LITTLE HOPE FROM MONETARY POLICY
Monetary policy operates through the financial system and banks are capital constrained. Even if intermediaries could lend, consumer balance sheets are not looking great with significant decline in asset values and the resulting increase in leverage. Credit off-take for capex will be cautious given that consumer demand is imploding. Conventional monetary policy is not working and will not work.
DIRECT GOVERNMENT SPENDING IN ISOLATION WILL LEAD TO A FEEBLE RECOVERY WHICH IN ALL LIKLIHOOD WON’T BE SELF SUSTAINING
There will certainly be a first round effect of direct government spending. But in the current environment, I remain doubtful about the efficacy of the multiplier effect. Heightened uncertainty will mute the second round effect due to a rising propensity to save. In fact, I think tax cuts will likely fail completely.
BEST USE OF GOVERNMENT MONEY IS A CLEANUP OF FINANCIAL INTERMEDIARIES
I do not think that bad loans need to be restructured or forgiven outright. Debt relief might create a perverse effect as it will encourage people who are current on their payments to default (worsening the problem). And I can’t think of a way to negate this incentive problem.
The value of doubtful loans or securitized assets needs to be written down fully. Private sector needs to step in with valuation expertise to lend credibility to the exercise. Once the quantum of capital shortage is known, the government needs to inject equity directly. I do not see any problem with public sector ownership of financial institutions as long as there is explicit commitment to privatize at some future date. The authorities need to step in when market failure is apparent. Right now is not the time to ensure allocative efficiency. The first priority is to allocate, then ensure that it is done the right way.
Once trust returns in the financial system, banks need to focus on the developing world to fund projects. The world needs to return to normal. The wealthy save and lend, the poor borrow and invest to create factories, roads and bridges.
MEANWHILE MONETIZED DEFICITS ARE REQUIRED TO SIDESTEP THE DYSFUNCTIONAL POLICY TRANSMISSION AND PREVENT DEFLATIONARY EXPECTATIONS FROM GETTING ENTRENCHED
Given the complexity of the politico-economic process, this clean up will take time and the government needs to sidestep the normal economic process and spend by borrowing directly from the central bank. This will provide a floor to the economy and prevent deflationary expectations from gaining traction.
EVEN WHEN DONE RIGHT; THIS PROCESS WILL TAKE A LONG TIME AND THE DEVELOPING WORLD WILL TAKE THE LEAD IN GROWTH
The factories in Asia that catered to U.S. consumers will no longer be viable. Consumer preferences differ in the less developed economies due to cultural dissimilarity and income levels. No amount of monetary easing can create huge demand for certain luxury goods in countries such as India and China. Production structures in export oriented economies will need to undergo painful restructuring to cater to domestic demand.
IF MY IDEAL POLICY WORLD MATERIALIZES, DEVELOPING COUNTRY EQUITIES WILL OUTPERFORM OVER THE NEXT COMPLETE CYCLE
The world worked in the following way over the last 5-years: home prices climbed leading to a positive wealth effect on U.S. consumption. This demand spilt into the external sector resulting in a higher current account deficit. Similar housing booms in Spain, Ireland and U.K. also reflected in expanding trade deficits. Export oriented economies such as Japan, China, Rest of Asia, and Germany expanded rapidly. This synchronous global upturn led to spiraling commodity prices, transmitting the up-cycle to countries such as Russia, Chile, and Australia. International investors financed the burgeoning U.S. external gap by driving the dollar lower. Global central banks bought U.S. assets (treasuries and agencies) to prevent their currencies from appreciating to benefit from U.S. demand. This chain is broken. Implications are obvious.
Tuesday, February 17, 2009
Sunday, February 8, 2009
CAN ECONOMIC DATA BE USED FOR EQUITY MARKET TIMING? MANUFACTURING ISM CALLING FOR A REVERSAL IN STOCKS ONE-YEAR DOWN
Most economic data is useless for the purpose of predicting markets. Asset prices discount the future economic outlook and are often used as leading indicators. Forecasting financial instrument returns therefore requires predicting the course of leading indicators – basically two steps ahead of the economy.
