Guest Post SuperReview Part III of VI: The Occupy Handbook Part I and a little Part II: Where We Are Now
Moving on from Lewis’ cute Bloomberg column reprint, we come to the next essay in the series:
Indefatigable pair Paul Krugman and Robin Wells (KW hereafter) contribute one of the several original essays in the book, but the content ought to be familiar if you read the New York Times, know something about economics or practice finance. Paul Krugman is prolific, and it isn’t hard to be prolific when you have to rewrite essentially the same column every week; question, are there other columnists who have been so consistently right yet have failed to propose anything that the polity would adopt? Political failure notwithstanding, Krugman leaves gems in every paragraph for the reader new to all this. The title “The Widening Gyre” comes from an apocalyptic William Yeats Butler poem. In this case, Krugman and Wells tackle the problem of why the government responded so poorly to the crisis. In their words:
By 2007, America was about as unequal as it had been on the eve of the Great Depression – and sure enough, just after hitting this milestone, we lunged into the worst slump since the Depression. This probably wasn’t a coincidence, although economists are still working on trying to understand the linkages between inequality and vulnerability to economic crisis.
Here, however, we want to focus on a different question: why has the response to crisis been so inadequate? Before financial crisis struck, we think it’s fair to say that most economists imagined that even if such a crisis were to happen, there would be a quick and effective policy response [editor's note: see Kautsky et al 2016 for a partial explanation]. In 2003 Robert Lucas, the Nobel laureate and then president of the American Economic Association, urged the profession to turn its attention away from recessions to issues of longer-term growth. Why? Because he declared, the “central problem of depression-prevention has been solved, for all practical purposes, and has in fact been solved for many decades.”
Famous last words from Professor Lucas. Nevertheless, the curious failure to apply what was once the conventional wisdom on a useful scale intrigues me for two reasons. First, most political scientists suggest that democracy, versus authoritarian system X, leads to better outcomes for two reasons.
1. Distributional – you get a nicer distribution of wealth (possibly more productivity for complicated macro reasons); economics suggests that since people are mostly envious and poor people have rapidly increasing utility in wealth, democracy’s tendency to share the wealth better maximizes some stupid social welfare criterion (typically, Kaldor-Hicks efficiency).
2. Information – democracy is a better information aggregation system than dictatorship and an expanded polity makes better decisions beyond allocation of produced resources. The polity must be capable of learning and intelligent OR vote randomly if uninformed for this to work. While this is the original rigorous justification for democracy (first formalized in the 1800s by French rationalists), almost no one who studies these issues today believes one-person one-vote democracy better aggregates information than all other systems at a national level. “Well Leon,” some knave comments, “we don’t live in a democracy, we live in a Republic with a president…so shouldn’t a small group of representatives better be able to make social-welfare maximizing decisions?” Short answer: strong no, and US Constitutionalism has some particularly nasty features when it comes to political decision-making.
Second, KW suggest that the presence of extreme wealth inequalities act like a democracy disabling virus at the national level. According to KW extreme wealth inequalities perpetuate themselves in a way that undermines both “nice” features of a democracy when it comes to making regulatory and budget decisions.* Thus, to get better economic decision-making from our elected officials, a good intermediate step would be to make our tax system more progressive or expand Medicare or Social Security or…Well, we have a lot of good options here. Of course, for mathematically minded thinkers, this begs the following question: if we could enact so-called progressive economic policies to cure our political crisis, why haven’t we done so already? What can/must change for us to do so in the future? While I believe that the answer to this question is provided by another essay in the book, let’s take a closer look at KW’s explanation at how wealth inequality throws sand into the gears of our polity. They propose four and the following number scheme is mine:
1. The most likely explanation of the relationship between inequality and polarization is that the increased income and wealth of a small minority has, in effect bought the allegiance of a major political party…Needless to say, this is not an environment conducive to political action.
2. It seems likely that this persistence [of financial deregulation] despite repeated disasters had a lot do with rising inequality, with the causation running in both directions. On the one side the explosive growth of the financial sector was a major source of soaring incomes at the very top of the income distribution. On the other side, the fact that the very rich were the prime beneficiaries of deregulation meant that as this group gained power- simply because of its rising wealth- the push for deregulation intensified. These impacts of inequality on ideology did not in 2008…[they] left us incapacitated in the face of crisis.
3. Conservatives have always seen seen [Keynesian economics] as the thin edge of the wedge: concede that the government can play a useful role in fighting slumps, and the next thing you know we’ll be living under socialism.
4. [Krugman paraphrasing Kalecki] Every widening of state activity is looked upon by business with suspicion, but the creation of employment by government spending has a special aspect which makes the opposition particularly intense. Under a laissez-faire system the level of employment to a great extend on the so-called state of confidence….This gives capitalists a powerful indirect control over government policy: everything which may shake the state of confidence must be avoided because it would cause an economic crisis.
All of these are true to an extent. Two are related to the features of a particular policy position that conservatives don’t like (countercyclical spending) and their cost will dissipate if the economy improves. Isn’t it the case that most proponents and beneficiaries of financial liberalization are Democrats? (Wall Street mostly supported Obama in 08 and barely supported Romney in 12 despite Romney giving the house away). In any case, while KW aren’t big on solutions they certainly have a strong grasp of the problem.
Take a Stand: Sit In by Phillip Dray
As the railroad strike of 1877 had led eventually to expanded workers’ rights, so the Greensboro sit-in of February 1, 1960, helped pave the way for passage of the Civil Rights Act of 1964 and the Voting Rights Act of 1965. Both movements remind us that not all successful protests are explicit in their message and purpose; they rely instead on the participants’ intuitive sense of justice. 
I’m not the only author to have taken note of this passage as particularly important, but I am the only author who found the passage significant and did not start ranting about so-called “natural law.” Chronicling the (hitherto unknown-to-me) history of the Great Upheaval, Dray does a great job relating some important moments in left protest history to the OWS history. This is actually an extremely important essay and I haven’t given it the time it deserves. If you read three essays in this book, include this in your list.
Inequality and Intemperate Policy by Raghuram Rajan (no URL, you’ll have to buy the book)
Rajan’s basic ideas are the following: inequality has gotten out of control:
Deepening income inequality has been brought to the forefront of discussion in the United States. The discussion tends to center on the Croesus-like income of John Paulson, the hedge fund manager who made a killing in 2008 betting on a financial collapse and netted over $3 billion, about seventy-five-thousand times the average household income. Yet a more worrying, everyday phenomenon that confronts most Americans is the disparity in income growth rates between a manager at the local supermarket and the factory worker or office assistant. Since the 1970s, the wages of the former, typically workers at the ninetieth percentile of the wage distribution in the United States, have grown much faster than the wages of the latter, the typical median worker.
But American political ideologies typically rule out the most direct responses to inequality (i.e. redistribution). The result is a series of stop-gap measures that do long-run damage to the economy (as defined by sustainable and rising income levels and full employment), but temporarily boost the consumption level of lower classes:
It is not surprising then, that a policy response to rising inequality in the United States in the 1990s and 200s – whether carefully planned or chosen as the path of least resistance – was to encourage lending to households, especially but not exclusively low-income ones, with the government push given to housing credit just the most egregious example. The benefit – higher consumption – was immediate, whereas paying the inevitable bill could be postponed into the future. Indeed, consumption inequality did not grow nearly as much as income inequality before the crisis. The difference was bridged by debt. Cynical as it may seem, easy credit has been used as a palliative success administrations that been unable to address the deeper anxieties of the middle class directly. As I argue in my book Fault Lines, “Let them eat credit” could well summarize the mantra of the political establishment in the go-go years before the crisis.
Why should you believe Raghuram Rajan? Because he’s one of the few guys who called the first crisis and tried to warn the Fed.
A solid essay providing a more direct link between income inequality and bad policy than KW do.
The 5 percent’s [consisting of the seven million Americans who, in 1934, were sixty-five and older] protests coalesced as the Townsend movement, launched by a sinewy midwestern farmer’s son and farm laborer turned California physician. Francis Townsend was a World War I veteran who had served in the Army Medical Corps. He had an ambitious, and impractical plan for a federal pension program. Although during its heyday in the 1930s the movement failed to win enactment of its [editor's note: insane] program, it did play a critical role in contemporary politics. Before Townsend, America understood the destitution of its older generations only in abstract terms; Townsend’s movement made it tangible. “It is no small achievment to have opened the eyes of even a few million Americans to these facts,” Bruce Bliven, editor of the New Republic observed. “If the Townsend Plan were to die tomorrow and be completely forgotten as miniature golf, mah-jongg, or flinch [editor's note: everything old is new again], it would still have left some sedimented flood marks on the national consciousness.” Indeed, the Townsend movement became the catalyst for the New Deal’s signal achievement, the old-age program of Social Security. The history of its rise offers a lesson for the Occupy movement in how to convert grassroots enthusiasm into a potent political force – and a warning about the limitations of even a nationwide movement.
Does the author live up to the promises of this paragraph? Is the whole essay worth reading? Does FDR give in to the people’s demands and pass Social Security?!
Yes to all. Read it.
This is a great essay. I’m going to outsource the review and analysis to:
because it basically sums up my thoughts. You all, go read it.
If you know nothing about Wall Street, then the essay is worth reading, otherwise skip it. There are two common ways to write a bad article in financial journalism. First, you can try to explain tiny index price movements via news articles from that day/week/month. “Shares in the S&P moved up on good news in Taiwan today,” that kind of nonsense. While the news and price movements might be worth knowing for their own sake, these articles are usually worthless because no journalist really knows who traded and why (theorists might point out even if the journalists did know who traded to generate the movement and why, it’s not clear these articles would add value – theorists are correct).
The other way, the Cassidy! way is to ask some subgroup of American finance what they think about other subgroups in finance. High frequency traders think iBankers are dumb and overpaid, but HFT on the other hand, provides an extremely valuable service – keeping ETFs cheap, providing liquidity and keeping shares the right level. iBankers think prop-traders add no value, but that without iBanking M&A services, American manufacturing/farmers/whatever would cease functioning. Low speed prop-traders think that HFT just extracts cash from dumb money, but prop-traders are reddest blooded American capitalists, taking the right risks and bringing knowledge into the markets. Insurance hates hedge funds, hedge funds hate the bulge bracket, the bulge bracket hates the ratings agencies, who hate insurance and on and on.
You can spit out dozens of articles about these catty and tedious rivalries (invariably claiming that financial sector X, rivals for institutional cash with Y, “adds no value”) and learn nothing about finance. Cassidy writes the article taking the iBankers side and surprises no one (this was originally published as an article in The New Yorker).
