I'm going away for a few days to enjoy some of the finest scenery that Canada has to offer.
I'll be back next week and rested up to look at the local real estate board reports.
Cheers - mohican.
Wednesday, August 27, 2008
Vacation
I'm going away for a few days to enjoy some of the finest scenery that Canada has to offer.
I'll be back next week and rested up to look at the local real estate board reports.
Cheers - mohican.
Tuesday, August 26, 2008
Musings - Don't Panic!
Prayer -
'Lord, lord, lord, protect me from knowing what I don't need to know.
Protect me from even knowing that there are things to know that I don't know.
Protect me from knowing that I decided not to know about the things that I decided not to know about.
Amen.
"There's another prayer that goes with it that's very important, so you'd better jot this down, too."
'Lord, lord, lord. Protect me from the consequences of the above prayer. Amen...'
And that's it. Most of the trouble people get into in life comes from missing out that last part.'
Courtesy of Douglas Adams via his book - The Hitchhiker's Guide to the Galaxy.
Friday, August 22, 2008
The Dimensions of Stock Returns
Abbreviated from here.
By Truman A. Clark
An ongoing objective of financial research is to explain the behavior of stock returns. Factors are sought that explain both differences among the returns of individual stocks in any given time period and the variation of stock returns through time. If a factor does both, it is said to explain the common variation of returns. In addition, if a factor is related to non-diversifiable risk and possesses explanatory power independent of other factors, the factor is considered a "dimension" of stock returns.
Fama and French (1992) found that two factors related to company size and book-to-market ratio (BtM) together explain much of the common variation of stock returns and that these factors are related to risk. Small cap stocks have higher average returns than large cap stocks, and high BtM (or "value") stocks have higher average returns than low BtM (or "growth") stocks. Based on Fama and French's findings, size and BtM are dimensions of stock returns.
Fama and French also investigated a market factor. A market factor is needed to distinguish stocks from fixed income securities, and it is important in explaining the variation of stock returns through time. But, among stocks in a given time period, differences in their sensitivities to the market factor are unrelated to differences in their average returns, so the market factor is not a dimension of stock returns.
The Fama/French results have important implications for domestic equity portfolio design. Large capitalization growth stocks constitute large portions of traditional "market-like portfolios" based on indexes such as the S&P 500, the Russell 3000 and the Wilshire 5000. Domestic equity portfolios with greater commitments to small cap stocks and value stocks offer higher average returns than conventional market-like portfolios.
Size and BtM also are dimensions of international and emerging markets stock returns. This confirms Fama and French's interpretation of size and BtM effects as rewards for bearing risk that cannot be eliminated by diversification.
Risk and Return
Controlling for differences in BtM by comparing large cap value to small cap value and comparing large cap growth to small cap growth, small cap stocks had higher average returns than large cap stocks. Controlling for differences in size by comparing large cap growth to large cap value and comparing small cap growth to small cap value, value stocks had higher average returns than growth stocks. The higher average returns of small cap and value stocks represent rewards for bearing risk.
If standard deviation were a complete measure of risk, average returns would increase as standard deviations increase. Controlling for differences in BtM, a direct relation between average returns and standard deviations is found when large cap stocks are compared to small cap stocks. But, controlling for differences in size, a discrepancy appears. Small growth stocks had a lower average return and a higher standard deviation than small value stocks. Since greater standard deviations are not consistently associated with higher average returns, standard deviation is not a reliable measure of risk.
Size, Book-to-Market and Earnings
Seeking a risk-based explanation for the relations of size and BtM to average returns, Fama and French (1995) investigated the behavior of the earnings of stocks grouped by size and BtM. Measuring profitability by the ratio of annual earnings to book value of equity, Figure 2 illustrates the evolution of profitability over long periods before and after stocks are classified by size and BtM. BtM is associated with persistent differences in profitability. On average, low BTM stocks are more profitable than high BtM stocks of similar size for at least five years before and after portfolio formation. Low BtM indicates sustained high earnings that are characteristic of firms that are growing and financially robust. High BtM indicates protracted low earnings that are typical of firms experiencing financial distress.
