In his March 4, 2010 blog, Steve LeCompte (webmaster of CXOAG) examines the daily data I provide to estimate the value of the S&P 500 using RYT, since July 2009. His conclusion is that RYT has little or no predictive power from one day to the next. I think Steve LeCompte provides a great service to the Finance community by surveying key discoveries in academia and distilling their essence and usefulness to practitioners. I also do not disagree with Steve's conclusions. However, I find them a bit misplaced. Let me explain. During the Vancouver Olympics you would not expect the scoring system for PAIRS skating to be used for judging ice dancing. Wikipedia's definition states that "Ice dance differs from pair skating by having different requirements for lifts, requiring spins to be performed as a team in a dance hold, and by disallowing throws and jumps." Of course, I'd love to see ice dancers do triple lutzes...
The data I publish on my website is meant to represent the CURRENT state of the valuation of the S&P 500 index not where it is likely to be the NEXT DAY. What I argue is that in order to predict you first have to have a thorough understanding of the mechanisms underlying market valuation. As Steve points out, on average my estimate has had a 0.19% percentage error from the actual index over the period, on a DAILY basis. I do not know of ANY other market valuation model out there that can come close to matching that. Right now, my model is NOT a predictive model.
Now, we teach in Finance departments that if a stock is undervalued then you (the investor) should buy because it is likely to rise in the FUTURE. Of course, it is true under two conditions: 1) the valuation takes a middle of the road view of long-term growth assumptions and future cash-flows, broadly correct rather than accurately wrong and 2) that the company and the economy do NOT experience drastic changes in terms of unforeseen events with BIG consequences (I am not necessarily referring to Black Swans here. For example, a sudden jump in investment opportunities suffices).
My technique is trying to pinpoint as ACCURATELY as possible why the S&P 500 is valued the way it is. In fact, the information as it is currently displayed is NON-actionable. Why? Because I use updated estimates of EPS, inflation risk premium, government yields, which change everyday. I do not make middle of the road assumptions about future growth etc... This fact is actually perfectly consistent with Efficient Market Hypothesis (semi-strong form) that all available information (in particular forecasts) is already included in the pricing of stocks. My discovery in publishing the daily data was actually how strong this result is, given that the flow of new information seems to significantly disturb prices from one day to the next. In other words, contemporaneous DAILY mismatches are not informative of next day's direction.
I understand that Steve is looking for exploit-ability of this techniques, as in the end this is where the rubber meets the road. We actually had a friendly e-mail exchange about it months ago. But I think Steve is asking too much of the model as it is right now. Before GPS was invented, people had a vague notion of their location as they were driving around looking at a map. GPS brought accuracy with respect to that information. This service is useful. Now, you wouldn't say that GPS failed because it wasn't giving driving direction when it first came out.
The next step for RYT IS prediction. I take Steve's analysis seriously and interpret it as a call to get moving in that direction. Agreed. For example, I am currently working on a paper that shows that daily movements in Treasury yields are strongly correlated with S&P 500 price movements during the financial crisis. This and other predictive models for the inputs in the RYT formula will lead to a testable model for trading.
Albany, March 7, 2010
Sunday, March 7, 2010
Thursday, July 16, 2009
Prediction and Three Necessary and Overlooked Ingredients for a Stock Market Recovery
I have been running my S&P 500 RYT valuation model (see paper) since May 1st 2009, and on a daily basis since June 1st, with about a 2-2.5% deviation from actual index value on average. The question in my mind is where is the index going to be in a year from now (target date July 2010)? There are some obvious ingredients for a market recovery that every educated investor knows about: higher corporate earnings and higher earnings growth. I want to discuss a couple of other factors that are overlooked in the current state of understanding in Finance. First of all, marginal tax rates on dividend and interest income, as well as on capital gains do appear in some calculations, but the press and the blogosphere do not seem too worried about these.
Much less obvious are three factors I pay close attention to as affecting the stock market: 1) Inflation expectations, especially the Fed’s target; 2) the inflation risk premium due to uncertainties about the path of future inflation and 3) the yield on 30-year Treasuries. The last two ingredients have actually played the biggest role in my valuation formula in terms of my RYT estimates tracking the daily ups and downs of the index. Before I get into the specifics of what these inputs should be and how they matter, let me zoom out and put down some ideas about what the larger quest is about here…. If I may…
First stop: the dreaded minefield of prediction. One point I’d like to make is that market prediction is not as hopeless as many academics and naysayers may think. In fact, the reason that prediction in Finance appears impossible is twofold. First of all, Finance does not have a complete understanding of the mechanisms that underlie how the stock market is priced…. answering that the mechanism is “the law of supply and demand” doesn’t count, as even if it is true, it explains zilch. Secondly, the previous issue is confounded with the presence of another layer of complexity that is the occurrence of random shocks to the system. Unfortunately, shocks to the system appear unpredictable and their consequences are often ambiguous.
One way to think about random shocks is as omitted relevant variables in a universal model of reality. Randomness is a catchall category for things we can’t account for and do not yet understand, or even believe that we can’t model mathematically to a great degree of accuracy… take your pick. Think about a physical system of two frictionless large scale objects coming at each other on a two-dimensional plane with established speeds and masses. Newtonian physics can predict the future trajectory of these objects after they hit one another. Usually, the prediction is very close to the actual outcome.
If you could decompose all the physical forces going into a coin toss, you could predict which side the coin would land on. The ideal world of 50/50 chances is just that: an ideal world that does not take side with respect to underlying forces or mechanisms, because it assumes no such forces are applied. Statistical distributions are a way to make sense of outcomes we observe but for which the generating processes are not understood. It is like taking a photograph of a landscape and describing the distribution of pixels and saying that the distribution of pixels has certain properties that capture reality, whereas the landscape is a 3D composition that grew out of the natural process of life and competition between species for habitats, for which the laws of existence and development are knowable.
Quantum mechanics is bit more tortuous. Randomness certainly appears to play a role, as for example in the Heisenberg Uncertainty Principle. Observation affects the outcome of the experiment (motion of particles) and there is no way to know beforehand what the outcome will be without interacting with the experiment by observing it. I like the irony of the name “Uncertainty” principle, which captures the essence of my definition of randomness. Einstein himself was dissatisfied with the reliance upon probabilities in quantum mechanics. But even more fundamentally, he believed that nature exists independently of the observer, and the motions of particles are precisely determined. In his view, it is the job of the physicist to uncover the laws of nature that govern these motions, which, in the end, will not require statistical theories. Thus prediction is not hard once you understand the complete model of reality. Of course, I am not trying to make any claims of omniscience… That’s for the “Godhead” to know.
It is interesting that even in the field of behavioral finance, which emerged from a rejection of the standard “rational” economic model; market behavior is talked about as if it were unknowable. The culprit being the famous “irrational” and therefore erratic injection of psychological twists and biases in economic behavior…. See how it is almost like we are talking about randomness again. An interesting schizophrenic aspect of behavioral finance is that scientists there are still trying to detect patterns in these behaviors, i.e. understandable and repeatable logical sequences going from premise to predictable consequences, which they believe they can decipher, maybe only for a brief time when these effects are present.
Forecasting the Forecasters
People who try to chart the course of future stock prices are navigating dangerous and treacherous waters. What needs to be done? We understand that today’s prices factor in today’s expectations about the future. To keep things simple assume that the only three things we have to worry about are next year’s earnings, today’s discount rate (the interest rate used to find the present value of these future earnings) and the growth rate of earnings. Is the discount rate fully determined by current conditions? Not quite. In fact, as Irving Fisher argued more than a century ago, the interest rate should factor in the investors expectations of inflation. In the mid 1970’s Marty Feldstein and Michael Darby showed that marginal tax rates matter, as investors want to insure a fair return after tax as well. In the end, we need to know investors’ expectations of earnings, growth, tax rates and inflation.
To predict ahead we have to find out about the process by which expectations are formed and predict the conditions that will be prevalent at that time in the future when these expectations will be made…. In other words, we need to forecast the forecasters! In economics we believe that expectations at the time they are made must include all the information relevant to the decider. We call those “rational” expectations. Investors learn from the past and do not get fooled again if they become aware that they forgot a key piece of information the second time around.