Ms. McLean holds immense talent. It was always pretty obvious that the bottom twenty-percent, i.e. the vast majority of subprime loan recipients, who are generally poor at planning, were using mortgages to get quick cash rather than buy houses. Regulators and high finance, after resisting for a good twenty years, gave in for reasons explained in Rajan’s essay.
A legit essay by a future Nobelist in Econ. Read it.
Anthro-hack Appadurai writes:
I first came to this country in 1967. I have been either a crypto-anthropologist or professional anthropologist for most of the intervening years. Still, because I came here with an interest in India and took the path of least resistance in choosing to retain India as my principal ethnographic referent, I have always been reluctant to offer opinions about life in these United States.
His instincts were correct. The essay reads like an old man complaining about how bad the weather is these days. Skip it.
Editor Byrne has amazing powers of persuasion or, a lot of authors have had some essays in the desk-drawer they were waiting for an opportunity to publish. In any case, Rogoff and Reinhart (RR hereafter) have summed up a couple hundred studies and two of their books in a single executive summary and given it to whoever buys The Occupy Handbook. Value. RR are Republicans and the essay appears to be written in good faith (unlike some people *cough* Tyler Cowen and Veronique de Rugy *cough*). RR do a great job discovering and presenting stylized facts about financial crises past and present. What to expect next? A couple national defaults and maybe a hyperinflation or two.
Shiller has always been ahead of the curve. In 1981, he wrote a cornerstone paper in behavioral finance at a time when the field was in its embryonic stages. In the early 1990s, he noticed insufficient attention was paid to real estate values, despite their overwhelming importance to personal wealth levels; this led him to create, along with Karl E. Case, the Case-Shiller index – now the Case-Shiller Home Prices Indices. In March 2000**, Shiller published Irrational Exuberance, arguing that U.S. stocks were substantially overvalued and due for a tumble. [Editor's note: what Brandon Adams fails to mention, but what's surely relevant is that Shiller also called the subprime bubble and re-released Irrational Exuberance in 2005 to sound the alarms a full three years before The Subprime Solution]. In 2008, he published The Subprime Solution, which detailed the origins of the housing crisis and suggested innovative policy responses for dealing with the fallout. These days, one of his primary interests is neuroeconomics, a field that relates economic decision-making to brain function as measured by fMRIs.
Shiller is basically a champ and you should listen to him.
Shiller was disappointed but not surprised when governments bailed out banks in extreme fashion while leaving the contracts between banks and homeowners unchanged. He said, of Hank Paulson, “As Treasury secretary, he presented himself in a very sober and collected way…he did some bailouts that benefited Goldman Sachs, among others. And I can imagine that they were well-meaning, but I don’t know that they were totally well-meaning, because the sense of self-interest is hard to clean out of your mind.”
Shiller understates everything.
Verdict: Read it.
And so, we close our discussion of part I. Moving on to part II:
In Ms. Byrne’s own words:
Part 2, “Where We Are Now,” which covers the present, both in the United States and abroad, opens with a piece by the anthropologist David Graeber. The world of Madison Avenue is far from the beliefs of Graeber, an anarchist, but it’s Graeber who arguably (he says he didn’t do it alone) came up with the phrase “We Are the 99 percent.” As Bloomberg Businessweek pointed out in October 2011, during month two of the Occupy encampments that Graeber helped initiate and three moths after the publication of his Debt: The First 5,000 Years, “David Graeber likes to say that he had three goals for the year: promote his book, learn to drive, and launch a worldwide revolution. The first is going well, the second has proven challenging and the third is looking up.” Graeber’s counterpart in Chile can loosely be said to be Camila Vallejo, the college undergraduate, pictured on page 219, who, at twenty-three, brought the country to a standstill. The novelist and playwright Ariel Dorfman writes about her and about his own self-imposed exile from Chile, and his piece is followed by an entirely different, more quantitative treatment of the subject. This part of the book also covers the indignados in Spain, who before Occupy began, “occupied” the public squares of Madrid and other cities – using, as the basis for their claim on the parks could be legally be slept in, a thirteenth-century right granted to shepherds who moved, and still move, their flocks annually.
In other words, we’re in occupy is the hero we deserve, but not the hero we need territory here.
*Addendum 1: Some have suggested that it’s not the wealth inequality that ought to be reduced, but the democratic elements of our system. California’s terrible decision-making resulting from its experiments with direct democracy notwithstanding, I would like to stay in the realm of the sane.
**Addendum 2: Yes, Shiller managed to get the book published the week before the crash. Talk about market timing.
This is a review of Part I of The Occupy Handbook. Part I consists of twelve pieces ranging in quality from excellent to awful. But enough from me, in Janet Byrne’s own words:
Part 1, “How We Got Here,” takes a look at events that may be considered precursors of OWS: the stories of a brakeman in 1877 who went up against the railroads; of the four men from an all-black college in North Carolina who staged the first lunch counter sit-in of the 1960s; of the out-of-work doctor whose nationwide, bizarrely personal Townsend Club movement led to the passage of Social Security. We go back to the 1930s and the New Deal and, in Carmen M. Reinhart and Kenneth S. Rogoff‘s “nutshell” version of their book This Time Is Different: Eight Centuries of Financial Folly, even further.
Ms. Byrne did a bang-up job getting one Nobel Prize Winner in economics (Paul Krugman), two future Economics Nobel Prize winners (Robert Shiller, Daron Acemoglu) and two maybes (sorry Raghuram Rajan and Kenneth Rogoff) to contribute excellent essays to this section alone. Powerhouse financial journalists Gillian Tett, Michael Hilztik, John Cassidy, Bethany McLean and the prolific Michael Lewis all drop important and poignant pieces into this section. Arrogant yet angry anthropologist Arjun Appadurai writes one of the worst essays I’ve ever had the misfortune of reading and the ubiquitous Brandon Adams make his first of many mediocre appearances interviewing Robert Shiller. Clocking in at 135 pages, this is the shortest section of the book yet varies the most in quality. You can skip Professor Appadurai and Cassidy’s essays, but the rest are worth reading.
Advice from the 1 Percent: Lever Up, Drop Out by Michael Lewis
Framed as a strategy memo circulated among one-percenters, Lewis’ satirical piece written after the clearing of Zucotti Park begins with a bang.
The rabble has been driven from the public parks. Our adversaries, now defined by the freaks and criminals among them, have demonstrated only that they have no idea what they are doing. They have failed to identify a single achievable goal.
Indeed, the absurd fixation on holding Zuccotti Park and refusal to issue demands because doing so “would validate the system” crippled Occupy Wall Street (OWS). So far OWS has had a single, but massive success: it shifted the conversation back to the United States’ out of control wealth inequality managed to do so in time for the election, sealing the deal on Romney. In this manner, OWS functioned as a holding action by the 99% in the interests of the 99%.
We have identified two looming threats: the first is the shifting relationship between ambitious young people and money. There’s a reason the Lower 99 currently lack leadership: anyone with the ability to organize large numbers of unsuccessful people has been diverted into Wall Street jobs, mainly in the analyst programs at Morgan Stanley and Goldman Sachs. Those jobs no longer exist, at least not in the quantities sufficient to distract an entire generation from examining the meaning of their lives. Our Wall Street friends, wounded and weakened, can no longer pick up the tab for sucking the idealism out of America’s youth.We on the committee are resigned to all elite universities becoming breeding grounds for insurrection, with the possible exception of Princeton.
Michael Lewis speaks from experience; he is a Princeton alum and a 1 percenter himself. More than that however, he is also a Wall Street alum from Salomon Brothers during the 1980s snafu and wrote about it in the original guide to Wall Street, Liar’s Poker. Perhaps because of his atypicality (and dash of solipsism), he does not have a strong handle on human(s) nature(s). By the time of his next column in Bloomberg, protests had broken out at Princeton.
Ultimately ineffectual, but still better than…
Lewis was right in the end, but more than anyone sympathetic to the movement might like. OccupyPrinceton now consists of only two bloggers, one of which has graduated and deleted all his work from an already quiet site and another who is a senior this year. OccupyHarvard contains a single poorly written essay on the front page. Although OccupyNewHaven outlasted the original Occupation, Occupy Yale no longer exists. Occupy Dartmouth hasn’t been active for over a year, although it has a rather pathetic Twitter feed here. Occupy Cornell, Brown, Caltech, MIT and Columbia don’t exist, but some have active facebook pages. Occupy Michigan State, Rutgers and NYU appear to have had active branches as recently as eight months ago, but have gone silent since. Functionally, Occupy Berkeley and its equivalents at UCBerkeley predate the Occupy movement and continue but Occupy Stanford hasn’t been active for over a year. Anecdotally, I recall my friends expressing some skepticism that any cells of the Occupy movement still existed.
As for Lewis’ other points, I’m extremely skeptical about “examined lives” being undermined by Wall Street. As someone who started in math and slowly worked his way into finance, I can safely say that I’ve been excited by many of the computing, economic, and theoretical problems quants face in their day-to-day work and I’m typical. I, and everyone who has lived long-enough, knows a handful of geniuses who have thought long and hard about the kinds of lives they want to lead and realized that A. there is no point to life unless you make one and B. making money is as good a point as any. I know one individual, after working as a professional chemist prior to college,who decided to in his words, “fuck it and be an iBanker.” He’s an associate at DB. At elite schools, my friend’s decision is the rule rather than the exception, roughly half of Harvard will take jobs in finance and consulting (for finance) this year. Another friend, an exception, quit a promising career in operations research to travel the world as a pick-up artist. Could one really say that either the operations researcher or the chemist failed to examine their lives or that with further examinations they would have come up with something more “meaningful”?
One of the social hacks to give lie to Lewis-style idealism-emerging-from-an attempt-to-examine-ones-life is to ask freshpeople at Ivy League schools what they’d like to do when they graduate and observe their choices four years later. The optimal solution for a sociopath just admitted to a top school might be to claim they’d like to do something in the peace corp, science or volunteering for the social status. Then go on to work in academia, finance, law or tech or marriage and household formation with someone who works in the former. This path is functionally similar to what many “average” elite college students will do, sociopathic or not. Lewis appears to be sincere in his misunderstanding of human(s) nature(s). In another book he reveals that he was surprised at the reaction to Liar’s Poker – most students who had read the book “treated it as a how-to manual” and cynically asked him for tips on how to land analyst jobs in the bulge bracket. It’s true that there might be some things money can’t buy, but an immensely pleasurable, meaningful life do not seem to be one of them. Today for the vast majority of humans in the Western world, expectations of sufficient levels of cold hard cash are necessary conditions for happiness.
