Mining for Gold - the 3 reports for searching unscheduled treatment

When I worked in a dental practice, we still had paper charts until 2009 when I helped my practice transition to a paperless environment. This means that we had a ton of treatment sitting in these paper charts that we had to search for manually in order to find patients who were unscheduled. It was an extremely time-consuming process but it is what we had to do in order to keep our doctor’s schedule full.

When you are working in a chartless environment and all your treatment plans are sitting in the computer, you still must follow up with patients who are not scheduled … but the process is much more streamlined. What I love about computer data is that it is all trackable and you can filter it onto a report. I call it “Mining for Gold.”

In Dentrix, there are actually three reports for you to use to track down unscheduled treatment to follow up with patients. I do have a favorite but I will let you decide for yourself what works best in your practice.

1. The Unscheduled Treatment Plan Reporthas been in Dentrix forever and it is what I used back in the olden days. This report gives you information on unscheduled treatment but you have to print it in order to work it and it can be very lengthy. Also, we have to remember that every time we print a report, things will change quickly and then your report is obsolete. To find this report, go to the Office Manager > Reports > Lists > Unscheduled Treatment Plans.
2. The Practice Treatment Case Report is also a printed report but you can filter it by very specific data points that you cannot find in other reports. What I like about this report is that you can search for treatment that has been Accepted, Rejected or Proposed if you are marking your cases with this status. This is good information for your doctor to see. In this report, you can also search by the case severity if you are using the stop light feature (for more information on this, CLICK HERE). This report can only be found on the Patient Chart and Treatment Planner modules. Click on the printer icon as if you were printing a treatment plan estimate, but instead click on Practice Treatment Case Report. Then select the parameters you want and click OK.
3. The Treatment Manager Report This is by far my favorite report in Dentrix. It offers you a way to search for patients with unscheduled treatment and create your own interactive spreadsheet where you can resort the columns to organize the report any way you want. You also do not have to print this report because everything you need to follow up with the patient is at your fingertips. For more details about using this report, refer to the blog I wrote called “Holes in your Doctor’s Schedule?” and learn more.

“Mining for Gold” is my motto when it comes to looking for patients with unscheduled treatment. You need to be proactive when it comes to following up with patients. You cannot expect them to pick up the phone and call you. Keeping your schedule full is something that requires a little bit of work from you. I hope these report options help make the task a little more efficient.

A Behavioral new-Keynesian Model

Here are comments on Xavier Gabaix' "A Behavioral new-Keynesian model." Xavier presented at the October 21 NBER Economic Fluctuations and growth meeting, and I was the discussant. Slides here

Short summary: It's a really important paper. I think it's too important to be true.

Gabaix' irrationality fixes the pathologies of the standard model by making a stable model unstable, and hence locally determinate. Gabaix' irrationality parameter M in [0,1] can thus substitute for the usual Taylor principle that interest rates move more than one for one with inflation.


Gabaix imagines -- after three papers worth of careful math -- that people pay less attention to future income when deciding on consumption than they should.  Making today's consumption less sensitive to future income, means expectations of future income are larger for any amount of today's consumption. Thus, it makes model dynamics unstable.

But just a little irrationality won't do. If you move a stable eigenvalue, say 0.8, by a bit, say 0.85, it's still stable. You have to move it all the way past 1 before it does any good at all.

Thus, Gabaix puts irrationality right in the  middle of monetary policy. If Gabaix is right, you simply cannot explain monetary policy in simple terms with money supply and money demand, or interest rate rises lower investment and inflation via a Phillips curve, as simple approximations that more complex models, perhaps involving some irrationality, improve on. Monetary policy is centrally about the Fed exploiting irrationality, full stop, and cannot be explained or understood at all without that feature.

More in the comments. There are too many equations and figures to mirror it here, so you have to get the pdf if you're interested. This is for academics anyway.