Maybe it is not as hard as it seems. Let’s see. Imagine that earnings next year are actually going to be at 90 for the S&P 500, but that the current forecast is at 80. First interesting discovery is that today’s price does not depend on how wrong the expectation is from actual. In fact, the actual earnings next year could be 85 and this would still not affect the price today… Does it mean that it doesn’t matter how wrong forecasters are? On the face of it, how could we accept that? What we mean by not being “wrong” is that investors must get a sense that these expectations are rational; i.e. that they are the best forecasts using all information available. A forecast has to be believable! So being wrong is not about missing the target, it is not even about by how much you missed the target, it is about whether you gave a good honest effort that makes sense to people.... Of, course it is useful if the forecast ends-up close to reality ex-post... But let’s get back to that point a bit later…
Secondly, assume that you want to forecast what the market price will be in three months. Do you need to know exactly what the economic conditions will be in three months? Not necessarily. For example, assume that the Fed pursues a credible policy of maintaining an inflation target of 2%. While there may be temporary deviations, with every new forecasting horizon you would reset the clock at 2% for the next 12 months. The stability of monetary policy generates the power to forecast inflation accurately. Dealing with taxes is the same thing. The stability of the tax code also enables tight forecasting. The growth of earnings can be a little bit trickier, because it depends on the two forecasts at the ends of the forecasting period. If the base is lower than you thought, the growth rate will be higher than anticipated starting 3 months from now. Can you know anything about that? Well, there might be something to rely on that makes the growth rate forecast reasonable.
In fact, the only way to gauge that a forecast is reasonable is when it is based on the idea of mean reversion. Getting a sense of return to normal conditions, from a set of either normal business or abnormal business conditions has a comforting aspect to the human mind, and also tends to happen to economic variables such as detrended earnings or earnings growth. Forecasts that are all over the place do not establish a good use of this property and do not instill confidence. This is probably the reason why forecasters use models that have central statistical tendencies, and also why good forecasters will be on average closer to the actuals, given that the actuals hug a trend.
Required Yield Theory (RYT) and the Two Key Ingredients for a Recovery
In the article that sets the foundation for RYT, we did test the model back to the 1950s on a quarterly basis looking at the valuation of the S&P 500, and did very well. Recently, in June 2009, I started looking at daily valuation. It has been surprising to me how volatile the predictions have been and how much in synch they are with the actual figures. This is mostly due to three inputs. The first is expected bottom-up S&P operating earnings, which I adjust on a daily basis by interpolating 12 months forecasts located between now and next month. The second input is the inflation risk premium and the last one is the 30-year Treasury yield. Changes in operating earnings are very small; a few cents every day. Not enough to produce the volatility we observe.
On the other hand, inputting the daily 30-year Treasury yield gets us the desired volatility and directionality. The inflation risk premium I initially assumed was around 0.20% (I consider it is for a 10-year Treasury). I have updated on a bi-weekly basis and the range has been 0.2%-03%. This is an aspect of the model that needs to be improved upon as I am using a two-weeks moving average of implied risk premia (implied by making the RYT formula equal to actual) for my next two weeks worth of estimates. Ideally, I would like to come-up with independent measures. My implied values are very stable however, and they are economically sensible. A 0.2% value as an inflation risk premium is 1/10th of the expected inflation target, which makes sense as the inflation risk premium should probably be a small fraction of the actual expectations. I have reviewed multiple works by Fed economists in that matter and they seem to me all over the place and not really consistent with my observations.
Last but not least, let’s talk about the role of the 30-year Treasury yield. In our RYT article, we show that the 30-year Treasury is related to a measure I call the Fear Premium. When there is flight to safety, investors bid up the price of the 30-year Treasury, and its yield falls. When the 30-year T-yield falls below what we call the required yield; i.e. the yield that makes investors whole after-tax and inflation; we measure that gap as the Fear Premium. Investors are willing to accept a lower yield than the minimum real after-tax return they should be getting (around 2.21%) due to fear. On the other hand, the yield on the stock market will rise by a symmetric amount, because investors are fleeing from stocks to safe bonds. This will depress prices. Hence the 30-year T-yield and S&P 500 prices are moving in the same direction in times of fear.
With the Fed now involved in buying back the 30-year Treasury, this artificially depresses the yields on the 30-year. While this phenomenon is not due to fear, investors who are trying to hedge to preserve an expected return are not able to do so, and if they stay with stocks they will require a higher yield there to compensate them. In other words, my conclusion is that if the Fed tries to keep long-term yields low, it will depress the S&P 500 index.
All in all, it is critical that fear subsides in our economy as it is the precursor of movements in the 30-year Treasury which work to depress the stock market by the mechanism demonstrated above. The Fed’s actions will have only a temporary depressing effect on market values, as the buying program is limited in scope and time. Eventually the yield on the 30-year Treasury must bounce back, which will help the stock market as it will be a sign of liberated pent-up confidence and also will help large scale lenders such as mutual funds and pension funds. Below are my expectations for next year (July 2010):
1. Expected S&P 500 operating earnings for 2011 have to be back above $74 range.
2. Expected BV per share growth for 2011 above the 5.3% range.
3. 30-year Treasury yield must be above 4.5%.
4. Long-run inflation expectations have to remain anchored at 2%.
5. Inflation risk premium below 0.4%
6. Marginal taxes rates remain the same.
7. In that case, the S&P 500 index should end-up above 1045 in a year, as per RYT calculation.
Albany, July 16, 2009
Much less obvious are three factors I pay close attention to as affecting the stock market: 1) Inflation expectations, especially the Fed’s target; 2) the inflation risk premium due to uncertainties about the path of future inflation and 3) the yield on 30-year Treasuries. The last two ingredients have actually played the biggest role in my valuation formula in terms of my RYT estimates tracking the daily ups and downs of the index. Before I get into the specifics of what these inputs should be and how they matter, let me zoom out and put down some ideas about what the larger quest is about here…. If I may…
First stop: the dreaded minefield of prediction. One point I’d like to make is that market prediction is not as hopeless as many academics and naysayers may think. In fact, the reason that prediction in Finance appears impossible is twofold. First of all, Finance does not have a complete understanding of the mechanisms that underlie how the stock market is priced…. answering that the mechanism is “the law of supply and demand” doesn’t count, as even if it is true, it explains zilch. Secondly, the previous issue is confounded with the presence of another layer of complexity that is the occurrence of random shocks to the system. Unfortunately, shocks to the system appear unpredictable and their consequences are often ambiguous.
One way to think about random shocks is as omitted relevant variables in a universal model of reality. Randomness is a catchall category for things we can’t account for and do not yet understand, or even believe that we can’t model mathematically to a great degree of accuracy… take your pick. Think about a physical system of two frictionless large scale objects coming at each other on a two-dimensional plane with established speeds and masses. Newtonian physics can predict the future trajectory of these objects after they hit one another. Usually, the prediction is very close to the actual outcome.
If you could decompose all the physical forces going into a coin toss, you could predict which side the coin would land on. The ideal world of 50/50 chances is just that: an ideal world that does not take side with respect to underlying forces or mechanisms, because it assumes no such forces are applied. Statistical distributions are a way to make sense of outcomes we observe but for which the generating processes are not understood. It is like taking a photograph of a landscape and describing the distribution of pixels and saying that the distribution of pixels has certain properties that capture reality, whereas the landscape is a 3D composition that grew out of the natural process of life and competition between species for habitats, for which the laws of existence and development are knowable.
Quantum mechanics is bit more tortuous. Randomness certainly appears to play a role, as for example in the Heisenberg Uncertainty Principle. Observation affects the outcome of the experiment (motion of particles) and there is no way to know beforehand what the outcome will be without interacting with the experiment by observing it. I like the irony of the name “Uncertainty” principle, which captures the essence of my definition of randomness. Einstein himself was dissatisfied with the reliance upon probabilities in quantum mechanics. But even more fundamentally, he believed that nature exists independently of the observer, and the motions of particles are precisely determined. In his view, it is the job of the physicist to uncover the laws of nature that govern these motions, which, in the end, will not require statistical theories. Thus prediction is not hard once you understand the complete model of reality. Of course, I am not trying to make any claims of omniscience… That’s for the “Godhead” to know.
It is interesting that even in the field of behavioral finance, which emerged from a rejection of the standard “rational” economic model; market behavior is talked about as if it were unknowable. The culprit being the famous “irrational” and therefore erratic injection of psychological twists and biases in economic behavior…. See how it is almost like we are talking about randomness again. An interesting schizophrenic aspect of behavioral finance is that scientists there are still trying to detect patterns in these behaviors, i.e. understandable and repeatable logical sequences going from premise to predictable consequences, which they believe they can decipher, maybe only for a brief time when these effects are present.