In short and contra Lewis, little has changed. As of this moment, Occupy has proven so harmless to existing institutions that during her opening address Princeton University’s president Shirley Tilghman called on the freshmen in the class of 2016 to “Occupy” Princeton. No freshpeople have taken up her injunction. (Most?) parts of Occupy’s failure to make a lasting impact on college campuses appear to be structural; Occupy might not have succeeded even with better strategy. As the Ivy League became more and more meritocratic and better at discovering talent, many of the brilliant minds that would have fallen into the 99% and become its most effective advocates have been extracted and reached their so-called career potential, typically defined by income or status level. More meritocratic systems undermine instability by making the most talented individuals part of the class-to-be-overthrown, rather than the over throwers of that system. In an even somewhat meritocratic system, minor injustices can be tolerated: Asians and poor rural whites are classes where there is obvious evidence of discrimination relative to “merit and the decision to apply” in elite gatekeeper college admissions (and thus, life outcomes generally) and neither group expresses revolutionary sentiment on a system-threatening scale, even as the latter group’s life expectancy has begun to decline from its already low levels. In the contemporary United States it appears that even as people’s expectations of material security evaporate, the mere possibility of wealth bolsters and helps to secure inequities in existing institutions.
Hence our committee’s conclusion: we must be able to quit American society altogether, and they must know it.The modern Greeks offer the example in the world today that is, the committee has determined, best in class. Ordinary Greeks seldom harass their rich, for the simple reason that they have no idea where to find them. To a member of the Greek Lower 99 a Greek Upper One is as good as invisible.
He pays no taxes, lives no place and bears no relationship to his fellow citizens. As the public expects nothing of him, he always meets, and sometimes even exceeds, their expectations. As a result, the chief concern of the ordinary Greek about the rich Greek is that he will cease to pay the occasional visit.
Michael Lewis is a wise man.
I can recall a conversation with one of my Professors; an expert on Democratic Kampuchea (American: Khmer Rouge), she explained that for a long time the identity of the oligarchy ruling the country was kept secret from its citizens. She identified this obvious subversion of republican principles (how can you have control over your future when you don’t even know who runs your region?) as a weakness of the regime. Au contraire, I suggested, once you realize your masters are not gods, but merely humans with human characteristics, that they: eat, sleep, think, dream, have sex, recreate, poop and die – all their mystique, their claims to superior knowledge divine or earthly are instantly undermined. De facto segregation has made upper classes in the nation more secure by allowing them to hide their day-to-day opulence from people who have lost their homes, job and medical care because of that opulence. Neuroscience will eventually reveal that being mysterious makes you appear more sexy, socially dominant, and powerful, thus making your claims to power and dominance more secure (Kautsky et. al. 2018).*
If the majority of Americans manage to recognize that our two tiered legal system has created a class whose actual claim to the US immense wealth stems from, for the most part, a toxic combination of Congressional pork, regulatory and enforcement agency capture and inheritance rather than merit, there will be hell to pay. Meanwhile, resentment continues to grow. Even on the extreme right one can now regularly read things like:
Now, I think I’d be downright happy to vote for the first politician to run on a policy of sending killer drones after every single banker who has received a post-2007 bonus from a bank that received bailout money. And I’m a freaking libertarian; imagine how those who support bombing Iraqi children because they hate us for our freedoms are going to react once they finally begin to grasp how badly they’ve been screwed over by the bankers. The irony is that a banker-assassination policy would be entirely constitutional according to the current administration; it is very easy to prove that the bankers are much more serious enemies of the state than al Qaeda. They’ve certainly done considerably more damage.
The rest of part I reviewed tomorrow. Hang in there people.
Addendum 1: If your comment amounts to something like “the Nobel Prize in Economics is actually called the The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel” and thus “not a real Nobel Prize” you are correct, yet I will still delete your comment and ban your IP.
*Addendum 2: More on this will come when we talk about the Saez-Delong discussion in part III.
It has become a truism that as the amount of news and information generated per moment continues to grow, so too does the value of aggregation, curation and editing. A point less commonly made is that these aggregators are often limited by time in the sense, whatever the topic, the value of news for the median reader decays extremely rapidly. Some extremists even claim that it’s useless to read the newspaper, so rapidly do things change. The forty eight hours news cycle, in addition to destroying context, has made it impossible for both reporters and viewers to learn from history. See “Is News Memoryless?” (Kautsky et. al. 2014).
A more promising approach to news aggregation (for those who read the news with purpose) is to organize pieces by subject and publish those articles in a book. Paul Krugman did this for himself in The Great Unraveling, bundling selected columns from 1999 to 2003 into a single book, with chapters organized by subject and proceeding chronologically. While the rise and rise of Krumgan’s real-time blogging virtually guarantees he’ll never make such an effort again, a more recent try came from uber-journalist Michael Lewis in Panic!: The Story of Modern Financial Insanity. Financial journalists’ myopic perspective at any given point in time make financial column compilations of years past particularly fun(ny) to read.
Nothing is staler than yesterday’s Wall Street journal (financial news spoils quickly) and reading WSJ or Barron’s pieces from 10 to 20 years ago is just painful.
The title PANIC: The story of modern financial insanity led me to believe the book was about the current crises. The book does say, in very, very fine print “Edited by” Michael Lewis.
-Fritz Krieger, Amazon Reviewer and chief scientist at ISIS
Unfortunately, some philistines became angry in 2008 when they insta-purchased a book called Panic! by Michael Lewis and to their horror, discovered that it contained information about prior financial crises, the nerve of the author to bring us historical perspective, even worse…some of that perspective relating to nations other than the ole’ US of A.
As the more alert readers have noted, almost nothing in the book concerns the 2008 Credit Meltdown, but instead this is merely a collection of news clippings and old magazine articles about past financial crises. You might as well visit a chiropodist’s office and offer them a couple of bucks for their old magazines.
Granted, the articles are by some of today’s finest and most celebrated journalists (although some of the news clippings are unsigned), but do you really want to read more about the 1987 crash or the 1997 collapse of the Thai Baht?
Perhaps you do, but whoever threw this book together wasn’t very particular about the articles chosen. Page 193 reprints an article from “Barron’s” of March, 2000 in which Jack Willoughby presents a long list of Internet companies that he considered likely to run out of cash by 2001. “Some can raise more funds through stock and bond offerings,” he warns. “Others will be forced to go out of business. It’s Darwinian capitalism at work.” True, many of the companies he listed did go belly-up, but on his list of the doomed are
- Someone named Keith Otis Edwards
Perhaps because I was abroad for both the initial disaster and the entire Occupation of Zucotti Park, both events have held my attention. So it is with a mixture of hope and apprehension that I picked up Princeton alum Janet Byrne’s The Occupy Handbook from the public library. The Occupy Handbook is a collection of essays written from 2010 to 2011 by an assortment of first and second-rate authors that attempt to: show what Wall Street does and what it did that led to the most recent crash, explain why our policy apparatus was paralyzed in response to the crash, describe how OWS arose and how it compared with concurrent international movements and prior social movements in the US, and perhaps most importantly, provide policy solutions for the 99% in finance and economics. Janet Byrne begins with a heartfelt introduction:
One fall morning I stood outside the Princeton Club, on West 43rd Street in Manhattan. Occupy Wall Street, which I had visited several times as a sympathetic outsider, has passed its one month anniversary, and I thought the movement might be usefully analyzed by economists and financial writers whose pieces I would commission and assemble into a book that was analytical and- this was what really interested me – prescriptive. I’d been invited to breakfast to talk about the idea with a Princeton Club member and had arrived early out of nervousness.
It seemed a strange place to be discussing the book. I tried the idea out on a young bellhop…
And so it continues. The book is divided into three parts. Part I, broadly speaking, tries to give some economic background on the crash and the ensuing political instability that the crash engendered, up to the first occupation of Zuccotti Park. Part II, broadly speaking, describes the events in Zuccotti Park and around the world as they were in those critical months of fall 2011. Part III, broadly speaking, prescribes solutions to current depression. I say broadly speaking because, as you will see, several essays appear to be in the wrong part and in the worst cases, in the wrong book.
High frequency trading (HFT) is in the news. Politicians and regulators are thinking of doing something to slow stuff down. The problem is, it’s really complicated to understand it in depth and to add rules in a nuanced way. Instead we have to do something pretty simple and stupid if we want to do anything.
How it happened
In some ways HFT is the inevitable consequence of market forces – one has an advantage when one makes a good decision more quickly, so there was always going to be some pressure to speed up trading, to get that technological edge on the competition.
But there was something more at work here too. The NYSE exchange used to be a non-profit mutual, co-owned by every broker who worked there. When it transformed to a profit-seeking enterprise, and when other exchanges popped up in competition with it was the beginning of the age of HFT.
All of a sudden, to make an extra buck, it made sense to allow someone to be closer and have better access, for a hefty fee. And there was competition among the various exchanges for that excellent access. Eventually this market for exchange access culminated in the concept of co-location, whereby trading firms were allowed to put their trading algorithms on servers in the same room as the servers that executed the trades. This avoids those pesky speed-of-light issues when sitting across the street from the executing servers.
Not surprisingly, this has allowed the execution of trades to get into the mind-splittingly small timeframe of double-digit microseconds. That’s microseconds, where from wikipedia: “One microsecond is to one second as one second is to 11.54 days.”
What’s wrong with it
Turns out, when things get this fast, sometimes mistakes happen. Sometimes errors occur. I’m writing in the third-person passive voice because we are no longer talking directly about human involvement, or even, typically, a single algorithm, but rather the combination of a sea of algorithms which together can do unexpected things.
People know about the so-called “flash crash” and more recently Knight Capital’s trading debacle where an algorithm at opening bell went crazy with orders. But people on the inside, if you point out these events, might counter that “normal people didn’t lose money” at these events. The weirdness was mostly fixed after the fact, and anyway pension funds, which is where most normal people’s money lives, don’t ever trade in the thin opening bell market.
But there’s another, less well known example from September 30th, 2008, when the House rejected the bailout, shorting stocks were illegal, and the Dow dropped 778 points. The prices as such common big-ticket stocks such as Google plummeted and, in this case, pension funds lost big money. It’s true that some transactions were later nulled, but not all of them.
This happened because the market makers of the time had largely pulled their models out of the market after shorting became illegal – there was no “do this algorithm except make sure you’re never short” button on the algorithm, so once the rule was called, the traders could only turn it all of completely. As a result, the liquidity wasn’t there and the pension funds, thinking they were being smart to do their big trades at close, instead got completely walloped.