Yet, leading indicators (excluding asset prices themselves) could potentially be used to differentiate between a “bear market rally”/“false dawns” from a sustainable turn in the outlook. The big question is the following: if there is a divergence between stock returns and the underlying economic outlook defined by a leading indicator, do fundamentals prevail?
I used the manufacturing ISM as a proxy for the future economic outlook. The S&P composite monthly returns are positively correlated with coincident changes in the ISM. Also, the monthly divergence indicator does not add any value to intra-year investment decision-making. However, not all is lost.
The level of the ISM predicts 1-year forward S&P performance. The relationship is inverse and statistically significant. The current level of the ISM suggests a good 2009 for stocks.
ISM AND S&P 500 ARE COINCIDENT
Monthly equity returns covary positively with the change in ISM.
S&P Comp. = 0.60 + 0.15*ISM
Data: Monthly % Changes from January 1948 to December 2008
R^2 = 5%
t-statistic = 6.8
While this information is obvious and good to know, it does not help us too much in our market timing endeavor.
ISM AND S&P COMPOSITE DIVERGENCE INDICATOR NOT USEFUL
To test my divergence hypothesis, I conducted the following experiments:
1. If monthly return on the S&P is positive but the 3-month average (to filter out noise) change in the ISM is negative, I short the index.
Average monthly return: -0.72
Standard deviation: 3.32
No. of trades: 209
2. If monthly return on the S&P is negative but the 3-month average change in the ISM is positive, I go long the index.
Average monthly return: -0.13
Standard deviation: 3.71
No. of trades: 124
The results are disastrous and suggest that either there is momentum in monthly returns or that stocks predict the economy better than the ISM. The lead-lag structure of correlations indicates that S&P composite’s variations predict the future movements in the purchasing manager’s index.
Correlation (S&P (t), ISM (t)) = 0.24
Correlation (S&P (t-1), ISM (t)) = 0.19
Correlation (S&P (t), ISM (t-1)) = 0.02
While these results reinforce my belief in the futility of economic indicators in devising profitable trading strategies, all is not lost.
YEARLY RESULTS PRESENT A DIFFERENT PICTURE
There exists an inverse relationship between the level of the ISM and 1-year forward returns. The reason for this covariance, in my view, is that the current in point the cycle has implications for the likely evolution of the future economic landscape.
Medium to long-term, the invisible hand operates to correct deviations from equilibrium. For instance, high oil prices set into motion various forces to reverse the trend. At steeper valuations, new sources supply such as tar sands become viable. Certain alternative fuels also gain traction. Elasticity of demand also rises with time as people adjust behavior and switch to processes which economize on fuel. In addition, governments reinforce these processes by actual policy or pronouncements which affect expectations of economic agents.
Similarly, a high level of the purchasing manager’s index indicates a mature business cycle with incipient inflationary pressures. In a money targeting regime, rising money demand forces interest rates to rise, working to moderate the cycle. At economic extremes, public outcry makes the authorities more responsive - Central Banks reinforce market forces to increase the speed of reversion to equilibrium.
S&P = 40.63 – 0.60* ISM (t-1)
Data: Yearly from 1948 to 2008, ISM: levels; S&P Comp.: Changes
R^2 = 9%
t-statistic (beta) = -2.51
The coefficient of ISM is statistically significant at the 1% level. The latest manufacturing PMI reading indicates the possibility of solid returns (about 19%) over the next year.
As a next step, I tested the strategy of buying the index when ISM fell below 45. I only considered year-ends to get non-overlapping time intervals.
Mean: 19%
Standard deviation: 15%
Maximum drawdown: -14%
Maximum: 41%
Number of trades: 10
Importantly, 9 out 10 trades made money. The strategy of picking the bottom lost money in 2001. I also tested whether these results represent a chance outcome devoid of any economic significance. A t-test for the difference in mean reward of a buy-hold strategy and the one presented above point to superiority of the PMI based trading scheme and we can assert this with 95% statistical confidence.
THERE ARE ALWAYS CAVEATS; IS THIS TIME DIFFERENT?
Understanding of why the rule worked in the past can also highlight circumstances where it might fail. The only failure occurred in 2001. September 11 was an exogenous shock that severely dented confidence. Additionally, in early part of the decade, equities sported stratospheric valuations. Stocks are clearly no longer exuberantly priced.