Forecasting the Forecasters
People who try to chart the course of future stock prices are navigating dangerous and treacherous waters. What needs to be done? We understand that today’s prices factor in today’s expectations about the future. To keep things simple assume that the only three things we have to worry about are next year’s earnings, today’s discount rate (the interest rate used to find the present value of these future earnings) and the growth rate of earnings. Is the discount rate fully determined by current conditions? Not quite. In fact, as Irving Fisher argued more than a century ago, the interest rate should factor in the investors expectations of inflation. In the mid 1970’s Marty Feldstein and Michael Darby showed that marginal tax rates matter, as investors want to insure a fair return after tax as well. In the end, we need to know investors’ expectations of earnings, growth, tax rates and inflation.
To predict ahead we have to find out about the process by which expectations are formed and predict the conditions that will be prevalent at that time in the future when these expectations will be made…. In other words, we need to forecast the forecasters! In economics we believe that expectations at the time they are made must include all the information relevant to the decider. We call those “rational” expectations. Investors learn from the past and do not get fooled again if they become aware that they forgot a key piece of information the second time around.
Maybe it is not as hard as it seems. Let’s see. Imagine that earnings next year are actually going to be at 90 for the S&P 500, but that the current forecast is at 80. First interesting discovery is that today’s price does not depend on how wrong the expectation is from actual. In fact, the actual earnings next year could be 85 and this would still not affect the price today… Does it mean that it doesn’t matter how wrong forecasters are? On the face of it, how could we accept that? What we mean by not being “wrong” is that investors must get a sense that these expectations are rational; i.e. that they are the best forecasts using all information available. A forecast has to be believable! So being wrong is not about missing the target, it is not even about by how much you missed the target, it is about whether you gave a good honest effort that makes sense to people.... Of, course it is useful if the forecast ends-up close to reality ex-post... But let’s get back to that point a bit later…
Secondly, assume that you want to forecast what the market price will be in three months. Do you need to know exactly what the economic conditions will be in three months? Not necessarily. For example, assume that the Fed pursues a credible policy of maintaining an inflation target of 2%. While there may be temporary deviations, with every new forecasting horizon you would reset the clock at 2% for the next 12 months. The stability of monetary policy generates the power to forecast inflation accurately. Dealing with taxes is the same thing. The stability of the tax code also enables tight forecasting. The growth of earnings can be a little bit trickier, because it depends on the two forecasts at the ends of the forecasting period. If the base is lower than you thought, the growth rate will be higher than anticipated starting 3 months from now. Can you know anything about that? Well, there might be something to rely on that makes the growth rate forecast reasonable.
In fact, the only way to gauge that a forecast is reasonable is when it is based on the idea of mean reversion. Getting a sense of return to normal conditions, from a set of either normal business or abnormal business conditions has a comforting aspect to the human mind, and also tends to happen to economic variables such as detrended earnings or earnings growth. Forecasts that are all over the place do not establish a good use of this property and do not instill confidence. This is probably the reason why forecasters use models that have central statistical tendencies, and also why good forecasters will be on average closer to the actuals, given that the actuals hug a trend.
Required Yield Theory (RYT) and the Two Key Ingredients for a Recovery
In the article that sets the foundation for RYT, we did test the model back to the 1950s on a quarterly basis looking at the valuation of the S&P 500, and did very well. Recently, in June 2009, I started looking at daily valuation. It has been surprising to me how volatile the predictions have been and how much in synch they are with the actual figures. This is mostly due to three inputs. The first is expected bottom-up S&P operating earnings, which I adjust on a daily basis by interpolating 12 months forecasts located between now and next month. The second input is the inflation risk premium and the last one is the 30-year Treasury yield. Changes in operating earnings are very small; a few cents every day. Not enough to produce the volatility we observe.
On the other hand, inputting the daily 30-year Treasury yield gets us the desired volatility and directionality. The inflation risk premium I initially assumed was around 0.20% (I consider it is for a 10-year Treasury). I have updated on a bi-weekly basis and the range has been 0.2%-03%. This is an aspect of the model that needs to be improved upon as I am using a two-weeks moving average of implied risk premia (implied by making the RYT formula equal to actual) for my next two weeks worth of estimates. Ideally, I would like to come-up with independent measures. My implied values are very stable however, and they are economically sensible. A 0.2% value as an inflation risk premium is 1/10th of the expected inflation target, which makes sense as the inflation risk premium should probably be a small fraction of the actual expectations. I have reviewed multiple works by Fed economists in that matter and they seem to me all over the place and not really consistent with my observations.
Last but not least, let’s talk about the role of the 30-year Treasury yield. In our RYT article, we show that the 30-year Treasury is related to a measure I call the Fear Premium. When there is flight to safety, investors bid up the price of the 30-year Treasury, and its yield falls. When the 30-year T-yield falls below what we call the required yield; i.e. the yield that makes investors whole after-tax and inflation; we measure that gap as the Fear Premium. Investors are willing to accept a lower yield than the minimum real after-tax return they should be getting (around 2.21%) due to fear. On the other hand, the yield on the stock market will rise by a symmetric amount, because investors are fleeing from stocks to safe bonds. This will depress prices. Hence the 30-year T-yield and S&P 500 prices are moving in the same direction in times of fear.
With the Fed now involved in buying back the 30-year Treasury, this artificially depresses the yields on the 30-year. While this phenomenon is not due to fear, investors who are trying to hedge to preserve an expected return are not able to do so, and if they stay with stocks they will require a higher yield there to compensate them. In other words, my conclusion is that if the Fed tries to keep long-term yields low, it will depress the S&P 500 index.
All in all, it is critical that fear subsides in our economy as it is the precursor of movements in the 30-year Treasury which work to depress the stock market by the mechanism demonstrated above. The Fed’s actions will have only a temporary depressing effect on market values, as the buying program is limited in scope and time. Eventually the yield on the 30-year Treasury must bounce back, which will help the stock market as it will be a sign of liberated pent-up confidence and also will help large scale lenders such as mutual funds and pension funds. Below are my expectations for next year (July 2010):
1. Expected S&P 500 operating earnings for 2011 have to be back above $74 range.
2. Expected BV per share growth for 2011 above the 5.3% range.
3. 30-year Treasury yield must be above 4.5%.
4. Long-run inflation expectations have to remain anchored at 2%.
5. Inflation risk premium below 0.4%
6. Marginal taxes rates remain the same.
7. In that case, the S&P 500 index should end-up above 1045 in a year, as per RYT calculation.
Albany, July 16, 2009
Saturday, July 4, 2009
Government and Markets: a Call for Sensible Solutions to the Financial Crisis
March 2009. The latest news is another historic rise in the unemployment level and more troubles with major banks and corporations… What to do? I believe that government and markets can work hand in hand to help solve this crisis. Below are three ideas in that direction.
How to Help Homeowners, Mortgage Banks and Restart the Housing Market
Many honest people got into houses that they could not afford based on bad mortgage counseling. But speculators used interest-only or adjustable rate mortgages, with the idea of reselling these houses quickly. These people gambled and lost. They should not get a clean bill of health. This undermines the trust in the rescue package, and the willingness of banks to lend again. The Treasury’s new plan “The Home Affordable Refinance Program” to essentially renegotiate these loans is only partially viable.
Proposal #1: I propose to introduce a new type of mortgage into the marketplace: the Upside Shared Equity Mortgage (USEM). These mortgages would be renegotiated at lower interest rate based on the initial mortgage principal. However, a bank would add a clause in these new contracts that it (the bank) is entitled to getting a set percentage of the capital gains when the family sells the house or transfers the property to another owner. Bequests would be treated the same way. To avoid manipulations, the bank would for example specify that the bank’s cut cannot be smaller than say 20% of the capital gains computed based on the most current tax assessment of the property. If the tax assessment is too stale, banks could mandate that three market value assessments be made by independent assessors. Banks can certainly model the risk associated with capital gains and how this translates into a reduction of the mortgage interest rate. They may take some losses but not as much as under the current Treasury plan, and speculators would be more penalized.
How to Halt the Domino Effect of Toxic Assets Bringing Down the Real Economy
Credit Default Swaps are over-the-counter unregulated insurance contracts. In September 2008, about $60 trillion worth of Credit Default Swaps contracts constituted “contingent” debt for the financial sector which recorded these transactions off-balance sheet! This debt is triggered when third parties default on their debt, mainly mortgage pools. Around that time, the total liabilities shown in the aggregated balance sheet of the financial sector was roughly $25 trillion (including commercial banking, property and casualty and life insurances companies, and excluding pension funds, mutual funds etc..). If one estimates at $30 trillion the effective liabilities resulting from CDSs, by excluding companies on both sides of the market and contracts that expired or got unwound, this means that these credit default swaps were effectively doubling the size of the financial sector liabilities, under the worst case scenario.
