Mathematicians and the Prevention of Recessions
Note: I completed a PhD in Mathematics from University of Illinois under the direction of Nathan Dunfield in 2011. I worked as a research analyst at GiveWell from April 2012 to May 2013. All views expressed here are my own.
About this post: I’ve long been interested in ways in which mathematicians can contribute high social value. In this post, I discuss a tentative idea along these lines. My thoughts are very preliminary in nature, and my intent in making this post is to provide a launching point for further exploration of the subject, rather than to persuade.
Recessions as a serious threat to global welfare
In 2008, the US housing bubble popped, precipitating the Great Recession. The costs of this were staggering:
It’s been claimed that the cost to US taxpayers in bank bailouts was $9 trillion.
The Dow Jones Industrial Average dropped by almost 50% and took over 4 years to recover.
US unemployment jumped from ~5% to ~10%, and has only gradually been declining.
Budget cuts were especially great for government support of activities with unusually high humanitarian value to those without political constituency, such as investment in global health.
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It’s been claimed that recessions cause a drop in prosocial behavior.
All told, the Great Recession had massive negative humanitarian disvalue, and preventing another such recession would have massive humanitarian value.
Transparent financial analysis as a possible solution
There are actors in finance who accurately predicted that there was a housing bubble that was on the brink of popping, and who bet heavily against subprime mortgages, reaping enormous profits as a result. The most prominent example is John Paulson, who made $3.7 billion in a 2007 alone, starting from a base of less than $1 billion. There are less extreme examples that are nevertheless very striking.
It’s difficult to determine the relative roles that skill and luck played in these peoples’ success, and the situation is further obscured by hindsight bias. Nevertheless, it seems possible that the financial success of Paulson and others was a consequence of careful analysis and shrewdness, and that other people of sufficiently high intellectual caliber and rationality would have been able to predict it as well.
As is always the case in finance, those who recognized the impending pop of the housing bubble kept their analysis secret, because sharing it would have allowed others to partially close the arbitrage opportunity, reducing the potential to profit. If these people had made their thinking public, it could have resulted in other people betting against the housing bubble earlier on, popped the housing bubble when it was smaller, possibly substantially lessening the severity of the ensuing recession. While there were people who publicly voiced concern, a large number of people would have had a bigger impact
This suggests that transparent financial analysis by intellectual elites could carry massive humanitarian value.
Mathematicians as unusually well positioned to perform such analysis
In the course of my graduate school days, I became familiar with mathematical community. There’s a wide cultural gulf between pure math and finance. My experience was that mathematicians generally view finance as “dirty business,” on account of:
Often having left-wing political beliefs
Discomfort with the zero-sum and/or negative-sum nature of finance
Not identifying with materialism
Disliking messy problems that are less intrinsically interesting than problems in pure math.
I believe that this gulf has led to a potential opportunity being overlooked: mathematicians may be ideally suited to perform transparent financial analysis that reduces damage from financial bubbles.
This idea occurred to me a few weeks ago. Ideas for philanthropic interventions generally fall apart upon closer examination, and so I wasn’t too optimistic about it holding up. So I was surprised when Neal Koblitz (co-creator of elliptic curve cryptography) raised the same idea in unrelated correspondence:
If mathematicians had been noticing the dubious ways that people in the financial world were claiming to be applying mathematics, and if they had publicly and loudly criticized the misuse of mathematics, then the world might have been spared the collapse of 2008 (or, rather, it wouldn’t have been as bad). If mathematicians could have played a role stopping the credit-derivatives bubble before it got out of hand, the economic value of doing that would have been in the trillions of dollars.
When an idea occurs to two people independently, the case for it being a good idea is strengthened. Moreover, Koblitz has a long history of involvement with humanitarian efforts and so can be expected to have perspective on them.
Some reasons why mathematicians seem unusually well suited to the task are:
Transferable Skills — Most mathematicians are unfamiliar with some of most important tools used in finance: statistics, data analysis & programming. But there’s a historical track record of mathematicians being able to pick up these skills and use them to powerful effect. James Simons transitioned from differential geometry to quantitative finance, and became one of the most successful hedge fund managers ever. Cathy O’Neil did a PhD in algebraic number theory under Barry Mazur’s direction, and got a job at DE Shaw, which is one of the most prestigious hedge funds. Mathematicians who are motivated to learn these skills are well positioned to do so.
There are other skills that are very important for successful financial analysis – in particular, one has to have a good eye for empirical data. This is a skill that’s not directly transferable, but it still seems likely that a nontrivial fraction of mathematicians could develop high facility with it.
Intellectual Caliber — The mathematics community has a very dense concentration of intellectual power. James Simons offers a direct point of comparison between math and finance:
Simons won the Oswald Veblan Prize in Geometry before leaving academia to start Renaissance Technologies. There are 25 living mathematicians who have won this prize. The prize is awarded exclusively for work in geometry/topology, and if one looks more broadly at all mathematical fields, one can generate a list of about 100 living mathematicians who were at least as accomplished as Simons at the same age.
After leaving academia, Simons made $10 billion in quantitative finance. What I find most interesting about this is that the situation is not that Simons succeeded where other mathematicians of the same caliber had failed – rather, Simons is virtually the only pure mathematician of his caliber to have left academia. This raises the possibility that there are a handful of elite mathematicians who could make much better financial predictions than most present day actors in finance. Less accomplished but capable mathematicians may also do very well.
Cautiousness — Mathematicians are naturally intellectually conservative, as they spend much of their time rigorously examining arguments for flaws. Thus, they’re unusually unlikely to succumb to greed and fear, which are factors that are thought to play a large role in the behavior of financial markets, and which lead to speculative bubbles. This is corroborated by some of Cathy O’Neil’s remarks on finance.
Implications
The above considerations suggest that mathematicians could contribute enormous social value by engaging in transparent financial analysis.
Many mathematicians who I know wish that they could contribute more social value. In the essay Is there beauty in mathematical theories?, the great mathematician Robert Langlands wrote:
In a letter to A.-M.Legendre of 1830, which I came across while preparing this lecture, Jacobi famously wrote
It is true that Mr. Fourier thought that the principal goal of mathematics was their public utility and their use in explaining natural phenomena. A philosopher like him should have known that the only goal of Science is the honor of the human spirit, and that as such, a question in number theory is worth a question concerning the system of the world.
I am not sure it is so easy. I have given a great deal of my life to matters closely related to the theory of numbers, but the honor of the human spirit is, perhaps, too doubtful and too suspect a notion to serve as vindication. […] Moreover, the appeal to the common welfare as a goal of mathematics is, if not then at least now, often abusive. So it is not easy to find an apology for a life in mathematics.
A fair number of mathematicians don’t have any choice but to do pure math. Gromov wrote:
You become a mathematician, a slave of this insatiable hunger of your brain, of everybody’s brain, for making structures of everything that goes into it.
I’m very sympathetic to Gromov’s remark, and I think that for people who constituted in this way, it’s probably best not to try to suppress these urges, as such attempts tend to be unsustainable and result in lower contributions to global welfare rather than higher ones.
But for mathematicians who are:
Tenured professors who don’t have to worry about career considerations
Able to enjoy financial analysis
Strongly motivated to do an excellent job
there may be a major opportunity to contribute enormous social value by conducting transparent high quality financial analysis.