The second caveat is that the equilibrating forces I mentioned earlier, fail to operate as hypothesized or there is a longer lag than usual. Indeed, there is reason to believe that this scenario could be relevant today. The transmission mechanism of monetary policy has been rendered dysfunctional by the financial sector crisis.
Yet, given that the authorities are willing to do everything possible to avert debt deflation, I think it is imprudent to bet for a significant decline from current levels. Not that another downdraft is not possible, just that it requires another Great Depression-like outcome. I would bet against that low probability scenario.
Data Source:
http://www.irrationalexuberance.com/
http://www.ism.ws/
Yet, leading indicators (excluding asset prices themselves) could potentially be used to differentiate between a “bear market rally”/“false dawns” from a sustainable turn in the outlook. The big question is the following: if there is a divergence between stock returns and the underlying economic outlook defined by a leading indicator, do fundamentals prevail?
I used the manufacturing ISM as a proxy for the future economic outlook. The S&P composite monthly returns are positively correlated with coincident changes in the ISM. Also, the monthly divergence indicator does not add any value to intra-year investment decision-making. However, not all is lost.
The level of the ISM predicts 1-year forward S&P performance. The relationship is inverse and statistically significant. The current level of the ISM suggests a good 2009 for stocks.
ISM AND S&P 500 ARE COINCIDENT
Monthly equity returns covary positively with the change in ISM.
S&P Comp. = 0.60 + 0.15*ISM
Data: Monthly % Changes from January 1948 to December 2008
R^2 = 5%
t-statistic = 6.8
While this information is obvious and good to know, it does not help us too much in our market timing endeavor.
ISM AND S&P COMPOSITE DIVERGENCE INDICATOR NOT USEFUL
To test my divergence hypothesis, I conducted the following experiments:
1. If monthly return on the S&P is positive but the 3-month average (to filter out noise) change in the ISM is negative, I short the index.
Average monthly return: -0.72
Standard deviation: 3.32
No. of trades: 209
2. If monthly return on the S&P is negative but the 3-month average change in the ISM is positive, I go long the index.
Average monthly return: -0.13
Standard deviation: 3.71
No. of trades: 124
The results are disastrous and suggest that either there is momentum in monthly returns or that stocks predict the economy better than the ISM. The lead-lag structure of correlations indicates that S&P composite’s variations predict the future movements in the purchasing manager’s index.
Correlation (S&P (t), ISM (t)) = 0.24
Correlation (S&P (t-1), ISM (t)) = 0.19
Correlation (S&P (t), ISM (t-1)) = 0.02
While these results reinforce my belief in the futility of economic indicators in devising profitable trading strategies, all is not lost.
YEARLY RESULTS PRESENT A DIFFERENT PICTURE
There exists an inverse relationship between the level of the ISM and 1-year forward returns. The reason for this covariance, in my view, is that the current in point the cycle has implications for the likely evolution of the future economic landscape.
Medium to long-term, the invisible hand operates to correct deviations from equilibrium. For instance, high oil prices set into motion various forces to reverse the trend. At steeper valuations, new sources supply such as tar sands become viable. Certain alternative fuels also gain traction. Elasticity of demand also rises with time as people adjust behavior and switch to processes which economize on fuel. In addition, governments reinforce these processes by actual policy or pronouncements which affect expectations of economic agents.
Similarly, a high level of the purchasing manager’s index indicates a mature business cycle with incipient inflationary pressures. In a money targeting regime, rising money demand forces interest rates to rise, working to moderate the cycle. At economic extremes, public outcry makes the authorities more responsive - Central Banks reinforce market forces to increase the speed of reversion to equilibrium.
S&P = 40.63 – 0.60* ISM (t-1)
Data: Yearly from 1948 to 2008, ISM: levels; S&P Comp.: Changes
R^2 = 9%
t-statistic (beta) = -2.51
The coefficient of ISM is statistically significant at the 1% level. The latest manufacturing PMI reading indicates the possibility of solid returns (about 19%) over the next year.
As a next step, I tested the strategy of buying the index when ISM fell below 45. I only considered year-ends to get non-overlapping time intervals.