It is now recognized that AIG’s downfall was precipitated by them offering up to $450 billion worth of CDSs contracts. When the risk of the underlying debt rose up, it led to a downgrade of AIG and calls for more collateral they did not have. Most of the outstanding CDSs contracts in our economy are “Naked” CDSs; that is transactions in which none of the parties have a direct stake in the underlying insured debt. In other words, these are side-bets on the health of a third party.
Proposal #2: 1) NO money provided by the tax payers for bailing out institutions should be allocated to pay for side-bets “naked” CDSs. More importantly, 2) the Fed and Treasury should work in concert with the financial industry and declare a one-time moratorium on all “naked” CDSs side-bets. The point is that these side-bets can bring the real economy down to its knees by spreading bankruptcies to the real sector, and they should not be allowed to do that. While some hedge funds will cry foul, all these side-bets are based on gambling money. Financial institutions should write-off these side-bets CDSs and bank regulators should allow for temporary modifications regarding the conditions that lead to declaring financial institutions’ insolvency based on the revised equity and assets and capital reserve ratios. Institutions heavily involved (90% or more of the value of assets in CDSs prior to the crisis) in these side-bets should be allowed to fail and should not be rescued by the government.
Another “Philadelphia Experiment”: Gathering the Best Minds in the Country
From May to September 1787 a group of fifty-five of the brightest collection of minds of that time gathered and confined themselves to a meeting room in Philadelphia to hammer out what has become one of the highest and noblest declarations of human potential: the American Constitution. This is such time of emergency and great potential.
Proposal #3: The Federal Government in concert with financial institutions should immediately convene a 4-weeks long convention of the 100 brightest academic and business minds in the country who are experts in Financial Economics, to put their mind at work for solving this crisis. At the end of four weeks, taskforces would report on the best solutions they can come-up with. These solutions would be proposed with the utmost scientific objectivity and with full disclosure of any ideological and political bias. The reports should be presented to congress and legislation should be drafted in the following month to adjust and rectify ongoing policies as seen fit.
How to Help Homeowners, Mortgage Banks and Restart the Housing Market
Many honest people got into houses that they could not afford based on bad mortgage counseling. But speculators used interest-only or adjustable rate mortgages, with the idea of reselling these houses quickly. These people gambled and lost. They should not get a clean bill of health. This undermines the trust in the rescue package, and the willingness of banks to lend again. The Treasury’s new plan “The Home Affordable Refinance Program” to essentially renegotiate these loans is only partially viable.
Proposal #1: I propose to introduce a new type of mortgage into the marketplace: the Upside Shared Equity Mortgage (USEM). These mortgages would be renegotiated at lower interest rate based on the initial mortgage principal. However, a bank would add a clause in these new contracts that it (the bank) is entitled to getting a set percentage of the capital gains when the family sells the house or transfers the property to another owner. Bequests would be treated the same way. To avoid manipulations, the bank would for example specify that the bank’s cut cannot be smaller than say 20% of the capital gains computed based on the most current tax assessment of the property. If the tax assessment is too stale, banks could mandate that three market value assessments be made by independent assessors. Banks can certainly model the risk associated with capital gains and how this translates into a reduction of the mortgage interest rate. They may take some losses but not as much as under the current Treasury plan, and speculators would be more penalized.
How to Halt the Domino Effect of Toxic Assets Bringing Down the Real Economy
Credit Default Swaps are over-the-counter unregulated insurance contracts. In September 2008, about $60 trillion worth of Credit Default Swaps contracts constituted “contingent” debt for the financial sector which recorded these transactions off-balance sheet! This debt is triggered when third parties default on their debt, mainly mortgage pools. Around that time, the total liabilities shown in the aggregated balance sheet of the financial sector was roughly $25 trillion (including commercial banking, property and casualty and life insurances companies, and excluding pension funds, mutual funds etc..). If one estimates at $30 trillion the effective liabilities resulting from CDSs, by excluding companies on both sides of the market and contracts that expired or got unwound, this means that these credit default swaps were effectively doubling the size of the financial sector liabilities, under the worst case scenario.
It is now recognized that AIG’s downfall was precipitated by them offering up to $450 billion worth of CDSs contracts. When the risk of the underlying debt rose up, it led to a downgrade of AIG and calls for more collateral they did not have. Most of the outstanding CDSs contracts in our economy are “Naked” CDSs; that is transactions in which none of the parties have a direct stake in the underlying insured debt. In other words, these are side-bets on the health of a third party.
Proposal #2: 1) NO money provided by the tax payers for bailing out institutions should be allocated to pay for side-bets “naked” CDSs. More importantly, 2) the Fed and Treasury should work in concert with the financial industry and declare a one-time moratorium on all “naked” CDSs side-bets. The point is that these side-bets can bring the real economy down to its knees by spreading bankruptcies to the real sector, and they should not be allowed to do that. While some hedge funds will cry foul, all these side-bets are based on gambling money. Financial institutions should write-off these side-bets CDSs and bank regulators should allow for temporary modifications regarding the conditions that lead to declaring financial institutions’ insolvency based on the revised equity and assets and capital reserve ratios. Institutions heavily involved (90% or more of the value of assets in CDSs prior to the crisis) in these side-bets should be allowed to fail and should not be rescued by the government.
Another “Philadelphia Experiment”: Gathering the Best Minds in the Country
From May to September 1787 a group of fifty-five of the brightest collection of minds of that time gathered and confined themselves to a meeting room in Philadelphia to hammer out what has become one of the highest and noblest declarations of human potential: the American Constitution. This is such time of emergency and great potential.
Proposal #3: The Federal Government in concert with financial institutions should immediately convene a 4-weeks long convention of the 100 brightest academic and business minds in the country who are experts in Financial Economics, to put their mind at work for solving this crisis. At the end of four weeks, taskforces would report on the best solutions they can come-up with. These solutions would be proposed with the utmost scientific objectivity and with full disclosure of any ideological and political bias. The reports should be presented to congress and legislation should be drafted in the following month to adjust and rectify ongoing policies as seen fit.
The Dire Need for CIOs: Chief Imagination Officers
An interesting trend is developing following the onset of the worst financial crisis since 1929: the rapid rise of Chief Risk Officers (CROs). In many businesses, risk management has become the equivalent of what the Chief Environmental Officers were in the 1990s: a necessity of business life. The CRO job is fairly new and I am a wondering whether this function really requires a full time position and whether it should be outsourced to specialized engineering firms. I can see the value though for institutions managing money, or the ones that require tremendous amounts of hedging in a global economy. The job has been around since the year 2000, and Enron even had its own CRO before the “Enron scandal”.
One such job description from 2006 states: “One key to success for CROs is the ability to see the range of risk variations that can crop up across the enterprise. At the AAA Inc., a mortgage insurance company in BBB, the CRO position was created in 2003 to monitor international credit-risk operations. But the position's description has since been expanded to encompass risk throughout the company, including strategic, operational, external, financial, IT and security (both data and physical) operations.” The job seems to entails a strong familiarity with forecasting and risk modeling. The function is often associated with Enterprise Resource Management systems.
In this essay, I argue that there are good reasons why CROs and the risk models and forecasting methods which existed prior to the crisis did not protect these firms against the crisis and that a new breed of analyst is needed: the CIO or Chief Imagination Officer.
Black Swans and Fat Tail Distributions
One of the arguments put forth by Nicholas Nassim Taleb in his book Black Swans is that distribution of stocks returns have fat tails so that large losses have a greater probability of occurrence than the ones predicted by the typical Value at Risk Models. Taleb defines a black swan as follows: “First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. Second, it carries an extreme impact. Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.” Taleb’s point however is not that we need better models that incorporate these new types of distribution. It would be too simplistic and would not fix the underlying problem. No. Taleb is after a more nihilistic view of Finance. The world of Finance is not underpinned by scientific laws and immutable principles; it is a great big wheel of fortune. Any systematic attempt at modeling random processes in Finance is futile and the whiplash of randomness will hit you right back in the face in due time.
Taleb however, takes an interesting position as he himself alludes to his own success in profiting from these rare and conflagrating events. Intellectually, his position is untenable if it is viewed it as a claim that he has an edge in anticipating rare events. How can it be so if a priori such events are extremely rare and not predictable? More likely is that he is capitalizing on judgment biases demonstrated by market participants, who contrary to Taleb, do not recognize that they are walking towards a cliff, staring at the edge without seeing it. Some portfolio managers have a tendency to fall prey to the self-confirmation bias, which makes them think that reality is “wrong”, when it turns against their view of the world, and are confident that reality will correct itself. Focusing too much on fat tails and black swans is also a distraction. In Taleb’s narrative, he builds himself as an expert with the same heavy quantitative training that he is decrying. I believe that his true trading skill is his imagination and web-like understanding of markets and economic interactions. Thus, while fat-tail distributions can add information, the correct understanding of confluences of trading and economic patterns is primordial.