Keep this in mind, before you go blaming the politicians on this one because the immediate cause was the short-sighted short-selling ban: the HFT firms regularly pull out of the market in times of stress, or when they’re updating their algorithms, or just whenever they want. In other words, it’s liquidity when you need it least.
Moreover, just because two out of three times were relatively benign for the 99%, we should not conclude that there’s nothing potentially disastrous going on. The flash crash and Knight Capital have had impact, namely they serve as events which erode our trust in the system as a whole. The 2008 episode on top of that proved that yes, we can be the victims of the out-of-control machines fighting against each other.
Quite aside from the instability of the system, and how regular people get screwed by insiders (because after all, that’s not a new story at all, it’s just a new technology for an old story), let’s talk about resources. How much money and resources are being put into the HFT arena and how could those resources otherwise be used?
Putting aside the actual energy consumed by the industry, which is certainly non-trivial, let’s focus for a moment on money. It has been estimated that overall, HFT firms post about $80 billion in profits yearly, and that they make on the order of 10% profit on their technology investments. That would mean that there’s in the order of $800 billion being invested in HFT each year. Even if we highball the return at 25%, we still have more than $300 billion invested in this stuff.
And to what end?
Is that how much it’s really worth the small investor to have decreased bid-ask spreads when they go long Apple because they think the new iPhone will sell? What else could we be doing with $800 billion dollars? A couple of years of this could sell off all of the student debt in this country.
What should be done
Germany has recently announced a half-second minimum for posting an share order. This is eons in current time frames, and would drastically change how trading is done. They also want HFT algorithms to be registered with them. You know, so people can keep tabs on the algorithms and understand what they’re doing and how they might interact with each other.
Um, what? As a former quant, let me just say: this will not work. Not a chance in hell. If I want to obfuscate the actual goals of a model I’ve written, that’s easier than actually explaining it. Moreover, the half-second rule may sound good but it just means it’s a harder system to game, not that it won’t be gameable.
Other ideas have been brought forth as to how to slow down trading, but in the end it’s really hard to do: if you put in delays, there’s always going to be an algorithm employed which decides whose trade actually happens first, and so there will always be some advantage to speed, or to gaming the algorithm. It would be interesting but academically challenging to come up with a simple enough rule that would actually discourage people from engaging in technological warfare.
The only sure-fire way to make people think harder about trading so quickly and so often is a simple tax on transactions, often referred to as a Tobin Tax. This would make people have sufficient amount of faith in their trade to pay the tax on top of the expected value of the trade.
And we can’t just implement such a tax on one market, like they do for equities in London. It has to be on all exhange-traded markets, and moreover all reasonable markets should be exchange-traded.
Oh, and while I’m smoking crack, let me also say that when exchanges are found to have given certain of their customers better access to prices, the punishments for such illegal insider information should be more than $5 million dollars.
My friend Nik recently sent me a PandoDaily article written by Francisco Dao entitled Looterism: The Cancerous Ethos That Is Gutting America.
He defines looterism as the “deification of pure greed” and says:
The danger of looterism, of focusing only on maximizing self interest above the importance of creating value, is that it incentivizes the extraction of wealth without regard to the creation or replenishment of the value building mechanism.
I like the term, I think I’ll use it. And it made me think of this recent Bloomberg article about private equity and hedge funds getting into the public schools space. From the article:
Indeed, investors of all stripes are beginning to sense big profit potential in public education.
The K-12 market is tantalizingly huge: The U.S. spends more than $500 billion a year to educate kids from ages five through 18. The entire education sector, including college and mid-career training, represents nearly 9 percent of U.S. gross domestic product, more than the energy or technology sectors.
Traditionally, public education has been a tough market for private firms to break into — fraught with politics, tangled in bureaucracy and fragmented into tens of thousands of individual schools and school districts from coast to coast.
Now investors are signaling optimism that a golden moment has arrived. They’re pouring private equity and venture capital into scores of companies that aim to profit by taking over broad swaths of public education.
The conference last week at the University Club, billed as a how-to on “private equity investing in for-profit education companies,” drew a full house of about 100.
[I think I know why that golden moment arrived, by the way. The obsession with test scores, a direct result of No Child Left Behind, is both pseudo-quantitative (by which I mean it is quantitative but is only measuring certain critical things and entirely misses other critical things) and has broken the backs of unions. Hedge funds and PE firms love quantitative things, and they don't really care if they numbers are meaningful if they can meaningfully profit.]
Their immediate goal is out-sourcing: they want to create the Blackwater (now Academi) of education, but with cute names like Schoology and DreamBox.
Lest you worry that their focus will be on the wrong things, they point out that if you make kids drill math through DreamBox “heavily” for 16 weeks, they score 2.3 points higher in a standardized test, although they didn’t say if that was out of 800 or 20. Never mind that “heavily” also isn’t defined, but it seems safe to say from context that it’s at least 2 hours a day. So if you do that for 16 weeks, those 2.3 points better be pretty meaningful.
So either the private equity guys and hedge funders have the whole child in mind here, or it’s maybe looterism. I’m thinking looterism.
This is a guest post by George Bailey, who is part of Occupy the SEC. I just want insert here a congratulations to Occupy the SEC for submitting their public comments letter yesterday, and to point out that the organization SIFMA below is the same SIFMA I mentioned here and here (those guys are everywhere, defending the interests of the banks).
Today is “Volcker Day” and Paul Volcker was on a tear.
Mr Volcker added in a formal submission to regulators Monday that “proprietary trading is not an essential commercial bank service that justifies taxpayer support,” and that banks should stop “stonewalling.”
He went on:
“There should not be a presumption that evermore market liquidity brings a public benefit,” Volcker, 84, wrote in a letter submitted yesterday to regulators in defense of the rule curtailing banks’ bets on asset prices with their own money. “At some point, great liquidity, or the perception of it, may itself encourage more speculative trading (see here and here for the full story).
But then Jamie Dimon came along and bitch slapped Tall Paul. Ouch.
“Paul Volcker by his own admission has said he doesn’t understand capital markets,” Dimon told Francis in the Fox Business interview. “He has proven that to me.”
SIFMA, on behalf of the industry, took over to explain in detail just what it is that Mr. Volcker doesn’t understand in their comment letter. They reiterate their dire warning about the devastating effects on ‘corporate liquidity’’ from the Volcker Rule. Yet surprisingly, no non-financial corporate bond issuers filed any comments to acknowledge or object to this danger.
In fact, there are no comment letters from any non-financial companies. They did haul out the widely lampooned Oliver Wyman study to bolster their comment that ‘corporate’ America would suffer horribly if Volcker is enacted. But that just serves to remind us again that the corporate bond liquidity that will be affected is the liquidity in dodgy financial company ‘corporate’ bonds, like CDOs and other drek. They conclude the only solution is a rewrite . They request the rule makers go back and start all over again.
The SIFMA comment letter runs to 175 pages. I haven’t read all the other financial company letters, but the ones I’ve skimmed conform to SIFMAs position.
The Occupy the SEC comment letter logs in at 325 pages and oddly enough draws the exact opposite conclusions to each of SIFMAs objections. It’s an interesting contrast. For some reason (some familiarity with the subject matter and public interest primarily) the group seems to have understood and articulated Volcker’s (and the electorate’s) intent pretty effectively.
Of the comment letters received about 90% are from financial institutions, and another 5% are from foreign governments objecting to the priority the US regulators have gifted to US traders in US Government Bonds. The remaining 5% are from ordinary folks, like Mr. Volcker, Occupy the SEC and other public interest groups.
Its interesting that 95% of the comments reflect the views of the 1%, and the views of the 99% are embodied in the comments of the remaining 5% of commenters. I’m confident the regulators will recognize that, for all its complexity, the rules are comprehensible and can be refined to serve the public’s demand for control over a runaway financial system.
There’s an uneasy relationship between economists and quants. Part of this stems from the fact that each discounts what the other is really good at.
Namely, quants are good at modeling, whereas economists generally are not (I’m sure there are exceptions to this rule, so my apologies to those economists who are excellent modelers); they either oversimplify to the point of uselessness, or they add terms to their models until everything works but by then the models could predict anything. Their worst data scientist flaw, however, is the confidence they have, and that they project, in their overfit models. Please see this post for examples of that overconfidence.
On the other hand, economists are good at big-picture thinking, and are really really good at politics and influence, whereas most quants are incapable of those things, partly because quants are hyper aware of what they don’t know (which makes them good modelers), and partly because they are huge nerds (apologies to those quants who have perspective and can schmooze).
Economists run the Fed, they suggest policy to politicians, and generally speaking nobody else has a plan so they get heard. The sideline show of the two different schools of mainstream economics constantly at war with each other doesn’t lend credence to their profession (in fact I consider it a false dichotomy altogether) but again, who else has the balls and the influence to make a political suggestion? Not quants. They basically wait for the system to be set up and then figure out how to profit.
I’m not suggesting that they team up so that economists can teach quants how to influence people more. That would be really scary. However, it would be nice to team up so that the underlying economic model is either reasonably adjusted to the data, or discarded, and where the confidence of the model’s predictions is better known.
To that end, Cosma Shalizi is already hard at work.
Generally speaking, economic models are ripe for an overhaul. Let’s get open source modeling set up, there’s no time to lose. For example, in the name of opening up the Fed, I’d love to see their unemployment prediction model be released to the public, along with the data used to train it, and along with a metric of success that we can use to compare it to other unemployment models.
One major weakness of quantitative trading is that it’s based on the concept of how correlated various instruments and instrument classes are. Today I’m planning to rant about this, thanks to a reader who suggested I should. By the way, I do not suggest that anything in today’s post is new- I’m just providing a public service by explaining this stuff to people who may not know about it.
Correlation between two things indicates how related they are. The maximum is 1 and the minimum is -1; in other words, correlation ignores the scale of the two things and concentrates only on the de-scaled relationship. Uncorrelated things have correlation 0.
All of the major financial models (for example Modern Portfolio Theory) depend crucially on the concept of correlation, and although it’s known that, at a point in time, correlation can be measured in many different ways, and even given a choice, the statistic itself is noisy, most of the the models assume it’s an exact answer and never bother to compute the sensitivity to error. Similar complaints can just as well be made to the statistic “beta”, for example in the CAPM model.
To compute the correlation between two instruments X and Y, we list their returns, defined in a certain way, for a certain amount of time for a given horizon, and then throw those two series into the sample correlation formula. For example we could choose log or percent returns, or even difference returns, and we could look back at 3 months or 30 years, or have an exponential downweighting scheme with a choice of decay (explained in this post), and we could be talking about hourly, daily, or weekly return horizons (or “secondly” if you are a high frequency trader).
