Two months back, I came across a Harvard Business Review article on what else but the financial crisis and the blame-game it spawned. Some cursed the rational exuberance mathematical models rewarded their users with; others blamed the underlying Wall Street greed. I promptly ignored the article then only to revisit it today! The article (Judgment Deficit) is by an Indian-born American academic and industry insider, Amir Bhide.
Bhide bemoans the loss of the face-to-face human i
nteraction of lenders with the borrowers – supplanted by “mechanistic finance”. Such a machine-run process becomes centralized and suffers from a similar fate as Soviet Union. So comes the phrase Stalinization of Finance. Bhide’s key contention is that the careful case-by-case judgment that was traditionally made by lenders, while verifying mortgages, was substituted by models based on statistical laws spawning predictions. Simply put, understanding the dynamics of human behavior through statistical patterns is quite fatal and even more so, if you let computers do it.
...Because natural laws and mathematical inferences cannot predict behavior, algorithms are built upon statistical models. But for all their econometric sophistication, statistical models are ultimately a simplified form of history, a terse numerical narrative of what happened in the past. (The simplifying assumptions of most statistical models are in fact so great that they can almost never be used successfully to reconstruct the very historical data used to construct the models.) They reveal broad tendencies and recurring patterns, but in a dynamic society shot through with willful and imaginative people making conscious choices, they cannot make reliable predictions...
Bhide is mindful of the use of models in their proper context but is worried over their spillover into studying human behavior.
In fact, hardly anyone now makes case-by-case mortgage credit judgments. Mortgages are granted or denied (and new mortgage products like option ARMs are designed) using complex models that are conjured up by a small number of faraway rocket scientists and take little heed of the specific facts on the ground….
The buyers of securitized mortgages don’t make case-by-case credit decisions, either. For instance, buyers of Fannie Mae or Freddie Mac paper weren’t, and still aren’t, making judgments about the risk that homeowners would default on the underlying mortgages. Rather, they were buying government debt—and earning a higher return than they would from Treasury bonds. Even when securities weren’t guaranteed, buyers ignored the creditworthiness of individual mortgages. They relied instead on the models of the wizards who developed the underwriting standards, the dozen or so banks (the likes of Lehman, Goldman, and Citicorp) that securitized the mortgages, and the three rating agencies that vouched for the soundness of the securities.
Dispensing with judgment has also helped funnel the mass production of derivatives into a few mega-institutions, posing systemic risks that their top executives and regulators cannot control.
So the bottom-line seems to be use the models but don’t eliminate the human element in the processing of any financial instrument.
I’ve few more things to say.
That complex financial derivatives lay at the root of the financial crisis is a seductive but wrong idea. When liabilities are complex, assets cannot be simple; otherwise we will have a mismatch between the two. The problem is not with the nature of derivatives but of the incentives. Financial regulators need to concentrate on incentives that reduce the build-up of lending in a boom, irrespective of the precise instrument of leverage.
So is banning complex derivatives such as CDS or banning banks from participating in certain activities the way out (the “Volcker rule”)? There are two points to be kept in mind.
1. These so-called complex derivatives were not bought by innocent retail investors in the first place. But by seemingly sophisticated institutional investors such as hedge funds (like Paulson & Co).
2. Banks that failed were jealous of these hedge funds and sold these instruments to their own subsidiaries.
It is important to remember that derivatives are merely insurance for the financial sector. Imagine you are a sugar cane grower and you lose money if the price of sugar cane falls below Rs 200 per quintal. You could insure yourself by buying a “put” on the price of sugar cane that paid you an increasing sum of money as the price fell below Rs 200.
Perhaps the government also has an income support mechanism when the price falls below Rs 150. You may then want to sell a new “put” where the combination of the two meant that your payout increased until Rs 150. The combination of buying a put and selling another is cheaper than just buying the first put. Affordable insurance.
You could lower the price further by receiving a premium for selling a “call”, which meant you have to pay to your counter-party an increasing amount whenever the sugar price rose above Rs 300, a point that made you so wealthy that you did not mind giving up some of these gains to make your downside insurance cheaper.
This is a highly complex (long a put, short a put and short a call), be-spoke derivative that would be traded over-the-counter because it is too specific to the production economics of your sugar business to trade more generally. Is banning this making the world a safer or a riskier place?
Risk management is about matching liabilities with assets. If our liabilities are highly complex, like the sugar producer, but in reality like all of our liabilities as parents, employees, home-owners, etc, then forcing us to only use simple assets will create mismatches between assets and liabilities that increase risk. Instruments are not born with original sin. It is what we do with them that matters. Behavior matters. Behavior is driven by incentives, not instruments. Financial regulators need to concentrate on incentives that reduce the build-up of lending in a boom, irrespective of the precise instrument of leverage this time around.
Given that the problem is a collective belief that risks have fallen and in essence it is a mispricing of risk by the market, the solution to financial crashes must be non-market: clever ideas around contingent capital and use of credit derivatives miss this critical point. This argues for going back to the future and utilizing some old-fashioned, administrative tools still used by the Reserve Bank of India but considered outdated elsewhere such as low ceilings to loan-to-value ratios or rising reserve requirements for above normal levels of credit growth.
The essential point however, to paraphrase the 1992 Democratic slogan, is that it is not instruments, “it’s the incentives stupid!”
Let me end with two quotes.
“..but the financial industry is pragmatic. While it [the industry] may genuflect to the old icons, it invests its research dollars in the search of newer and better gods.”
“The calculus of probability can doubtless never be applied to market activity, and the dynamics of the Exchange will never be an exact science. But it is possible to study mathematically the state of the market at a given instant – that is to say, to establish the laws of probability for price variation that the market at the instant dictates. If the market, in effect, does not predict its fluctuations, it does assess them as being more or less likely, and this likelihood can be evaluated mathematically.”