Tracking the ROI on your marketing campaigns in three easy steps

You spend a lot of money on marketing, direct mailers and advertising. I am

shocked sometimes when I see how much these advertising campaigns cost the practice. How do you know if they are working? Do you know the ROI on your investment? I can help you see the statistics you need to know to help make decisions on your referral programs and new patient acquisition.

First things first . . . your team must be recording where your new patients are being referred from. If you are starting a new patient referral program or just signed up with a marketing company that is launching some direct mail campaigns, I want you to add these into your referral sources. Go to the Office Manager > Reference > Referral Maintenance. Then click on add new and enter the name of the campaign. Make sure to check Non Person and then, at the bottom, click on Referred By. The reason you want to select Non Person is because these campaigns will be tracked as Referred by Marketing on the Practice Advisor Report.

Second, make sure your team is checking the new patients against the referring sources on a daily basis. The easiest way to do this is using the Daily Huddle Report. Look at the Daily Huddle each day and make sure the total number of new patients is accurate and make sure the total number of new patients matches the total number of referral sources. This makes sure that every new patient is tracked with how he or she found you.

Now the juicy stuff . . . you can run a report that will show you the total number of patients by each referral source, see how much their treatment plan is worth and how much production has been completed. This is invaluable because you can not only see how many patients this campaign is bringing you, but also how much revenue. AMAZING!

Go to the Office Manager > Reports > Management > Referred By Report. Next, select the referral dates, production dates and select the referring source you want to analyze. Then at the bottom make sure to check Show Production Detail.

This will be great information when you are looking to renew a campaign or just great feedback to your marketing company

Levinson on growth

I disagree rather profoundly with crucial parts of Marc Levison's essay "Why the Economy Doesn't Roar Anymore" in the Saturday Wall Street Journal.

Yes, growth is slow. Yes, the ultimate source of growth is productivity. But no, sclerotic productivity is not "just being ordinary." No, our economy is not generating as much productivity growth as is possible, so just get used to it. No, productivity does not fall randomly from the sky no matter what politicians do.

Mark starts well, with a nice and vivid review of the post WWII growth "miracles."

He stumbles a bit at the 1973 Yom Kippur war and oil embargo

"Politicians everywhere responded by putting energy high on their agendas. In the U.S., the crusade for “energy independence” led to energy efficiency standards, the creation of the Strategic Petroleum Reserve, large government investments in solar power and nuclear fusion, and price deregulation. [JC: ?? The 1970s had price controls, not deregulation!] But it wasn’t the price of gasoline that brought the long run of global prosperity to an end. It just diverted attention from a more fundamental problem: Productivity growth had slowed sharply."

"The consequences of the productivity bust were severe.."

More good descriptions of eurosclerosis follow. But you see him veer off course, as  he sees little connection between the litany of ham-handed responses to the oil shock and the decline in productivity.


Briefly back to a sensible point

"Government leaders in the 1970s knew, or thought they knew, how to use traditional methods of economic management—adjusting interest rates, taxes and government spending—to restore an economy to health. But when it came to finding a fix for declining productivity growth, their toolbox was embarrassingly empty."

Let us speak the word: the methods of Keynesian demand-side economic management were, as any honest Keynesian will tell you, utterly unsuited to solving productivity, the ultimate "supply" problem. Given the Economist's enthusiasm for fiscal stimulus a bit more honesty on this one would be appreciated.

But then then he veers off course entirely

"Conservative politicians such as Margaret Thatcher in the U.K., Ronald Reagan in the U.S. and Helmut Kohl in West Germany swept into power, promising that freer markets and smaller government would reverse the decline, spur productivity and restore rapid growth." 

