SYSTEMS AND METHODS FOR GENERATING INDUSTRY OUTLOOK SCORES
The present invention relates to systems and methods for the generation of industry outlook scores. Datasets that are factors for the industry being scored are collected. These datasets are then normalized and then transformed into the outlook score. Lastly, the resulting outlook score may be characterized, compared to prior scores to identify trends, and displayed to the user. The characterization may include grouping scores into quartiles and color coding the scores accordingly.
1 . A computerized method for generating industry outlook scores, useful in association with a forecasting engine, the method comprising:
determining industry for which an outlook score is desired;
receiving selected datasets for the determined industry;
normalizing the selected datasets;
generating an outlook score for the industry by transforming the datasets by a macro formula;
subtracting a prior outlook score from the generated outlook score to determine a trend;
characterizing the generated outlook score; and
displaying the generated outlook score, trend and characterization.
2 . The method of claim 1 wherein the normalizing includes:
smoothing volatility from the elected datasets;
aligning the datasets by similar dates;
classifying the datasets as normal, inverted or diffusion;
determining month-to-month change of each dataset based upon the classification; and
adjusting to equalize volatility between datasets.
3 . The method of claim 2 wherein the macro formula includes:
generating a growth rate index;
summing the growth rates to equate trends to a coincidence index;
computing an index with a symmetric percent change formula;
rebasing the index to average 100;
converting the index to a three period year over year percent change; and
converting the three period year over year percent change to a normalized scale.
4 . The method of claim 2 wherein the smoothing volatility from the elected datasets utilizes a Hodrick Prescott filter.
5 . The method of claim 3 wherein the normal classified datasets are procyclic to the index, the inverse classified datasets are counter-cyclic to the index, and the diffusion classified datasets are measures of the proportion of the dataset that are positive impacts on the index.
6 . The method of claim 5 wherein:
determining month-to-month change of normal classified datasets is calculated by:
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determining month-to-month change of inverse classified datasets is calculated by:
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and, determining month-to-month change of diffusion classified datasets equals monthly levels.
7 . The method of claim 1 wherein the selected datasets include at least three of residential architectural billings index, consumer sentiment scores, ISM manufacturing index of new orders, Moody's Seasoned Aaa Corporate bond yield, personal savings rate, consumer price index for urban consumers, commercial architectural billings index, Cass Freight index of expenditures, economic policy uncertainty index for the United States, NFIB small business optimism index, United States Non-Manufacturing Business Tendency Survey: Business Situation and Activity, an adjusted S&P 500 score, ISM manufacturing index of new orders, industrial production and capacity utilization rate for chemicals, architectural billings index for new projects inquiries, real average hourly earnings, producer price index for chemical manufacturing, an adjusted materials select sector index, S&P Case-Shiller 10-City home price sales pair count, average weekly hours of production employees in the chemical sector, ISM PMI composite, an adjusted J&J stock price, S&P Case-Shiller 10-City home sales arima 2, Prevedere retail leading indicator composite, Prevedere industrial production leading indicator composite, Prevedere residential construction leading indicator composite, NFIB small business optimism index, Bank of America Merrill Lynch US corporate AAA option adjusted spread, real personal consumption expenditures for durable goods, an adjusted score of American Express Company stock price, value of manufacturers' new orders for durable goods for the electrical equipment industry, total business sales, commercial paper outstanding, construction employment, S&P Case-Shiller 20-City home price sales pair count, new homes sold in the United States, assets and liabilities of commercial banks in the United States, forecasts of non-farm job openings, real disposable personal income, food service spread, adjusted consumer discrete select sector SPDR, personal savings rates, a volatility measure of the S&P 500, non-branch merchant wholesalers durable goods inventory to sales ratio, an adjusted United States Steel Corporation stock price, value of manufacturers' new orders for durable goods for iron and steel mills, and the value of manufacturers' new orders for the communication equipment industries.
8 . The method of claim 3 wherein the converting the three period year over year percent change to the normalized scale includes setting the minimum value of the three period year over year percent change to zero and the maximum value of the three period year over year percent change to 1000 on a linear scale.
9 . The method of claim 1 wherein the characterizing the generated outlook score includes segregating the score into linear quartiles.