All of those choices matter, and you’ll end up with a different answer depending on what you decide. This is essentially never mentioned in basic quantitative modeling texts but (obviously) does matter when you put cash money on the line.
But in some sense the biggest problem is the opposite one. Namely, that people in finance all make the same choices when they compute correlation, which leads to crowded trades.
Think about it. Everyone shares the same information about what the daily closes are on the various things they trade on. Correlation is often computed using log returns, at a daily return horizon, with an exponential decay weighting typically 0.94 or 0.97. People in the industry thus usually agree more or less on the correlation of, say, the S&P and crude.
[I'm going to put aside the issue that, in fact, most people don't go to the trouble of figuring out time zone problems, which is to say that even though the Asian markets close earlier in the day than the European or U.S. markets, that fact is ignored in computing correlations, say between country indices, and this leads to a systematic miscalculation of that correlation, which I'm sure sufficiently many quantitative traders are busy arbing.]
Why is this general agreement a problem? Because the models, which are widely used, tell you how to diversify, or what have you, based on their presumably perfect correlations. In fact they are especially widely used by money managers, so those guys who move around pension funds (so have $6 trillion to play with in this country and $20 trillion worldwide), with enough money involved that bad assumptions really matter.
It comes down to a herd mentality thing, as well as cascading consequences. This system breaks down at exactly the wrong time, because after everyone has piled into essentially the same trades in the name of diversification, if there is a jolt on the market, those guys will pull back at the same time, liquidating their portfolios, and cause other managers to lose money, which results in that second tier of managers to pull back and liquidate, and it keeps going. In other words, the movements among various instruments become perfectly aligned in these moments of panic, which means their correlation approaches 1 (or perfectly unaligned, so their correlations approach -1).
The same is true of hedge funds. They don’t rely on the CAPM models, because they are by mandate trying to be market neutral, but they certainly rely on a factor-model based risk model, in equities but also in other instrument classes, and that translates into the fact that they tend to think certain trades will offset others because the correlation matrix tells them so.
These hedge fund quants move around from firm to firm, sharing their correlation matrix expertise, which means they all have basically the same model, and since it’s considered to be in the realm of risk management rather than prop trading, and thus unsexy, nobody really spends too much time trying to make it better.
But the end result is the same: just when there’s a huge market jolt, the correlations, which everyone happily computed to be protecting their trades, turn out to be unreliable.
One especially tricky thing about this is that, since correlations are long-term statistics, and can’t be estimated in short order (unless you look at very very small horizons but then you can’t assume those correlations generalize to daily returns), even if “the market is completely correlated” on one day doesn’t mean people abandon their models. Everyone has been trained to believe that correlations need time to bear themselves out.
In this time of enormous political risk, with the Eurozone at risk of toppling daily, I am not sure how anyone can be using the old models which depend on correlations and sleep well at night. I’m pretty sure they still are though.
I think the best argument I’ve heard for why we saw crude futures prices go so extremely high in the summer of 2008 is that, at the time, crude was believed to be uncorrelated to the market, and since the market was going to hell, everyone wanted “exposure” to crude as a hedge against market losses.
What’s a solution to this correlation problem?
One step towards a solution would be to stop trusting models that use greek letters to denote correlation. Seriously, I know that sounds ridiculous, but I’ve noticed a correlation between such models and blind faith (I haven’t computed the error on my internal estimate though).
Another step: anticipate how much overcrowding there is in the system. Assume everyone is relying on the same exact estimates of correlations and betas, take away 3% for good measure, and then anticipate how much reaction there will be the next time the Euroleaders announce a new economic solution and then promptly fail to deliver, causing correlations to spike.
I’m sure there are quants out there who have mastered this model, by the way. That’s what quants do.
At a higher perspective, I’m saying that we need to stop relying on correlations as fixed over time, and start treating them as volatile as prices. We already have markets in volatility; maybe we need markets in correlations. Or maybe they already exist formally and I just don’t know about them.
At an even higher perspective, we should just figure out a better system altogether which doesn’t put people’s pensions at risk.
I love this New York Times article, first because it shows how much the Occupy Wall Street movement has resonated with young people, and second because my friend Chris Wiggins is featured in it making witty remarks. It’s about the investment bank recruiting machine on college campuses (Yale, Harvard, Columbia, Dartmouth, etc.) being met with resistance from protesters. My favorite lines:
Ms. Brodsky added that she had recently begun openly questioning the career choices of her finance-minded friends, because “these are people who could be doing better things with their energy.”
Kate Orazem, a senior in the student group, added that Yale students often go into finance expecting to leave after several years, but end up staying for their entire careers.
“People are naïve about how addictive the money is going to be,” she said.
Amen to that, and wise for you to know that! There are still plenty of my grown-up friends in finance who won’t admit that it’s a plain old addiction to money keeping them in a crappy job where they are unhappy, and where they end up buying themselves expensive trips and toys to try to combat their unhappiness.
And here’s my friend Chris:
“Zero percent of people show up at the Ivy League saying they want to be an I-banker, but 25 and 30 percent leave thinking that it’s their calling,” he said. “The banks have really perfected, over the last three decades, these large recruitment machines.”
Another piece of really excellent new: Judge Rakoff has come through big time, and rejected the settlement between the SEC and Citigroup. Woohoo!! From this Bloomberg article:
In its complaint against Citigroup, the SEC said the bank misled investors in a $1 billion fund that included assets the bank had projected would lose money. At the same time it was selling the fund to investors, Citigroup took a short position in many of the underlying assets, according to the agency.
“If the allegations of the complaint are true, this is a very good deal for Citigroup,” Rakoff wrote in today’s opinion. “Even if they are untrue, it is a mild and modest cost of doing business.”
A revised settlement would probably have to include “an agreement as to what the actual facts were,” said Darrin Robbins, who represents investors in securities fraud suits. Robbins’s firm, San Diego-based Robbins Geller Rudman & Dowd LLP, was lead counsel in more settled securities class actions than any other firm in the past two years, according to Cornerstone Research, which tracks securities suits.
Investors could use any admissions by Citigroup against the bank in private litigation, he said.
This raises a few questions in my mind. First, do we really have to depend on a randomly chosen judge having balls to see any kind of justice around this kind of thing? Or am I allowed to be hopeful that Judge Rakoff has now set a precedent for other judges to follow, and will they?
Second, something that came up on Sunday’s Alt Banking group meeting. Namely, how many more cases are there that the SEC hasn’t even bothered with, even just with Citigroup? I’ve heard the SEC was only scratching the surface on this, since that’s their method.
Even if they only end up getting $285m, plus the admission that they did wrong by their clients, could the SEC go back and prosecute them for 30 other deals for 30x$285m = $8.55b? Would that give us enough leverage to break up Citigroup and start working on our “Too Big to Fail” problems? And how about the other banks? What would this litigation look like if the SEC were really trying to kick some ass?
One of my readers sent me a link to this blogpost by James Wimberley, which talks intelligently about safety nets and their secondary effects (it also has a nifty link to the history of bankruptcy laws in the U.S.).
I want to hone in on one aspect he describes, namely how, in spite of people in the U.S. considering themselves entrepreneurial, we are not so much. His theory is that it’s because of a lack of safety net: people are worried about losing their health insurance so they don’t leave the safety of their job. Here’s Wimberley’s chart of entry density, defined as the rate of registration of new limited liability companies per thousand adults of working age, by country:
The question of who takes risks is interesting to me, and made me think about my experiences in my various jobs. In fact this dovetails quite well with another subject I want to post on soon, namely who learns from mistakes; I have a theory that people who don’t take risks also don’t learn from mistakes well. But back to risktakers.
It kind of goes without saying that people in academics are not risk-taking entrepreneurs, but I’ll say it anyway – they aren’t. In fact it was one reason I wanted out- I’m much more turned on by risks than the people I met inside academics. In particular I don’t want to have the same job with the same conditions for the rest of my life, guaranteed. I want adventure and variation and the excitement of not knowing what’s next. When I went to a hedge fund I thought I would find my peeps.
However, most of the people I worked with at D.E. Shaw were really not risk takers at all, in spite of the finance cowboy image that they are so proud of. In fact, these were deeply risk averse people who wanted total control over their and their children’s destinies.
Moreover, the students I meet in finance programs (I took a few classes at Columbia’s when I knew I was leaving academic math) and who hope to someday work at JP Morgan are some of the most risk averse people ever. They are essentially trying to lock in a huge salary in return for working like slaves for a huge system.
Fine, so finance attracts people who are risk averse (and love money). That may be the consequence of its reputation and its age. So where are the risk takers? They must be some other field. How about startups?
What has surprised me working at a startup is that a majority of them are also not what I’d consider risk takers. There are a few though. These few tend to be young men with no families. Kind of the “Social Network” model of college aged boys working out of their dorm rooms. The women tend to be unmarried.
This is completely in line with Wimberley’s theory of safety nets, since it seems like once these men find a wife and have a kid they settle (speaking in general) into a risk averse mode. Once the women get married they tend to leave altogether.
In fact I’m kind of an oddball in that I’m married and have three kids and I actually love risk taking. Part of this is that I get to depend on my husband for health insurance, but that’s clearly not the only factor, since you’d expect lots of women whose husbands had steady jobs to be joining startups, but that’s not true.
I also have a feeling that the enormous amount of effort people tend to put into proving their credentials has something to do with all of this- when you take risks you are without title, you win or lose on your own luck and hard work. For a culture with a strong desire to be credentialed that’s a tough one.
I don’t really have a conclusion today but I’m thinking that the story is slightly more complicated than just safety nets. I feel like maybe it starts out as a safety net issue but then it becomes a cultural assumption.
After my talk on Monday there were lots of questions and comments, which is always awesome (will blog the contents soon).
One person in the audience asked me if I’d ever heard of CompTop, which I hadn’t. And actually, even though I vaguely understand what they’re talking about, I still don’t understand it sufficiently to blog about it- but it reminds me of something else which I would like to blog about, and which combines topology and modeling.
Maybe they’re even the same thing! But if so (especially if so), I’d like to get my idea down onto electronic paper before I read theirs. This is kind of like my thing about not googling something until you’ve tried to work it out for yourself.
So here’s the setup. In different fields in finance, there’s a “space” you work in. I worked in Futures, which you’ve heard of because when they talk about the price of barrels of oil going up (or maybe down, but you don’t hear about it as much when that happens), they are actually talking about futures prices. This also happens with basic food prices such as corn and wheat; corn and oil are linked of course through ethanol production. There are also futures on the S&P (or any other major stock index), bonds, currencies, other commodities, or even options on stock indices.
