Dueling Mortgage Gurus and Uncertainty

uncertaintyOne of the things that makes finance endlessly fascinating (to me!) is that perfectly sound logic for one situation turns out to be perfect madness in another situation.

In my best moments I appreciate the ironies and contradictions. In my worst moments I despair for people whipsawed by the seeming complexities of financial choices.

Most middle-class folks grapple with one of these important choices – a home mortgage – at least once in their life. I’m a big fan of the choice to buy a home with a mortgage but even there, a controversial battle rages.


Dave Ramsey

On one side of the ring stand the anti-debt gurus like Dave Ramsey. While Ramsey really wants his followers to pay cash for their homes (which is fairly absurd), he has strong rules about what to do if you decide to borrow. For example, Ramsey says

  1. Always make at least a 20% down payment, to avoid high interest charges and expensive private mortgage insurance.
  2. Always get a 15-year mortgage rather than a 30-year mortgage because you will pay it off sooner, typically enjoy a lower interest rate, and you’ll pay significantly less interest over the life of the loan.
  3. Never take on mortgage debt with a monthly payment that will command more than 25% of your take-home pay.
  4. Always avoid adjustable rate mortgages which shift the risk of higher interest rates from the lender to you.
  5. Never borrow additional home equity in the form of a home equity loan or line of credit.1

Ramsey – who built a real estate fortune and then went bankrupt by the age of 30 – preaches a low-debt or (preferably) no-debt financial lifestyle as a curative for people with past debt problems. He knows of what he speaks, and he has a certain strong logic for his points.

On the other hand, personally, I’ve broken each and every one of his rules. So I can’t actually advocate following his advice.


On the opposite side of the ring, another financial guru Ric Edelman advocates the opposite approach.

Ric Edelman

Since Edelman’s contrarian position flies in the face of conventional wisdom, I enjoy presenting his points even more.

  1. Only make the bare minimum down payment on your house – thereby freeing up your remaining capital for investing in the market, where you can earn an annual return higher than what you pay on your mortgage debt.
  2. Always get a 30-year mortgage rather than a 15-year mortgage, to take advantage of the tax deduction on mortgage interest.
  3. Never pay off your mortgage early or at all, because mortgages are the best way to borrow extremely cheaply. Again, use the borrowed money to invest profitably in the market.
  4. If the value of your house rises, consider freeing up the equity to invest, through a home equity loan or line, rather then let your net worth stay locked up and unused in the form of your house. That way, Edelman says, if the value of your house drops you’ll at least have withdrawn the money and have use of it for emergencies.
  5. Quickly paying down and eliminating your monthly mortgage payment is not an important goal because, as a homeowner, you’ll always have to pay insurance and real estate taxes anyway. Since you can’t eliminate those obligations, why bother trying to eliminate your mortgage payment?

You get the idea. When Ramsey says “Zig” Edelman says “Zag.”

Edelman presents some compelling math for his arguments. If you accept his assumptions then you could end up wealthier in the long run.

However, Edelman does not account for the psychological difficulty of saving money. Specifically, many of us benefit from the ‘forced savings’ of paying a mortgage, and few will have the discipline to take the extra monthly cash flow as a result of a 30 year mortgage and invest it for the long run, rather than squander it on iced latte frappuccinos.

As a result, I’m pretty sure some portion of people who take Edelman’s advice to heart will end up like the proverbial broke guy having to wear a barrel for pants. It kind of all depends on your specific situation.

My choices

In my own life I’ve had both adjustable rate mortgages and fixed rate mortgages. I’ve borrowed more than the conventional 80% limit. I’ve had 15-year and 30-year loans. I’ve paid extra principal on a biweekly basis, and I’ve also borrowed heavily against my home equity line of credit. I’d like to think I had compelling logic for each decision, or at least a sober mind for understanding what I was doing.

How to decide

I think my point is that the more wholly convinced a guru is, the less certain you should be. The stronger they lean in, the less likely they are to be correct in all circumstances, for all people. Ramsey’s got a great plan, for example, for people who’ve been bankrupt in the past or who have a history of debt problems. Edelman’s approach is closer to my own experience because he’s linking some risk-taking to long-term wealth creation, which I tend to do in my own life. But where you fall on the risk spectrum is a key determinant of their relevancy to your own situation.

