Thứ Hai, 30 tháng 5, 2011

Composite Index of Leading Indicators

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Composite Index of Leading Indicators

An index published monthly by the Conference Board used to predict the direction of the economy's movements in the months to come. The index is made up of 10 economic components, whose changes tend to precede changes in the overall economy. These 10 components include:
1. the average weekly hours worked by manufacturing workers
2. the average number of initial applications for unemployment insurance
3. the amount of manufacturers' new orders for consumer goods and materials
4. the speed of delivery of new merchandise to vendors from suppliers
5. the amount of new orders for capital goods unrelated to defense
6. the amount of new building permits for residential buildings
7. the S&P 500 stock index
8. the inflation-adjusted monetary supply (M2)
9. the spread between long and short interest rates
10. consumer sentiment.

Investopedia Says: The Composite Index of Leading Indicators is a number that is used by many economic participants to judge what is going to happen in the near future. By looking at the Composite Index of Leading Indicators in the light of business cycles and general economic conditions, investors and businesses can form expectations about what's ahead, and make better-informed decisions.
Business Cycle
The recurring and fluctuating levels of economic activity that an economy experiences over a long period of time. The five stages of the business cycle are growth (expansion), peak, recession (contraction), trough and recovery. At one time, business cycles were thought to be extremely regular, with predictable durations. But today business cycles are widely known to be irregular - varying in frequency, magnitude and duration.
Investopedia Says:
Since the Second World War, most business cycles have lasted three to five years from peak to peak. The average duration of an expansion is 44.8 months and the average duration of a recession is 11 months. As a comparison, the Great Depression - which saw a decline in economic activity from 1929 to 1933 - lasted 43 months from peak to trough.
See Also: Boom, Business Cycle Indicators - BCI, Coincident Indicator, Contraction, Economy, Expansion, Investment Climate, Lagging Indicator, Leading Indicator, Recession
Related Links:
You need to understand the various phases of the market cycle to avoid bubbles and also maximize your returns. Understanding Cycles - The Key To Market Timing
It ensures that you have a realistic outlook, and a solid strategy. We show you why and how. Having A Plan: The Basis Of Success
Understanding the business cycle and your own investment style can help you cope with an economic decline. Recession: What Does It Mean To Investors?
Investing during an economic downturn simply means changing your focus. Discover the benefits of defensive stocks. Cyclical Versus Non-Cyclical Stocks
business cycle

Economic Waves series
(see Business cycles)
Cycle/Wave Name
Years
Kitchin inventory
3-5
Juglar fixed investment
7-11
Kuznets
15-25
Bronson Asset Allocation
~30
Kondratiev wave
45-60

Enlarge
An abstract business cycle
The business cycle or economic cycle refers to the ups and downs seen somewhat simultaneously in most parts of an economy. The cycle involves shifts over time between periods of relatively rapid growth of output (recovery and prosperity), alternating with periods of relative stagnation or decline (contraction or recession). These fluctuations are often measured using the real gross domestic product.
To call those alternances "cycles" is rather misleading as they don't tend to repeat at fairly regular time intervals. Most observers find that their lengths (from peak to peak, or from trough to trough) vary, so that cycles are not mechanical in their regularity. Since no two cycles are alike in their details, some economists dispute the existence of cycles and use the word "fluctuations" (or the like) instead. Others see enough similarities between cycles that the cycle is a valid basis of studying the state of the economy. A key question is whether or not there are similar mechanisms that generate recessions and/or booms that exist in capitalist economies so that the dynamics that appear as a cycle will be seen again and again.
Types of business cycle
Traditional business cycle models
The main types of business cycles enumerated by Joseph Schumpeter and others in this field have been named after their discoverers or proposers:
  1. the Kitchin inventory cycle (3-5 years) - after Joseph Kitchen.
  2. the Juglar fixed investment cycle (7-11 years) -- after Clement Juglar.
  3. the Kuznets infrastructural investment cycle (15-25 years) -- after Simon Kuznets, Nobel Laureate.