This question warrants further investigation.
I have an economist friend who predicted the tech bubble, but shorted too early, and ended up losing quite a bit of money in the process. Knowing that chickens are coming home to roost is not enough; you need a good idea of when they’re going to arrive, and to have the solvency to be able to stick to your beliefs. (If Keynes’s comment—”The market can stay irrational longer than you can stay solvent”—fits you, then shorting bubbles seems unwise.)
I do believe there were people predicting the bubble would burst before it burst, and some of them were even people who don’t predict a bubble bursting every year. It’s not clear to me that a handful more mathematicians pointing out impending disaster would significantly shift public opinion, and hoping that the government would act to burst a bubble early rather than keep it inflated seems like an antihistorical hope.
One wonders if they would have seen the minority aspect of the meltdown coming, then. Qualitative reasoning can be as suspect as quantitative reasoning, and it’s not clear to me mathematicians are that much less prone to qualitative errors.
A professor in his 40s once told me that the department where he got his PhD had run the numbers, and financial success was inversely correlated with grades / research skills / intellectual ability; the top people got professorships and were middle class, whereas the people who were at the bottom of the class had left academia, and several of them made millions on Wall Street.
I also hear that the supply of physicists as quants is much larger now than it was ~20 years ago, and so it’s a respectable living but not a massively profitable one; it’s not clear that you could start a competitor to Renaissance today and do nearly as well.
Much of what you say seems like valid counterpoint. I’m getting out of my depth, insofar as I know very little about finance :-). I hope that other more knowledgable people will step in.
I don’t find this at all convincing, e.g.:
It’s only one department
He may have been misremembering the situation
“Bottom of the class” could reflect motivation rather than just intellectual ability.
It’s well known that academia doesn’t pay so well.
It seems to me to be weak support for the claim “if top tier math/physics talent went into finance, they could plausibly make billions, since fourth tier talent make millions.” (Fourth tier meant in the sense of this comment, but third tier might be more appropriate.)
There are also famous high-profile failures, though, such as Scholes at LTCM. (He did only win the Nobel prize in Econ, though, which is not a particularly strong predictor of math ability.)
There’s Renaissance technologies making obscene returns consistently for decades.
According to Wikipedia, the founder advised people to invest with Bernie Madoff...
Madoff fooled Simons for 10 years. He told Stony Brook to invest in Madoff in 1991. In 2003, Renaissance decided that Madoff was a fraud and pulled out. Simons told Stony Brook to pull out, but they left in 50%.
Many people investing with Madoff thought he was a fraud, just that he was defrauding someone else (eg, frontrunning his brokerage clients). It’s not clear that they pulled out because they realized he was a fraud or because they worried that Spitzer would investigate him. Their suspicions triggered a failed investigation of Madoff not because they complained to the SEC, but because when the SEC audited RenTech, it found emails discussing Madoff.
Oddly, the wikipedia article seems to be based on the first WSJ article I linked, not the Bloomberg one it cites.
Hindsight bias?
It’s mild evidence against the “top level math talent will be able to identify bubbles before they burst” hypothesis, but only mild because I don’t think Madoff was transparent enough to be obviously unstable in the way that the housing market was.
Yeah, it’s a cheap shot. Madoff fooled a lot of people, and detecting fraud takes a different skill set.
How would we go about making it such that someone who contemplated perpetrating that sort of fraud would be very likely to conclude, “Nah, there’s no way that would work; if I tried that, I’d just end up in prison like Madoff”?
Trump up the trials, studiously avoid mentioning the obvious fact that there’s a bunch of people living large on top of a Ponzi scheme right now.
Considering that the wikipedia article about Madoff didn’t exist until the scandal was unveiled … probably. (I checked the Wikipedia history when I first heard about it in Dec 08, check the history for yourself. )
Hmm. I thought I mentioned Renaissance in this post, but for some reason it says Medallion, which is one of their funds. Apparently I derped earlier, fixing it now.
You’re a PhD in math with no specialist knowledge of finance. Do you think that you personally could do better financial analysis than, say, a Business major with a CFA and a decade or two of industry experience? If no, why do you think mathematicians are the way forward for the field?
I would expect the mathematician to be more likely to be a good coder, and more likely to lean on data analysis that she understood from first principles.
The closest thing to finance from first principles is just to assume the EMH, use the CAPM theory to constuct a market portfolio, and practice a policy of pure commission/tax avoidance. Finance from first principles is incredibly boring—the only way to make it interesting is to start getting into second-order effects.
Finance != data analysis.
The comment was “lean on data analysis”, as in “lean on data analysis to do finance”. Yes, the analysis itself is not “finance” per se, but most of the data analysis done in finance is simple math that gets used in complex ways. It’s easy enough to calculate P/E, but figuring out whether that implies you wanting to buy or sell is a much harder question. About the only way to use first principles to actually make buy/sell decisions is, as I said, to use the CAPM and just embrace your ignorance.
That seems a little extreme; presumably there’s a difference between using statistical tests as a heuristic you don’t understand, and using statistical tests in a well-understood way, even if you’re not deriving finance from first principles.
Also, CAPM isn’t actually true (i.e. assumptions never hold in the real world), whereas statistics is.
Did your friend think about purchasing put options on a continuous basis? No short squeeze risk and you wouldn’t have to worry about timing. I assume the information from your friend’s purchases would have been arbitraged in to the price of the underlying asset somehow.
(I’m not a quant and there might be something wrong with this idea.)
The problem is that a put strategy bleeds money on option costs, whereas a short doesn’t. (Shorts bleed on dividends, but in a lot of industries that’s cheaper). Also, long-term puts deeply out of the money(which you want, to minimize costs and maximize leverage) are an incredibly thin market, which tends to make trading difficult and expensive.
That shouldn’t be an inherent problem… there are some strategies that tend to make money most of the time but occasionally go drastically wrong, and this is an example of a strategy that tends to lose money most of the time but occasionally goes drastically right. Just calculate the expected value as usual, right?
It’s not a crippling problem. Nassim Taleb, the Black Swan author, ran a hedge fund with precisely that strategy(though his options were omnidirectional—his thesis was that we underestimate all forms of tail risk), and he made a mint in 2008. But it’s something that needs to be kept in mind.
My understanding was that his fund was a failure and shut down before 2008, and the only evidence for his claim to have made money in 2008 was his word (with nothing about how well his strategy has performed on net).
Per Wikipedia:
Taleb reportedly became financially independent after the crash of 1987[15] and made a multi-million dollar fortune during the financial crisis that began in 2007, a development which he attributed to the mismatch between statistical distributions used in finance and reality.[36] Universa is a fund which is based on the “black swan” idea and to which Taleb is a principal adviser. Separate funds belonging to Universa made returns of 65% to 115% in October 2008.[20][37]
That said, the fund he founded, one by the name of Empirica, was shut down in 2004, though it was actually producing positive returns at the time. Apparently he was seriously ill, and wanted to become an author full-time as well. However, the “ran” in my above post was incorrect.
There’s at least one leprechaun there: citation 36 says nothing at all about whether Taleb made any money during the 2007 crisis, much less whether he realized a net return since 1987 greater than indexing. I’ve replaced it with a citation-needed.