Big Ideas vs Little Ideas

Nate Silver’s 2012 book The Signal And The Noise presents the dichotomy of a guru or pundit’s ‘big idea’ vs. ‘small ideas.’ While punditry rewards people who have ‘big ideas’ and ‘hot takes’ on topics, the reality is that certainty and big ideas come at a cost. Predicting the future – one of Nate Silver’s specialties – is a difficult business for people with big ideas. They rarely get it right. Instead, Silver advocates adopting a nimbler approach to observing the world.
When I read gurus like Ramsey and Edelman, I remind myself that their certainty is a sign of the ‘big idea’ thinking that Silver warns against, when we might be served better by smaller ideas, more responsive to changing conditions.

The more certain I am, the less likely I am to be wholly right.


A version of this post ran in the San Antonio Express News.

Please see related posts:

Book Review: The Signal And The Noise by Nate Silver

My 15-year mortgage – I am a Golden God

Rent vs. Buy a house

Home Equity Lines of Credit are awesome

The Latte Effect in my own life




Post read (3729) times.

  1. I have strong pro-HELOC views, as I’ve written about in the past.

Political Markets: Democrats’ Chances Of Holding The Senate Just Doubled

I generally trust markets when it comes to political forecasting, which is why I dabbled in trading contracts on the Iowa Political Markets in both 2008 and 2012.

iowa political marketsI’d rather trust in people’s actual money-on-the-line to indicate an aggregated belief in who will win an election, rather than your average poll – or worse – a political commentator. Markets are great at collecting and reflecting back prices that reflect expectations of future results. Markets can be wrong, and markets can be irrational, but generally and in the long run they tend to be right.

This is a sort of restatement of the efficient market hypothesis, which you can read more about either from Nate Silver or Burton Malkiel in A Random Walk Down Wall Street.

At the very least, you should know what the markets say about the future before you go leaping in a different direction.

Anyway…I checked back in the Iowa Political Markets Senate race today, and its totally different today – than it has been any time in the last few months.

Conventional wisdom, and the Iowa political markets, had only given Dems a 20% chance or less of holding the Senate after next week’s election.

Suddenly, today, the ‘market’ has jumped to a 40% chance of Democrats retaining the Senate, on the Iowa Political Markets.

The interesting, quirky, thing about the Iowa Political Markets is that they operate on tiny amounts of money in the system – by design – as individuals may only seed their account with a maximum of $500 total. In addition, the markets don’t see much volume much of the time, except in the hottest moments of a Presidential race, which we’re not in now. That has always meant that the Iowa markets could be temporarily manipulated – presumably for political reasons – without a tremendous amount of effort.

And yet…I don’t know.

Nate Silver’s 538.com says that the probability of Republicans taking over the Senate has stayed consistently around 63% for the past month, presumably leaving Dems with a 37% chance of retaining control.

iowa political market senate race
The “DS.hold14” (The price of a “Democrats hold the Senate” contract) price doubled since yesterday

Yet the Iowa market ‘price’ (roughly, the chance of Democrats retaining control) has bounced around well below 20% for the past month. Until today…now it’s at 40%.

Why did the Democrats’ chance of retaining the Senate just double from yesterday to today?


Post read (770) times.

The Efficient Market Hypothesis: The 7 Levels of Nate Silver

Nate_SilverOne of the most important, but controversial, ideas of investing is the ‘efficient market’ hypothesis.

I say important, because it provides a great starting point for approaching investing and markets humbly, as well as for approaching the Financial Infotainment Industrial Complex with a healthy dose of skepticism.  Most investors would be better off if they understood and believed in the efficient market hypothesis.

I say controversial because extreme – or rigid – versions of the efficient market hypothesis can be either disproven or mocked or shown to be untrue in a variety of ways.

Why is this on my mind?

In the next few days I’ll be posting a podcast interview I did a few months ago with author Lars Kroijer, whose book Investing Demystified builds on the basic idea that very few of us actually have an ‘edge’ in the markets. As a result, we would be better off adopting a simple, low-cost approach to our investments.

Reviewing that podcast interview reminded me of my favorite presentation of the efficient market hypothesis, from Nate Silver’s The Signal and The Noise.