"But these leaders’ policies—deregulation, privatization, lower tax rates, balanced budgets and rigid rules for monetary policy—proved no more successful at boosting productivity than the statist policies that had preceded them. Some insist that the conservative revolution stimulated an economic renaissance, but the facts say otherwise: Great Britain’s productivity grew far more slowly under Thatcher’s rule than during the miserable 1970s, and Reagan’s supply-side tax cuts brought no productivity improvement at all. [My emphasis] Even the few countries that seemed to buck the trend of sluggish productivity growth in the 1970s and 1980s, notably Japan, did so only temporarily. A few years later, they found themselves mired in the same productivity slump as everyone else.."

This is just a whopper of... what to call it... factual error.

The US embarked on a second boom from 1980 to 2000. See John Taylor's excellent response, "Take off the muzzle and the economy will roar" for more discussion, and the graph reproduced at the left. Call it the Reagan-Bush-Clinton boom if it makes you feel better. But the boom was real.

(Update: A correspondent writes "the author's claim that productivity growth was worse in the UK under Thatcher than in the 1970s is very wrong.  Indeed, productivity growth was one area in which I thought there was wide agreement that the Thatcher period was a success (see, for example, Krugman's chapter on the UK in his book Peddling Prosperity).")

From off course, Levinson arrives at a strange harbor. His bottom line is the astonishing proposition that productivity growth just happens; manna from heaven (or not) dissociated from any economic or political structure:

"Productivity, in historical context, grows in fits and starts. Innovation surely has something to do with it, but we have precious little idea how to stimulate innovation—and no way at all to predict which innovations will lead to higher productivity..."

"It is tempting to think that we know how to do better, that there is some secret sauce that governments can ladle out to make economies grow faster than the norm. But despite glib talk about “pro-growth” economic policies, productivity growth is something over which governments have very little control. Rapid productivity growth has occurred in countries with low tax rates but also in nations where tax rates were sky-high. Slashing government regulations has unleashed productivity growth at some times and places but undermined it at others. The claim that freer markets and smaller governments are always better for productivity than a larger, more powerful state is not one that can be verified by the data."

I'm sorry, the data -- and the immense literature that study that data -- come to the opposite conclusion. There is a reason that this manna seems to fall on the US and not, say, on Haiti. There is a reason it falls on South Korea and not North  Korea -- the most tragic but decisive controlled experiment known to economics.

Yes, the answers are not as simplistic as the minor tweaks represented by "pro-growth" policies of established parties in western democracies. But experience and formal analysis tell us clearly that innovation and productivity happen where there is rule of law, simple and predictable regulation, property rights, reasonable taxation, an open and competitive economy, and decent public infrastructure.  These, politicians do have ample control over, and ample opportunity to screw up.

(Update and clarification. Levinson is thinking about the experience over time in single countries. There, indeed, the variation of policies is small, and its correlation with growth hard to tease out. Growth takes a while to get going or to kill, and causality can run both ways. Countries often reform after bad times, and squeeze the golden goose after good times. I'm thinking more about the variation across countries. If you look at the yawning gaps in "pro-growth" policy across US, UK, China, India, North Korea, say, you see also yawning gaps in productivity.)

"Here is the lesson: What some economists now call “secular stagnation” might better be termed “ordinary performance.” ... 

"Ever since the Golden Age vanished amid the gasoline lines of 1973, political leaders in every wealthy country have insisted that the right policies will bring back those heady days. Voters who have been trained to expect that their leaders can deliver something more than ordinary are likely to find reality disappointing."

I've got news for Mr. Levinson. "Ordinary performance" is what people experienced from the beginning of time to about 1750. Steady grinding poverty, 0% growth rate, each farming in his parents' footsteps. Even 2% was the result of an amazing and unprecedented set of "pro-growth" political institutions.

Not only can we do better we can do worse. A lot worse.

If  good policy does not help, then it follows that bad policies do not hurt. No matter how much our politicians abandon "pro-growth" policies, to nativism, trade barriers, over-regualation, legal capture, arbitrarily high taxes, more controlled markets and larger government, growth will just bumble along at 2% anyway. Both the US and UK may soon put that one to the test.