Eh, maybe it wasn’t bleeding too badly, but 2004 wasn’t a year anyone should be posting negative returns, and at least one of their funds was shut down for failing so badly:
(Yes, 2001 was a loss. As Tavakoli asks, how the deuce does a ‘black swan’ fund lose money after 9/11?)
Articles I read phrased it as he ‘feared’ a recurrence of throat cancer, which honestly sounds a bit like ‘our CEO is resigning to spend more time with his family’.
Spitznagel is more of an Austrian than a black-swan guy; since he started in 2007 and apparently managed small amounts like <$100m, some wins are not very strong evidence… I tried to find any estimate of Universa’s net return to investors from 2007 to now after fees etc, but I only found a fluffy Wikipedia article claiming “Spitznagel’s investment performance ranks as one of the top returns on capital of the financial crisis, as well as over a career” and citing The Dao of Capital—written by Spitznagel. So...
My first quote on Taleb was from memory, and looking into it it seems that my memory was a lot more black-and-white than the facts are. I’d love to get some concrete data on the two fund families, but hedge funds are notoriously hard to pry data out of, so I’m not sure we’ll get any.
And in this case, a career is being built partially out of claiming that one of the families was a great success and proves the worth of a notoriously egotistic man’s ideas, so the situation is even worse than usual.
If risk/reward profiles that bleed money most of the time but occasionally make it big look inherently less attractive to investors, should we expect those strategies to be underplayed relative to their expected value?
Probably, but it doesn’t need to be a pure strategy. A normal portfolio hedged with a bit of crash insurance in the form of deep-OTM puts can be a sensible play in ordinary times. I don’t know how many people actually do that, though—judging by the market size, not many.
I’m not sure; I can ask the next time I talk to him (which will be over a month from now).
Interesting idea.
However, if you’re trying to prove that mathematicians are known to have highly transferable skills, don’t cite a few famous people, I would expect that in any field. Cite companies hiring mathematicians with the intent on training them in a different area.
Finance firms seem to aggressively hire math talent from MIT, both as traders and quants (I considered it, but decided that I could probably do more good directly). I don’t know the numbers, but I know many good math folk, particularly from the high school math contest crowd, and most of them who are interested seem to get offers.
This isn’t enough to justify the proposal at all, but it’s definitely a point in favor. They also hire good people from other majors, but math seems to be in highest demand.
As I said, these are very preliminary thoughts. I don’t yet have access to such data, and would welcome pointers to possible sources.
Actually, as soon as I posted this I thought about how MIRI does exactly this! But this is just one group, and we would need more data.
There’s no problem with suggesting a hypothesis based on a limited data set, but that’s not how the post reads. It sounds like it is making a definitive claim, but does not present the evidence to back this up.
Of course, much of what MIRI hires mathematicians for is to do mathematics research.
There’s a disclaimer at the beginning :-).
Note that I wrote “Mathematicians who are motivated to learn these skills are well positioned to do so,” which is a weaker claim than that mathematicians are known to have highly transferable skills. I’m fairly confident that what I said is true in most cases, but this is based on a lot of tacit knowledge, which is difficult to externalize.
We already have a whole industry built up around the topic of analyzing the markets. Why would this provide any improvements? I know of commentators who thought housing was a bubble as soon as 2002, but a few people talking doesn’t get a bubble popped. You’re talking about putting a handful of ivory-tower nerds up against the entire weight of first-world culture. I do not imagine that this could actually be successful. And frankly, given the abysmal track record of smart people poking their heads into fields they don’t understand and trying to overhaul the whole thing from scratch, I suspect this effort stands a real chance of doing more harm than good.
I encourage all folks to stand up for sanity at any given time. People talking about how houses were a bubble in 2005 were doing God’s work, so to speak. But making it a special effort is weird, and expecting a bunch of neophytes to do better analysis than professionals because they can do pure path is absurd. Finance done by pure mathematical methods leads to things like the Gaussian cupola fiasco—the math was impeccable, the assumptions were absurd, and the instruments underpinned by this analysis burned down, fell over, and then sank into the swamp.
My fundamental assumption of public policy is humility. Know your limits, and work within them. Do not assume that raw intelligence, good intentions, and clarity of purpose are enough to solve the world’s problems, because that’s the sort of mindset that leads to most of the laws we have today(and quite a few genocides as well). The market is not perfect, but it’s the original superhuman artificial intelligence, and it’s a pretty good one. A couple guys with PhDs in topology are not going to one-up it just by walking by and pointing out how everyone else is stupid.
(Also, anyone who actually is capable of doing really good financial analysis already is providing enormous social value. We call them “successful hedge fund managers”.)
Your comments on this thread are unnecessarily inflammatory, something that tends to lower the epistemic value of discourse, and you’re not steelmanning my arguments (that is, you’re falling short of DH7 here).
Putting that aside, I appreciate your willingness to engage. Here are some responses to points in your comments on this thread.
I certainly don’t think that I’m well equipped to make better predictions than existing actors in finance. I think that it would take many years of training for me to have a shot at doing so. My underlying assumption was that the mathematicians in question would spend a great deal of time developing expertise.
I have reasons to believe that mathematics is stacked with “transferable intellectual talent” to a significantly greater degree than finance is. But my view here is largely based on tacit knowledge, and so is difficult to argue for in a few paragraphs.
I agree that there’s a danger of doing more harm than good, and think that people doing this would have to exhibit high self-skepticism and solicit and consider feedback from many different sources.
Successful hedge fund managers could make markets even more efficient by sharing information with each other.
The taxpayer burden is only one factor that I listed supporting the claim that the recession was really, really bad. So even if it doesn’t hold up, my central point stands.
I’d be interested in hearing what you think the real cost to taxpayers (both current and future) was, with detailed justification.
I don’t believe DH7 really applies here, because the status quo is the steelman of your proposal for change. We already have open-source financial analysis and financial models, and mathematicians do work in the industry. I’m unclear on what concrete changes you’re actually proposing here that haven’t already been implemented decades ago.
1) I suspect this is the reason why Simons is so unusual, and one of the main stumbling blocks to your plan. Getting to high levels of mathematical skill and achievement takes years, and spending more years learning finance is naturally going to be unappealing. Surely they won’t all make $10B, and trying to get folks who’ve spent their lives getting the famously conservative institution of tenure to give it up for the famously chaotic world of Wall Street seems like a serious challenge.
2) I would agree with this—mathematics is a frame of mind as much as anything, and a pretty useful one. While finance has some elements of the same effect, “anything can be traded” is far less transferrable, and the more specific knowledge base lacks the out-of-field utility of math.
3) True, but then that’s true of most fields where people want to do a good job.
4) I’m not convinced of that. One of the good things about finance is how incredibly well incentives are aligned(modulo Congress) - someone who guesses right makes billions, someone who guesses wrong loses billions. Having managers share information and analysis destroys that accountability. It also would tend to encourage groupthink − 20 separate paths of analysis will inherently vary more than one collective pool of analysis. Given that groupthink is a major contributor to bubbles, this seems a real danger.
5) Granted.