  4. the Kondratiev wave or cycle (45-60 years) -- after Nikolai Kondratiev.
Even longer cycles are occasionally proposed, often as multiples of the Kondratiev cycle.
Edward R Dewey, who formed The Foundation for the Study of Cycles, studied cycles in everything -- including economic data. Based on Dewey's periods, the cycles' average periods are: Kondratieff 53.3 years; Kuznets 17.75 years; Juglar 8.88 years; and Kitchen an interaction of 2.96 years, 4.44 years and 3.39 years.
Juglar cycle
In the Juglar cycle, which is sometimes called "the" business cycle, recovery and prosperity are associated with increases in productivity, consumer confidence, aggregate demand, and prices. In the cycles before World War II or that of the late 1990s in the United States, the growth periods usually ended with the failure of speculative investments built on a bubble of confidence that bursts or deflates. In these cycles, the periods of contraction and stagnation reflect a purging of unsuccessful enterprises as resources are transferred by market forces from less productive uses to more productive uses. Cycles between 1945 and the 1990s in the United States were generally more restrained and followed political factors, such as fiscal policy and monetary policy. Automatic stabilization due the government's budget helped moderate the cycle even without conscious action by policy-makers.
Politically-based business cycle models
Another set of models tries to derive the business cycle from political decisions.
The partisan business cycle suggests that cycles result from the successive elections of administrations with different policy regimes. Regime A adopts expansionary policies, resulting in growth and inflation, but is voted out of office when inflation becomes unacceptably high. The replacement, Regime B, adopts contractionary policies reducing inflation and growth, and the downwards swing of the cycle. It is voted out of office when unemployment is too high, being replaced by Party A.
The political business cycle is an alternative theory stating that when an administration of any hue is elected, it initially adopts a contractionary policy to reduce inflation and gain a reputation for economic competence. It then adopts an expansionary policy in the lead up to the next election, hoping to achieve simultaneously low inflation and unemployment on Polling Day.
Preventing Business Cycles
Because the periods of stagnation are painful for many who lose their jobs, pressure arises for politicians to try to smooth out the oscillations. An important goal of all Western nations since the Great Depression has been to limit the dips, and until 2001 or so, a comparable period of economic malaise was avoided. Government intervention in the economy can be risky, however. For instance, some of Herbert Hoover's efforts (including tax increases) are widely, though not universally, believed to have deepened the depression.
No-one argues that managing economic policy to even out the cycle is an easy job in a society with a complex economy, even when Keynesian theory is applied. According to some theorists, notably nineteenth-century advocates of communism, this difficulty is insurmountable. Karl Marx in particular claimed that the recurrent business cycle crises of capitalism were inevitable results of the system's operations. In this view, all that the government can do is to change the timing of economic crises. The crisis could also show up in a different form, for example as severe inflation or a steadily increasing government deficit. Worse, by delaying a crisis, government policy is seen as making it more dramatic and thus more painful.
Therefore, good forecasts of cyclical turning points are critical to improve policy decisions. The Economist noted that the Weekly Leading Index published by the Economic Cycle Research Institute (ECRI) is a successful real-time indicator to watch. ECRI was founded by Geoffrey H. Moore, who created the first ever Index of Leading Economic Indicators in the 1950s.
Alternative Interpretations of Business Cycles
Austrian School
The Austrian School of economics rejects the suggestion that the business cycle is an inherent feature of an unregulated economy and argues that it is caused by intervention in the money supply. Austrian School economists, following Ludwig von Mises, point to the role of the interest rate as the price of investment capital, guiding investment decisions. In an unregulated (free-market) economy, it is posited that the interest rate reflects the actual time preference of lenders and borrowers. Some follow Knut Wicksell to call this the "natural" interest rate.[1] Government control of the money supply through central banks disturbs this equilibrium such that the interest rate no longer reflects the real supply of and demand for investment capital. Austrian School economists conclude that, if the interest rate is artificially low, then the demand for loans will be higher than the actual supply of willing lenders, and if the interest rate is artificially high, the opposite situation will occur. This misinformation leads investors to misallocate capital, borrowing and investing either too much or too little in long-term projects. Periodic recessions, then, are seen as necessary "corrections" following periods of fiat credit expansion, when unprofitable investments are liquidated, freeing capital for new investment.