Note: I use block quotes and embedded graphs. These show up on the original blogger verision of this post. I notice they get garbled at various other feeds. If you want better formatting, come back to the original

Five Books to Change Liberals' Minds

"Five Books to Change Liberals' Minds" is the title of a remarkable post by Cass Sunstein.

It can be easy and tempting, especially during a presidential campaign, to listen only to opinions that mirror and fortify one's own. That’s not ideal, because it eliminates learning and makes it impossible for people to understand what they dismiss as “the other side.”

If you think that Barack Obama has been a terrific president (as I do) and that Hillary Clinton would be an excellent successor (as I also do), then you might want to consider the following books, to help you to understand why so many of your fellow citizens disagree with you:

“Seeing Like A State: How Certain Schemes to Improve the Human Conditions Have Failed,” by James Scott.....

and  closes

Having read these books, you might continue to believe that progressives are more often right than wrong, and that in general, the U.S. would be better off in the hands of Democrats than Republicans. But you’ll have a much better understanding of the counterarguments -- and on an issue or two, and maybe more, you’ll probably end up joining those on what you once saw as “the other side.”

Most public intellectual commentary these days takes a tone of parochial demonization -- the hilarious "how Paul Krugman made Donald Trump possible" is good to ponder. When such people even consider views the other side, it's  bulveristic speculation -- did bad childhoods make them evil, or are they bought? The next sentence usually bemoans polarization. This piece by Sunstein is a breath of fresh air.

Those who listen buy themselves an ear.  I usually find I disagree with Sunstein about most things (though his attempt to rein in regulation from inside the Administration is both praiseworthy and instructive in its failure). But knowing that his opinions come from such consideration, they carry more weight. It's more effective than upping low Krugmanian insult to high Bergeracian disdain.

I'm sure many of my blog readers could suggest additional books for Mr. Sunstein -- Friedman, Sowell, Murray, and so on. That's not the point. When grandma sends you books about how to clean your room, you never read them. If you want to send suggestions, send good liberal and progressive books that lovers of freedom should read.

Personal goals = personal growth = practice success

Did one of my recent articles get you thinking about planning for the end of the year? I talked about how you can make adjustments in your appointment book to make room for those last-minute new patients who are trying to get in before the end of the year so you can make your new patient goal number. I also discussed how you can forecast your appointment book production numbers to see if you are coming up short for your production goals. If you would like to re-read this article, CLICK HERE to be redirected.

These practice goals are not only important, but they are critical to the health of the office overhead and the stress level of the team. But what about you? What are your personal goals? When I was working in a practice, I got my hands on as much CE as I could handle. Do you know what goal I think you could set and accomplish by the end of the year? You could become a Dentrix Master and receive a certificate to prove it. This could be your personal end of the year goal.

Clinical CE is great and, for some of you in the dental practice, it is a requirement in order for you to maintain your license. Your entire day revolves around your practice management software and most offices have zero training or continuing education for the software you use every day. You all must maintain a certain level of knowledge of Dentrix in order to function on a daily basis. I am asking you to up your game and not just function, but excel. Become a Dentrix Master.

It’s easy to start and extremely rewarding to finish. The Dentrix Mastery Tracks were launched to help you learn more about your software, things you might not know exist and things you want to learn more about. This is your chance to set a personal goal and help your office at the same time.  I believe in you.

Your first test is free! 

To get started CLICK HERE and Create New User. Then enter code FreeTestDOM

Volume and Information

This is a little essay on the puzzle of volume, disguised as comments on a paper by Fernando Alvarez and Andy Atkeson, presented at the Becker-Friedman Institute Conference in Honor of Robert E. Lucas Jr. (The rest of the conference is really interesting too, but I likely will not have time to blog a summary.) 

Like many others, I have been very influenced by Bob, and I owe him a lot personally as well. Bob pretty much handed me the basic idea for a "Random walk in GNP" on a silver platter. Bob's review of a report to the OECD, which he might rather forget, inspired the Grumpy Economist many years later. Bob is a straight-arrow icon for how academics should conduct themselves. 