6) The primary cost is going to be reduced tax receipts and increased welfare costs, plus the stimulus. The bank bailout actually made a small profit, the auto bailout would probably have happened either way by now, but the damage to the economy(especially the top strata that pays a disproportionate amount of the taxes) really did hurt the government’s finances. Also, for ease of data collection, I’m limiting my numbers to the US. Most developed economies will have a similar money lost:GDP ratio(Canada and Japan less, PIIGS more, but it’s assumed to work out in the end). All numbers from this wonderful document: http://www.gpo.gov/fdsys/pkg/BUDGET-2014-TAB/pdf/BUDGET-2014-TAB.pdf
Tax receipts: We’ll measure these peak-to-peak, so 2000-2007, and use that to establish a trend line. In 2000, the USG took in $2025B, in 2007 it took in $2568B, for an average growth of 3.45%. We’ll assume that inflation and population growth are constant over the time period, so the trend will stay flat.
Year Trend Actual Loss
2007 2,568 2,568 0
2008 2,657 2,524 133
2009 2,748 2,105 643
2010 2,843 2,163 680
2011 2,941 2,303 638
2012 3,043 2,450 593
That’s a cumulative loss of $2687 billion in tax receipts thus far.
Social spending: It’s hard to define “social spending” precisely. Social Security, for example, should remain basically stable during economic change, while food stamps will vary wildly. I’m going to use the “Income Security” function and “Health” function(which is primarily Medicaid—Medicare is separate) to approximate this. There’s some stray charges in there(federal employee pensions, occupational health and safety, etc.), but those should be fairly stable modulo recession, and I don’t know enough about what precisely is in the categories to get too fine in my distinctions. I’m using the same 3.45% growth assumption.
Year Trend Actual Loss
2007 632 632 0
2008 654 712 −58
2009 677 868 −191
2010 700 991 −291
2011 724 970 −246
2012 749 888 −139
This yields a total of $924 billion in added welfare costs.
Stimulus: The ARRA was estimated at $831B of total costs, though a significant fraction of that has been counted above - $288B was tax cuts, $155B was health spending, and $82B was welfare of various sorts. The remainder is $306 billion on education, infrastructure, and research.
The sum of the above categories is $3917 billion, to the end of FY 2012. However, we also need to factor in the effect of lower interest rates—we’re spending less on debt interest today than we were in 2006, despite a 137% increase in the size of the debt. This works out to a $228B savings versus trend(again, assuming 3.45% YOY growth). Throw in a few miscellaneous billions for uncounted programs, we can estimate the cost to date at $3.7 trillion. Final cost will likely be some $6-8 trillion, depending how long this mess continues for.
It’s worth noting, though, that if you phrase it in terms of cost to taxpayers, the loss of receipts shouldn’t really be counted—after all, it doesn’t lose me money to have my taxes go down. Actual new spending as a result of the recession is only about a trillion thus far, the remainder is simply deferred taxes.
(Yes, these numbers are a bit ham-fisted in places. I’m not the CBO, I don’t intend to get too far into the details for the sake of an internet discussion. It’s a good enough estimate to work with)
Is this accurate?
It seems like there is no ceiling to profits, but a clear floor on losses from the bettor’s perspective. If a $100 Billion company bets on a coin toss, it can be $150B bet or a $250B bet and if it wins, it gets $150B or $250B, but if it loses, the company itself is bankrupt, and loses $100B. The portion greater than its value is assumed by its creditors, and doesn’t really factor into the decision.
How is my reasoning faulty here?
My implicit assumption is that the “someone” in question has billions to lose. Obviously, you or I are not going to start losing billions.
I’m raising the possibility of more work along these lines.
Yes, but the idea may not have occurred to some of them, so pointing it out could be helpful. Even if very few are interested, sharing this information could be helpful at the margin (provided that this is a good idea, which I recognize may not be the case).
Ok
We’re on the same page here.
Good point.
Ok
Thanks, this is useful info.
Bryan Caplan seems to think that the meltdown scenario’s effects were accurately estimated: http://econlog.econlib.org/archives/2013/05/conditional_ins.html
Mathematicians sound useful for answering the former question, yet no more useful than anyone else for answering the latter question.
No! The bailouts, and the loans, have been paid back; the government made a profit on the AIG bailout. There was risk, but long run tax burden has not risen by $9 trillion.
According to Wikipedia:
But TARP was just a small part of the whole picture. What concerns me is that there seem to have been somewhere between $1.2 trillion and $16 trillion in
secret loans from the Fed to big financial institutions and other corporations. Even if they’ve been repaid, the low interest rates might represent a big transfer of wealth from the poor to the wealthy. And the fact that I’m seeing figures that differ by more than an order of magnitude is far from reassuring, too! The GAO report seems to be worth digging into.
Can you give a reference?
According to Wikipedia:
But TARP was just a small part of the whole picture. What concerns me is that there seem to have been somewhere between $1.2 trillion and $16 trillion in secret loans from the Fed to big financial institutions and other corporations. Even if they’ve been repaid, the low interest rates might represent a big transfer of wealth from the poor to the wealthy. And the fact that I’m seeing figures that differ by more than an order of magnitude is far from reassuring, too! The GAO report seems to be worth digging into. If not mathematicians, at least accountants could be helpful for things like this!
The Fed’s balance sheet isn’t anywhere approaching $16T—the last I looked it was under $2T. About half of that is growth since the recession hit, in the form of the Fed printing money and loaning it out. If the $16T number is anything more than a fever dream, the number comes from something like “We loaned $1 to you yesterday, you paid it back, now we’re loaning $1 to my mother, so that’s $2 of loans”—literally true, but you’re counting the same dollar multiple times.
They might represent a transfer from taxpayers to bondholders and shareholders of banks, but not to the tune of $9 billion.
Also thank you for providing the reference I was in too much of a hurry to.
Typo: you meant, “trillion”.
Taxpayers aren’t on the hook for “loans” made by the Federal Reserve. And we’re in a liquidity trap—until the economy recovers and nominal interest rates rise above the zero lower bound, the Fed can print all the cash it wants without causing inflation (because people are going to save, rather than spend or invest, that cash). Which is pretty much what happened. The U.S. monetary base tripled between 2008 and 2011 and yet prices have not risen accordingly.
Well from 2009-2013, the US government spent $18.3 trillion in total. Do you really think that half of that went to banks, instead of old people and soldiers?
Saying the bailouts cost $9T is like saying that the derivative market is measured in trillions of trade a day—yes, that’s the face value, but it’s not the actual amount of cash trading hands, any more than the travel insurance I bought today with $10M of coverage is a $10M contract.
This claim seems incomplete. If I have secret knowledge of an inaccurate price, my motivation is to keep that secret… for as much as a few hours, if that’s how much is required for me to decide on, place, and verify trades which take advantage of that price. Once I’ve made my trades, I would prefer that the price adjusts to a more accurate value as soon as possible so that I can cash out and re-diversify my portfolio. Unless my secret knowledge was some illegal insider thing, I’d be shouting it to the rooftops as soon as I could.
This applies in triple if the trades I’m making are something like a leveraged short sale, like you’d probably want to do for a bubble. The longer people are unaware of the bubble, the more danger it has of growing to the point where I lose my shirt before it pops.
That might be illegal, and even if not would people listen? You’re talking about taking a position and then loudly announcing that other people should make trades such that you make money.