Expected Returns and the Cost of Capital
Financial markets channel funds from suppliers of capital to users of capital. Expected returns are the rewards investors anticipate for supplying capital. Investors require a higher rate of return (or risk premium) for bearing greater risk. Risk is something that investors collectively shun and that cannot be eliminated by diversification.
The cost of capital is the price users of capital must pay to obtain financing. Competition forces users of capital to bid higher prices to obtain funding for more risky ventures.
In equilibrium, the expected rate of return and the cost of capital are determined jointly as the price at which the demand for and supply of capital are equal. In financial markets that function efficiently, investors only receive risk premiums for bearing risk. As risk increases, the expected rate of return and the cost of capital increase.
High BtM and small size often indicate companies that are experiencing some degree of financial distress. On average, they have higher costs of capital because they tend to be riskier than companies with low BtM and large market capitalization. The higher average returns of small stocks and value stocks reflect compensation for exposure to non-diversifiable risk factors.
The Three-Factor Model
The findings of Fama and French suggest that much of the variation in stock returns is explained by three systematic risk factors.
· The market factor measured by the returns of stocks minus the returns of Treasury bills.
· The size factor measured by the returns of small stocks minus the returns of big stocks.
· The value factor measured by the returns of high-BtM stocks minus the returns of low-BtM stocks.
Portfolio Engineering
Many investors commit high proportions of their domestic equity holdings to portfolios resembling the S&P 500, Russell 3000 or other market-like proxies. Large cap growth stocks are the dominant holdings of the S&P 500 and the Russell 3000. As a result, market-like proxies are poor portfolio structures for investors seeking exposure to the size and/or value factors. Investors can get such exposure by increasing their relative holdings of small cap and/or value stocks.
The potential increases in expected returns due to these tilts can be estimated with the three-factor model. For purposes of illustration, it is assumed that the expected risk premiums are six percent per year for the market factor and three percent per year for both the size and value factors.
Words of Caution
Structured portfolios offer the prospect of higher long-term returns than market-like portfolios, but the expected risk premiums are not sure things. Factor premiums vary widely and randomly. For the 1927-2001 period, the standard deviations of the annual premiums were approximately 21% per year for the market factor, 14% for the size factor and 14% for the value factor. Owing to their high variability, it may take decades before rewards for bearing increased size and value risk are realized.
Cumulative premiums for each factor are computed by adding successive monthly premiums for the period January 1927 through December 2001. Although the cumulative premiums tend to rise over long periods of time, each moves erratically with lengthy episodes of downward drift. The market premium declined from December 1967 to July 1982—a period of more than 14 years. The size premium declined from December 1983 to December 2001—a period of 18 years (and still counting). The value premium declined from December 1987 to December 2000—a period of 13 years.
Structured portfolios are not appropriate for all investors. Structured portfolios have higher expected returns because they are riskier than market-like portfolios. Over periods of less than 20 years, structured portfolios often will have lower returns than market-like portfolios. It is only over periods of 20 years or more that it becomes more probable that structured portfolios will outperform market-like portfolios. Investors with short horizons or aversion to risk should stick with market-like portfolios. Structured portfolios only make sense for investors with long time horizons and sufficient tolerance for increased risk.
International and Emerging Markets Equities
Size and value effects are not confined to US equity markets. The MSCI EAFE Index represents a portfolio of international stocks from developed countries similar to the S&P 500. EAFE is composed predominantly of large cap growth stocks. During 1975-2001, international small cap stocks had a higher average return than EAFE indicating a size effect, and international large cap value stocks had a higher average return than EAFE indicating a value effect.Based on the limited amount of data available, size and value effects also appear in emerging markets. The IFC Investables Total Return Index represents a portfolio of tradable stocks in emerging markets countries that non-resident investors are permitted to own. During 1989-2001, emerging markets small cap stocks and value stocks had higher average returns than the IFC index.
The international findings are consistent with Fama and French's interpretation of the size and value effects as rewards for bearing non-diversifiable risk. If size and value effects were related to risk factors unique to the US, forming globally diversified portfolios could eliminate them. Instead, the existence of similar size and value effects in both domestic and international stock returns demonstrates that these effects are global phenomena reflecting exposures to ubiquitous sources of risk.
