MPRWeb General FAQ |
Q. What is Meat Price Relationships?
- Meat Price Relationships is a product from Informa Economics designed to assist users forecast meat prices.
It is based on the idea that relatively consistent relationships between the prices of meat items exist and can be exploited for forecasting purposes.
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Q. Who should I call with questions about my subscription or the system in general?
- Informa Economics at 901-202-4448
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Q. What information is produced by this system?
- The system generates two basic pricing tools, (1)seasonal factors and (2)pricing ratios
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Q. What purpose do seasonal factors serve?
- Seasonal factors can be used to forecast prices in future time periods. The seasonal factor method takes the current spot price and assumes a normal seasonal pattern going forward in order to arrive at a forecast.
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Q. What purpose do ratios serve?
- Ratios can also be used to forecast prices in future time periods. The ratio describes the normal relationship between the prices of two items (most often a cut of meat and the live animal that it is derived from).
If one already has a forecast of the animal price in a future time period, the ratio can be used to convert that forecast to a forecast for the meat cut of interest.
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Q. What is the frequency and length of the price series used in the seasonal and ratio tools?
- Both approaches make use of weekly average price data and both use the most recent ten years when calculating seasonal factors or ratios.
For a few items, a lack of data forces the use of less than 10 years in calculating the factors or ratios.
These instances are duly noted in the file.
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Q. How often are the prices updated on the system?
- Prices are updated each week shortly after they become available. Weekly averages are generally published late on Friday afternoon and the system will be updated with those new numbers by the following Monday morning.
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Q. What happens when a price is not quoted?
- For weeks where no price was quoted, the price from the week prior is carried forward.
If reporting on a particular price series becomes so thin that the quote does not change for long periods, then the cut is dropped from the system.
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Q. How do I tell which files are seasonal factors and which are ratios?
- Ratio files are titled using the name of the cut, division symbol (/) and the name of the live animal series used in calculating the ratio. Seasonal factor files are labeled with the name of the cut only. Example:
Seasonal File: 112a lip-on ribeye, heavy, Choice
Ratio File: 112a lip-on ribeye, heavy, Choice/Texas Panhandle Cattle
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Q. Where are the graphs?
- The graphs are located on page 2 of the pdf file for both seasonal factors and ratios
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Seasonal Factor FAQ |
Q. How are seasonal factors calculated?
- The following steps are followed to calculate seasonal factors:
- An annual average is calculated for each item for each year.
- Each weekly or monthly data point is divided by the corresponding annual average. This variable represents the percentage that each observation is of the annual average.
- The weekly percentages are averaged across the same week for the ten years of data. This is the seasonal factor displayed in the graph and tables of data.
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Q. How do I use the seasonal factors to produce forecasts?
- The following technique can be used to forecast prices using current prices, seasonal factors and a simple mathematical formula.
- Determine the current weekly or monthly average price.
- Refer to the table to find the seasonal factor for the current period and for the period to be forecast.
- To calculate the forecast price, divide the current price by the current seasonal factor. Next, multiply this number by the seasonal factor for the time period to be forecast.
SEASONAL FORECASTING EQUATION
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Example:
180 Strip Loin, 0x1 Boneless, Choice
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Current Price
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Forecasted Price
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Week
Number
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Current Year
Dates
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Seasonal
Factor
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435.08
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1
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Jan 7
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.8343
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2
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Jan
14
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.8431
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3
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Jan
21
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.8610
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4
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Jan
28
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.8549
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5
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Feb
4
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.8536
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6
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Feb
11
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.8726
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7
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Feb
18
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.8972
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8
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Feb
25
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.9179
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500.01
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9
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Mar
4
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.9588
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10
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Mar
11
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.9969
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11
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Mar
18
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1.0043
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12
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Mar
25
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1.0021
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13
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Apr
1
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1.0217
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As an example, let’s
use the seasonal factors to forecast a price for 180 Strip Loins for the
week of March 4 based on the average price of 180 Strips during the week
of January 7.
First, find the
seasonal factors corresponding to the week of January 7 and March 4, .8343
and .9588 respectively.
Next, divide the
known price for week of January 7 of $435.08 by the seasonal factor for
that week, .8343, $435.08 ÷ .8343 = $521.49.
Now, multiply the
seasonally adjusted price for the week of January 7, ($521.49) by the
seasonal factor for the forecast week, March 4 (.9588). $521.49 x .9588 =
$500.01
To recap:
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Q. What is the shaded area on the seasonal factor graphs (page 2)?
- The shaded area represents the 95% confidence interval for the seasonal factors. In the above example we are using the 10-year average seasonal factors as an estimate of what the seasonal factors will be over the forecasting period.
Of course, in any given year, the seasonal factor for a particular week could be different from 10-year average. Based on historical data, we would expect future seasonal factors to lie within the shaded area 95% of the time (19 out of 20 years).
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Q. I see that the most recent price data for the current year is included in the seasonal factor tables. Is the current year used in calculating the 10-year average seasonal factors and how should I use this current-year data in the forecasting process.
- Price data for the current year are not included in the seasonal factor calculations.