A Missing Component of Risk and Forecast Models: Imagination
From what I can surmise the actual job of the CRO is not to improve understanding of the possible mechanisms that will generate the next seismic economic event(s) affecting the firm, but rather he/she is to serve as Chicken Little. By definition of the job, the CRO must show activity by crying wolf at any chance he/she gets, and attempts to put a lid on risk-taking in the businesses they oversee. This bias is probably the reason why CROs did not have veto power against buying and selling financial instruments delivering “easy money” in the run-up to the crisis. It is also true that in most of the companies that were first casualties of the crisis, the risk models were ineffective and their CROs did not have privy knowledge to warn their bosses of an impending meltdown, or if they did, they weren’t listened to.
I remember in the 1970s, the buzz around Alvin Toffler who was a self-proclaimed futurist, and made a career out of it over the next 30 years. Since the 1980s, the Pentagon and CIA employ futurists (people with very big imaginations; essentially sci-fi writers) and game theorists to analyze possible geopolitical catastrophic scenarios. What is the difference between quantitative forecasting models and sci-fi? The big picture! Sci-Fi writers can create stylized brave new worlds, with all their complexity, interactivity at the social, technological and economic levels. Quantitative models are very simple minded. Even AI and game theoretical models do not have the “realistic” flavor that worlds created by the imagination have.
Recently, I have been intrigued by the work of NYU Political Science Prof. De Mesquita, who forecasts geopolitical events using a game-theoretic model. His work was used by the CIA, and seems to have a good track record of predictions based on describing a web of geopolitical interactions and conflicting interests. In the realm of international politics, De Mesquita appears to have augmented the basic game theoretic framework with more narrative and complex structure similar to building a “sci-fi” world and assigning probabilities to alternate futures. He states (in Theory Talk #31):
“The forecasting and policy engineering model I developed assumes that stakeholders on any policy issue care about two things: the outcome on the issue and the extent to which they are seen as instrumental in putting an agreement together (or blocking one). The model specifies a rather simple game and solves the game, in the process estimating how much each stakeholder values the policy outcome relative to being seen as instrumental in shaping the outcome. It also estimates how each player perceives its relationship with each other player, what proposals players make to each other regarding resolution of the issue (including no proposal at all) on a round by round basis. The model estimates how player positions change and also updates player estimates of the willingness of others to take risks. It does quite a bit more as well. This model depends on expert inputs based on an intensive interview process that elicits who the stakeholders are who will try to influence an outcome, what outcome they currently argue for, how much persuasive clout they could bring to bear, and how salient the issue is to them compared to other issues on their plate. Experts are not asked how they think the issue will be resolved and the model frequently disagrees with the conventional wisdom on what is likely to happen.”
What Taleb calls the failure of post-facto rationalization, I contrast with the possibility of enhancing economic rational models with imagination and more complex narratives, the same way De Mesquita has done in Political Science. The CIA is currently looking to hire laid-off Wall-Street analysts. I think it is easier to teach financial and risk models than to teach people to use their imagination and to sort out complex interactions. Thus, the hiring should probably go in the other direction. A new breed of financial analysts combining imagination of future webs of market interactions with the understanding of human interactions and judgment biases, basic arbitrage strategies and the laws of asset valuation should be able to produce a very useful service and lucrative business in the next decade.
One such job description from 2006 states: “One key to success for CROs is the ability to see the range of risk variations that can crop up across the enterprise. At the AAA Inc., a mortgage insurance company in BBB, the CRO position was created in 2003 to monitor international credit-risk operations. But the position's description has since been expanded to encompass risk throughout the company, including strategic, operational, external, financial, IT and security (both data and physical) operations.” The job seems to entails a strong familiarity with forecasting and risk modeling. The function is often associated with Enterprise Resource Management systems.
In this essay, I argue that there are good reasons why CROs and the risk models and forecasting methods which existed prior to the crisis did not protect these firms against the crisis and that a new breed of analyst is needed: the CIO or Chief Imagination Officer.
Black Swans and Fat Tail Distributions
One of the arguments put forth by Nicholas Nassim Taleb in his book Black Swans is that distribution of stocks returns have fat tails so that large losses have a greater probability of occurrence than the ones predicted by the typical Value at Risk Models. Taleb defines a black swan as follows: “First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. Second, it carries an extreme impact. Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.” Taleb’s point however is not that we need better models that incorporate these new types of distribution. It would be too simplistic and would not fix the underlying problem. No. Taleb is after a more nihilistic view of Finance. The world of Finance is not underpinned by scientific laws and immutable principles; it is a great big wheel of fortune. Any systematic attempt at modeling random processes in Finance is futile and the whiplash of randomness will hit you right back in the face in due time.
Taleb however, takes an interesting position as he himself alludes to his own success in profiting from these rare and conflagrating events. Intellectually, his position is untenable if it is viewed it as a claim that he has an edge in anticipating rare events. How can it be so if a priori such events are extremely rare and not predictable? More likely is that he is capitalizing on judgment biases demonstrated by market participants, who contrary to Taleb, do not recognize that they are walking towards a cliff, staring at the edge without seeing it. Some portfolio managers have a tendency to fall prey to the self-confirmation bias, which makes them think that reality is “wrong”, when it turns against their view of the world, and are confident that reality will correct itself. Focusing too much on fat tails and black swans is also a distraction. In Taleb’s narrative, he builds himself as an expert with the same heavy quantitative training that he is decrying. I believe that his true trading skill is his imagination and web-like understanding of markets and economic interactions. Thus, while fat-tail distributions can add information, the correct understanding of confluences of trading and economic patterns is primordial.
A Missing Component of Risk and Forecast Models: Imagination
From what I can surmise the actual job of the CRO is not to improve understanding of the possible mechanisms that will generate the next seismic economic event(s) affecting the firm, but rather he/she is to serve as Chicken Little. By definition of the job, the CRO must show activity by crying wolf at any chance he/she gets, and attempts to put a lid on risk-taking in the businesses they oversee. This bias is probably the reason why CROs did not have veto power against buying and selling financial instruments delivering “easy money” in the run-up to the crisis. It is also true that in most of the companies that were first casualties of the crisis, the risk models were ineffective and their CROs did not have privy knowledge to warn their bosses of an impending meltdown, or if they did, they weren’t listened to.
I remember in the 1970s, the buzz around Alvin Toffler who was a self-proclaimed futurist, and made a career out of it over the next 30 years. Since the 1980s, the Pentagon and CIA employ futurists (people with very big imaginations; essentially sci-fi writers) and game theorists to analyze possible geopolitical catastrophic scenarios. What is the difference between quantitative forecasting models and sci-fi? The big picture! Sci-Fi writers can create stylized brave new worlds, with all their complexity, interactivity at the social, technological and economic levels. Quantitative models are very simple minded. Even AI and game theoretical models do not have the “realistic” flavor that worlds created by the imagination have.
Recently, I have been intrigued by the work of NYU Political Science Prof. De Mesquita, who forecasts geopolitical events using a game-theoretic model. His work was used by the CIA, and seems to have a good track record of predictions based on describing a web of geopolitical interactions and conflicting interests. In the realm of international politics, De Mesquita appears to have augmented the basic game theoretic framework with more narrative and complex structure similar to building a “sci-fi” world and assigning probabilities to alternate futures. He states (in Theory Talk #31):
“The forecasting and policy engineering model I developed assumes that stakeholders on any policy issue care about two things: the outcome on the issue and the extent to which they are seen as instrumental in putting an agreement together (or blocking one). The model specifies a rather simple game and solves the game, in the process estimating how much each stakeholder values the policy outcome relative to being seen as instrumental in shaping the outcome. It also estimates how each player perceives its relationship with each other player, what proposals players make to each other regarding resolution of the issue (including no proposal at all) on a round by round basis. The model estimates how player positions change and also updates player estimates of the willingness of others to take risks. It does quite a bit more as well. This model depends on expert inputs based on an intensive interview process that elicits who the stakeholders are who will try to influence an outcome, what outcome they currently argue for, how much persuasive clout they could bring to bear, and how salient the issue is to them compared to other issues on their plate. Experts are not asked how they think the issue will be resolved and the model frequently disagrees with the conventional wisdom on what is likely to happen.”