The problem is not doing transparent high quality analysis… it’s getting that analysis believed and acted upon. And this becomes much harder when there are people with a vested interest in seeing that analysis not acted upon. As the Upton Sinclair quote says, “It is difficult to get a man to understand something when his salary depends upon his not understanding it.”
One idea is to build a track record the way most hedge funds build a track record, right? Trade assets, record your annual return, and whatnot. You could even run a regular old hedge fund and make a bunch of money. Then when you started getting some credibility, give away your insights for free.
(Personally, I’m not convinced that you’d do more good by giving your insights away for free than you would do by keeping your insights secret and donating the money you made to effective charities, but I’ll listen to anyone who wants to persuade me.)
By the way, if people on LW want to start a hedge fund, I’m interested. I’m not a mathematician, but people seem to think I’m pretty solid at programming.
Can you flesh out your thoughts here? Surely not everyone in a position of influence has a vested interest in seeing the analysis not acted on.
True, but you don’t always need everyone. How many “greater fools” do you actually need to have an asset price bubble?
Subissue 1: You can find lots of financial analyses around, some available for free, some not. Why should this one be given any more weight than others, when you can find someone else with good credentials saying the exact opposite? And accurately assessing the quality of an analysis is almost as hard as doing the analysis yourself; it’s very easy to be fooled by bullshit.
Subissue 2: When you have the appearance of equal and opposite experts, many people are going to end up believing what they want to believe, and acting accordingly.
The people in a position of influence in the housing market were primarily the homebuyers. Needless to say, they didn’t want to believe that the value of their biggest asset was about to go up in smoke.
Behind The Housing Crash is a collection of interesting stories written by a loan analyst who had predicted the housing crash several years in advance. The book can be boiled down fairly simply: “Nobody listened.”
You’re looking to get voices. Until those voices have ears, they can shout all they want to no effect.
The Sumner critique says that the Great Recession was more ‘caused’ by failure to supply enough money (when the market was clearly anticipating almost zero inflation) to keep NGDP on a level growth path, suggesting that the main path toward resolving this problem would be figuring some way to further convince the Federal Reserve to adopt NGDP level targeting. Also, as I understand it, even some of the people making money off shorting the housing market were quite loud about it—it’s not obvious that a lack of transparency is the problem. To make money you need to time your short correctly which the now-famous winners may have done by coincidence, and this is always the problem with bubbles. For that matter, it’s not obvious that the housing market would still have collapsed if the Fed had committed to an NGDP path.
Yes, people who short are usually loud about it. The sooner people agree with them, the sooner the bubble pops.
Which, FWIW, is what happened. The question is, “why didn’t it happen sooner?” with perhaps a follow-up of “what’s the soonest we can reasonably expect these things to happen?”
Convincing the US Federal Reserve and the European Central Bank to adopt more intelligent monetary policy (something like NGDP targeting) would be big win for the world.
I’ll admit that I’m tragically under-read on the NGDP thesis, but it seems like a restatement of the old Phillips Curve nonsense that caused so much of the wreckage of the 1970s. It basically amounts to “inflate when times are bad to spike employment”, which does work in the short-term, but the long-term second-order effect of increased inflation expectations saps all the value out of it quickly.
Is there something I’m missing here, or is this just failed policy from 50 years ago with a new label on it?
First, I think ‘flexible inflation targeting’ is still the conventional wisdom, so I don’t think people have abandoned “inflate when times are bad to spike employment”.
Second, the case for NGDP targeting is more subtle than that. Market Monetarists claim that an NGDP targeting provides approximately the correct amount of money for the economy. A key difference is that one targets a time-path for the level of NGDP rather than the rate of growth.
There’s definitely something you’re missing, its not just a dressed up old theory. I wish I had a great starting point to start reading about this stuff, but I don’t. If you’re very interested I can try to find some especially good articles. This post of mine (and the ones it links to) tries to explain the process of monetary disequilibrium, which I think starts to give you some intuitions for why NGDP targeting might be a good idea (but is certainly not the whole story).
Thanks Eliezer. I’ll look into this.
So the problem was that authorities didn’t (do enough to) prop up one of the 20th-century correlates of economic shock resistance? If only more money had entered the economy in the form of massive, cheap loans to much the same intermediaries that failed to make their business plans resilient against such shocks, the economy could have reorganized quickly into sustainable modes of production?
When NGDP/NGDI, the total amount of money the economy spends, goes down below trend, so does RGDP. If the Fed had e.g. bought government bonds, the amount of circulating money could have been prevented from going down (without any need for “fiscal stimulus”, by the way) without making loans to any particular trading companies (no need for bailouts! let them go bust!), and the bonds wouldn’t be monetized, they would be sold again as velocity picked up. I recommend http://themoneyillusion.com/ - your viewpoint here is too distant from sane macroeconomics, and too close to crazy populist economics, for me to tackle all the individual problems. But what you’re saying is more or less literally, exactly what people were saying about banks needing to take their medicine just before the Great Depression. Seriously, look it up, that was what made people realize that monetary velocity slowdowns needed to be made up by (temporary) monetary supply increases.
Only under “all else equal” assumptions, which are weakest in this situation. Any exogenous forcing of higher trading would imply that the extra trades have lower consumer surplus (perhaps negative relative to the no-forcing baseline).
In other words, if what you’re saying is true, then threatening to murder anyone who hoards more than 1% of their income (relative to a baseline year, etc) would also solve the problem. But then the cost of such a policy would be too large relative to its benefits for the most diehard anti-Sayer to ignore.
You’re acting like I haven’t already been to the site or thought about these issues, when I actually followed it for the entire time Sumner was making his case, hence why I could make comments like in this thread—which I, for my part, recommend.
FWIW, sane macro doesn’t have the implications that interest rates can be forced to zero without cost, or any money not immediately spent is pure waste, or that the temporary surge in economic indicators you get from looting the rich is evidence of policy success.
So? They were just as correct then: people suddenly expected (and expect now, less suddenly) to have their cake and eat it too with banking. Specifically, they wanted their banks to provide them with instant, guarateed access to their money, while also investing it in illiquid, return-generating ventures. That inevitably leads to situations where banks can’t redeem the investments for cash, and yet people do want it in that form. That’s not the basis for a sane economy.
Yes, we’d all be better off if the short-long trade was outlawed and banks issued bonds on a liquid market, possibly insured by private counterparties, to their customers, instead of claiming that their money was available on demand. But nobody is actually doing that, or has any incremental incentive to adopt it so long as governments supply free insurance, and we have to consider what second-best options are available. Money has no intrinsic value (I assume we both take this as axiomatic) and the utility ‘money’ provides to civilization comes from increasing the number of positive-sum trades. When people attempting to use money as a guaranteed store of value keep that money instead of spending it, the velocity of positive-sum trades goes down. This doesn’t mean that they’re evil hoarders, though I do think that preferring money as a store of value generally indicates something wrong. But it does mean that supplying more money is a positive-sum move because it increases the number of positive-sum trades occurring, so long as there is significant unutilized capacity.
To the degree that money is used as a store of value, the money supply available for ‘positive-sum’ trades decreases. Let us say that the supply of goods and services on the market stays the same, then with less money available to potentially purchase theses goods and services, the price of the goods and services decreases; microeconomics supply and demand curve. This incentivizes people who are not holding money as a store of value to participate in more positive-sum trades.