The general idea, which is given away by the name, is that when you buy a futures contract, you are placing a bet on the future price of something. Futures were started as a way for farmers to hedge their risks when they were growing food. But clearly other things have happened since then.
There’s a way of measuring the dimension of this space of instruments, which is less trivial than counting them. For example, there is a “2 year U.S. bond” future as well as a “5 year U.S. bond future” and you may guess (and you’d be right) that these don’t really represent independent dimensions.
Indeed there’s a concept of independence which one can use coming from statistics (so, statistical independence), which is pretty subjective in that it depends on what time period and how much data you use to measure it (and lately we’ve seen less independence in general). But even so, you can go blithely forward and count how many dimensions your space has, and you generally got something like 15, at least before the credit crisis hit. This process is called PCA, and I’ll write a post on it sometime.
Depending on which instruments you counted, and how liquid you expected them to be, you could get a few more “independent” instruments, but you also may be fooling yourself with idiosyncratic noise caused by those instruments being not very liquid. So there are some subtleties.
Once you have your space measured in terms of dimension, you can choose a basis and look at things along the basis vectors. You can see how your different models behave, for example. You might see how the bond model you worked on places no bet on the basis vectors corresponding to lean hog futures.
That made me wonder the following question. If we can measure the space of instruments, can we also measure the space of models? Is this some kind of dual? If so, is there some kind of natural upper bound on the number of (independent) models we could ever have which all make profit?
Note there’s also a way of making sure that models are statistically independent, so this part of the question is well-defined. But it’s not clear what property of the space of instruments you are measuring when you ask for a model on that space which “makes profit”.
Another related question is whether such a question can really only be asked at a given time horizon (if it can be asked at all). I’ll explain.
The horizon of a model is essentially how long you expect a given bet to last in terms of time. For example, a weekly horizon model is something you’d typically only see on a slow-moving instrument class like bonds. There are plenty of daily models on equities, but there are also incredibly hyper fast “high frequency” models, say on currencies, which care about the speed of light and how different computers in the same room, being at different internal temperatures, can’t place consistent timestamps on ticker data.
These different horizons have such different textures, it makes me wonder if the question of an upper bound on the number of profitable models, if true, is true at each horizon.
Another related question: what about topological weirdness inside the space of instruments? If you plot some of this (take as a baby model three instruments that are essentially independent, choose a time horizon, and plot the simultaneous returns) the main characteristic you’ll see is that it’s a bounded blob. But inside that blob are certainly inconsistencies; in particular the density is not everywhere the same. Is the lack of consistency a signal that there’s a model there? Does the market know about holes, for example? Maybe not, which would mean that the space of (profitable) models is perhaps better understood as a space whose basis consists of something like “holes in the instrument space”, rather than a dual.
This is verging on something like what CompTop is talking about. Maybe. I’ll have to go read what they’re doing now.
I’ve decided to talk about how to set up a linear regression with Bayesian priors because it’s super effective and not as hard as it sounds. Since I’m not a trained statistician, and certainly not a trained Bayesian, I’ll be coming at it from a completely unorthodox point of view. For a more typical “correct” way to look at it see for example this book (which has its own webpage).
The goal of today’s post is to abstractly discuss “bayesian priors” and illustrate their use with an example. In later posts, though, I promise to actually write and share python code illustrating bayesian regression.
The way I plan to be unorthodox is that I’m completely ignoring distributional discussions. My perspective is, I have some time series (the ‘s) and I want to predict some other time series (the ) with them, and let’s see if using a regression will help me- if it doesn’t then I’ll look for some other tool. But what I don’t want to do is spend all day deciding whether things are in fact student-t distributed or normal or something else. I’d like to just think of this as a machine that will be judged on its outputs. Feel free to comment if this is palpably the wrong approach or dangerous in any way.
A “bayesian prior” can be thought of as equivalent to data you’ve already seen before starting on your dataset. Since we think of the signals (the ‘s) and response () as already known, we are looking for the most likely coefficients that would explain it all. So the form a bayesian prior takes is: some information on what those ‘s look like.
The information you need to know about the ‘s is two-fold. First you need to know their values and second you need to have a covariance matrix to describe their statistical relationship to each other. When I was working as a quant, we almost always had strong convictions about the latter but not the former, although in the literature I’ve been reading lately I see more examples where the values (really the mean values) for the ‘s are chosen but with an “uninformative covariance assumption”.
Let me illustrate with an example. Suppose you are working on the simplest possible model: you are taking a single time series and seeing how earlier values of predict the next value of . So in a given update of your regression, and each is of the form for some
What is your prior for this? Turns out you already have one (two actually) if you work in finance. Namely, you expect the signal of the most recent data to be stronger than whatever signal is coming from older data (after you decide how many past signals to use by first looking at a lagged correlation plot). This is just a way of saying that the sizes of the coefficients should go down as you go further back in time. You can make a prior for that by working on the diagonal of the covariance matrix.
Moreover, you expect the signals to vary continuously- you (probably) don’t expect the third-from recent variable to have a positive signal but the second-from recent variable to have a negative signal (especially if your lagged autocorrelation plot looks like this). This prior is expressed as a dampening of the (symmetrical) covariance matrix along the subdiagonal and superdiagonal.
In my next post I’ll talk about how to combine exponential down-weighting of old data, which is sacrosanct in finance, with bayesian priors. Turns out it’s pretty interesting and you do it differently depending on circumstances. By the way, I haven’t found any references for this particular topic so please comment if you know of any.
I’ve enjoyed how many people are reading the post I wrote about hiring a data scientist for a business. It’s been interesting to see how people react to it. One consistent reaction is that I’m just saying that a data scientist needs to know undergraduate level statistics.
On some level this is true: undergrad statistics majors can learn everything they need to know to become data scientists, especially if they also take some computer science classes. But I would add that it’s really not about familiarity with a specific set of tools that defines a data scientist. Rather, it’s about being a craftsperson (and a salesman) with those tools.
To set up an analogy: I’m not a chef because I know about casserole dishes.
By the way, I’m not trying to make it sound super hard and impenetrable. First of all I hate it when people do that and second of all it’s not at all impenetrable as a field. In fact I’d say it the other way: I’d prefer smart nerdy people to think they could become data scientists even without a degree in statistics, because after all basic statistics is pretty easy to pick up. In fact I’ve never studied statistics in school.
To get to the heart of the matter, it’s more about what a data scientist does with their sometimes basic tools than what the tools are. In my experience the real challenges are things like
- Defining the question in the first place: are we asking the question right? Is an answer to this question going to help our business? Or should we be asking another question?
- Once we have defined the question, we are dealing with issues like dirty data, too little data, too much data, data that’s not at all normally distributed, or that is only a proxy to our actual problem.
- Once we manhandle the data into a workable form, we encounter questions like, is that signal or noise? Are the errorbars bigger than the signal? How many more weeks or months of data collection will we need to go through before we trust this signal enough to bet the business on it?
- Then of course we go back to: should we have asked a different question that would have not been as perfect an answer but would have definitely given us an answer?
In other words, once we boil something down to a question in statistics it’s kind of a breeze. Even so, nothing is ever as standard as you would actually find in a stats class – the chances of being asked a question similar to a stats class is zero. You always need to dig deeply enough into your data and the relevant statistics to understand what the basic goal of that t-test or statistic was and modify the standard methodology so that it’s appropriate to your problem.
My advice to the business people is to get someone who is really freaking smart and who has also demonstrated the ability to work independently and creatively, and who is very good at communicating. And now that I’ve written the above issues down, I realize that another crucial aspect to the job of the data scientist is the ability to create methodology on the spot and argue persuasively that it is kosher.
A useful thing for this last part is to have broad knowledge of the standard methods and to be able to hack together a bit of the relevant part of each; this requires lots of reading of textbooks and research papers. Next, the data scientist has to actually understand it sufficiently to implement it in code. In fact the data scientist should try a bunch of things, to see what is more convincing and what is easier to explain. Finally, the data scientist has to sell it to everyone else.
Come to think of it the same can be said about being a quant at a hedge fund. Since there’s money on the line, you can be sure that management wants you to be able to defend your methodology down to the tiniest detail (yes, I do think that being a quant at a hedge fund is a form of a data science job, and this
guy woman agrees with me).
I would argue that an undergrad education probably doesn’t give enough perspective to do all of this, even though the basic mathematical tools are there. You need to be comfortable building things from scratch and dealing with people in intense situations. I’m not sure how to train someone for the latter, but for the former a Ph.D. can be a good sign, or any person that’s taken on a creative project and really made something is good too. They should also be super quantitative, but not necessarily a statistician.
One of the positive things about working at D.E. Shaw was the discipline shown in determining whether a model had a good chance of working before spending a bunch of time on it. I’ve noticed people could sometimes really use this kind of discipline, both in their data mining projects and in their normal lives (either personal lives or with their jobs).
Some of the relevant modeling questions were asked and quantified:
- How much data do you expect to be able to collect? Can you pool across countries? Is there proxy historical data?
- How much signal do you estimate could be in that data? (Do you even know what the signal is you’re looking for?)
- What is the probability that this will fail? (not good) That it will fail quickly? (good)
- How much time will it take to do the initial phase of the modeling? Subsequent phases?
- What is the scope of the model if it works? International? Daily? Monthly?
- How much money can you expect from a model like this if it works? (takes knowing how other models work)
- How much risk would a model like this impose?
- How similar is this model to other models we already have?
- What are the other models that you’re not doing if you do this one, and how do they compare in overall value?
Even if you can’t answer all of these questions, they’re certainly good to ask. Really we should be asking questions like these about lots of projects we take on in our lives, with smallish tweaks:
- What are the resources I need to do this? Am I really collecting all the resources I need? What are the resources that I can substitute for them?
- How good are my resources? Would better quality resources help this work? Do I even have a well-defined goal?
- What is the probability this will fail? That it will fail quickly?
- How long will I need to work on this before deciding whether it is working? (Here I’d say write down a date and stick to it. People tend to give themselves too much extra time doing stuff that doesn’t seem to work)
- What’s the best case scenario?
- How much am I going to learn from this?
- How much am I going to grow from doing this?
- What are the risks of doing this?
- Have I already done this?
- What am I not doing if I do this?
Previously I’ve talked about the quant culture of D.E. Shaw as well as the tendencies of people working there. Today I wanted to add a third part about the experience of being “on the inside looking out” during the credit crisis.