10 . The method of claim 9 wherein the characterizing the generated outlook score includes coloring the graphical representation of the score according to quartile.
11 . A industry outlook score generator, useful in association with a forecasting engine, the system comprising:
a user interface for receiving input to determine industry for which an outlook score is desired;
a database for receiving selected datasets for the determined industry;
a processor for normalizing the selected datasets, generating an outlook score for the industry by transforming the datasets by a macro formula, subtracting a prior outlook score from the generated outlook score to determine a trend, and characterizing the generated outlook score; and
the user interface further able to display the generated outlook score, trend and characterization.
12 . The system of claim 11 wherein the processor is configured to normalize the datasets by:
smoothing volatility from the elected datasets;
aligning the datasets by similar dates;
classifying the datasets as normal, inverted or diffusion;
determining month-to-month change of each dataset based upon the classification; and
adjusting to equalize volatility between datasets.
13 . The system of claim 12 wherein the processor is configured to generate the outlook score by:
generating a growth rate index;
summing the growth rates to equate trends to a coincidence index;
computing an index with a symmetric percent change formula;
rebasing the index to average 100;
converting the index to a three period year over year percent change; and
converting the three period year over year percent change to a normalized scale.
14 . The system of claim 12 wherein the processor is configured to smooth volatility from the elected datasets utilizing a Hodrick Prescott filter.
15 . The system of claim 13 wherein the normal classified datasets are procyclic to the index, the inverse classified datasets are counter-cyclic to the index, and the diffusion classified datasets are measures of the proportion of the dataset that are positive impacts on the index.
16 . The system of claim 15 wherein the processor:
determines month-to-month change of normal classified datasets is calculated by:
x
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t
)
=
200
(
x
t
-
x
t
-
1
)
(
x
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+
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-
1
)
determines month-to-month change of inverse classified datasets is calculated by:
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=
200
(
x
t
-
1
-
x
t
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(
x
t
+
x
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-
1
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and, determines month-to-month change of diffusion classified datasets equals monthly levels.
17 . The system of claim 11 wherein the selected datasets include at least three of residential architectural billings index, consumer sentiment scores, ISM manufacturing index of new orders, Moody's Seasoned Aaa Corporate bond yield, personal savings rate, consumer price index for urban consumers, commercial architectural billings index, Cass Freight index of expenditures, economic policy uncertainty index for the United States, NFIB small business optimism index, United States Non-Manufacturing Business Tendency Survey: Business Situation and Activity, an adjusted S&P 500 score, ISM manufacturing index of new orders, industrial production and capacity utilization rate for chemicals, architectural billings index for new projects inquiries, real average hourly earnings, producer price index for chemical manufacturing, an adjusted materials select sector index, S&P Case-Shiller 10-City home price sales pair count, average weekly hours of production employees in the chemical sector, ISM PMI composite, an adjusted J&J stock price, S&P Case-Shiller 10-City home sales arima 2, Prevedere retail leading indicator composite, Prevedere industrial production leading indicator composite, Prevedere residential construction leading indicator composite, NFIB small business optimism index, Bank of America Merrill Lynch US corporate AAA option adjusted spread, real personal consumption expenditures for durable goods, an adjusted score of American Express Company stock price, value of manufacturers' new orders for durable goods for the electrical equipment industry, total business sales, commercial paper outstanding, construction employment, S&P Case-Shiller 20-City home price sales pair count, new homes sold in the United States, assets and liabilities of commercial banks in the United States, forecasts of non-farm job openings, real disposable personal income, food service spread, adjusted consumer discrete select sector SPDR, personal savings rates, a volatility measure of the S&P 500, non-branch merchant wholesalers durable goods inventory to sales ratio, an adjusted United States Steel Corporation stock price, value of manufacturers' new orders for durable goods for iron and steel mills, and the value of manufacturers' new orders for the communication equipment industries.
18 . The system of claim 13 wherein the processor converts the three period year over year percent change to the normalized scale by setting the minimum value of the three period year over year percent change to zero and the maximum value of the three period year over year percent change to 1000 on a linear scale.
19 . The system of claim 11 wherein the processor characterizes the generated outlook score by segregating the score into linear quartiles.
20 . The system of claim 19 wherein the processor characterizes the generated outlook score by coloring the graphical representation of the score according to quartile.