Posts tagged ‘Grantham Mayo Van Otterloo’
Investing in a World of Black Swans
In the world of modern finance, there has always been the search for the Holy Grail. Ever since the advent of computers, practitioners have looked to harness the power of computing and direct it towards the goal of producing endless profits. Today the buzz words being used across industries include, “AI – Artificial Intelligence,” “Machine Learning,” “Neural Networks,” and “Deep Learning.” Regrettably, nobody has found a silver bullet, but that hasn’t slowed down people from trying. Wall Street has an innate desire to try to turn the ultra-complex field of finance into a science, just as they do in the field of physics. Even banking stalwart JPMorgan Chase (JPM) and its renowned CEO/Chairman Jamie Dimon suffered billions in losses in the quest for infinite income, due in large part to their over-reliance on pseudo-science trading models.
Preceding JPM’s losses, James Montier of Grantham Mayo van Otterloo’s asset allocation team gave a keynote speech at a CFA Institute Annual Conference in Chicago, where he gave a prescient talk explaining why bad models were the root cause of the financial crisis. Montier noted these computer algorithms essentially underappreciate the number and severity of Black Swan events (low probability negative outcomes) and the models’ inability to accurately identify predictable surprises.
What are predictable surprises? Here’s what Montier had to say on the topic:
“Predictable surprises are really about situations where some people are aware of the problem. The problem gets worse over time and eventually explodes into crisis.”
When Dimon was made aware of the 2012 rogue trading activities, he strenuously denied the problem before reversing course and admitting to the dilemma. Unfortunately, many of these Wall Street firms and financial institutions use value-at-risk (VaR) models that are falsely based on the belief that past results will repeat themselves, and financial market returns are normally distributed. Those suppositions are not always true.
Another perfect example of a Black Swan created by a bad financial model is Long Term Capital Management (LTCM) – see also When Genius Failed. Robert Merton and Myron Scholes were world renowned Nobel Prize winners who single-handedly brought the global financial market to its knees in 1998 when LTCM lost $500 million in one day and required a $3.6 billion bailout from a consortium of banks. Their mathematical models worked for a while but did not fully account for trading environments with low liquidity (i.e., traders fleeing in panic) and outcomes that defied the historical correlations embedded in their computer algorithms. The “Flash Crash” of 2010, in which liquidity evaporated due to high-frequency traders temporarily jumping ship, is another illustration of computers wreaking havoc on the financial markets.
The problem with many of these models, even for the ones that work in the short-run, is that behavior and correlations are constantly changing. Therefore any strategy successfully reaping outsized profits in the near-term will eventually be discovered by other financial vultures and exploited away.
Another pundit with a firm hold on Wall Street financial models is David Leinweber, author of Nerds on Wall Street. As Leinweber points out, financial models become meaningless if the data is sliced and diced to form manipulated and nonsensical relationships. The data coming out can only be as good as the data going in – “garbage in, garbage out.”
In searching for the most absurd data possible to explain the returns of the S&P 500 index, Leinweiber discovered that butter production in Bangladesh was an excellent predictor of stock market returns, explaining 75% of the variation of historical returns. By tossing in U.S. cheese production and the total population of sheep in Bangladesh, Leinweber was able to mathematically “predict” past U.S. stock returns with 99% accuracy. To read more about other financial modeling absurdities, check out a previous Investing Caffeine article, Butter in Bangladesh.
Generally, investors want precision through math, but as famed investor Benjamin Graham noted more than 50 years ago, “Mathematics is ordinarily considered as producing precise, dependable results. But in the stock market, the more elaborate and obtuse the mathematics, the more uncertain and speculative the conclusions we draw therefrom. Whenever calculus is brought in, or higher algebra, you can take it as a warning signal that the operator is trying to substitute theory for experience.”
If these models are so bad, then why do so many people use them? Montier points to “intentional blindness,” the tendency to see what one expects to see, and “distorted incentives” (i.e., compensation structures rewarding improper or risky behavior).
Montier’s solution to dealing with these models is not to completely eradicate them, but rather recognize the numerous shortcomings of them and instead focus on the robustness of these models. Or in other words, be skeptical, know the limits of the models, and build portfolios to survive multiple different environments.
Investors seem to be discovering more financial Black Swans over the last few years in the form of events like the Lehman Brothers bankruptcy, Flash Crash, and Greek sovereign debt default. Rather than putting too much faith or dependence on bad financial models to identify or exploit Black Swan events, the over-reliance on these models may turn this rare breed of swans into a large bevy.
See Full Article on Montier: Failures of Modern Finance
Wade W. Slome, CFA, CFP®
Plan. Invest. Prosper.
DISCLOSURE: Sidoxia Capital Management (SCM) and some of its clients own JPM and certain exchange traded funds, but at the time of publishing SCM had no direct position in Lehman Brothers, or any other security referenced in this article. No information accessed through the Investing Caffeine (IC) website constitutes investment, financial, legal, tax or other advice nor is to be relied on in making an investment or other decision. Please read disclosure language on IC “Contact” page.