Mean: 19%
Standard deviation: 15%
Maximum drawdown: -14%
Maximum: 41%
Number of trades: 10
Importantly, 9 out 10 trades made money. The strategy of picking the bottom lost money in 2001. I also tested whether these results represent a chance outcome devoid of any economic significance. A t-test for the difference in mean reward of a buy-hold strategy and the one presented above point to superiority of the PMI based trading scheme and we can assert this with 95% statistical confidence.
THERE ARE ALWAYS CAVEATS; IS THIS TIME DIFFERENT?
Understanding of why the rule worked in the past can also highlight circumstances where it might fail. The only failure occurred in 2001. September 11 was an exogenous shock that severely dented confidence. Additionally, in early part of the decade, equities sported stratospheric valuations. Stocks are clearly no longer exuberantly priced.
The second caveat is that the equilibrating forces I mentioned earlier, fail to operate as hypothesized or there is a longer lag than usual. Indeed, there is reason to believe that this scenario could be relevant today. The transmission mechanism of monetary policy has been rendered dysfunctional by the financial sector crisis.
Yet, given that the authorities are willing to do everything possible to avert debt deflation, I think it is imprudent to bet for a significant decline from current levels. Not that another downdraft is not possible, just that it requires another Great Depression-like outcome. I would bet against that low probability scenario.
Data Source:
http://www.irrationalexuberance.com/
http://www.ism.ws/
Sunday, February 1, 2009
TRADING GOVT. SECURITIES USING THE SENSEX; EQUITIES LEAD BONDS BY 7-MONTHS
POSITIVE RELATIONSHIP BETWEEN S&P 500 RETURNS AND 10-YEAR U.S. TREASURY YIELD CHANGES
There is a well known positive relationship between U.S. Treasury yields and the S&P 500 returns. There are two interpretations of this covariance. First, risk appetite is pro-cyclical. Likelihood of a strong economy results in allocation in favor of equities versus treasuries. Second, stocks discount the profit outlook that lies ahead and bonds the likely Fed actions. A recession implies earnings downgrades and Fed cuts. An upswing in aggregate demand has the opposite consequences.
10-year U.S. Treasury Yield = -0.41 + 0.88* S&P 500
Data: Monthly percentage changes - July 1990 to December 2008
R^2 = 2%
t-statistic (beta) = 2.02
NEGATIVE RELATIONSHIP BETWEEN SENSEX RETURNS AND 10-YEAR INDIAN GOVERNMENT SECURITY YIELDS
To test the relationship for India, I ran the following regressions:
MONTHLY DATA
10-year GOISEC Yield = -0.02 - 0.75* SENSEX
Data: Monthly percentage changes - January 2000 to December 2007.
R^2 = 10%
t-statistic (beta) = -3.03
10-year GOISEC Yield = -0.26 - 0.41* SENSEX
Data: Monthly percentage changes - November 1998 to December 2008.
R^2 = 2%
t-statistic (beta) = -1.79
DAILY DATA
10-year GOISEC Yield = -0.003 - 0.27* SENSEX
Data: Daily percentage changes – July 2002 to January 2009.
R^2 = 1%
t-statistic (beta) = -4.40
I experimented with different periodicity and time periods just to be sure of the robustness of the results. The message is clear. Contrary to economic logic, an inverse relationship exists between Indian government bond yield changes and equity returns.
THE MARKET THAT GETS IT RIGHT CAN BE USED TO TRADE THE ONE THAT GETS IT WRONG
In my view, the reason behind this counter-intuitive relationship is that different players dominate the landscape in stocks and bonds. Foreign institutional investors determine the rhythm of the SENSEX. Nationalized banks rule the roost in the government securities world.
Based on the above, it seems likely to me that equities get the economic outlook more correct on average. Firstly, international investors have much better access to research and have a keener eye on the global cycle. Secondly, there is the big question of incentives. FIIs are paid to get things right and have to face the music for wrong bets. That is certainly not the case at nationalized banks where there is only downside for assuming risk.
SENSEX GETS IT RIGHT ON AVERAGE
Empirical evidence supports my hypothesis. RBI’s research suggests (Business Cycles and Leading Indicators of Industrial Activity in India, RBI Occasional Papers (2003)) that the SENSEX leads industrial production by about 12-months.
BOND YIELDS LAG EQUITY RETURNS BY ABOUT 7-MONTHS
I ran simple regressions using various monthly lags for stock market fluctuations. The fit parameters work out best for the 7th month.