What Taleb calls the failure of post-facto rationalization, I contrast with the possibility of enhancing economic rational models with imagination and more complex narratives, the same way De Mesquita has done in Political Science. The CIA is currently looking to hire laid-off Wall-Street analysts. I think it is easier to teach financial and risk models than to teach people to use their imagination and to sort out complex interactions. Thus, the hiring should probably go in the other direction. A new breed of financial analysts combining imagination of future webs of market interactions with the understanding of human interactions and judgment biases, basic arbitrage strategies and the laws of asset valuation should be able to produce a very useful service and lucrative business in the next decade.
The Nature of what is Knowable about Financial Markets and the Conditions for Profitable Trades
Imagine you wake-up tomorrow, turn on the TV to the money channel and hear the following news: “A group of researchers is scheduled for a press conference at the AAA-think-tank later this morning. An announcement is expected that the mystery of how financial securities are priced has finally been cracked. They claim they have found a way to accurately predict daily prices for stocks and other financial assets such as government bonds.” What would your reaction be? Indifference? Disbelief? A tinge of curiosity or perhaps excitement? In all likelihood, you may welcome this piece of news with a healthy dose of skepticism.
Let us for a minute suspend disbelief and assume that this claim is indeed true. The first question one may ask is: what is that mechanism behind the determination of securities’ prices? But, as the architect of the Matrix in the second movie installment of the series says when addressing Neo: “Your first question while pertinent is not necessarily the most relevant one.” There are more pressing questions:
• What do they mean by predicting asset prices?
• Have they found a basis for telling the true worth of financial assets?
First of all, these people can’t be serious in claiming to foretell stock market prices. Most of us understand and accept that the future is unknowable. The best that anyone can do is to make a market forecast given all the information and expectations available at the time. Near term forecasts are more reliable than farsighted ones. Hence, while these researchers may have solved the riddle of how stocks and Treasuries are priced, it is a different and much harder ballgame to predict future stock prices. Taking an extreme example: who outside conspiracy theorists could have foretold the tragedy of 9/11 and its human and economic toll, even hours prior to the event?
Without knowing how our group of researchers solved this riddle, we should first understand a very important basic fact about how financial assets are priced. Since assets are claims against future cash flows, their prices must be based on forecasts of these cash flows and surrounding financial and economic circumstances, or what we economists call expectations. More importantly, it is often overlooked that while expectations are a primordial input, they do not have to be correct in the sense that they will be actually realized. Of course expectations cannot deviate from reality too much or for too long…. More on that in another article….
Markets continuously react to new information. Future information is by definition not yet available to forecasters at the time of their forecast. The TV commentator and our team of researchers used the word “predict” a bit cavalierly. Rather it is likely that what they mean is that they have achieved a eureka-type of understanding of the process by which these financial assets are priced, one of these fundamental insights that sometimes brings all of us to exclaim “I got it!” Predicting is not the same as understanding and explaining.
Explaining is an important and necessary step but predicting may be even more important, because that is what allows human beings to take action. Prediction allows us to make sense of the likely consequences certain actions will have. Thus we avoid some actions and embrace others. Physics can explain the motion of large celestial bodies in simple two or three bodies systems. However, the whole venture would be quite worthless if Physics could not then predict planetary and rocket trajectories when we send astronauts to the moon. In Finance, the difficulty of prediction is compounded over two layers of information: 1) the forecasts of actual economic data and 2) how investors’ (subjective) expectations are set in response to these forecasts.
Back to our team and their discovery. How do they know their theory is correct? In a sense, their theory has to make an assessment regarding stock market prices and match reality. It is the comparison of their assessment of the stock price versus what it actually is, which will confirm or infirm their theory. Many researchers back test their theories. Looking in the past, our economists must be able to match the prices very accurately, given that in hindsight it is easy to piece together all the information about the correct set of expectations relevant at the time, some of which may have escaped us even then.
For the purpose of simplicity, let us imagine that our researchers incorporate all the relevant information and have come-up with a formula to price the market in real time. Their formula matches the actual stock market price tick by tick. For example, the stock market index is at 1,500 on November 2nd at 1:30 pm and our group of economists simultaneously issues a statement: “Yes, our calculations confirm that the index is worth exactly 1,500”. Anytime the index moves, the same statement is re-issued by our economists.
What a wonderful but boring world this would be! It is a bit like the movie “Stepford Wives”, in which “imperfect” human beings perceived as disagreeable by their spouses are replaced by plastic-perfect and predictable robots. There would be no chances to be taken, no possibility to make a profit by buying at a lower price than what it is worth. Having found the key to valuing these securities could in fact be very disappointing for many of us. On the other hand, this outcome may be fine for many investors, since they are getting exactly what they pay for. There is no win-lose situation here. A trader is still able to realize a positive return. For example, buying a fair priced Treasury at a quoted yield of 5%, would earn you a $50 coupon year after year. However, there does not seem to be any room for making huge profits, or is there?
Some traders may still book great profits because of the flow of new information and the game of predicting future price movements. For example, “bad” news about the subprime mortgage market, and the losses incurred by financial companies may temporarily depress the value of the whole stock market. Investors may infer that when the environment gets better and toxic assets have been cleaned-up, the index will get back to its “normal” range because the profitability of these firms will then revert back to normal given the other areas they operate in (consumer loans, corporate loans, insurance etc…), and thus prices may eventually rise faster than if the information had been neutral in the first place. In fact, some investors having cash on hand may see this as a great buying opportunity.
Assume that the assessment of the future path of recovery of the index is shared by the demand side of the market. The decision to buy the index at that time reflects the view of how the index’s price is likely to rise in the future. On the other hand, the seller (supply-side) must either be facing an immediate need for cash, i.e. they need to sell exactly at this time to get cash, or they must share a more somber view of the future price path. In their mind the index is likely to drop further. At any rate, they feel the recovery is uncertain and don’t want to hold the stock.
It is then interesting to note at this point that a complete and exact understanding of how securities are priced does not remove or prevent profit opportunities. Profit opportunities are in part the result of disagreements amongst traders about the future. In a way, this is a version of what economists call the “efficient market hypothesis” that stock prices are unpredictable. The reason is that future events which may affect stock prices are unpredictable. However, if stock prices are explainable they must contain a non-random or stable component. In fact, we have suggested in our example that the mean-reversion of corporate profitability to a normal range may provide the ground to establish that stable component.
I compare Finance to driving an automobile at night on a narrow country road in France. You know you will eventually get to your final destination and you know the principles of driving a car, but you don’t know that there is a sharp turn coming up in 10 miles. You can only react to the new set of information as your headlights reveal the road ahead. New information is critical and can also affect the outcome quite a bit if your fall in the ravine when missing your turn. You are also affected by yours and other drivers’ expectations of road conduct and risk taking. Physics by contrast is like riding a train (in France). You have a precise idea of how long the trip will be, there is no question that you will arrive at your exact destination and so you can close your eyes and relax because the train does not need any extraneous information along the way, as it is positioned on a fixed track and its speed is predetermined and there is only a remote probability for unexpected changes in the surrounding environment.
Notwithstanding, our imagined new financial theory is still an exaggeration and no theory in the realm of social sciences can claim to achieve that level of reality matching. In other words, while they are fully confident about their insight our team of economists is facing a bit of a conundrum. Assume the stock market index is at 1,300, and their theory finds that it should be trading at 1,275. To keep it simple, assume that over the last month the index has stayed at that same level of 1,300, and our team derived the same result that the index is worth 1,275. Are the economists correct, leading to a trading opportunity, or are they wrong? If the economists are right, then the stock market is currently overpriced and investors should short (sell) the index.
If the discrepancy is due to a systematic error in the theory, then this puts the theory in jeopardy. On the other hand, if it is true that the values that the economists keep predicting over time and on average are closer to actual prices than any other theory, this must be a good sign that the theory is superior to existing ones. What explains the difference between actual and theory-induced values then? Economists call it residual noise, i.e. unaccounted and unpredictable factors. Indeed, the best our economists can aspire to is to have figured out a reliable mechanism.
Because mechanical engineers understand how power generating turbines work does not necessarily mean they can always account for the behavior and performance of a given gas turbine. Obtaining all the environmental conditions to make that determination would be too costly. They can however explain performance within a tight and satisfactory range. This is, I believe, the best that can be expected from us in Finance.
Let us for a minute suspend disbelief and assume that this claim is indeed true. The first question one may ask is: what is that mechanism behind the determination of securities’ prices? But, as the architect of the Matrix in the second movie installment of the series says when addressing Neo: “Your first question while pertinent is not necessarily the most relevant one.” There are more pressing questions:
• What do they mean by predicting asset prices?
• Have they found a basis for telling the true worth of financial assets?