Investing in a World of Black Swans
In the world of modern finance, there has always been the search for the Holy Grail. Ever since the advent of computers, practitioners have looked to harness the power of computing and direct it towards the goal of producing endless profits. Regrettably, nobody has found the silver bullet, but that hasn’t slowed down people from trying. Wall Street has an innate desire to try to turn the ultra-complex field of finance into a science, just as they do in the field of physics. Even JPMorgan Chase (JPM) and its CEO Jamie Dimon are already on their way to suffering more than $2 billion in losses in the quest for infinite income, due in large part to their over-reliance on pseudo-science trading models.
James Montier of Grantham Mayo van Otterloo’s asset allocation team was recently a keynote speaker at the CFA Institute Annual Conference in Chicago. His prescient talk, which preceded JP Morgan’s recent speculative trading loss announcement, explained why bad models were the root cause of the financial crisis. Essentially these computer algorithms under-appreciate the number and severity of Black Swans (low probability negative outcomes) and the models’ inability to accurately identify predictable surprises.
What are predictable surprises? Here’s what Montier had to say on the topic:
“Predictable surprises are really about situations where some people are aware of the problem. The problem gets worse over time and eventually explodes into crisis.”
Just a month ago, when Dimon was made aware of the rogue trading activities, the CEO strenuously denied the problem before reversing course and admitting the dilemma last week. Unfortunately, many of these Wall Street firms and financial institutions use value-at-risk (VaR) models that are falsely based on the belief that past results will repeat themselves, and financial market returns are normally distributed. Those suppositions are not always true.
Another perfect example of a Black Swan created by a bad financial model is Long Term Capital Management (LTCM). Robert Merton and Myron Scholes were world renowned Nobel Prize winners who single handedly brought the global financial market to its knees in 1998 when LTCM lost $500 million in one day and required a $3.6 billion bailout from a consortium of banks. Their mathematical models worked for a while but did not fully account for trading environments with low liquidity (i.e., traders fleeing in panic) and outcomes that defied the historical correlations embedded in their computer algorithms. The “Flash Crash” of 2010, in which liquidity evaporated due to high frequency traders temporarily jumping ship, is another illustration of computers wreaking havoc on the financial markets.
The problem with many of these models, even for the ones that work in the short-run, is that behavior and correlations are constantly changing. Therefore any strategy successfully reaping outsized profits in the near-term will eventually be discovered by other financial vultures and exploited away.
Another pundit with a firm hold on Wall Street financial models is David Leinweber, author of Nerds on Wall Street. As Leinweber points out, financial models become meaningless if the data is sliced and diced to form manipulated and nonsensical relationships. The data coming out can only be as good as the data going in – “garbage in, garbage out.”
In searching for the most absurd data possible to explain the returns of the S&P 500 index, Leinweiber discovered that butter production in Bangladesh was an excellent predictor of stock market returns, explaining 75% of the variation of historical returns. By tossing in U.S. cheese production and the total population of sheep in Bangladesh, Leinweber was able to mathematically “predict” past U.S. stock returns with 99% accuracy. To read more about other financial modeling absurdities, check out a previous Investing Caffeine article, Butter in Bangladesh.
Generally, investors want precision through math, but as famed investor Benjamin Graham noted more than 50 years ago, “Mathematics is ordinarily considered as producing precise, dependable results. But in the stock market, the more elaborate and obtuse the mathematics, the more uncertain and speculative the conclusions we draw therefrom. Whenever calculus is brought in, or higher algebra, you can take it as a warning signal that the operator is trying to substitute theory for experience.”
If these models are so bad, then why do so many people use them? Montier points to “intentional blindness,” the tendency to see what one expects to see, and “distorted incentives” (i.e., compensation structures rewarding improper or risky behavior).
Montier’s solution to dealing with these models is not to completely eradicate them, but rather recognize the numerous shortcomings of them and instead focus on the robustness of these models. Or in other words, be skeptical, know the limits of the models, and build portfolios to survive multiple different environments.
Investors seem to be discovering more financial Black Swans over the last few years in the form of events like the Lehman Brothers bankruptcy, Flash Crash, and Greek sovereign debt default. Rather than putting too much faith or dependence on bad financial models to identify or exploit Black Swan events, the over-reliance on these models may turn this rare breed of swans into a large bevy.
See Full Article on Montier: Failures of Modern Finance
Wade W. Slome, CFA, CFP®
Plan. Invest. Prosper.
DISCLOSURE: Sidoxia Capital Management (SCM) and some of its clients own certain exchange traded funds, but at the time of publishing SCM had no direct position in JPM, Lehman Brothers, or any other security referenced in this article. No information accessed through the Investing Caffeine (IC) website constitutes investment, financial, legal, tax or other advice nor is to be relied on in making an investment or other decision. Please read disclosure language on IC “Contact” page.