The Austrian theory also predicts that the imposition of artificially low interest rates, and the resulting increase in the supply of fiat credit, generates inflation, which obliges the central bank to increase the supply of credit yet further to maintain the artificially low interest rate, thus prolonging the "boom" and worsening the inevitable "correction." Austrian School economists point to the dot-com investment frenzy as a modern example of artificially abundant credit subsidizing unsustainable overinvestment.
In the Keynesian view, this Austrian theory assumes that the "natural" rate of interest is unique at any given time and cannot be affected by policy. To Keynesian economists, this rate is only unique if the economy is assumed to always be at full employment. If the economy is operating with less than full employment, i.e., with high unemployment above the NAIRU, then in theory monetary policy and fiscal policy can have a positive role to play rather than simply creating booms that necessarily collapse on themselves.
Marxist Views
Michal Kalecki's [2] Marxian-influenced "political business cycle" theory blames the government: he argued that no democratic government under capitalism would allow the persistence of full employment, so that recessions would be caused by political decisions: persistent full employment would mean increasing workers' bargaining power to raise wages and to avoid doing unpaid labor, potentially hurting profitability. (He did not see this theory as applying under fascism, which would use direct force to destroy labor's power.) In recent years, proponents of the "electoral business cycle" theory have argued that incumbent politicians encourage prosperity before elections in order to ensure re-election -- and make the citizens pay for it with recessions afterwards.
Cycles or fluctuations?
In recent years economic theory has moved towards the study of economic fluctuation rather than a 'business cycle' - though some economists use the phrase 'business cycle' as a convenient shorthand.
Rational expectations theory states that no deterministic cycle can persist because it would consistently create arbitrage opportunities. Much economic theory also holds that the economy is usually at or close to equilibrium.
These views led to the formulation of the idea that observed economic fluctuations can be modelled as shocks to a system.
A moving average of a stochastic stationary variable also bears resemblance to a graph of an economic time-series, such as inflation, unemployment, or investment. Such graphs arguably resemble actual events more closely than deteministic cycle formulae.
These fluctuations can be modelled in terms of fluctuations of aggregate demand. However, the main influence in this direction has been real business cycle models which consider fluctuations in supply (technology shocks). This theory is most associated with Finn E. Kydland and Edward C. Prescott, winners of the 2004 Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel.
Problems of Measurement
Some argue that modern business cycle theory often measures growth by using the flawed measure of the economy's aggregate production, i.e., real gross domestic product, which is not useful for measuring well-being. Accordingly, there is a mismatch between the state of economic health as perceived by many individuals and that perceived by the bankers and economists, which most likely drives them further apart politically. However, unlike with issues of long-term economic growth, the economists and bankers may be right to use real GDP when studying business cycles. After all, it is fluctuations in real GDP, not those of measures of well-being, that cause changes in employment, unemployment, interest rates, and inflation, i.e. economic issues which are their main concern of business cycle experts.
Business cycle theory has been most effective in microeconomics where it aids in the preparation of risk management scenarios and timing investment, especially in infrastructural capital that must pay for itself over a long period, and which must fund itself by cashflow in late years. When planning such large investments, it is often useful to use the anticipated business cycle as a baseline, so that unreasonable assumptions, e.g. constant exponential growth, are more easily eliminated.