On Volume:  (also pdf here) 

Volume and Information. Comments on “Random Risk Aversion and Liquidity: a Model of Asset Pricing and Trade Volumes” by Fernando Alvarez and Andy Atkeson 

John H. Cochrane

October 7 2016 

This is a great economics paper in the Bob Lucas tradition: Preferences, technology, equilibrium, predictions, facts, welfare calculations, full stop.

However, it’s not yet a great finance paper. It’s missing the motivation, vision, methodological speculation, calls for future research — in short, all the BS — that Bob tells you to leave out. I’ll follow my comparative advantage, then, to help to fill this yawning gap.

Volume is The Great Unsolved Problem of Financial Economics. In our canonical models — such as Bob’s classic consumption-based model — trading volume is essentially zero.

The reason is beautifully set out in Nancy Stokey and Paul Milgrom’s no-trade theorem, which I call the Groucho Marx theorem: don’t belong to any club that will have you as a member. If someone offers to sell you something, he knows something you don’t.

More deeply, all trading — any deviation of portfolios from the value-weighted market index — is zero sum. Informed traders do not make money from us passive investors, they make money from other traders.

It is not a puzzle that informed traders trade and make money. The deep puzzle is why the uninformed trade, when they could do better by indexing.

Here’s how markets “should” work: You think the new iPhone is great. You try to buy Apple stock, but you run in to a wall of indexers. “How about $100?” “Sorry, we only buy and sell the whole index.” “Well, how about $120?” “Are you deaf?” You keep trying until you bid the price up to the efficient-market value, but no shares trade hands.

As Andy Abel put it, financial markets should work like the market for senior economists: Bids fly, prices change, nobody moves.

And, soon, seeing the futility of the whole business, nobody serves on committees any more. Why put time and effort into finding information if you can’t profit from it? If information is expensive to obtain, then nobody bothers, and markets cannot become efficient. (This is the Grossman-Stiglitz theorem on the impossibility of efficient markets.)

I gather quantum mechanics is off by 10 to the 120th power in the mass of empty space, which determines the fate of the universe. Volume is a puzzle of the same order, and importance, at least within our little universe.

Stock exchanges exist to support information trading. The theory of finance predicts that stock exchanges, the central institution it studies, the central source of our data, should not exist. The tiny amounts of trading you can generate for life cycle or other reasons could all easily be handled at a bank. All of the smart students I sent to Wall Street for 20 years went to participate in something that my theory said should not exist.

And it’s an important puzzle. For a long time, I think, finance got by on the presumption that we’ll get the price mostly right with the zero-volume theory, and you microstructure guys can have the last 10 basis points. More recent empirical work makes that guess seem quite wrong. It turns out to be true that prices rise when a lot of people place buy orders, despite the fact that there is a seller for each buyer. There is a strong correlation between the level of prices and trading volume — price booms involve huge turnover, busts are quiet.

At a deeper level, if we need trading to make prices efficient, but we have no idea how that process works, we are in danger that prices are quite far from efficient. Perhaps there is too little trading volume, as the rewards for digging up information are not high enough! (Ken French’s AFA presidential speech artfully asks this question.)

Our policy makers, as well as far too many economists, jump from not understanding something, to that something must be wrong, irrational, exploitative, or reflective of “greed” and needs to be stopped. A large transactions tax could well be imposed soon. Half of Washington and most of Harvard believes there is “too much” finance, meaning trading, not compliance staff, and needs policy interventions to cut trading down. The SEC and CFTC already regulate trading in great detail, and send people to jail for helping to incorporate information in to prices in ways they disapprove of. Without a good model of information trading those judgments are guesses, but equally hard to refute.

How do we get out of this conundrum? Well, so far, by a sequence of ugly patches.