First of all, these people can’t be serious in claiming to foretell stock market prices. Most of us understand and accept that the future is unknowable. The best that anyone can do is to make a market forecast given all the information and expectations available at the time. Near term forecasts are more reliable than farsighted ones. Hence, while these researchers may have solved the riddle of how stocks and Treasuries are priced, it is a different and much harder ballgame to predict future stock prices. Taking an extreme example: who outside conspiracy theorists could have foretold the tragedy of 9/11 and its human and economic toll, even hours prior to the event?
Without knowing how our group of researchers solved this riddle, we should first understand a very important basic fact about how financial assets are priced. Since assets are claims against future cash flows, their prices must be based on forecasts of these cash flows and surrounding financial and economic circumstances, or what we economists call expectations. More importantly, it is often overlooked that while expectations are a primordial input, they do not have to be correct in the sense that they will be actually realized. Of course expectations cannot deviate from reality too much or for too long…. More on that in another article….
Markets continuously react to new information. Future information is by definition not yet available to forecasters at the time of their forecast. The TV commentator and our team of researchers used the word “predict” a bit cavalierly. Rather it is likely that what they mean is that they have achieved a eureka-type of understanding of the process by which these financial assets are priced, one of these fundamental insights that sometimes brings all of us to exclaim “I got it!” Predicting is not the same as understanding and explaining.
Explaining is an important and necessary step but predicting may be even more important, because that is what allows human beings to take action. Prediction allows us to make sense of the likely consequences certain actions will have. Thus we avoid some actions and embrace others. Physics can explain the motion of large celestial bodies in simple two or three bodies systems. However, the whole venture would be quite worthless if Physics could not then predict planetary and rocket trajectories when we send astronauts to the moon. In Finance, the difficulty of prediction is compounded over two layers of information: 1) the forecasts of actual economic data and 2) how investors’ (subjective) expectations are set in response to these forecasts.
Back to our team and their discovery. How do they know their theory is correct? In a sense, their theory has to make an assessment regarding stock market prices and match reality. It is the comparison of their assessment of the stock price versus what it actually is, which will confirm or infirm their theory. Many researchers back test their theories. Looking in the past, our economists must be able to match the prices very accurately, given that in hindsight it is easy to piece together all the information about the correct set of expectations relevant at the time, some of which may have escaped us even then.
For the purpose of simplicity, let us imagine that our researchers incorporate all the relevant information and have come-up with a formula to price the market in real time. Their formula matches the actual stock market price tick by tick. For example, the stock market index is at 1,500 on November 2nd at 1:30 pm and our group of economists simultaneously issues a statement: “Yes, our calculations confirm that the index is worth exactly 1,500”. Anytime the index moves, the same statement is re-issued by our economists.
What a wonderful but boring world this would be! It is a bit like the movie “Stepford Wives”, in which “imperfect” human beings perceived as disagreeable by their spouses are replaced by plastic-perfect and predictable robots. There would be no chances to be taken, no possibility to make a profit by buying at a lower price than what it is worth. Having found the key to valuing these securities could in fact be very disappointing for many of us. On the other hand, this outcome may be fine for many investors, since they are getting exactly what they pay for. There is no win-lose situation here. A trader is still able to realize a positive return. For example, buying a fair priced Treasury at a quoted yield of 5%, would earn you a $50 coupon year after year. However, there does not seem to be any room for making huge profits, or is there?
Some traders may still book great profits because of the flow of new information and the game of predicting future price movements. For example, “bad” news about the subprime mortgage market, and the losses incurred by financial companies may temporarily depress the value of the whole stock market. Investors may infer that when the environment gets better and toxic assets have been cleaned-up, the index will get back to its “normal” range because the profitability of these firms will then revert back to normal given the other areas they operate in (consumer loans, corporate loans, insurance etc…), and thus prices may eventually rise faster than if the information had been neutral in the first place. In fact, some investors having cash on hand may see this as a great buying opportunity.
Assume that the assessment of the future path of recovery of the index is shared by the demand side of the market. The decision to buy the index at that time reflects the view of how the index’s price is likely to rise in the future. On the other hand, the seller (supply-side) must either be facing an immediate need for cash, i.e. they need to sell exactly at this time to get cash, or they must share a more somber view of the future price path. In their mind the index is likely to drop further. At any rate, they feel the recovery is uncertain and don’t want to hold the stock.
It is then interesting to note at this point that a complete and exact understanding of how securities are priced does not remove or prevent profit opportunities. Profit opportunities are in part the result of disagreements amongst traders about the future. In a way, this is a version of what economists call the “efficient market hypothesis” that stock prices are unpredictable. The reason is that future events which may affect stock prices are unpredictable. However, if stock prices are explainable they must contain a non-random or stable component. In fact, we have suggested in our example that the mean-reversion of corporate profitability to a normal range may provide the ground to establish that stable component.
I compare Finance to driving an automobile at night on a narrow country road in France. You know you will eventually get to your final destination and you know the principles of driving a car, but you don’t know that there is a sharp turn coming up in 10 miles. You can only react to the new set of information as your headlights reveal the road ahead. New information is critical and can also affect the outcome quite a bit if your fall in the ravine when missing your turn. You are also affected by yours and other drivers’ expectations of road conduct and risk taking. Physics by contrast is like riding a train (in France). You have a precise idea of how long the trip will be, there is no question that you will arrive at your exact destination and so you can close your eyes and relax because the train does not need any extraneous information along the way, as it is positioned on a fixed track and its speed is predetermined and there is only a remote probability for unexpected changes in the surrounding environment.
Notwithstanding, our imagined new financial theory is still an exaggeration and no theory in the realm of social sciences can claim to achieve that level of reality matching. In other words, while they are fully confident about their insight our team of economists is facing a bit of a conundrum. Assume the stock market index is at 1,300, and their theory finds that it should be trading at 1,275. To keep it simple, assume that over the last month the index has stayed at that same level of 1,300, and our team derived the same result that the index is worth 1,275. Are the economists correct, leading to a trading opportunity, or are they wrong? If the economists are right, then the stock market is currently overpriced and investors should short (sell) the index.
If the discrepancy is due to a systematic error in the theory, then this puts the theory in jeopardy. On the other hand, if it is true that the values that the economists keep predicting over time and on average are closer to actual prices than any other theory, this must be a good sign that the theory is superior to existing ones. What explains the difference between actual and theory-induced values then? Economists call it residual noise, i.e. unaccounted and unpredictable factors. Indeed, the best our economists can aspire to is to have figured out a reliable mechanism.
Because mechanical engineers understand how power generating turbines work does not necessarily mean they can always account for the behavior and performance of a given gas turbine. Obtaining all the environmental conditions to make that determination would be too costly. They can however explain performance within a tight and satisfactory range. This is, I believe, the best that can be expected from us in Finance.
Why the Fed Should Not Mess with the 30-year Treasury Bond
In these uncertain times, the pressure is squarely on the Federal Reserve’s shoulders to deliver policies that will prop up investors’ confidence, so that the U.S. and other world economies can get back to the business of raising mankind’s standards of living without excessive disruption. Policies that aim at easing credit are appropriate when they target short-term interest rates and are coupled with financial regulations that promote risk transparency and commensurate fair returns.
On the other hand, a policy that aims at flattening the yield curve and encouraging long-term borrowing, while it may originate from the best of intentions, is likely to have damaging economic effects. Firstly, it undercompensates lenders for the cost of expected inflation. At the top of the list are foreign governments who will not earn a fair return. Secondly, this policy depresses the stock market via its effect on what we call the fear premium.
Welcoming (Back) the Fisher Effect
Chairman Bernanke has long been a proponent of inflation targeting. Since the early 1990s, he has done research on the topic and at least since 2003 he has advocated that the Federal Reserve commit to a long-run annual inflation target of about 2%, similar to what the Bank of England and the European Central Bank are currently doing. Spelling out a clear inflation target is extremely useful for the economy. Businesses can make stable plans regarding pricing policies and investors can price assets to earn a real return without fearing inflationary jumps, which helps reduce asset price volatility.
Whereas the concern these days is that a protracted deflation will set in, a long-term commitment to a 2% inflation target, if credible, will diffuse that risk by setting the expectations of future inflation in the minds of economic agents. The Fed is correct in towing that line. In fact, about ten years ago (2008 Economics Nobel Prize winner) Paul Krugman put forth a similar argument to help Japan recover from its own decade long deflationary recession, with some moderate success.
On the other hand, Yale economist Irving Fisher in 1896 and UCLA’s Michael Darby with Harvard’s Marty Feldstein eighty years later have taught us that investors want to be compensated for the loss of purchasing power due to expected inflation and taxes. The contention is that all investors seek to earn a positive real after-tax return (the after-tax Fisher Effect). With the one-year Treasury yield currently at 0.5% nominal, there is no way that investors are earning a positive real return after-tax there. The Fed’s action of pushing short-term rates down to kick start the credit market is right out of the playbook on modern monetary policy, which Bernanke helped to write.