See also
External links
  1. Do business cycles really exist?
  2. Climate-driven cycles
  3. Over-investment cycles
  4. Psychological & lead/lag cycles
  5. Monetary cycles
  6. Underconsumption theories
  7. Exogenous shock-based cycles
  8. Keynesian theories of the cycle:
    1. Oxford/Cambridge theories
    2. Accelerator/multiplier theories
    3. Endogenous theories of the cycle
This entry is from Wikipedia, the leading user-contributed encyclopedia. It may not have been reviewed by professional editors (see full disclaimer)
business cycle is mentioned in the following topics:
countercyclical
Reference date (economics)
conjuncture
José Víctor Ríos Rull
cycle theory
Federal Stores
trough
Industrial Production Index
Lucas critique
the Dogs of the Dow

business cycle: A period during which business activity reaches a low point, recovers, expands, reaches a high point, decreases to a new low point, and so on.
exponential growth
In mathematics, a quantity that grows exponentially (or geometrically) is one that grows at a rate proportional to its size. Such growth is said to follow an exponential law (but see also Malthusian growth model). This implies that for any exponentially growing quantity, the larger the quantity gets, the faster it grows. But it also implies that the relationship between the size of the dependent variable and its rate of growth is governed by a strict law, of the simplest kind: direct proportion. It is proved in calculus that this law requires that the quantity is given by the exponential function, if we use the correct time scale. This explains the name.
Intuition
The phrase exponential growth is often used in nontechnical contexts to mean merely surprisingly fast growth. In a strictly mathematical sense, though, exponential growth has a precise meaning and does not necessarily mean that growth will happen quickly. In fact, a population can grow exponentially but at a very slow absolute rate (as when money in a bank account earns a very low interest rate, for instance), and can grow surprisingly fast without growing exponentially. And some functions, such as the logistic function, approximate exponential growth over only part of their range. The "technical details" section below explains exactly what is required for a function to exhibit true exponential growth.
But the general principle behind exponential growth is that the larger a number gets, the faster it grows. Any exponentially growing number will eventually grow larger than any other number which grows at only a constant rate for the same amount of time (and will also grow larger than any function which grows only subexponentially). This is demonstrated by the classic riddle in which a child is offered two choices for an increasing weekly allowance: the first option begins at 1 cent and doubles each week, while the second option begins at $1 and increases by $1 each week. Although the second option, growing at a constant rate of $1/week, pays more in the short run, the first option eventually grows much larger:
Week:     0   1   2   3    4    5    6    7      8      9      10      11      12      13       14       15
Option 1: 1c, 2c, 4c, 8c, 16c, 32c, 64c, $1.28, $2.56, $5.12, $10.24, $20.48, $40.96, $81.92, $163.84, $327.68
Option 2:$1, $2, $3, $4,  $5,  $6,  $7,  $8,    $9,   $10,    $11,    $12,    $13,    $14,     $15,     $16
We can describe these cases mathematically. In the first case, the allowance at week n is 2n cents; thus, at week 15 the payout is 215 = 32768c = $327.68. All formulas of the form kn, where k is an unchanging number greater than 1 (e.g., 2), and n is the amount of time elapsed, grow exponentially. In the second case, the payout at week n is simply n + 1 dollars. The payout grows at a constant rate of $1 per week.
This image shows a slightly more complicated example of an exponential function overtaking subexponential functions:

The red line represents 50x, similar to option 2 in the above example, except increasing by 50 a week instead of 1. Its value is largest until x gets around 7. The green line represents the polynomial x3. Polynomials grow subexponentially, since the exponent (3 in this case) stays constant while the base (x) changes. This function is larger than the other two when x is between about 7 and 9. Then the exponential function 2x takes over and becomes larger than the other two functions for all x greater than about 10.
Anything that grows by the same percentage every year (or every month, day, hour etc.) is growing exponentially. For example, if the average number of offspring of each individual (or couple) in a population remains constant, the rate of growth is proportional to the number of individuals. Such an exponentially growing population grows three times as fast when there are six million individuals as it does when there are two million. Bank accounts with fixed-rate compound interest grow exponentially provided there are no deposits, withdrawals or service charges. Mathematically, the bank account balance for an account starting with s dollars, earning an annual interest rate r and left untouched for n years can be calculated as s(1 + r)n. So, in an account starting with $1 and earning 5% annually, the account will have \$1\times(1+0.05)^1=\$1.05after 1 year, \$1\times(1+0.05)^{10}=\$1.62after 10 years, and $131.50 after 100 years. Since the starting balance and rate don't change, the quantity \$1\times(1+0.05)=\$1.05can work as the value k in the formula kn given earlier.