Implications for Global Equity Allocation
EAFE is the international equivalent of the S&P 500. EAFE returns, expressed in US dollars, are determined jointly by stock returns computed in local currencies and foreign-exchange gains or losses against the dollar. Because the two indexes contain stocks with similar size and value characteristics, it is reasonable to assume that the costs of capital of EAFE and the S&P 500 are approximately equal. If it is also assumed that currencies have zero expected returns, EAFE should have about the same expected gross rate of return as the S&P 500.
Concluding Comments
The identification of size and value factors by Fama and French has important implications for equity portfolio design. Relative to traditional market-like portfolios, portfolios with greater exposures to the size and value factors offer higher expected long-term rates of return.
Structured portfolios can be designed that provide targeted sensitivities to the size and value factors. International and emerging markets equity returns also exhibit size and value effects.
Structured portfolios only make sense for investors with long time horizons and sufficient tolerance for increased risk. For the right investors, structured portfolios are promising alternatives to old-fashioned market-like portfolios.
By Truman A. Clark
An ongoing objective of financial research is to explain the behavior of stock returns. Factors are sought that explain both differences among the returns of individual stocks in any given time period and the variation of stock returns through time. If a factor does both, it is said to explain the common variation of returns. In addition, if a factor is related to non-diversifiable risk and possesses explanatory power independent of other factors, the factor is considered a "dimension" of stock returns.
Fama and French (1992) found that two factors related to company size and book-to-market ratio (BtM) together explain much of the common variation of stock returns and that these factors are related to risk. Small cap stocks have higher average returns than large cap stocks, and high BtM (or "value") stocks have higher average returns than low BtM (or "growth") stocks. Based on Fama and French's findings, size and BtM are dimensions of stock returns.
Fama and French also investigated a market factor. A market factor is needed to distinguish stocks from fixed income securities, and it is important in explaining the variation of stock returns through time. But, among stocks in a given time period, differences in their sensitivities to the market factor are unrelated to differences in their average returns, so the market factor is not a dimension of stock returns.
The Fama/French results have important implications for domestic equity portfolio design. Large capitalization growth stocks constitute large portions of traditional "market-like portfolios" based on indexes such as the S&P 500, the Russell 3000 and the Wilshire 5000. Domestic equity portfolios with greater commitments to small cap stocks and value stocks offer higher average returns than conventional market-like portfolios.
Size and BtM also are dimensions of international and emerging markets stock returns. This confirms Fama and French's interpretation of size and BtM effects as rewards for bearing risk that cannot be eliminated by diversification.
Risk and Return
Controlling for differences in BtM by comparing large cap value to small cap value and comparing large cap growth to small cap growth, small cap stocks had higher average returns than large cap stocks. Controlling for differences in size by comparing large cap growth to large cap value and comparing small cap growth to small cap value, value stocks had higher average returns than growth stocks. The higher average returns of small cap and value stocks represent rewards for bearing risk.
If standard deviation were a complete measure of risk, average returns would increase as standard deviations increase. Controlling for differences in BtM, a direct relation between average returns and standard deviations is found when large cap stocks are compared to small cap stocks. But, controlling for differences in size, a discrepancy appears. Small growth stocks had a lower average return and a higher standard deviation than small value stocks. Since greater standard deviations are not consistently associated with higher average returns, standard deviation is not a reliable measure of risk.
Size, Book-to-Market and Earnings
Seeking a risk-based explanation for the relations of size and BtM to average returns, Fama and French (1995) investigated the behavior of the earnings of stocks grouped by size and BtM. Measuring profitability by the ratio of annual earnings to book value of equity, Figure 2 illustrates the evolution of profitability over long periods before and after stocks are classified by size and BtM. BtM is associated with persistent differences in profitability. On average, low BTM stocks are more profitable than high BtM stocks of similar size for at least five years before and after portfolio formation. Low BtM indicates sustained high earnings that are characteristic of firms that are growing and financially robust. High BtM indicates protracted low earnings that are typical of firms experiencing financial distress.
Expected Returns and the Cost of Capital
Financial markets channel funds from suppliers of capital to users of capital. Expected returns are the rewards investors anticipate for supplying capital. Investors require a higher rate of return (or risk premium) for bearing greater risk. Risk is something that investors collectively shun and that cannot be eliminated by diversification.