Silver doesn’t accept the discredited rigid definition of the efficient market hypothesis, but rather builds a series of increasingly accurate versions through steps 1 through 7.  I read this portion of the Silver book to Kroijer, so I thought I’d just post the transcript of our interview here, as a preview to the upcoming podcasts:


Michael:          My favorite version of the efficient-market hypothesis was written by Nate Silver in The Signal and the Noise. Have you read that book?

Lars:                No.

Michael:          Do you know who he is?

Lars:                The New York Times guy? [More recently, ESPN, of course]

Michael:          Yeah and Fivethirtyeight.com where he does political forecasting. He’s an interesting thinker and I really recommend the book. But he has a statement of the efficient-market hypothesis that matches his view of the world, which is a probabilistic view in which you end up saying things much less certainly about the future, but maybe more accurately, because the future itself is uncertain. He has seven levels of the efficient-market hypothesis, which I just want to read to you, because it’s really fun.

Level 1:  “No investor can beat the market.”

Okay, that’s very strong, very simple.

Level 2:  “No investor can beat the stock market over the long run.”

That’s a bit, more qualified.

Level 3:  “No investor can beat the stock market over the long run, relative to his level of risk.”

Okay, that’s more sophisticated.

Level 4:  “No investor can beat the stock market over the long run, relative to his level of risk, and accounting for transaction costs.”

Okay, makes sense.

Level 5:  “No investor can beat the stock market over the long run, relative to his level of risk, and accounting for transaction costs, unless he has inside information.”

Makes sense.  The second-to-last one is:

Level 6:  “Few investors can beat the stock market, over the long run, relative to their level of risk, and accounting for the transaction costs, unless they have inside information.”

Finally, the most complete version of the efficient-market hypothesis, which makes sense to me.

Level 7: “It is hard to tell how many investors beat the stock market, over the long run, because the data is very noisy. But we know that most cannot, relative to their level of risk, since trading produces no net excess return, but entails transaction costs. So unless you have inside information, you’re probably better off investing in an index fund.”

Lars:                I like that.

Michael:          He’s a good writer and he has an awesome way of talking about how do we understand the future in a probabilistic way. It’s a sophisticated way of talking about the different levels of extreme efficient-market hypothesis, versus a more – probably correct – nuanced way.

Lars:                I think unfortunately the way it’s sort of been very roughly done in theory, it’s sort of been discredited.

Michael:          If you go to the extreme version, you can discredit it, I think.

Lars:                So no one will really look at it today. It’s not something widely discussed. One of the reasons being is that not many people are really all that interested in discussing it widely. I like what he’s talking about.


Please see related post book review of The Signal and The Noise, by Nate Silver

The Signal and The Noise

Please see related post book review of Investing Demystified, by Lars Kroijer

Investing Demystified

Post read (4592) times.

Red Sox as an illustration of Bayesian Probability Theory

DaveWill the Red Sox win the World Series this year?  What are their chances?

What are their chances of going all the way, if they win their first game of the playoffs this Friday?

Aha!  I have a chance to apply Bayesian probability theory!

I recently reviewed Nate Silver’s excellent The Signal and The Noise: Why So Many Predictions Fail – But Some Don’t , which at its core, advocates we adopt Bayesian probability methods for forecasting complex events.  Like Red Sox World Series championships.

Nate Silver’s Big Idea

Silver’s big idea is for us to move away from “I have the explanation and I know what’s going to happen,” to a different way of understanding the world characterized by “I can articulate a range of outcomes and attach meaningful probabilities to the possible outcomes.”

Bayesian probability

Bayes’ theory, Silver explains, helps us come up with the most accurate probability of some event occurring.  Fortunately, it’s not too complicated.

The Red Sox, of course, defy all probabilities

As we approach the MLB playoffs I’m fully aware of the irony of applying rational Bayesian probability to something as totally irrational, magical, and unlikely as Red Sox playoff outcomes.

My childhood and young adulthood consisted of them repeatedly snatching defeat from the jaws of victory.  Both the Game Six World Series loss in 1986 to the Mets and the 2003 ALCS loss to the Yankees[1] defied all semblance of probability – we didn’t need a mathematical theorem to tell us that.

At the time, all we knew was that God personally intervened in baseball outcomes and that she enjoyed torturing us.  And we hoped that God had plans for our redemption, some day.