Grossman and Stiglitz added “noise traders.” Why they trade rather than index is just outside the model.

Another strand, for example Viral Acharya and Lasse Pedersen’s liquidity based asset pricing model, uses life cycle motives, what you here would recognize as an overlapping generations model. They imagine that people work a week, retire for a week, and die without descendants. Well, that gets them to trade. But people are not fruit flies either.

Fernando and Andy adopt another common trick — unobservable preference shocks. If trade fundamentally comes from preferences rather than information then we avoid the puzzle of who signs up to lose money.

I don’t think it does a lot of good to call them shocks to risk aversion, and tie them to habit formation, as enamored as I am of that formulation in other contexts. Habit formation induces changes in risk aversion from changes in consumption. That makes risk aversion shocks observable, and hence contractable, which would undo trading.

More deeply, to explain volume in individual securities, you need a shock that makes you more risk averse to Apple and less risk averse to Google. It can be done, but it is less attractive and pretty close to preferences for shares themselves.

Finally, trading is huge, and hugely concentrated. Renaissance seems to have a preference shock every 10 milliseconds. I last rebalanced in 1994.

The key first principle of modern finance, going back to Markowitz, is that preferences attach to money — to the payoffs of portfolios — not to the securities that make up portfolios. A basket of stocks is not a basket of fruits. It’s not the first time that researchers have crossed this bright line. Fama and French do it. But if it is a necessary condition to generate volume, it’s awfully unpalatable. Do we really need to throw out this most basic insight of modern finance?

Another strain of literature supposes people have “dogmatic priors” or suffer from “overconfidence.” (José Scheinkman and Wei Xiong have a very nice paper along these lines, echoing Harrison and Kerps much earlier.) Perhaps. I ask practitioners why they trade and they say “I’m smarter than the average.” Exactly half are mistaken.

At one level this is a plausible path. It takes just a little overconfidence in one’s own signal to undo the no-trade-theorem information story — to introduce a little doubt into the “if he’s offering to sell me something he knows something I don’t” recursion.

On the other hand, understanding that other people are just like us, and therefore inferring motives behind actions, is very deep in psychology and rationality as well. Even chimps, offered to trade a banana for an apple, will check to make sure the banana isn’t rotten.

(Disclaimer: I made the banana story up. I remember seeing a science show on PBS about how chimps and other mammals that pass the dot test have a theory of mind, understand that others are like them and therefore question motives. But I don’t have the reference handy. Update: A friend sends this and this.)

More deeply, if you are forced to trade, a little overconfidence will get it going. But why trade at all? Why not index and make sure you’re not one of the losers? Inferring information from other’s offer to trade is only half of the no-trade theorem. The fact that rational people don’t enter a zero-sum casino in the first place is the other, much more robust, half. That line of thought equates trading with gambling — also a puzzle — or other fundamentally irrational behavior.

But are we really satisfied to state that the existence of exchanges, and the fact that information percolates into prices via a series of trades, are facts only “explainable" by human folly, that would be absent in a more perfect (or perfectly-run) world?

Moreover, that “people are idiots” (what Owen Lamont once humorously called a “technical term of behavioral finance”) might be a trenchant observation on the human condition. But, by being capable of “explaining” everything, it is not a theory of anything, as Bob Lucas uses the word “theory.”

The sheer volume of trading is the puzzle. All these non-information mechanisms — life-cycle, preference shocks, rebalancing among heterogeneous agents (Andy Lo and Jiang Wang), preference shifts, generate trading volume. But they do not generate the astronomical magnitude and concentration of volume that we see.

We know what this huge volume of trading is about. It’s about information, not preference shocks. Information seems to need trades to percolate into prices. We just don’t understand why.

Does this matter? How realistic do micro foundations have to be anyway? Actually, for Andy and Fernando’s main purpose, and that of the whole literature I just seemed to make fun of, I don’t think it’s much of a problem at all.