The cost of capital is the price users of capital must pay to obtain financing. Competition forces users of capital to bid higher prices to obtain funding for more risky ventures.
In equilibrium, the expected rate of return and the cost of capital are determined jointly as the price at which the demand for and supply of capital are equal. In financial markets that function efficiently, investors only receive risk premiums for bearing risk. As risk increases, the expected rate of return and the cost of capital increase.
High BtM and small size often indicate companies that are experiencing some degree of financial distress. On average, they have higher costs of capital because they tend to be riskier than companies with low BtM and large market capitalization. The higher average returns of small stocks and value stocks reflect compensation for exposure to non-diversifiable risk factors.
The Three-Factor Model
The findings of Fama and French suggest that much of the variation in stock returns is explained by three systematic risk factors.
· The market factor measured by the returns of stocks minus the returns of Treasury bills.
· The size factor measured by the returns of small stocks minus the returns of big stocks.
· The value factor measured by the returns of high-BtM stocks minus the returns of low-BtM stocks.
Portfolio Engineering
Many investors commit high proportions of their domestic equity holdings to portfolios resembling the S&P 500, Russell 3000 or other market-like proxies. Large cap growth stocks are the dominant holdings of the S&P 500 and the Russell 3000. As a result, market-like proxies are poor portfolio structures for investors seeking exposure to the size and/or value factors. Investors can get such exposure by increasing their relative holdings of small cap and/or value stocks.
The potential increases in expected returns due to these tilts can be estimated with the three-factor model. For purposes of illustration, it is assumed that the expected risk premiums are six percent per year for the market factor and three percent per year for both the size and value factors.
Words of Caution
Structured portfolios offer the prospect of higher long-term returns than market-like portfolios, but the expected risk premiums are not sure things. Factor premiums vary widely and randomly. For the 1927-2001 period, the standard deviations of the annual premiums were approximately 21% per year for the market factor, 14% for the size factor and 14% for the value factor. Owing to their high variability, it may take decades before rewards for bearing increased size and value risk are realized.
Cumulative premiums for each factor are computed by adding successive monthly premiums for the period January 1927 through December 2001. Although the cumulative premiums tend to rise over long periods of time, each moves erratically with lengthy episodes of downward drift. The market premium declined from December 1967 to July 1982—a period of more than 14 years. The size premium declined from December 1983 to December 2001—a period of 18 years (and still counting). The value premium declined from December 1987 to December 2000—a period of 13 years.
Structured portfolios are not appropriate for all investors. Structured portfolios have higher expected returns because they are riskier than market-like portfolios. Over periods of less than 20 years, structured portfolios often will have lower returns than market-like portfolios. It is only over periods of 20 years or more that it becomes more probable that structured portfolios will outperform market-like portfolios. Investors with short horizons or aversion to risk should stick with market-like portfolios. Structured portfolios only make sense for investors with long time horizons and sufficient tolerance for increased risk.
International and Emerging Markets Equities
Size and value effects are not confined to US equity markets. The MSCI EAFE Index represents a portfolio of international stocks from developed countries similar to the S&P 500. EAFE is composed predominantly of large cap growth stocks. During 1975-2001, international small cap stocks had a higher average return than EAFE indicating a size effect, and international large cap value stocks had a higher average return than EAFE indicating a value effect.Based on the limited amount of data available, size and value effects also appear in emerging markets. The IFC Investables Total Return Index represents a portfolio of tradable stocks in emerging markets countries that non-resident investors are permitted to own. During 1989-2001, emerging markets small cap stocks and value stocks had higher average returns than the IFC index.
The international findings are consistent with Fama and French's interpretation of the size and value effects as rewards for bearing non-diversifiable risk. If size and value effects were related to risk factors unique to the US, forming globally diversified portfolios could eliminate them. Instead, the existence of similar size and value effects in both domestic and international stock returns demonstrates that these effects are global phenomena reflecting exposures to ubiquitous sources of risk.