Of course, people might end up taking their store-of-value money and investing it, allowing the creation of capital goods that make more efficient production possible. But that’s another story.
Specifically, then they wouldn’t be using money as a store of value anymore since they wouldn’t be holding money but securities.
Hence the standard belief that reluctance to lower nominal prices, or delay in lowering nominal prices, is key (along with nominal debt contracts) to explaining the observed fact that deflation is destructive of RGDP, which is why Scott Sumner’s blog is called “The Money Illusion”.
Well, that’s the core of our disagreement (indeed, my disagreement with anyone who buys into either the Sumner or Krugman view).
Specifically, on what basis can you even begin to make a case that there is “too much” hoarding without a framework for tabulating its benefits? That’s like saying that someone isn’t “eating healthy enough” while only counting the benefits of eating healthy.
And yet (as best demonstrated by the Landsburg link), any time such proponents are asked how to tabulate the relative benefits of hoarding to hoarders against its social costs, the answer is somwhere between silence and “assume we want to get people spending”.
That should be a much bigger red flag for you in the opposite direction.
I don’t think you’re engaging your opponents strongest arguments here. Yes most proponents (even economists or scott sumner) of active monetary policy can’t articulate why it might be a good idea that is solidly grounded, but other people can (myself, nick rowe, other market monetarists or the austrian monetary desequilibriumists), and you should engage the arguments they make.
I know you know of a way to tabulate the benefits because we’ve had extensive discussions about it. The benefits are the marginal utility of holding money (convenience etc.), the costs/benefits are the changes in money holdings you impose on other people when you trade or don’t trade with them.
Btw, I don’t remember if you read the write up I did of my simple mathematical model of monetary disequilibrium.
Hold on—Sumner is the one everyone refers back to when advocating monetary policies he likes, he’s blogged about it for years, at tremendous length, starting from the crisis, he’s become (supposedly) influential in policy circles, and you’re saying I’m attacking a weak point? Sumner is exactly who I should be engaging, which is why it’s all the more saddening that his arguments fail simple checks:
Doesn’t this mean the Fed should be loaning to any ol’ person who can put up the collateral at 0%, not just large banks?
Why is it bad to “hoard” money for a year, but not five days? Why 5% NGDP growth and not some other number?
Why is it a “crisis” when interbank short-term loan rates “spike” from 4% to 6%? That just means their borrowing cost went up in the parts per million. (His entire response to this was something like “yes, ‘for want of a nail’ and all that”.)
If more exchanges are good, why aren’t hyperinflation scenarios (in which people treat money as a hot potato) a social optimum? (Or lesser scenarios that make people trade more than they otherwise would, like bans on home cooking.)
No, those weren’t the main benefits of holding money, and your citing of them means I somehow didn’t communicate the benefits to you. On your specific illustrative examples of convenience in our past discussion, I found them to completely assume away the important social benefits of hoarding.
In short, every example you gave was a case in which people knew in advance what they were going to spend the money on (e.g. having money for the bus). But these are emphatically opposite of the cases where money is most important and uniquely valuable—i.e., when you don’t yet know what you wish to redeem the money for and prefer to keep its option value.
The main benefit of holding money is that it signals the higher social demand for option value, which in turn is indicative of “discoordination”, or the lack of confidence that people can find stable comparative advantages. Money is only the extreme end of a scale that includes goods of increasingly multiple use—but in such scenarios, anything if valued if and to the extend that it will be useful in a broader array of situations.
You can suppress that signal, but only by worsening the economic allocation problem, just as you can suppress spiking oil prices, but only by shifting around the inefficiencies.
So I don’t think anyone, including you, has really engaged with the benefits of hoarding and so don’t have a satisfactory framework for evaluating whether there’s “too much”.
I phrased that badly. You’re not engaging a weak point so much as not steelmanning, which is what you should be doing. Fix your opponents arguments.
Yes, everyone refers to Sumner. He is the popularizer of market monetarism. Yes, Sumner doesn’t produce a defense of his views that is solidly grounded in economics. He is none the less vastly better than most people talking about this.
You should say things like ‘The best arguments for Sumners views are made by Nick Rowe and others (link to work or something) and are sometimes called ‘monetary disequilibrium theory’, however I think that this theory misses the important effect of Discoordination which works like this …’.
Basically no one discussing monetary economics (including the Austrians and others against active monetary policy) manages to ground their arguments in solid theory except Nick Rowe, a couple other market monetarists and the monetary disequilibriumist Austrians. The quality of debate is bad, but that doesn’t mean its OK for you not to engage the strongest arguments.
And that’s fair enough. I didn’t get that you were referring to that.
However,It is highly misleading to say
Because monetary disequilibrium is an internally consistent theory which does talk about the benefits of holding money and which can assess whether people hold ‘too much’ or not within the theory. You do not appear to deny this.
As far as I can tell, you additionally claim that this theory does not describe the important effects of Discoordination which provide additional social benefits to holding money. This is well and good! You should acknowledge what monetary disequilibrium theory does do well, and then attempt to improve upon its deficiencies. I’m actually very interested in hearing those arguments!
Your previous posts do not make this at all clear that this is what you were arguing, even to me. Since I probably know more about your views than anyone else other than you, this means they were probably also unclear to everyone else.
You’re speaking language I don’t recognize. This has nothing to do with ‘hoarding’. It’s about sticky nominal prices and fixed nominally priced debt contracts implying that when monetary velocity goes down, monetary supply should be increased to maintain the velocity of positive-sum trade at full capacity. Nothing you’ve said contradicts this, at least not in any language I recognize.
I would say your language is at least as unrecognizable if you’re going to propose measures to stop people from hoar… not spending money fast enough, while saying the problem “isn’t about that” (another red flag term), but instead “really about” this other new thing you just brought up.
Regardless, about that thing: yes, it certainly sucks when you can’t retroactively change the numbers in a contract you signed, and don’t understand how it embrittles your business plan to guarantee a stream of payments like that. However, sane policies should not favor those who failed to plan against eventualities, nor are sane economies predicated thereon.
I’m afraid that you do have to employ the concept of hoarding (or some isomorphic one) when you want to claim that welfare-enhan… positive sum trades are maximized when NGDP grows at n% like clockwork, regardless of how unmoored the economy has become, for some value of n that Sumner pulled out of thin air.
Remember our conversation! Sometimes negative interest rates are appropriate. If on the margin its impossible to make more investments that have a positive return, and people still want to transfer more wealth to the future, then negative rates reflect the actual social costs and benefits of holding money.
Also, I don’t understand why you insist on talking about adding more money to the economy as ‘forcing of higher trading’. Remember that money is a product, and that it can be correct to increase or decrease the supply in responses to changes in demand.
Even so, the case you describe wouldn’t require central banking to bring about zero interest rates, and yet their action is needed to do exactly that.
Then why do you understand why it’s talked about in exactly that manner, i.e. used to prop up total nominal expenditures? How do you differentiate e.g. quantitative easing from from the murder policy I described, or the “ban on home cooking” that I described in past discussions with you. Both get people to spend more money.
1) Fair enough, I think targeting US bond interest rates is probably a bad policy too.
2) Because one attempts to solve a shortage of money by adding more money and one tries to solve the same problem by much worse means?