I started my quant job in June 2007, which was perfect timing to never actually experience unbridled profit and success; within two months of starting, there was a major disruption in the market which caused enough momentary panic and uncertainty that the Equities group decided to liquidate their holdings. This was a big deal and meant they lost quite a bit of money on transaction costs as well as losing money because other investors were pulling out of similar trades at the same time.
The August 2007 market disruption was referred to internally as “the kerfuffle”. I’ve grown to think that this slightly dismissive term, which connotates more of an awkward misunderstanding than any real underlying problem, was indicative of a larger phenomenon. Namely, there was a sense that nothing really bad was afoot, that the system couldn’t be at risk, and that as long as we kept our trades on balance market neutral, we would be fine, except for possibly bizarre moments of exception. The tone would be something like, if an upper class man went to a restaurant and his credit card was denied- the waiter would return the credit card with almost an apology, assuming that it must have expired or something, that surely it is a mistake more than an exposure of underlying bankruptcy.
This framing of the world around us, as individual exceptional moments, as mysterious, almost amusing singularities in an otherwise smooth manifold, continued throughout the credit crisis (I left in May 2009), with the exception of the days after Lehman collapsed (Lehman was a 20% owner of D.E. Shaw at the time of its collapse, as well as a one of our major brokers).
But Lehman fell kind of late in the game, actually, for those in the industry. In other words there were months and months of disturbing signs, especially in the overnight lending market (where banks lend to each other for just the night or over the weekend) leading up to the Lehman moment. I remember one experience during those times that still baffles me.
It was a company-wide event, an invitation to see Larry Summers, Robert Rubin, and Alan Greenspan chat with each other and with us at the Rainbow Room in Rockefeller Center. It started with a lavish spread, fit for the dignitaries that were visiting, as well as introductory remarks wherein David Shaw described Larry Summer’s appointment as managing director at D.E. Shaw a “promotion” from being President at Harvard (just to be clear, this was a joke – even David Shaw isn’t that arrogant). In incredibly collegial terms, each of the three spoke for some time and reminisced about working together in the Clinton administration. Whatever, that’s not the important part, although it is kind of strange to think about now.
The important part, in retrospect, was later, near the end, when Alan Greenspan started talking about CMO‘s and how worried he was that anybody investing in them was in for a world of hurt. When I had gotten to D.E. Shaw, one of the first presentations I’d ever gone to was by a guy describing how he thought the same thing, and how we had divested ourselves of any such holdings, at least for the high-risk kind. So when Greenspan asserted these warnings, I sensed quite a bit of smugness in the crowd around me. It made me imagine us investors as a bunch of people playing illegal poker in the back of a club, where the smartest ones in the game get told a few minutes before the cops come and they leave out the back (except in this case it wasn’t actually illegal, and it was retired cops- Greenspan left the Fed at the end of 2006).
I wish I could remember when exactly that Rainbow Room event was, because I specifically remember Rubin saying absolutely nothing and looking uncomfortable when Greenspan was going on about CMOs and the danger in their future. Way later, it was revealed that Rubin, who was being paid obscene amounts by Citigroup at the time, claimed not to know about how toxic those mortgage-backed securities were (nor did he claim to know how much Citibank had invested in them- which begs the question of what he actually did for Citigroup) back when he could do something about it. He was booted in January 2009.
I wanted to mention one other specific thing I remember about this attitude of bemused nonchalance in the face of the world crumbling. When Lehman fell, and the overnight lending market froze for some weeks leading to government intervention, there was a term for this at D.E. Shaw, attributed (perhaps wrongly) to Larry Summers. Namely, the term was “magic liquidity dust”, implying that all we needed, to solve the problems around us and the apparent irrational panic of the markets, was for a fairy to come down to us and shake her wand, spreading this liquidity dust generously in our otherwise functional and robust system.
The saddest part of all of this is that, in a very real sense, these guys were essentially right not to worry. There has been no real restructuring of the system that led to this, just its continuation and backing.
In my next installation I’ll talk about why I think people in finance were, and to some extent still are, so insulated from reality.
There’s a nice blog post here by Quantivity which explains why we choose to define market returns using the log function:
where denotes price on day .
I mentioned this question briefly in this post, when I was explaining how people compute market volatility. I encourage anyone who is interested in this technical question to read that post, it really explains the reasoning well.
I wanted to add two remarks to the discussion, however, which actually argue for not using log returns, but instead using percentage returns in some situations.
The first is that the assumption of a log-normal distribution of returns, especially over a longer term than daily (say weekly or monthly) is unsatisfactory, because the skew of log-normal distribution is positive, whereas actual market returns for, say, S&P is negatively skewed (because we see bigger jumps down in times of panic). You can get lots of free market data here and try this out yourself empirically, but it also makes sense. Therefore when you approximate returns as log normal, you should probably stick to daily returns.
Second, it’s difficult to logically combine log returns with fat-tailed distributional assumptions, even for daily returns, although it’s very tempting to do so because assuming “fat tails” sometimes gives you more reasonable estimates of risk because of the added kurtosis. (I know some of you will ask why not just use no parametric family at all and just bootstrap or something from the empirical data you have- the answer is that you don’t ever have enough to feel like that will be representative of rough market conditions, even when you pool your data with other similar instruments. So instead you try different parametric families and compare.)
Mathematically there’s a problem: when you assume a student-t distribution (a standard choice) of log returns, then you are automatically assuming that the expected value of any such stock in one day is infinity! This is usually not what people expect about the market, especially considering that there does not exist an infinite amount of money (yet!). I guess it’s technically up for debate whether this is an okay assumption but let me stipulate that it’s not what people usually intend.
This happens even at small scale, so for daily returns, and it’s because the moment generating function is undefined for student-t distributions (the moment generating function’s value at 1 is the expected return, in terms of money, when you use log returns). We actually saw this problem occur at Riskmetrics, where of course we didn’t see “infinity” show up as a risk number but we saw, every now and then, ridiculously large numbers when we let people combine “log returns” with “student-t distributions.” A solution to this is to use percentage returns when you want to assume fat tails.
Yesterday it was announced that the short selling ban in France, Italy, and Spain for financial stocks would be continued; there’s also an indefinite short selling ban in Belgium. What is this and does it make sense?
Short selling is mathematically equivalent to buying the negative of a stock. To see the actual mechanics of how it works, please look here.
Typically people at hedge funds use shorts to net out their exposure to the market as a whole: they will go long some bank stock they like and then go short another stock that they are neutral to or don’t like, with the goal of profiting on the difference of movements of the two – if the whole market goes up by some amount like 2%, it will only matter to them how much their long position outperformed their short. People also short stocks for direct negative forecasts on the stock, like when they detect fraud in accounting of the company, or otherwise think the market is overpricing the company. This is certainly a worthy reason to allow short selling: people who take the time to detect fraud should be rewarded, or otherwise said, people should be given an incentive to be skeptical.
If shorting the stock is illegal, then it generally takes longer for “price discovery” to happen; this is sort of like the way the housing market takes a long time to go down. People who bought a house at 400K simply don’t want to sell it for less, so they put it on the market for 400K even when the market has gone down and it is likely to sell for more like 350K. The result is that fewer people buy, and the market stagnates. In the past couple of years we’ve seen this happen in the housing market, although banks who have ownership of houses through foreclosures are much less quixotic about prices, which is why we’ve seen prices drop dramatically more recently.
The idea of banning short-selling is purely political. My favorite quote about it comes from Andrew Lo, an economist at M.I.T., who said, “It’s a bit like suggesting we take heart patients in the emergency room off of the heart monitor because you don’t want to make doctors and nurses anxious about the patient.” Basically, politicians don’t want the market to “panic” about bank stocks so they make it harder to bet against them. This is a way of avoiding knowing the truth. I personally don’t know good examples of the market driving down a bank’s stock when the bank is not in terrible shape, so I think even using the word “panic” is misleading.
When you suddenly introduce a short-selling ban, extra noise gets put into the market temporarily as people “cover their shorts”; overall this has a positive effect on the stocks in question, but it’s only temporary and it’s completely synthetic. There’s really nothing good about having temporary noise overwhelm the market except for the sake of the politicians being given a few extra days to try to solve problems. But that hasn’t happened.
Even though I’m totally against banning short selling, I think it’s a great idea to consider banning some other instruments. I actually go back and forth about the idea of banning credit default swaps (CDS), for example. We all know how much damage they can do (look at AIG), and they have a particularly explosive pay-off system, by design, since they are set up as insurance policies on bonds.
The ongoing crisis in Europe over debt is also partly due to the fact that the regulators don’t really know who owns CDS’s on Greek debt and how much there is out there. There are two ways to go about fixing this. First we could ban owning CDS unless you also own the underlying bond, so you are actually protecting your bond; this would stem the proliferation of CDS’s which hurt AIG so badly and which could also hurt the banks holding Greek bonds and who wrote Greek CDS protection. Alternatively, you could enforce a much more stringent system of transparency so that any regulator could go to a computer and do a search on where and how much CDS exposure (gross and net) people have in the world. I know people think this is impossibly difficult but it’s really not, and it should be happening already. What’s not acceptable is having a political and psychological stalemate because we don’t know what’s out there.
There are other instruments that definitely seem worthy of banning: synthetic over-the-counter instruments that seem created out of laziness (since the people who invented them could have approximated whatever hedge they wanted to achieve with standard exchange-traded instruments) and for the purpose of being difficult to price and to assess the risk of. Why not ban them? Why not ban things that don’t add value, that only add complexity to an already ridiculously complex system?
Why are we spending time banning things that make sense and ignoring actual opportunities to add clarity?
When people tell me they are interested in working at a hedge fund, I always tell them a few things. First I talk about the atmosphere and culture, to make sure they would feel comfortable with it. Then I talk to them about which hedge fund they’re thinking about, because I think it makes a huge difference, especially how old a hedge fund is.
Here’s the way I explain it. When a hedge fund is new, a baby, it either works or it doesn’t. If it doesn’t, you never even hear about it, a kind of survivorship bias. So the ones you hear about work well, and their founders do extremely well for themselves.
Then the hedge fund hires a bunch of people, and this first round of people also does well, and they start filling up the ranks of MD’s (managing directors). Maybe at this point you’d say the hedge fund is an adolescent. Once you have a bunch of MD’s that are rich and smart, though, they become pretty protective of the pot of money they generate each year, especially if the pot isn’t as big as it once was, because of competition from other hedge funds.
However, this doesn’t always mean they stop hiring. In fact, they often hire people at this stage, young, smart, incredibly hard working people, who are generally screwed in the sense that they have very little chance of being successful or ever becoming MD. This is what I’d term an adult hedge fund. They have complicated rules which make sense for the existing MD’s but which keep new people from ever succeeding.