Implications for Global Equity Allocation
EAFE is the international equivalent of the S&P 500. EAFE returns, expressed in US dollars, are determined jointly by stock returns computed in local currencies and foreign-exchange gains or losses against the dollar. Because the two indexes contain stocks with similar size and value characteristics, it is reasonable to assume that the costs of capital of EAFE and the S&P 500 are approximately equal. If it is also assumed that currencies have zero expected returns, EAFE should have about the same expected gross rate of return as the S&P 500.
Concluding Comments
The identification of size and value factors by Fama and French has important implications for equity portfolio design. Relative to traditional market-like portfolios, portfolios with greater exposures to the size and value factors offer higher expected long-term rates of return.
Structured portfolios can be designed that provide targeted sensitivities to the size and value factors. International and emerging markets equity returns also exhibit size and value effects.
Structured portfolios only make sense for investors with long time horizons and sufficient tolerance for increased risk. For the right investors, structured portfolios are promising alternatives to old-fashioned market-like portfolios.
Tuesday, August 19, 2008
Mohican Buys House
Yes it is true and here is how it happened.
Me, Mrs Mohican, and Baby Mohican are expecting another addition to the family soon. We don't have enough space for the new baby in our current rental unit and we were considering our options. We were looking at renting a reasonable suite or townhouse as a first option since we don't like the prospect of spending more to own than rent and we especially don't like the value of our property dropping before we even move in.
The rents for the places that would be suitable ranged from $1400 to $1700 / month and the quality range was dramatic. Suffice to say that for me and the Mrs to be happy, we would need to spend $1600 / month to rent a suitable place for a family of four in the Fraser Valley. We would also have the prospect of moving again once we found a suitable place to purchase and we didn't really like the thought of moving with two small children.
The question then came up, what could we purchase for the same amount of money? Here is the formula which you may be familiar with by now:
Fair Value = 100/(5 Year Mortgage Rate) * (Annual Rent - Taxes - Strata - Maintenance)
Fair Value = 100/5.35 * (19,200 - 3,200)
Fair Value = $299,000
So the question became, can we find something to purchase that meets that criteria? The answer, as you've probably figured out by now, is yes we did. This would have been impossible even 3 months ago as we found a brand new unit available from a developer who was trying to unload the last unit in the complex at nearly 20% off the last sale price (May 2008) and more than 20% off current listings in the development. They had several offers fall through due to others being unable to remove subjects (sale of own home) and they were very willing to consider our lowball offer. We came to an agreement at well below the Mohican Fair Market Value and the deal was done.
We are comfortable with securing a residence which costs us less per month than renting an equivalent unit. We are also cognizant of the fact that our residence will likely fall in value over the next few years, a loss that is compensated for by not having to move again and the fact we are paying less than rent to own our new home. Some solace also comes from paying $50,000 less than my neighbour did for a similar unit only three months ago. Additionally, we have a 10 year mortgage payoff plan. The plan is quite realistic so although our Loan to Value ratio is low (60%) to begin with it will decline very quickly toward zero.
I still believe that prices will likely correct 30% to 50% from peak pricing and that some areas and housing types will correct more than others. I have never been tied to the idea of buying at the perfect 'bottom of the market' so I am free to rely on my cash flow / opportunity cost metrics instead or market timing. I am comfortable buying a home for personal use using the formula above but buying an investment property is another issue. We can examine that further at a later date.
Me, Mrs Mohican, and Baby Mohican are expecting another addition to the family soon. We don't have enough space for the new baby in our current rental unit and we were considering our options. We were looking at renting a reasonable suite or townhouse as a first option since we don't like the prospect of spending more to own than rent and we especially don't like the value of our property dropping before we even move in.
The rents for the places that would be suitable ranged from $1400 to $1700 / month and the quality range was dramatic. Suffice to say that for me and the Mrs to be happy, we would need to spend $1600 / month to rent a suitable place for a family of four in the Fraser Valley. We would also have the prospect of moving again once we found a suitable place to purchase and we didn't really like the thought of moving with two small children.