Grossman and Stiglitz, and their followers, want to study information traders, liquidity providers, bid-ask spreads, and other microstructure issues. Noise traders, “overconfidence,” short life spans, or preference shocks just get around the technicalities of the no-trade theorem to focus on the important part of the model, and the phenomena in the data it wants to match. Andy and Fernando want a model that generates the correlations between risk premiums and volume. For that purpose, the ultimate source of volume and why some people don’t index is probably unimportant.

We do this all the time. Bob’s great 1972 paper put people on islands and money in their hands via overlapping generations. People live in suburbs and hold money as a transactions inventory. OLG models miss velocity by a factor of 100 too. (OLG money and life-cycle volume models are closely related.) So what? Economic models are quantitative parables. You get nowhere if you fuss too much about micro foundations of peripheral parts. More precisely, we have experience and intuition that roughly the same results come from different peripheral micro foundations.

If I were trying to come up with a model of trading tomorrow, for example to address the correlation of prices with volume (my “Money as stock” left that hanging, and I’ve always wanted to come back to it), that’s what I’d do too.

At least, for positive purposes. We also have experience that models with different micro foundations can produce much the same positive predictions, but have wildly different welfare implications and policy conclusions. So I would be much more wary of policy conclusions from a model in which trading has nothing to do with information. So, though I love this paper’s answer (transactions taxes are highly damaging), and I tend to like models that produce this result, that is no more honest than most transactions tax thought, which is also an answer eternally in search of a question.

At this point, I should summarize the actual contributions of the paper. It’s really a great paper about risk sharing in incomplete markets, and less about volume. Though the micro foundations are a bit artificial, it very nicely gets at why volume factors seem to generate risk premiums. For that purpose, I agree, just why people trade so much is probably irrelevant. But, having blabbed so much about big picture, I’ll have to cut short the substance.

How will we really solve the volume puzzle, and related just what “liquidity” means? How does information make its way into markets via trading? With many PhD students in the audience, let me emphasize how deep and important this question is, and offer some wild speculations.


As in all science, new observations drive new theory. We’re learning a lot about how information gets incorporated in prices via trading. For example, Brian Weller and Shrihari Santosh show how pieces of information end up in prices through a string of intermediaries, just as vegetables make their way from farmer to your table — and with just as much objection from bien-pensant economists who have decried “profiteers” and “middlemen” for centuries.

Also, there is a lot of trading after a discrete piece of information hits the market symmetrically, such as a change in Federal Funds rate. Apparently it takes trading for people to figure out what the information means. I find this observation particularly interesting. It’s not just my signal and your signal.

And new theory demands new technique too, something that we learned from Bob. (Bob once confessed that learning the math behind dynamic programming had been really hard.)

What is this “information” anyway? Models specify a “signal” about liquidating dividends. But 99% of “information” trading is not about that at all. If you ask a high speed trader about signals about liquidating dividends, they will give you a blank stare. 99% of what they do is exactly inferring information from prices — not just the level of the price but its history, the history of quotes, volumes, and other data. This is the mechanism we need to understand.

Behind the no-trade theorem lies a classic view of information — there are 52 cards in the deck, you have three up and two down, I infer probabilities, and so forth. Omega, F, P. But when we think about information trading in asset markets, we don’t even know what the card deck is. Perhaps the ambiguity or robust control ideas Lars Hansen and Tom Sargent describe, or the descriptions of decision making under information overload that computer scientists study will hold the key. For a puzzle this big, and this intractable, I think we will end up needing new models of information itself. And then, hopefully, we will not have to throw out rationality, the implication that trading is all due to human folly, or the basic principles of finance such as preferences for money not securities.

Well, I think I’ve hit 4 of the 6 Bob Lucas deadly sins — big picture motivation, comments about about whole classes of theories, methodological musings, and wild speculation about future research. I’ll leave the last two — speculations about policy and politics, and the story of how one thought about the paper — for Andy and Fernando!