This is because the first step in calculating a seasonal factor requires the annual average price be calculated (see above). The annual average price cannot be calculated until the year is complete.
The current-year price data supplied in the table can be used as an indication of the most recent spot price in the forecast calculation. This data is updated each week.
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Q. What is the red dotted line on the seasonal factor graph?
- This line is a plot of price pattern so far this year. Read the prices for the red line from the y-axis on the right side of the graph. Use this tool to determine if prices this year have been tracking in a normal seasonal pattern (do they follow the black line?) or not.
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Q. What are the limitations of using seasonal factors for forecasting?
- Seasonal projections are a useful way to develop a forecast of prices and price changes.
However, no tool can accurately forecast prices or direction of price change 100% of the time.
For this reason, a number of other factors should also be considered (i.e. econometric models, surveys from buyers, etc.)
and indicators (i.e. market psychology, government actions, upcoming retail activity, etc.) before making a price prediction.
The main risk lies in the reality that the actual seasonal factors calculated after the fact will be different from the ones used in generating the forecast.
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Price Ratio FAQ |
Q. How are price ratios calculated?
- Price ratios are calculated by dividing the weekly average price of the meat item by the average cash animal value (or carcass value where appropriate) in the same week.
These ratios are then averaged across weeks for the last 10 years.
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Q. How do I use price ratios to produce a forecast for a meat item in the future?
- In order to accomplish this, you need a forecast for the live animal in the week for which you are generating the forecast. Multiply the price ratio for the forecast week by the expected live animal cost to generate a forecasted price for the meat item.
For example, the historical ratio of 109E Choice Ribeye, Bone-In to Texas Panhandle Choice steer prices in mid May is 5.557. That is, the price of Bone-In Ribeye in mid May have averaged 5.557 times the price of fed steers over the past ten years.
If Choice steers are expected to average $85 in mid May, then Bone-In Ribeye should be around $472.35, ($85 x 5.557 = $472.35).
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Q. How do I use the standard deviations that are printed in the ratio tables?
- The standard deviations are a measure of variability in the ratio. We know that it is very unlikely that this year’s ratio will be exactly equal to the 10-year average. However, we do know that there is a 68% chance that this year’s ratio will fall within plus or minus one standard deviation of the average. Thus, the standard deviation can be used to develop a range for the price forecast.
For example, the standard deviation around the average Bone-In Ribeye ratio for mid May is .687. Thus, the odds are about 2 out of 3 that the actual ratio will fall between the average plus one standard deviation and the average minus one standard deviation (5.557 + .687 = 6.244 and 5.557 - .687 = 4.870). Then, with the $85 fed cattle price projection for mid May, Bone-In Ribeye prices likely will range between $530.74 (6.244 x $85) and $413.95 (4.870 x $85). Large standard deviations around the mean price ratio suggests that price relationships between the individual cut and live animal are more variable from year to year. Large standard deviations also suggest price relationships are less predictable
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Q. What are the shaded areas on the price ratio graphs?
- The shaded areas represent the mean plus or minus two standard deviations. This represents the 95% confidence interval for the ratio.
In other words, based on historical data, we can expect future ratios to fall in the shaded area with a 95% probability (19 years out of 20).
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Q. What is the red dotted line on the ratio graphs?
- This line plots the ratios so far this year. This helps the user to identify if the current ratio is typical (near the black line) or if the ratio is running unusually high or low. When cut prices have been high relative to the price of live animals recently,
the red dotted line will lie above the black line (10-year average ratio).
When the price of a cut is weak relative to the live animal the red line will lie below the black line.
Forecasters can use this information to fine-tune their forecasts.
For instance, if the ratio is running high relative to the average, one might expect that condition to persist and may want to consider utilizing a ratio that is higher than the 10-year average to generate their forecast for an upcoming time period.
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Q. Is it possible to use these price ratios in conjunction with futures prices to generate forecasts?
- Yes, but one additional piece of information will be needed.
You must provide an estimate of the basis (cash price minus futures price) in the time period being forecast.
Add the basis to the nearby futures price to get an forecasted cash price.
From there the process proceeds as detailed above.
For example, the live cattle futures market can also be used to develop a price estimate for the individual cut: (Live cattle futures + basis) x ratio = cut price. For example, June live cattle are trading at $85; the average May basis is $2.85 (cash $2.85 over June futures); thus, based on current futures prices, Texas Panhandle Choice steers are expected to average around $87.85 (May cash cattle price = June futures + May basis).
Using the ratio for a week in May, 5.557, Bone-In Ribeye prices are projected near $488.18 for the month ($87.85 x 5.557). This process could be repeated using the monthly average ratio for May in order to develop a forecast of the monthly average price of Bone-In Ribeyes. A better tool for utilizing futures prices to generate forecasts is the Informa Economics Futures Implied Pricing (FIP) System. This web-based system utilizes regression models in combination with real-time futures quotes to generate up-to-the-minute forecasts for a large number of beef and pork items.
Contact Informa Economics at 901-766-4668 if you are interested in subscribing to the FIP system.
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