The question then came up, what could we purchase for the same amount of money? Here is the formula which you may be familiar with by now:
Fair Value = 100/(5 Year Mortgage Rate) * (Annual Rent - Taxes - Strata - Maintenance)
Fair Value = 100/5.35 * (19,200 - 3,200)
Fair Value = $299,000
So the question became, can we find something to purchase that meets that criteria? The answer, as you've probably figured out by now, is yes we did. This would have been impossible even 3 months ago as we found a brand new unit available from a developer who was trying to unload the last unit in the complex at nearly 20% off the last sale price (May 2008) and more than 20% off current listings in the development. They had several offers fall through due to others being unable to remove subjects (sale of own home) and they were very willing to consider our lowball offer. We came to an agreement at well below the Mohican Fair Market Value and the deal was done.
We are comfortable with securing a residence which costs us less per month than renting an equivalent unit. We are also cognizant of the fact that our residence will likely fall in value over the next few years, a loss that is compensated for by not having to move again and the fact we are paying less than rent to own our new home. Some solace also comes from paying $50,000 less than my neighbour did for a similar unit only three months ago. Additionally, we have a 10 year mortgage payoff plan. The plan is quite realistic so although our Loan to Value ratio is low (60%) to begin with it will decline very quickly toward zero.
I still believe that prices will likely correct 30% to 50% from peak pricing and that some areas and housing types will correct more than others. I have never been tied to the idea of buying at the perfect 'bottom of the market' so I am free to rely on my cash flow / opportunity cost metrics instead or market timing. I am comfortable buying a home for personal use using the formula above but buying an investment property is another issue. We can examine that further at a later date.
Monday, August 18, 2008
The Undercover Economist
The Wisdom of Crowds?
A single economic forecast is usually wrong. But groups of economic forecasts are often just as mistaken. Why?
By Tim Harford
Posted Saturday, Aug. 9, 2008, at 6:44 AM ET
When people discover that I am an economist, they rarely ask me for my views on subjects that economists know a bit about—such as how to respond to climate change or pay less at a supermarket. Instead, they ask me what will happen to the economy.
Why is it that people won't take "I don't really know" for an answer? People often chuckle about the forecasting skills of economists, but after the snickers die down, they keep demanding more forecasts. Is there any reason to believe that economists can deliver?
One answer can be gleaned from previous forecasts. Back in 1995, economist and Financial Times columnist John Kay examined the record of 34 British forecasters from 1987 to 1994, and he concluded that they were birds of a feather. They tended to make similar forecasts, and then the economy disobligingly did something else, with economic growth usually falling outside the range of all 34 forecasters.
Perhaps forecasting technology has moved on since then, or the British economy is unusually unpredictable? To find out, I repeated John's exercise with forecasts for economic growth for the United Kingdom, United States, and Eurozone over the years 2002-08, diligently collected at the end of each previous year by Consensus Economics.
The results are an eerie echo of John Kay's: For 2004, for example, 20 out of 21 nongovernmental forecasts made in December 2003 were too pessimistic about economic growth in the United Kingdom. The Pollyannas of the U.K. treasury were more optimistic than almost any commercial forecaster and closer getting their forecast right. So, one might suspect that systematic pessimism is to blame.
But, no, in 2005, the economy grew more slowly than 19 out of 21 forecasters had expected at the end of the previous year. The Pollyannas of the U.K. treasury were yet again more optimistic than anyone and thus more wrong than anyone. A year later, all but one of the forecasters were too pessimistic again. Yet at the end of 2001, three-quarters of the forecasters were too optimistic about 2002.
2003 is an interesting anomaly: the one year for which the average U.K. forecast turned out to be close to reality but also the year where the spread between highest and lowest forecast was widest. The rare occasion that the forecasters couldn't agree happened to be the occasion on which they were (on average) right.
Recent U.S. forecasters have done a little better: The spread of forecasts is tighter, and the outcome sometimes falls within that spread. Still, five out of six were too pessimistic about 2003, almost everyone was too pessimistic about 2002, three-quarters were too optimistic about 2005, and nearly nine-tenths too optimistic about 2006. Perversely, the best quantitative end-of-year forecasts were made in December 2006, despite the fact that the credit crunch materialized eight months later to the surprise of almost everybody.
In the Eurozone, forecasting over the past few years has been so wayward that it is kindest to say no more.