Bhide bemoans the loss of the face-to-face human i
nteraction of lenders with the borrowers – supplanted by “mechanistic finance”. Such a machine-run process becomes centralized and suffers from a similar fate as Soviet Union. So comes the phrase Stalinization of Finance. Bhide’s key contention is that the careful case-by-case judgment that was traditionally made by lenders, while verifying mortgages, was substituted by models based on statistical laws spawning predictions. Simply put, understanding the dynamics of human behavior through statistical patterns is quite fatal and even more so, if you let computers do it....Because natural laws and mathematical inferences cannot predict behavior, algorithms are built upon statistical models. But for all their econometric sophistication, statistical models are ultimately a simplified form of history, a terse numerical narrative of what happened in the past. (The simplifying assumptions of most statistical models are in fact so great that they can almost never be used successfully to reconstruct the very historical data used to construct the models.) They reveal broad tendencies and recurring patterns, but in a dynamic society shot through with willful and imaginative people making conscious choices, they cannot make reliable predictions...
Bhide is mindful of the use of models in their proper context but is worried over their spillover into studying human behavior.
In fact, hardly anyone now makes case-by-case mortgage credit judgments. Mortgages are granted or denied (and new mortgage products like option ARMs are designed) using complex models that are conjured up by a small number of faraway rocket scientists and take little heed of the specific facts on the ground….
The buyers of securitized mortgages don’t make case-by-case credit decisions, either. For instance, buyers of Fannie Mae or Freddie Mac paper weren’t, and still aren’t, making judgments about the risk that homeowners would default on the underlying mortgages. Rather, they were buying government debt—and earning a higher return than they would from Treasury bonds. Even when securities weren’t guaranteed, buyers ignored the creditworthiness of individual mortgages. They relied instead on the models of the wizards who developed the underwriting standards, the dozen or so banks (the likes of Lehman, Goldman, and Citicorp) that securitized the mortgages, and the three rating agencies that vouched for the soundness of the securities.
Dispensing with judgment has also helped funnel the mass production of derivatives into a few mega-institutions, posing systemic risks that their top executives and regulators cannot control.
So the bottom-line seems to be use the models but don’t eliminate the human element in the processing of any financial instrument.
I’ve few more things to say.
That complex financial derivatives lay at the root of the financial crisis is a seductive but wrong idea. When liabilities are complex, assets cannot be simple; otherwise we will have a mismatch between the two. The problem is not with the nature of derivatives but of the incentives. Financial regulators need to concentrate on incentives that reduce the build-up of lending in a boom, irrespective of the precise instrument of leverage.
So is banning complex derivatives such as CDS or banning banks from participating in certain activities the way out (the “Volcker rule”)? There are two points to be kept in mind.
1. These so-called complex derivatives were not bought by innocent retail investors in the first place. But by seemingly sophisticated institutional investors such as hedge funds (like Paulson & Co).
2. Banks that failed were jealous of these hedge funds and sold these instruments to their own subsidiaries.
It is important to remember that derivatives are merely insurance for the financial sector. Imagine you are a sugar cane grower and you lose money if the price of sugar cane falls below Rs 200 per quintal. You could insure yourself by buying a “put” on the price of sugar cane that paid you an increasing sum of money as the price fell below Rs 200.
Perhaps the government also has an income support mechanism when the price falls below Rs 150. You may then want to sell a new “put” where the combination of the two meant that your payout increased until Rs 150. The combination of buying a put and selling another is cheaper than just buying the first put. Affordable insurance.
You could lower the price further by receiving a premium for selling a “call”, which meant you have to pay to your counter-party an increasing amount whenever the sugar price rose above Rs 300, a point that made you so wealthy that you did not mind giving up some of these gains to make your downside insurance cheaper.
This is a highly complex (long a put, short a put and short a call), be-spoke derivative that would be traded over-the-counter because it is too specific to the production economics of your sugar business to trade more generally. Is banning this making the world a safer or a riskier place?
Risk management is about matching liabilities with assets. If our liabilities are highly complex, like the sugar producer, but in reality like all of our liabilities as parents, employees, home-owners, etc, then forcing us to only use simple assets will create mismatches between assets and liabilities that increase risk. Instruments are not born with original sin. It is what we do with them that matters. Behavior matters. Behavior is driven by incentives, not instruments. Financial regulators need to concentrate on incentives that reduce the build-up of lending in a boom, irrespective of the precise instrument of leverage this time around.
Given that the problem is a collective belief that risks have fallen and in essence it is a mispricing of risk by the market, the solution to financial crashes must be non-market: clever ideas around contingent capital and use of credit derivatives miss this critical point. This argues for going back to the future and utilizing some old-fashioned, administrative tools still used by the Reserve Bank of India but considered outdated elsewhere such as low ceilings to loan-to-value ratios or rising reserve requirements for above normal levels of credit growth.
The essential point however, to paraphrase the 1992 Democratic slogan, is that it is not instruments, “it’s the incentives stupid!”
Let me end with two quotes.
“..but the financial industry is pragmatic. While it [the industry] may genuflect to the old icons, it invests its research dollars in the search of newer and better gods.”
“The calculus of probability can doubtless never be applied to market activity, and the dynamics of the Exchange will never be an exact science. But it is possible to study mathematically the state of the market at a given instant – that is to say, to establish the laws of probability for price variation that the market at the instant dictates. If the market, in effect, does not predict its fluctuations, it does assess them as being more or less likely, and this likelihood can be evaluated mathematically.”
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