices Received by Farmers, the Consumer Price Index, or
the Producer Price Index.
Deflation Procedure:
Deflated 99 =$2.23
Corn price Price Rec’d Real Price 100 = 100/99 x2.23
Yr1 Dec $2.23
99
$2.25
102=$2.27
Yr 2 Jan $2.27
102
$2.22
100=100/102 x 2.27
• Deflated prices can be plotted & new trend line is fitted. If
the new trend shows an increase of price of $0.20 from yr 9
to yr 13 then the average annual real increase in price is
$0.20/4 = $0.05 - i.e. we can forecast an $0.05/year real
price increase in corn before inflation. This increase could
reflect actual shifts in the demand or supply, or both.
• 2. Adjusting for Population Growth
• Popn can also distort time series data including consmption
of agric pdt over time. If cnsmptn of a pdt is rising yearly, it
is important to know if its caused by changes in consumer
tastes, lower prices, or a rising popn. Popn effects on
consmptn is removed by dividing consmptn by popn to get a
per capita consumption rate.
• If per capita consumption is rising or falling over time, it
could indicate a change in consumer tastes, the effects of
price shifts, or other variables but not population.
• Procedure for Removing Effect of Population
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Yr
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1971
1976
1981
Meat % chnge Popn
Csptn sn 1971
ml
40405
204.9
41456
2.6
216.0
40481
0.2
227.9
% chnge
sn 1971
5.4
11.2
Per Cap
Csptn
197.2
161.9
177.6
% chnge
sn 1971
- 2.7
- 9.9
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• Consumption of meat stayed almost the same
• But by removing the impact of population it shows that per capita
consumption of meat has declined over the yrs & this may be due to
change in consumers taste.
• 3. Adjusting for Short-time Fluctuations
• a. Using Moving Averages to Adjust for Fluctuations
• Is another way to get a clearer picture of change or trend in
the data & it helps to reduce the impact of short-run
fluctuations in the data thro the process of "smoothing out“
i.e. by plotting average of several data points.
• A 12-month moving average is calculated by adding the 12
most recent months of prices & dividing by 12. This is
repeated each subsequent months, with the oldest month's
value being dropped & replaced by the next month's price.
• Plotting the moving average for these prices against the
monthly prices shows the smoothing effect of the procedure
(figure 4-3).
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Farm-level Average Corn Prices, the Index of Prices Received by Farms, & Real Prices
Deflated
(1)
(2)
(3)
(4)
PRICE PER INDEX OF
REAL PRICE
DATE
BUSHEL PRICES RECVD PER BUSHEL
Yr 1 Dec.
$2.23
99
$2.25
Y2
Jan.
2.27
102
2.22
Feb.
2.29
105
2.18
Mar.
2.43
109
2.23
Apr.
2.51
114
2.20
May
2.72
118
2.30
June
2.68
118
2.27
July
2.49
117
2.13
Aug.
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2.35
116
Sept.
2.27
118
Oct.
2.20
125
Nov.
2.20
119
Dec.
2.45
122
Year 3 Jan.
2.48
127
Feb.
2.46
132
Mar.
2.54
134
To calculate 12-month Moving Average
(5)
MOVING-AV
PRICE/ BUSHEL
$2.11
2.09
2.07
2.03
2.04
1.92
1.76
1.85
2.01
1.95
1.86
1.90
2.01
2.00
1.98
1.99
2.03
2.08
2.11
• 1. Using price index calculate deflated or real prices ( i.e. to obtain column 4)
• Sum 1st twelve month prices in col. 4 (2.25 +2.18. . +1.76+1.85 = 25.34)
• Divide by 12 to find column 5 (25.34/12)
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• Futures Markets
Agribusinesses face many risks in working with
future agric prices to make pdn & mktg decisions.
Futures mkts were developed as a way to reduce this
price risk.
The risk reduction is done through futures contract,
where buyers & sellers meet & agree on a price/qty
to be traded at some time in the future.
• Importance of Futures Markets
1. Facilitates Carrying of Inventories: For storable
pdt (corn, soybeans, wheat etc) whose supply is
limited to one harvest a year, futures contract allows
spreading out supply over the year through storage
or return from storage.
• e.g. Oct 15, corn cash price = $3.00/bushel
next May's futures price
= $3.14/bushel,
Incentive to store not to sell = $0.14/bushel
(or $0.02/bushel/month return to storage).
• Producers who can store for less than $0.02/bus/mth
shld store & vice versa.
• 2. Predict or Provide Future Price info: - forecasting
of future prices for inputs/outputs. Info assist
producers in their pdn decisions & protect
processors from shifts in input prices.
• Nearest future prices provide better predictions of
future price levels & can be of greatest value to
producers decisions of agric commodities.
• Provide Forward Contracting and Price-risk
Aversion. Producer or processor can lock in futures
prices through Forward-contract or Hedging where
one takes equal & opposite positions in the cash &
futures markets.
• Hedging: Permits users to gain the price protection
benefits of using the futures market without actually
shipping to or receiving the pdt from some distant
point. They do this by "netting out“ (liquidating)
their position (i.e., taking equal but opposite futures
mkt positions for the same commodity).
• Hedging works cos price difference in cash & futures mkts
reflect only storage cost over the life of the futures contract.
• e.g. If on July 1, a buyer for breakfast firm looks at the
harvest price of wheat ($3.90/bus) & estimates that it will
cost the firm $0.16/bushel to store the wheat until it is
needed on March 1.
• July 1 Cost of wheat
$3.90/bushel
• Storage cost
+ $0.16/bushel
• Total cost
$4.06/bushel
• March 1 wheat future price= $4. 10 per bushel.
• Margin
= $0.04/bushel ($4.10 - $4.06)
can be made by buying wheat now & storing until March 1.
• To protect the firm against adverse price changes, a futures
contract is sold to deliver wheat in March at $4. 10/bushel.
• If on March I both futures price & cash price are
$4.30/bushel, the return in the cash mkt will be a savings of
$0.24/bushel ($430 - $4.06) from purchasing at harvest.
• The return in the futures market after buying a contract &
"netting out" of the mkt will be a loss of $0.20/bushel
($4.10 - $4.30).
• Combining the two yields a margin of + $0.04/bushel ($0.24
- $0.20).
• Cash Mkt
Futures Mkt
• July 1 Buy $4.06 Sell
$4.10
• March 1 Sell $4.30
Buy
$4.30
• Gain/Loss
$0.24
-$0.20
• Net Gain/Loss = $0.24-$0.20 = $0.04
• Thus, thro hedging, the firm can reduce its price risk for
agric pdts
• Benefit of Futures markets
• To Producers: It allows producers to be sure of a
selling price for their output.
• To Processors: It assures processors of adequate
supplies of inputs at predetermined prices so they
can operate their plants more efficiently.
• To Agribusiness System: It benefits the entire,
system by giving good forecasts of future price
levels that should lead to better prodn decisions.