On the other hand, the nominal yield on the 30-year Treasury stands at about 4.5%. This constitutes a miserly 1.38% real return after a 2% expected inflation and 24.1% interest income marginal tax rate. While we are still far from it, the break-even point would be at 2.63% nominal yield on the 30-year Treasury to provide a 0% expected real return after-tax.
Recently, a larger inflation risk premium has crept back in yields, possibly because the commitment to a 2% target does not seem prevalent in light of the Fed’s focus on the immediate crisis. By my own estimation, the inflation risk premium is around 0.37 percentage points contained in the after-tax 30-year Treasury yield. In that case, the break even point is now about 3.12% nominal to yield a 0% expected real return after tax! The Fed must watch its steps carefully when it artificially shocks the supply of loanable funds away from its natural state, as this prevents lenders from receiving a fair return after taxes and inflation. In particular, institutions seeking long-term risk-free investment vehicles, mainly foreign governments and U.S. large pension funds and mutual funds suffer as a result.
A Major Source of Stock Market Value Discounting: The Fear Premium
In a recent article entitled “A Required Yield Theory of Stock Market Valuation and Treasury Yield Determination”, which appeared in Financial Markets, Institutions and Instruments, 18 (1), 2009: 27-88, we show that the 30-year Treasury yield plays a major role in the current short-term fluctuations of the S&P 500 index. While this may appear counterintuitive that long-run bonds should have a short-term impact on equity valuation, the reason is that the 30-year Treasury plays the role of a safe haven investment in the current financial crisis (along with gold).
The fear premium, as we define it, is the shortfall of the 30-year T-yield (after-tax, inflation and inflation risk premium) from a 2% constant real return. The 2% return corresponds to the historical average long-term real GDP/capita growth in the U.S., which we explain in our article, is an anchor for valuing the S&P 500 and Treasuries. Although it is beyond the scope of this essay, we argue that on an after-tax and real basis the equity premium is zero with respect to a 30-year instrument. We show that on an after-tax and real basis the 30-year Treasury has historically yielded about the same as the S&P 500’s earnings yield...
In the context of the crisis, the fear premium is high when investors flee the stock market (S&P 500) and migrate in mass to buy the 30-year Treasury as a safe haven investment. The 30-year Treasury price is then driven up and the real after tax yield goes down. Due to flight to safety, the S&P 500 index then falls to the point where investors are indifferent between holding either type of asset. The fear premium leads investors to discount the S&P 500 index more heavily during the crisis. As the Fed pursues a policy of lowering the yield on the 30-year Treasury, it artificially inflates the fear premium, which undercuts the value of the S&P 500. While in this case the raised fear premium does not mean that investors are actually more fearful, lower yields on the 30-year Treasury offer less downside risk protection to investors. Thus, investors, who cannot hedge as well as before, will demand a higher yield on the S&P 500.
Now, it is true that we have seen 30-year T yields in the last year as low as 2.5% following the conflagration of the financial industry in September 2008. But these rates were justified then by investors’ flight to safety behavior. By my own calculations, an artificial and instantaneous Fed cut of the 30-year Treasury yield by 0.5% to about 4% would have the effect of lowering the S&P 500 index from about 890 where it is today to about 766 or a 14% drop, assuming no change in other economic conditions!
All in all, it is crucial that the Fed’s policy does not unfairly penalize long-term safe lenders as well as equity index shareholders.
On the other hand, a policy that aims at flattening the yield curve and encouraging long-term borrowing, while it may originate from the best of intentions, is likely to have damaging economic effects. Firstly, it undercompensates lenders for the cost of expected inflation. At the top of the list are foreign governments who will not earn a fair return. Secondly, this policy depresses the stock market via its effect on what we call the fear premium.
Welcoming (Back) the Fisher Effect
Chairman Bernanke has long been a proponent of inflation targeting. Since the early 1990s, he has done research on the topic and at least since 2003 he has advocated that the Federal Reserve commit to a long-run annual inflation target of about 2%, similar to what the Bank of England and the European Central Bank are currently doing. Spelling out a clear inflation target is extremely useful for the economy. Businesses can make stable plans regarding pricing policies and investors can price assets to earn a real return without fearing inflationary jumps, which helps reduce asset price volatility.
Whereas the concern these days is that a protracted deflation will set in, a long-term commitment to a 2% inflation target, if credible, will diffuse that risk by setting the expectations of future inflation in the minds of economic agents. The Fed is correct in towing that line. In fact, about ten years ago (2008 Economics Nobel Prize winner) Paul Krugman put forth a similar argument to help Japan recover from its own decade long deflationary recession, with some moderate success.
On the other hand, Yale economist Irving Fisher in 1896 and UCLA’s Michael Darby with Harvard’s Marty Feldstein eighty years later have taught us that investors want to be compensated for the loss of purchasing power due to expected inflation and taxes. The contention is that all investors seek to earn a positive real after-tax return (the after-tax Fisher Effect). With the one-year Treasury yield currently at 0.5% nominal, there is no way that investors are earning a positive real return after-tax there. The Fed’s action of pushing short-term rates down to kick start the credit market is right out of the playbook on modern monetary policy, which Bernanke helped to write.
On the other hand, the nominal yield on the 30-year Treasury stands at about 4.5%. This constitutes a miserly 1.38% real return after a 2% expected inflation and 24.1% interest income marginal tax rate. While we are still far from it, the break-even point would be at 2.63% nominal yield on the 30-year Treasury to provide a 0% expected real return after-tax.
Recently, a larger inflation risk premium has crept back in yields, possibly because the commitment to a 2% target does not seem prevalent in light of the Fed’s focus on the immediate crisis. By my own estimation, the inflation risk premium is around 0.37 percentage points contained in the after-tax 30-year Treasury yield. In that case, the break even point is now about 3.12% nominal to yield a 0% expected real return after tax! The Fed must watch its steps carefully when it artificially shocks the supply of loanable funds away from its natural state, as this prevents lenders from receiving a fair return after taxes and inflation. In particular, institutions seeking long-term risk-free investment vehicles, mainly foreign governments and U.S. large pension funds and mutual funds suffer as a result.
A Major Source of Stock Market Value Discounting: The Fear Premium
In a recent article entitled “A Required Yield Theory of Stock Market Valuation and Treasury Yield Determination”, which appeared in Financial Markets, Institutions and Instruments, 18 (1), 2009: 27-88, we show that the 30-year Treasury yield plays a major role in the current short-term fluctuations of the S&P 500 index. While this may appear counterintuitive that long-run bonds should have a short-term impact on equity valuation, the reason is that the 30-year Treasury plays the role of a safe haven investment in the current financial crisis (along with gold).
The fear premium, as we define it, is the shortfall of the 30-year T-yield (after-tax, inflation and inflation risk premium) from a 2% constant real return. The 2% return corresponds to the historical average long-term real GDP/capita growth in the U.S., which we explain in our article, is an anchor for valuing the S&P 500 and Treasuries. Although it is beyond the scope of this essay, we argue that on an after-tax and real basis the equity premium is zero with respect to a 30-year instrument. We show that on an after-tax and real basis the 30-year Treasury has historically yielded about the same as the S&P 500’s earnings yield...
In the context of the crisis, the fear premium is high when investors flee the stock market (S&P 500) and migrate in mass to buy the 30-year Treasury as a safe haven investment. The 30-year Treasury price is then driven up and the real after tax yield goes down. Due to flight to safety, the S&P 500 index then falls to the point where investors are indifferent between holding either type of asset. The fear premium leads investors to discount the S&P 500 index more heavily during the crisis. As the Fed pursues a policy of lowering the yield on the 30-year Treasury, it artificially inflates the fear premium, which undercuts the value of the S&P 500. While in this case the raised fear premium does not mean that investors are actually more fearful, lower yields on the 30-year Treasury offer less downside risk protection to investors. Thus, investors, who cannot hedge as well as before, will demand a higher yield on the S&P 500.
Now, it is true that we have seen 30-year T yields in the last year as low as 2.5% following the conflagration of the financial industry in September 2008. But these rates were justified then by investors’ flight to safety behavior. By my own calculations, an artificial and instantaneous Fed cut of the 30-year Treasury yield by 0.5% to about 4% would have the effect of lowering the S&P 500 index from about 890 where it is today to about 766 or a 14% drop, assuming no change in other economic conditions!
All in all, it is crucial that the Fed’s policy does not unfairly penalize long-term safe lenders as well as equity index shareholders.
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