10-year GOISEC Yield = -0.83 + 0.16* SENSEX (-7)
Data: Monthly percentage changes - November 1998 to December 2008.
R^2 = 8%
t-statistic (beta) = 3.23
LAST YEAR WENT BY THE BOOK; WHAT ABOUT 2009
Given my assessment of the Indian economic cycle, I would not sell government securities with yields at current levels. I get the same message from equities. Issues related to the transmission mechanism of monetary policy can be sorted out only over long periods. For now, the RBI needs to respond with the tools at hand. The only thing that the Reserve Bank can do is to reduce base rates, flood the system with liquidity and hope that deposit/lending rates respond in due course.
Bloomberg is the source for all data.
There is a well known positive relationship between U.S. Treasury yields and the S&P 500 returns. There are two interpretations of this covariance. First, risk appetite is pro-cyclical. Likelihood of a strong economy results in allocation in favor of equities versus treasuries. Second, stocks discount the profit outlook that lies ahead and bonds the likely Fed actions. A recession implies earnings downgrades and Fed cuts. An upswing in aggregate demand has the opposite consequences.
10-year U.S. Treasury Yield = -0.41 + 0.88* S&P 500
Data: Monthly percentage changes - July 1990 to December 2008
R^2 = 2%
t-statistic (beta) = 2.02
NEGATIVE RELATIONSHIP BETWEEN SENSEX RETURNS AND 10-YEAR INDIAN GOVERNMENT SECURITY YIELDS
To test the relationship for India, I ran the following regressions:
MONTHLY DATA
10-year GOISEC Yield = -0.02 - 0.75* SENSEX
Data: Monthly percentage changes - January 2000 to December 2007.
R^2 = 10%
t-statistic (beta) = -3.03
10-year GOISEC Yield = -0.26 - 0.41* SENSEX
Data: Monthly percentage changes - November 1998 to December 2008.
R^2 = 2%
t-statistic (beta) = -1.79
DAILY DATA
10-year GOISEC Yield = -0.003 - 0.27* SENSEX
Data: Daily percentage changes – July 2002 to January 2009.
R^2 = 1%
t-statistic (beta) = -4.40
I experimented with different periodicity and time periods just to be sure of the robustness of the results. The message is clear. Contrary to economic logic, an inverse relationship exists between Indian government bond yield changes and equity returns.
THE MARKET THAT GETS IT RIGHT CAN BE USED TO TRADE THE ONE THAT GETS IT WRONG
In my view, the reason behind this counter-intuitive relationship is that different players dominate the landscape in stocks and bonds. Foreign institutional investors determine the rhythm of the SENSEX. Nationalized banks rule the roost in the government securities world.
Based on the above, it seems likely to me that equities get the economic outlook more correct on average. Firstly, international investors have much better access to research and have a keener eye on the global cycle. Secondly, there is the big question of incentives. FIIs are paid to get things right and have to face the music for wrong bets. That is certainly not the case at nationalized banks where there is only downside for assuming risk.
SENSEX GETS IT RIGHT ON AVERAGE
Empirical evidence supports my hypothesis. RBI’s research suggests (Business Cycles and Leading Indicators of Industrial Activity in India, RBI Occasional Papers (2003)) that the SENSEX leads industrial production by about 12-months.
BOND YIELDS LAG EQUITY RETURNS BY ABOUT 7-MONTHS
I ran simple regressions using various monthly lags for stock market fluctuations. The fit parameters work out best for the 7th month.
10-year GOISEC Yield = -0.83 + 0.16* SENSEX (-7)
Data: Monthly percentage changes - November 1998 to December 2008.
R^2 = 8%
t-statistic (beta) = 3.23
LAST YEAR WENT BY THE BOOK; WHAT ABOUT 2009
Given my assessment of the Indian economic cycle, I would not sell government securities with yields at current levels. I get the same message from equities. Issues related to the transmission mechanism of monetary policy can be sorted out only over long periods. For now, the RBI needs to respond with the tools at hand. The only thing that the Reserve Bank can do is to reduce base rates, flood the system with liquidity and hope that deposit/lending rates respond in due course.
Bloomberg is the source for all data.
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