The new data seem to confirm Kay's original finding that economic forecasters all tend to be wrong in the same way. Their incentives to flock together are obvious enough.
What is less clear is why the flight of the flock is so often thought to augur much—but then, some astrologers are also profitably employed.
The curious thing is that forecasters often have something useful to say, but it is rarely conveyed in the numerical forecast itself on which so much attention is lavished. For instance, in December 2006, forecasters were warning of the risks of an oil price spike, a sharp rise in the cost of credit, and a dollar crash. The quantitative forecasts are usually wrong and not terribly helpful when right, but forecasters do say things worth hearing, if only you can work out when to listen.Tim Harford is a columnist for the Financial Times. He is the author of The Undercover Economist, and his latest book is The Logic of Life.
Article URL: http://www.slate.com/id/2196827/
A single economic forecast is usually wrong. But groups of economic forecasts are often just as mistaken. Why?
By Tim Harford
Posted Saturday, Aug. 9, 2008, at 6:44 AM ET
When people discover that I am an economist, they rarely ask me for my views on subjects that economists know a bit about—such as how to respond to climate change or pay less at a supermarket. Instead, they ask me what will happen to the economy.
Why is it that people won't take "I don't really know" for an answer? People often chuckle about the forecasting skills of economists, but after the snickers die down, they keep demanding more forecasts. Is there any reason to believe that economists can deliver?
One answer can be gleaned from previous forecasts. Back in 1995, economist and Financial Times columnist John Kay examined the record of 34 British forecasters from 1987 to 1994, and he concluded that they were birds of a feather. They tended to make similar forecasts, and then the economy disobligingly did something else, with economic growth usually falling outside the range of all 34 forecasters.
Perhaps forecasting technology has moved on since then, or the British economy is unusually unpredictable? To find out, I repeated John's exercise with forecasts for economic growth for the United Kingdom, United States, and Eurozone over the years 2002-08, diligently collected at the end of each previous year by Consensus Economics.
The results are an eerie echo of John Kay's: For 2004, for example, 20 out of 21 nongovernmental forecasts made in December 2003 were too pessimistic about economic growth in the United Kingdom. The Pollyannas of the U.K. treasury were more optimistic than almost any commercial forecaster and closer getting their forecast right. So, one might suspect that systematic pessimism is to blame.
But, no, in 2005, the economy grew more slowly than 19 out of 21 forecasters had expected at the end of the previous year. The Pollyannas of the U.K. treasury were yet again more optimistic than anyone and thus more wrong than anyone. A year later, all but one of the forecasters were too pessimistic again. Yet at the end of 2001, three-quarters of the forecasters were too optimistic about 2002.
2003 is an interesting anomaly: the one year for which the average U.K. forecast turned out to be close to reality but also the year where the spread between highest and lowest forecast was widest. The rare occasion that the forecasters couldn't agree happened to be the occasion on which they were (on average) right.
Recent U.S. forecasters have done a little better: The spread of forecasts is tighter, and the outcome sometimes falls within that spread. Still, five out of six were too pessimistic about 2003, almost everyone was too pessimistic about 2002, three-quarters were too optimistic about 2005, and nearly nine-tenths too optimistic about 2006. Perversely, the best quantitative end-of-year forecasts were made in December 2006, despite the fact that the credit crunch materialized eight months later to the surprise of almost everybody.
In the Eurozone, forecasting over the past few years has been so wayward that it is kindest to say no more.
The new data seem to confirm Kay's original finding that economic forecasters all tend to be wrong in the same way. Their incentives to flock together are obvious enough.
What is less clear is why the flight of the flock is so often thought to augur much—but then, some astrologers are also profitably employed.
The curious thing is that forecasters often have something useful to say, but it is rarely conveyed in the numerical forecast itself on which so much attention is lavished. For instance, in December 2006, forecasters were warning of the risks of an oil price spike, a sharp rise in the cost of credit, and a dollar crash. The quantitative forecasts are usually wrong and not terribly helpful when right, but forecasters do say things worth hearing, if only you can work out when to listen.Tim Harford is a columnist for the Financial Times. He is the author of The Undercover Economist, and his latest book is The Logic of Life.
Article URL: http://www.slate.com/id/2196827/
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