Technical details
Let x be a quantity growing exponentially with respect to time t. By definition, the rate of change dx/dt obeys the differential equation:
\!\, \frac{dx}{dt} = k x
where k > 0 is the constant of proportionality (the average number of offspring per individual in the case of the population). (See logistic function for a simple correction of this growth model where k is not constant). The solution to this equation is the exponential function \!\, x(t)=x_0 e^{kt}-- hence the name exponential growth ('e' being a mathematical constant). The constant \!\, x_0is determined by the initial size of the population.
In the long run, exponential growth of any kind will overtake linear growth of any kind (the basis of the Malthusian catastrophe) as well as any polynomial growth, i.e., for all α:
\lim_{x\rightarrow\infty} {x^\alpha \over Ce^x} =0
There is a whole hierarchy of conceivable growth rates that are slower than exponential and faster than linear (in the long run). Growth rates may also be faster than exponential. The linear and exponential models are merely simple candidates but are those of greatest occurrence in nature.
In the above differential equation, if k < 0, then the quantity experiences exponential decay.
Examples of exponential growth
  • Multi-level marketing
Exponential increases appear in each level of a starting member's downline as each subsequent member recruits more people.
Exponential stories
The surprising characteristics of exponential growth have fascinated people through the ages.
Rice on a chessboard
A courtier presented the Persian king with a beautiful, hand-made chessboard. The king asked what he would like in return for his gift and the courtier surprised the king by asking for one grain of rice on the first square, two grains on the second, four grains on the third etc. The king readily agreed and asked for the rice to be brought. All went well at first, but the requirement for 2n − 1 grains on the nth square demanded over a million grains on the 21st square, more than a million million on the 41st and there simply was not enough rice in the whole world for the final squares. (From Meadows et al. 1972, p.29 via Porritt 2005)
For variation of this see Second Half of the Chessboard in reference to the point where an exponentially growing factor begin to have a significant economic impact on an organization's overall business strategy.
The water lily
French children are told a story in which you imagine having a pond with water lily leaves floating on the surface. The lily doubles in size every day and if left unchecked will smother the pond in 30 days, killing all the other living things in the water. Day after day the plant seems small and so you decide to leave it grow until it half-covers the pond, before cutting it back. On what day will that occur? The 29th day, and then you will have just one day to save the pond. (From Meadows et al. 1972, p.29 via Porritt 2005)
See also
External links
  • Exponent calculator - One of the best ways to see how exponents work is to simply try different examples. This calculator enables you to enter an exponent and a base number and see the result.
References
Meadows, Donella H., Dennis L. Meadows, Jørgen Randers, and William W. Behrens III. (1972) The Limits to Growth. New York: University Books. ISBN 0-87663-165-0
Porritt, J. Capitalism as if the world matters, Earthscan 2005. ISBN 1-84407-192-8
Thomson, David G. Blueprint to a Billion: 7 Essentials to Achieve Exponential Growth, Wiley Dec 2005, ISBN 0-471-74747-5
This entry is from Wikipedia, the leading user-contributed encyclopedia. It may not have been reviewed by professional editors (see full disclaimer)
exponential growth is mentioned in the following topics:
exponential assembly
exponential
Arthrobacter
plastochrone
population growth rate
meme plague (computer jargon)
population growth
bacterial growth
biotic potential
Hagedorn temperature
The Conference Board announced today that the U.S. leading index decreased 0.1 percent, the coincident index increased 0.2 percent and the lagging index increased 0.3 percent in April.
  • The leading index decreased slightly in April. As a result of data revisions, the small decrease in March was revised up to a small increase. From October to April, the leading index grew 1.5 percent. This is slightly below the average six-month change (1.7 percent) in the first three months of the year. Housing permits made the largest negative contribution to the leading index in March. However, the strengths among the leading indicators have been widespread in recent months.