For example, when you get to a hedge fund, you start being assigned models to work on. You learn the techniques and follow the rules of the hedge fund, like making sure you don’t bet on the market, etc. If your model starts to look promising, they make sure you are not “remaking” an existing model that is currently being used. That is to say, they make sure, either by telling you what to do or asking you to do it yourself, that your bets are essentially orthogonal (in a statistical sense) to the current models. This often has the effect of removing the signal that your model had, or at least removing enough of it that your model no longer is statistically significant to go into production.
In other words, if the existing models are a relatively large collection, that perhaps spans the space of “current models that seem to work in the way we measure models” (I know this is a vague concept but I do think it means something), then you are kind of up shit’s creek to find a new model. By contrast, if you happened to start at a young hedge fund, or start your own hedge fund, then your model couldn’t be redundant, since there wouldn’t be anything to compete with it.
The older hedge funds have lots of working models, so there are lots of ways for your new, good-looking model to be swatted down before it has a chance to make money. And the way things work, you don’t ever get credit for a model that would have worked if there had been fewer models in production. In fact you only get credit if you came up with a new model which made shit tons of money.
Which is to say, under this system, the founders and the guys brought in during the first round of hiring are the most likely to get credit. Even if an MD retires, their working models don’t die, since they are algorithmic and they still work. But the money they generate goes into the company-wide pot, which is to say mostly goes to MD’s. So the MD’s have no incentive to change the system.
It also has another consequence, which is that the people hired in the second or further rounds slowly realize that their models are perfectly good but unused, and that they’ll never get promoted. So they end up leaving and starting their own funds or joining young funds, just so they can run the same models. So another consequence of adult hedge funds is that they spawn their own competition.
The only way I know of for a hedge fund to avoid this aging process is to never hire anyone after the first round. Or maybe to hire very few people, slowly, as the MD’s retire and as the models stop working and you need new ones, to be sure that the people they hire have a chance to succeed.
One of the major goals of this blog is to let people know how statistical modeling works. My plan is to explain as much as I can in simple plain English, with the least amount of confusion, and the maximum amount of elucidation at every possible level, so every reader can take at least a basic understanding away.
Why? What’s so important about you knowing about what nerds do?
Well, there are different answers. First, you may be interested in it from a purely cerebral perspective – you may yourself be a nerd or a potential nerd. Since it is interesting, and since there will be I suspect many more job openings coming soon that use this stuff, there’s nothing wrong with getting technical; it may come in handy.
But I would argue that even if it’s not intellectually stimulating for you, you should know at least the basics of this stuff, kind of like how we should all know how our government is run and how to conserve energy; kind of a modern civic duty, if you will.
Civic duty? Whaaa?
Here’s why. There’s an incredible amount of data out there, more than every before, and certainly more than when I was growing up. I mean, sure, we always kept track of our GDP and the stock market, that’s old school data collection. And marketers and politicians have always experimented with different ads and campaigns and kept track of what does and what doesn’t work. That’s all data too. But the sheer volume of data that we are now collecting about people and behaviors is positively stunning. Just think of it as a huge and exponentially growing data vat.
And with that data comes data analysis. This is a young field. Even though I encourage every nerd out there to consider becoming a data scientist, I know that if a huge number of them agreed to it today, there wouldn’t be enough jobs out there for everyone. Even so, there will be, and very soon. Each CEO of each internet startup should be seriously considering hiring a data scientist, if they don’t have one already. The power in data mining is immense and it’s only growing. And as I said, the field is young but it’s growing in sophistication rapidly, for good and for evil.
And that gets me to the evil part, and with it the civic duty part.
I claim two things. First, that statistical modeling can and does get out of hand, which I define as when it starts controlling things in a way that is not intended or understood by the people who built the model (or who use the model, or whose lives are affected by the model). And second, that by staying informed about what models are, what they aren’t, what limits they have and what boundaries need to be enforced, we can, as a society, live in a place which is still data-intensive but reasonable.
To give evidence to my first claim, I point you to the credit crisis. In fact finance is a field which is not that different from others like politics and marketing, except that it is years ahead in terms of data analysis. It was and still is the most data-driven, sophisticated place where models rule and the people typically stand back passively and watch (and wait for the money to be transferred to their bank accounts). To be sure, it’s not the fault of the models. In fact I firmly believe that nobody in the mortgage industry, for example, really believed that the various tranches of the mortgage backed securities were in fact risk-free; they knew they were just getting rid of the risk with a hefty reward and they left it at that. And yet, the models were run, and their numbers were quoted, and people relied on them in an abstract way at the very least, and defended their AAA ratings because that’s what the models said. It was a very good example of models being misapplied in situations that weren’t intended or appropriate. The result, as we know, was and still is an economic breakdown when the underlying numbers were revealed to be far far different than the models had predicted.
Another example, which I plan to write more about, is the value-added models being used to evaluate school teachers. In some sense this example is actually more scary than the example of modeling in finance, in that in this case, we are actually talking about people being fired based on a model that nobody really understands. Lives are ruined and schools are closed based on the output of an opaque process which even the model’s creators do not really comprehend (I have seen a technical white paper of one of the currently used value-added models, and it’s my opinion that the writer did not really understand modeling or at best tried not to explain it if he did).
In summary, we are already seeing how statistical modeling can and has affected all of us. And it’s only going to get more omnipresent. Sometimes it’s actually really nice, like when I go to Pandora.com and learn about new bands besides Bright Eyes (is there really any band besides Bright Eyes?!). I’m not trying to stop cool types of modeling! I’m just saying, we wouldn’t let a model tell us what to name our kids, or when to have them. We just like models to suggest cool new songs we’d like.
Actually, it’s a fun thought experiment to imagine what kind of things will be modeled in the future. Will we have models for how much insurance you need to pay based on your DNA? Will there be modeling of how long you will live? How much joy you give to the people around you? Will we model your worth? Will other people model those things about you?
I’d like to take a pause just for a moment to mention a philosophical point about what models do. They make best guesses. They don’t know anything for sure. In finance, a successful model is a model that makes the right bet 51% of the time. In data science we want to find out who is twice as likely to click a button- but that subpopulation is still very unlikely to click! In other words, in terms of money, weak correlations and likelihoods pay off. But that doesn’t mean they should decide peoples’ fates.
My appeal is this: we need to educate ourselves on how the models around us work so we can spot one that’s a runaway model. We need to assert our right to have power over the models rather than the other way around. And to do that we need to understand how to create them and how to control them. And when we do, we should also demand that any model which does affect us needs to be explained to us in terms we can understand as educated people.
Say you’re a math nerd, finishing your Ph.D. or a post-doc, and you’re wondering whether academics is really the place for you. Well I’ve got some advice for you! Actually I will have some advice for you, after you’ve answered a few questions. It’s all about fit. Since I know them best, I will center my questions and my advice around academic math vs. hedge fund quant vs. data scientist at a startup.
By the way, this is the advice I find myself telling people when they ask. It’s supposed to be taken over a beer and with lots of tongue in cheek.
1) What are your vices?
It turns out that the vices of the three jobs we are considering are practically disjoint! If you care about a good fit for your vices, then please pay attention.
NOTE: I am not saying that everyone in these fields has all of these vices! Far from it! It’s more like, if one or more of these vices drives you nuts, then you may get frustrated when you encounter them in these fields.
In academics, the major vices are laziness, envy, and arrogance. It’s perhaps true that laziness (at least outside of research) is typically not rewarded until after tenure, but at that point it’s pretty much expected, unless you want to be the fool who spends all of his(her) time writing recommendation letters and actually advising undergraduates. Envy is, of course, a huge deal in academics, because the only actual feedback is in the form of adulating rumor. Finally, arrogance in academics is kind of too obvious to explain.
At a hedge fund, the major vices are greed, covetousness, and arrogance. The number one source of feedback is pay, after all, so it’s all about how much you got (and how much your officemate got). Plus the isolation even inside your own office can lead to the feeling that you know more and more interesting, valuable, things than anyone else, thus the arrogance.
Finally, at a startup, the major vices are vanity, impatience, and arrogance. People really care about their image- maybe because they are ready to jump ship and land a better job as soon as they start to smell something bad. Plus it’s pretty easy in startups as well to live inside a bubble of self-importance and coolness and buzz. Thus the arrogance. On the flip side of vanity, startups are definitely the sexiest of the three, and the best source by far for good karaoke singers.
Okay it turns out they all have arrogance. Maybe that’s just a property of any job category.
2) What do you care about?
Do you care about titles? Don’t work at a startup.
Do you care about stability? Don’t work at a startup. Actually you might think I’d say don’t work at a hedge fund either, but I’ve found that hedge funds are surprisingly stable, and are full of people who are surprisingly risk averse. Maybe small hedge funds are unstable.
Do you care about feedback? Don’t work in academics.
Do you care about publishing? Don’t work outside academics (it’s sometimes possible to publish outside of academics but it’s not always possible and it’s not always easy).
Do you care about making lots of money? Don’t work in academics. In a startup you make a medium amount of money but there are stock options which may pan out someday, so it’s kind of in between academics and Wall St.
Do you care about being able to decide what you’re working on? Definitely stay in academics.
Do you care about making the world a better place? I’m still working on that one. There really should be a way of doing that if you’re a math nerd. It’s probably not Wall Street.
3) What do you not care about?
If you just like math, and don’t care exactly what kind of math you’re doing, then any of these choices can be really interesting and challenging.
If you don’t mind super competitive and quasi-ethical atmospheres, then you may really enjoy hedge fund quant work- the modeling is really interesting, the pay is good, and you are part of the world of finance and economics, which leaks into politics as well and is absolutely fascinating.
If you don’t mind getting nearly no vacation days and yet feeling like your job may blow up any minute, you may like working at a startup. The people there are real risk lovers, care about their quality of life (at least at the office!), and know how to throw a great party.
If you don’t mind being relatively isolated mathematically, and have enormous internal motivation and drive, then academics is a pretty awesome job, and teaching is really fun and rewarding. Also academic jobs have lots of flexibility as well as cool things like sabbaticals.
4) What about for women who want kids?
Let’s face it, the tenure clock couldn’t have been set up worse for women who want children. And startups have terrible vacation policies and child-care policies as well; it’s just the nature of living on a Venture Capitalist’s shoestring. So actually I’d say the best place to balance work and life issues is at an established hedge fund or bank, where the maternity policies are good; this is assuming though that your personality otherwise fits well with a Wall St. job. Actually many of the women I’ve met who have left academics for government research jobs (like at NASA or the NSA) are very happy as well.