(I’m confused why we’re having this argument again, I thought I had more or less convinced you on these issues).
But the dynamic is exactly the same! People are making all trades in which the stuff they get is better than enduring the “punishment” (arrest or loss of savings). This means the fraction of trades that only happened because of this policy are not, like other trades, Pareto-optimal. The fact that the means are better or worse among them (in gross terms) does not matter; they all are undermining the basis on which we can conclude that trades are positive sum. If your solution assumes away this loss of utility, you can get away with anything.
What gave you that impression? I thought we reached pretty intractable points, in that our core disagreement hinges on the question of the economic role of liquidity in goods, and the extent to which the market can thereby signal “discoordination” (a term you also claimed doesn’t operationalize).
2 - Yeah, you’re right, I was misremembering. We weren’t able to come up with a concrete description of discoordination (which is a new concept that is unknown in the academic literature including austrian as far as we know) that made sense to me, and I lacked the intuition about it you have. IIRC I was able to convince you that ignoring discoordination effects, minimizing monetary disequilibrium is the thing to do, and NGDP targeting does a decent job of that. You also convinced me there was a case where it wont (when there’s a big shift in market to non-market activities or vice versa). Is that right?
1 - Given our disagreement in 2, I think you should still agree with me here since you haven’t brought up any discoordination related arguments as far as I can tell.
To get pareto optimality you have to ignore any monetary externalities (i.e when I buy buy something, I increase the quantity of money you have and my decision does not take this into account) or assume the quantity of money is optimal. And if we’re doing this then: given the quantity of money, we’re going to get pareto optimality no matter what. In other words, the trades in the no-monetary-change and monetary-change case are going to have the same pareto optimality. All trade in both scenarios is voluntary, so given the quantity of money each trade is welfare improving in the same sense in both cases. So our basis for concluding the trades are positive sum is unaffected.
The effects of a monetary change occur through an externality.
Didn’t we already have a long debate about this? Didn’t I mostly convince you that NGDP stabilization was a good idea?
If I’m understanding my Krugman right, declaring an NGDP target and actually achieving that target are two different things. The Fed’s normal tools can’t actually produce any inflation when the economy is depressed and interest rates are at zero. - increasing the money supply will just lead to more cash under Apple Computer’s proverbial mattress instead of increased NGDP.
There actually is something a central bank can do when it’s up against the zero lower bound, but it’s risky. The interest rate won’t always be zero, so if the central bank can credibly promise to create high levels of future inflation by keeping interest rates low even after the economy recovers—to “credibly promise to be irresponsible”—it can affect real interest rates today. But good luck getting investors to believe a central bank is going to keep the proverbial printing presses going even beyond the point where it starts doing more harm than good.
It’s much easier under these circumstances to do the same thing with fiscal policy rather than monetary policy: have the government borrow and spend to put idle resources to work directly, increasing NGDP by increasing real GDP. (This is how World War II caused the end of the Great Depression.)
I think you’re actually just not understanding Krugman right. He often writes misleadingly, so that’s understandable. The Fed’s normal tools perhaps can’t be used to do that (but they probably can), but the Fed’s normal tools are stupid and it’s easy to produce inflation with slightly different tools (negative interest rates on reserves, level targeting, targeting the forecast etc., credible promise to keep interest rates low). This isn’t some new theory, Krugman has a famous paper about how the Japanese Central Bank could do exactly that.
Having a target and achieving the target are definitely different things, but achieving an NGDP target is not difficult because central banks have approximately infinite control over a single future nominal variable (e.g. NGDP, nominal interest rate or price level).
One of the big lessons from central banks response to this recession is that interest rates and inflation are a very misleading and confusing ways to talk about monetary policy.
This is likely an incorrect understanding.
If I’m understanding my Sumner right, Krugman is just plain wrong about this. Central banks can decrease interest rates, promise to keep future interest rates low, engage in quantitative easing, charge negative interest on reserves, and print physical money and drop it out of helicopters. “There is no zero bound” is a market monetarist slogan and I have to say it sounds a tad plausible from over here.
On this point, I’d just have to chalk it up as “beyond my current expertise”. (Part of Krugman’s argument is that although “unconventional” monetary policy is possible, political and other considerations make it much harder to do.)
My understanding is that Krugman is not wrong, he just writes correctly but very misleadingly for reasons that are not totally clear.
Oh, I think his reasons are pretty clear. They basically amount to politics.
I found the article “The Deflationist: How Paul Krugman found politics” educational on that point.
Holy crap: that article has “get to the point”:fluff ratio of about 5%. I appreciate the link, but just want to warn readers they need to skip past a lot of meandering about scenes from NYC to get the answer to the question.
Plausible enough to overcome the presumption against “printing money is a costless way to solve economic problems”?
As a blanket presumption, that’s dumb and you know it.
Certainly printing money is a near costless way to solve economic problems at some times otherwise we wouldn’t use money at all. Sumner doesn’t advocate printing money at all times and places, he advocates printing or destroying money until there is the correct amount (which he suggests is when NGDP is on-trend).
I think you’re confusing “presumption” with “categorial principle” or “axiom” or something. A presumption can be overridden, given sufficient evidence; my question was whether or how strongly EY takes a presumption against it. If you agree with the concept of criminalizing counterfeiting, you agree with that presumption.
Does not follow. Not all moneys arose in a Pareto-optimal fashion, so their use not evidence printing money being costless.
The problem isn’t that the Fed can’t reach (from low to hit) NGDP targets; the problem is that anyone can scheme to raise NGDP by conducting dummy transactions that nevertheless go on record in economic accounting as higher NGDP. At which point NGDP loses its evidential and causal power through Lucas/Goodhart effects.
Can there even exist a way to fix this without abandoning GDP-like indicators in favor of something closer to human level? Shifting resources around among enterprises, at higher and higher volume, is not the kind of economic activity worth wanting.
You are exactly correct. Policy must switch to a less gameable metric in order to avoid situations like that. But it’s important to remember that the problem isn’t just the potential for schemers, but that, even without schemers, the extra trades you’ve forced to goose NGDP are even less indicative of economic goodness than the normal “GDP = good” assumption requires!
And I don’t think anyone can come up with such a metric without solving the (holy grail level) problem of microeconomic foundations for macro.
Wait, now I’m confused. Micro-foundations for monetary economics exist, and I know you know, because we’ve talked about them at length. Maybe you mean that there’s more to macro beyond monetary economics?
I’m referring the commonly-known problem of deriving macro results from micro, which we also talked about in those exchanges, and during which you rejected the claim that failing to so derive results is a reason to reject the macro conclusions. If you remember differently, give a link.
Macro results are definitely derivable from micro principles: http://goodmorningeconomics.wordpress.com/2012/04/07/the-backrub-economy-a-simple-mathematical-model-of-monetary-disequilibrium/ .
I think we discussed that we haven’t seen the AS/AD models derived from micro principles. I’m not sure this can’t be done, its just not commonly discussed, and I don’t see a good use for those concepts so I haven’t tried to find a derivation.
Bubble != recession
Sufficiently large bubble popping ~= recession.
What if underlying structural issues put you into a very long term recession which only bubbles give the temporary appearance of rising out of...
Then in that case I’d advise reducing regulation, building nuclear power plants, and having more children.