Snoozing Your Way to Investment Prosperity
When it comes to investing, do you trade like Jim Cramer on Red Bull – grinding your teeth to every tick or news headline? With the advent of the internet, an unrelenting, real-time avalanche of news items spreads like a furious plague – just ask Anthony Weiner. As fear and greed incessantly permeate the web, and day-trading systems and software are increasingly peddled as profit elixirs, investors are getting itchier and itchier trading fingers. Just consider that investment holding periods have plummeted from approximately 10 years around the time of World War II to 8 months today (see GMO chart below). Certainly, the reduction in trading costs along with the ever-proliferating trend of technology advancements (see Buggy Whip Déjà Vu) is a contributor to the price of trading, but the ADHD-effect of information overload cannot be underestimated (see The Age of Information Overload).
Source: GMO (James Montier)
But fear not, there is a prescription for those addicted, nail-biting day-traders who endlessly pound away on their keyboards with bloody hangnails. The remedy is a healthy dosage of long-term growth investing in quality companies and sustainably expanding trends. I know this is blasphemy in the era of “de-risking” (see It’s All Greek to Me), short-term “risk controls” (i.e. panicking at bottoms and chasing performance), and “benchmark hugging,” but I believe T. Rowe Price had it right:
“The growth stock theory of investing requires patience, but is less stressful than trading, generally has less risk, and reduces brokerage commissions and income taxes.”
This assessment makes intuitive sense to me, but how can one invest for the long-term when there are structural deficits, inflation, decelerating GDP growth, international nuclear catastrophes, escalated gasoline prices, and Greek debt concerns? There are always concerns, and if there none, then you should in fact be concerned (e.g., when investors piled into equities during the “New Economy” right before the bubble burst in 2000). In order to gain perspective, consider what happened at other points in history when our country was involved in war; came out of recession; faced high employment; experienced Middle East supply fears; battled banking problems; handled political scandals; and dealt with rising inflation trends. One comparably bleak period was the 1974 bear market.
Let’s take a look at how that bear market compared to the current environment:
Then (1974) Now (2011)
End of Vietnam War End of Iraq War (battles in Afghanistan and Libya)
Exiting recession Exiting recession
9% Unemployment 9% Unemployment
Arab Oil Embargo Arab Spring and Israeli-Palestinian tensions
Watergate political scandal Anthony Weiner political scandal
Franklin National Bank failure Banking system bailout
Rising inflation trends Rising inflation trends
We can debate the comparability of events and degree of pessimism, but suffice it to say the outlook was not very rosy 37 years ago, nor is it today. History never repeats itself, but it does tend to rhyme. Although attitudes were dour four decades ago, the Dow Jones exploded from 627 in late 1974 to 12,004 today. I’m not calling for another near 20-fold increase in prices over the next 37 years, but a small fraction of that improvement would put a smile on equity investors’ faces. Jim Fullerton, the former chairman of the Capital Group of the American Funds understood pundits’ skepticism during times of opportunity when he wrote the following in November 1974:
“Today there are thoughtful, experienced, respected economists, bankers, investors and businessmen who can give you well-reasoned, logical, documented arguments why this bear market is different; why this time the economic problems are different; why this time things are going to get worse — and hence, why this is not a good time to invest in common stocks, even though they may appear low.”
Rather than getting glued to the TV horror story headline du jour, perhaps investors should take some of the sage advice provided by investment Hall of Famer, Peter Lynch (Lynch averaged a +29% annual return from 1977-1990 while at Fidelity Investments). Rather than try to time the market, he told investors to “assume the market is going nowhere and invest accordingly.” And Lynch offered these additional words of wisdom to the many anxious investors who fret about macroeconomics and timing corrections:
• “It’s lovely to know when there’s recession. I don’t remember anybody predicting 1982 we’re going to have 14 percent inflation, 12 percent unemployment, a 20 percent prime rate, you know, the worst recession since the Depression. I don’t remember any of that being predicted. It just happened. It was there. It was ugly. And I don’t remember anybody telling me about it. So I don’t worry about any of that stuff. I’ve always said if you spend 13 minutes a year on economics, you’ve wasted 10 minutes.”
• “Far more money has been lost by investors preparing for corrections, or trying to anticipate corrections, than has been lost in corrections themselves.”
• “Whatever method you use to pick stocks or stock mutual funds, your ultimate success or failure will depend on your ability to ignore the worries of the world long enough to allow your investments to succeed.”
Real money is not made by following the crowd. Real money is made by buying quality companies and securities at attractive prices. The prescription to generating above-average profits is finding those quality market leaders (or sustainable trends) that can compound earnings growth for multiple years, not chasing every up-tick and panicking out of every down-tick. Following these doctor’s orders will lead to a strong assured mind and a healthy financial portfolio – key factors allowing you to peacefully snooze to investment prosperity.
Wade W. Slome, CFA, CFP®
Plan. Invest. Prosper.
DISCLOSURE: Performance data from Morningstar.com. Sidoxia Capital Management (SCM) and some of its clients own certain exchange traded funds, but at the time of publishing SCM had no direct position in TROW, or any other security referenced in this article. No information accessed through the Investing Caffeine (IC) website constitutes investment, financial, legal, tax or other advice nor is to be relied on in making an investment or other decision. Please read disclosure language on IC “Contact” page.
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