  • The coincident index continued to increase steadily as it has since September 2005. From October to April, the coincident index grew 1.7 percent and all four components contributed about equally to this growth.
  • After slowing down in 2005, the leading index picked up somewhat in the first four months of 2006, but it is only slightly above its level at the end of 2005. Moreover, the small gains since December have not been very persistent. The current behavior of the leading index suggests economic growth should continue moderately in the near term.
LEADING INDICATORS. Three of the ten indicators that make up the leading index increased in April. The positive contributors - beginning with the largest positive contributor - were vendor performance, stock prices, and interest rate spread. The negative contributors - beginning with the largest negative contributor - were building permits, manufacturers' new orders for nondefense capital goods*, index of consumer expectations, average weekly initial claims for unemployment insurance (inverted), real money supply*, and manufacturers' new orders for consumer goods and materials*. The average weekly manufacturing hours held steady in April.
The leading index now stands at 138.9 (1996=100). Based on revised data, this index increased 0.4 percent in March and decreased 0.4 percent in February. During the six-month span through April, the leading index increased 1.5 percent, with eight out of ten components advancing (diffusion index, six-month span equals eighty percent).
COINCIDENT INDICATORS. All four indicators that make up the coincident index increased in April. The positive contributors to the index - beginning with the largest positive contributor - were industrial production, employees on nonagricultural payrolls, personal income less transfer payments*, and manufacturing and trade sales*.
The coincident index now stands at 122.6 (1996=100). This index increased 0.2 percent in both March and February. During the six-month period through April, the coincident index increased 1.7 percent.
LAGGING INDICATORS. The lagging index stands at 122.8 (1996=100) in April, with five of the seven components advancing. The positive contributors to the index - beginning with the largest positive contributor - were commercial and industrial loans outstanding*, average prime rate charged by banks, change in labor cost per unit of output*, average duration of unemployment (inverted), and ratio of manufacturing and trade inventories to sales*. The negative contributor was change in CPI for services. The ratio of consumer installment credit to personal income** held steady in April. Based on revised data, the lagging index increased 0.1 percent in March and remained unchanged in February.
DATA AVAILABILITY AND NOTES. The data series used by The Conference Board to compute the three composite indexes and reported in the tables in this release are those available "as of" 12 Noon on May 17, 2006. Some series are estimated as noted below.
* Series in the leading index that are based on The Conference Board estimates are manufacturers' new orders for consumer goods and materials, manufacturers' new orders for nondefense capital goods, and the personal consumption expenditure used to deflate the money supply. Series in the coincident index that are based on The Conference Board estimates are personal income less transfer payments and manufacturing and trade sales. Series in the lagging index that are based on The Conference Board estimates are inventories to sales ratio, consumer installment credit to income ratio, change in labor cost per unit of output, the consumer price index, and the personal consumption expenditure used to deflate commercial and industrial loans outstanding.
The procedure used to estimate the current month's personal consumption expenditure deflator (used in the calculation of real money supply and commercial and industrial loans outstanding) now incorporates the current month's consumer price index when it is available before the release of the U.S. Leading Economic Indicators.
Effective with the September 18, 2003 release, the method for calculating manufacturers' new orders for consumer goods and materials (A0M008) and manufacturers' new orders for nondefense capital goods (A0M027) has been revised. Both series are now constructed by deflating nominal aggregate new orders data instead of aggregating deflated industry level new orders data. Both the new and the old methods utilize appropriate producer price indices. This simplification remedies several issues raised by the recent conversion of industry data to the North American Classification System (NAICS), as well as several other issues, e.g. the treatment of semiconductor orders. While this simplification caused a slight shift in the levels of both new orders series, the growth rates were essentially the same. As a result, this simplification had no significant effect on the leading index.
Effective with the January 22, 2004 release a programming error in the calculation of the leading index -- in place since January 2002 -- has been corrected. The cyclical behavior of the leading index was not affected by either the calculation error or its correction, but the level of the index in the 1959-1996 period is slightly higher.


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