Certainly it is common for bubbles to lead to recessions, but with good monetary policy, it does not need to.
Do you have any good real-world examples of this happening?
I don’t off hand, but I also haven’t investigated this thoroughly. My understanding is that one of the stock crashes in the 20s lead to no recession, but I’m not finding the post I read.
Here’s the DJIA for 1920-1940: http://stockcharts.com/freecharts/historical/djia19201940.html
The only real crashes were 1921, which was mid-recession, 1929-33, which was caused by the Depression getting under way, and 1937, which was mid-Depression.
1987) is the example typically offered by propotents of that position.
For all the sound and fury at the time, it wasn’t all that big a bubble. The trough was only barely a 52-week low—the Dow closed Black Monday at 1739, the previous 52-week low was 1808, and by Wednesday it was back up over 2000. (Admittedly, it did keep bouncing around for a while).
I had thought of another way that mathematicians could contribute to global welfare using mostly math skills.
A lot of newcomers to bitcoin often mentioned that it looked to them that the calculations look wasted. Those who have read about bitcoin know that this is not true as the calculations are used to secure the network. The calculations really don’t have other uses.
The essence of a bitcoin like problem is - tough to crack, but easy to verify once the solution is in. A talented mathematician/chemist team could team up to try to map protein folding or some such problem to a bitcoin-like algorithm where regular increments proceed to solving a bigger problem.
The problem could involve a simple rope like structure and finding out the least energy state. Once cracked, then another segment is added and the next block requires solution with the added segment.
Or a 3d game of life simulation where you have to create von-neumann machines of a certain size. Then once that is cracked, you have to proceed towards creating a bigger von neumann machine.
Hopefully some of the learnings from the random designs that get generated from these networks can be used to crack actual protein folding or nanotechnology and take mankind to the next level.
As someone who’s wondering how much math to study, how do folks think the causality works here? Does studying math make it easier to pick up stuff (e.g. by training your brain on hard intellectual labor somehow), or do people who pick up stuff easily tend to study math?
I suspect that someone who likes hard fact and in-depth analysis, is comfortable with math, and is generally intelligent is going to have a skill set that’s transferrable into a wide variety of fields.
Seems plausible, but that doesn’t tell me whether I should study math for its own sake or not.
I believe that it’s partially a selection effect – things other than intellectual ability seem to matter more in other fields than they do in math. For example, in the experimental sciences, research often takes place in the context of a large lab, and I would guess that the principal investigator’s administrative skills probably make a big difference.
I believe that doing math does increase general intellectual caliber. But I believe that there are other activities that have the same effect. My experience has been that as long as the material is intellectually substantive and there are feedback loops to learn from (whether in the form of feedback from others, or data to test your beliefs against, or the test of being able to construct a valid proof), spending time thinking about it is conducive to intellectual growth. Math could be the best such thing that you have access to, but in principle there are other things that can fill the same role.
Thanks for your thoughts.
Minor quibble here...It’s my understanding that the DJIA is not useful indication of a cost to the economy.
The DJIA is very useful historically, because it’s the best index we have for the US before the S&P 500 came into being. In the present tense, it’s the finance equivalent of RBI in baseball—it’s easy to calculate and a lot of people like it because it’s been in the headlines since the 19th century, but there’s other numbers that do the same job more effectively.
I’m not sure whether it’s a question of math. In a field like bioinformatics most of the important software is open source. In fiance it isn’t.
If you want transparent fiancial analysis I would suggest to push for a big open source financial simulation. You might even want to start a new license to ensure that every one who contributes code also publishes his code.
If anyone is really good at it, why would they give it to you for free when they can give it to Goldman Sachs for six figures a year?
You have the same problem with getting mathematician into finance the way JonahSinick proposes. Someone who’s completely motivated by money probably won’t.
On the other hand being the head of a good open source financial simulation is probably a good way to get tenure as an economics professor.
Once you have a computer model that’s useful, academic publications are going to use it and cite it.
There’s also open source software that gets developed by companies that make good money. You could sell consulting to fit the software to particular business needs. There are a lot of companies besides Hedge funds and big banks that would profit from having access to good financial analysis tools.
If you customize your tool to help Walmart with their planning needs Walmart doesn’t have a particular problem with the tool being open source and other companies also profiting from the tool.
I’m in general agreement with most of that, but I suspect that the practice of copylefting will ruin the “customize for Walmart” plan—they won’t object to using an open-source tool, but they will object to the Walmart version being open-source. Also, anecdotally, academia places too low a value on toolmakers, instead preferring the analysts, which makes it a suboptimal way to get ahead—after all, what’s your journal publication history if you spend all your time working on a computer program?
There are already many mathematicians in finances, they’re the ones who came up the models showing that sub-prime backed securities warranted AAA ratings.
In fairness to the skills of mathematicians in finance, it should be noted that those ratings were evil, not stupid.
And yet other people looking at the ratings didn’t immediately notice there was a problem. I think it was a combination of evil and stupid with a lot more of the latter, specifically people did some cooking of their books but took everybody else’s numbers at face value.
I freely grant that lots of people who contributed to the meltdown were incompetent. However, I don’t think that’s the claim you were making in the original comment. It seems to me that your original comment’s implication is that mathematicians in finance were incompetent, this implication being supported by the fact that they came up with models that were later used (by the mathematicians themselves? by others?) to justify the AAA ratings. But it’s now apparent that those AAA ratings were the result of knowing fraud, not incompetence, so I don’t see how that can be evidence for your implied claim.
Well, my understanding of what happened is that the mathematicians came up with a model with certain assumptions. The model was applied to model securities even though the assumptions weren’t actually true in the real world with people assuming the assumptions were close enough. As it turns out they weren’t. This leaves the question of whether it was the mathematicians’ job to ensure their models were robust, or someone else’s (whose?) job to check the assumptions. In any case, however we assign blame it strikes me that Jonah’s proposal would suffer from the same problem.
I must have missed when this became apparent. (BTW, your link seems much more like the coping mechanism low level employees develop when it’s clear to them that their situation is absurd and complaining to their superiors won’t help than anything resembling knowing fraud.) I don’t dispute that there was fraud involved, but the point of the regulators, rating agencies, and the market itself is that they’re supposed to catch and/or correct for fraud. Here is where the incompetence comes in.
The dateline on the link is Feb 6, 2013 -- so fairly recently.
I think you’re correct -- I posit that the knowing fraud was perpetrated by the superiors, who I think must have ordered the relaxation of rating standards that the employees that were quoted in the link were complaining of.
I believe the sequence of events goes like this: Shadow banks (e.g., Lehmann Bros.) wanted to lay hands on large amounts of AAA-rated securities to use as collateral for the repo that funded their day-to-day activities. That plus the lack of regulation requiring mortgage issuers to hold some proportion of mortgages on their own books (and hence skin in the game) led to slipshod or outright fraudulent mortgage screening and wholesale securitization of the resulting mortgages, inflating the housing bubble.
In detail: since security issuers pay the rating agencies to rate the resulting securities, the three big rating agencies were forced to compete for share of this new market, and one way in which they did so was by relaxing rating standards so that super-senior tranches of crappy MBSes got undeserved AAA ratings and could then be sold to satisfy the shadow banks’ demand for AAA-rated securities.
I’m tapping out now.