We know now that, like the biblical story of Job, Red Sox Nation suffered for a reason.  We now own the Greatest Sports Victory of All Time, coming back impossibly from devastating losses in the first 3 ALCS games in 2004 to vanquish the Yankees and sweep the Cardinals.[2]  No sports victory has ever been as sweet as that.  It was all so improbable.  No math could ever explain that magic.

Greatest Sports Moments Ever, reduced to probabilities
Greatest Sports Moments Ever, reduced to probabilities

And yet, I insist we try to learn Bayesian Probability today

Fine then.

To use it, we need to define three known (or assumed) variables, in order to come up with a fourth, unknown variable, which is the thing we want to know, the probability of an event.

The known or assumed variables will be:

  1. X = an initial estimate of the likelihood of an event.  This is called a ‘prior’ since it’s our best guess of some probability prior to further investigation.  Before the playoffs even begin, how likely are the Red Sox to become World Series Champions?
  2. Y = The probability that if some condition is met, the event will happen.  In other words, how probable is it that a team that won the World Series had originally won their first game of the playoffs?
  3. Z = The probability that if that same condition is met, the event will not happen.  For a team that did not win the World Series, how probable is it that they won their first game of the playoffs?


The unknown variable, what we’re trying to determine, is our closest approximation of the probability of the event happening.

4. I’ll call that unknown variable V.  What is the probability of the Red Sox winning the World Series, if they win their first game on Friday?

The math formula of Bayes’ theorem, using these four variables, is:

V = (X*Y)/(X*Y + Z(1-X))

I understand that formula makes no sense in the abstract, so that’s why we’ll illustrate it with the Red Sox.

We need an example using numbers, please

Since it’s that time of year, I’ll ask the key question on everyone’s mind right now:

If they win on Friday, October 4th – their first game of the playoffs, will the Boston Red Sox go all the way on to win the World Series?

We can now define variables and assign probabilites

The variable V (This is the unknown what we’re trying to solve for)

V is the probability that the Red Sox win the World Series this year, if they win their first game of the playoffs.


Variable X, our prior

I will make our prior –the initial estimate for the Red Sox winning the World Series – 15%.  If all 8 playoff teams had an equal chance of winning the World Series my prior would be 12.5%, the percent equivalent of 1 divided by 8.  But given that the Sox had the best record in baseball this year – and they have studs like Big Papi and Pedroia – I have to boost their prior to 15%.


Variable Y, the conditional probability that the hypothesis is true

One of the requirements for using Bayesian probability theory is that we insert a conditional probability. We can simply express this hypothesis as “If this happens, this other thing is made more likely.”

In our example I’ll make the non-crazy hypothesis that there is some positive causal relationship between teams winning their first game of the playoffs and teams that eventually win the entire World Series.

Let’s assume we know, from historical data,[3] that teams that won the World Series had previously won their first game of the playoffs 58% of the time.  That’s our variable Y.


Variable Z, the false hypothesis variable

The false hypothesis variable in this example would be made from the 7 of 8 teams that historically begin the playoffs but do not go on to win the World Series.  Of these non-champions, what is the probability they won their first game?  I’ll estimate this at 45%[4]

Putting it all together

Using Bayes Theorem, we can now revise our estimate of the Red Sox winning the World Series, after the first playoff game has been played.

If the Red Sox win on October 4th, we can plug in variables X, Y and Z to determine the new probability of a glorious Red Sox World Series victory, variable V.

Remember: V = X*Y / (X*Y + Z*(1-X))

Plugging in our known and assumed probabilities, we get the

following math:

V = (15% * 58%) / ((15% * 58%) + (45%*(100%-15%)))

Solving that in an Excel Spreadsheet we get

V = 18.5%

Summed up, if the Red Sox win their first game Friday[5], we would revise our probability of them winning the World Series up to 18.5% from 15%.

Intuitively, this makes some sense.  There should be only a modest increase in the probability of a World Series championship after one game.

There’s a small positive correlation between winning the first game in the playoffs and eventually winning the World Series.

But even if it’s a blowout one way or another, let’s not get carried away.  The chances of them going all the way is only up to 18.5%.

bloody sock
Martyrdom & bloody sacrifice go beyond rational thought

Anchoring effect of priors

We should note, and Silver emphasizes, that the anchoring effect of priors greatly influences our updated probabilities.  In plainer English, our starting point for how we think the Red Sox are likely to do limits our ending point.

If we start with a prior that the Red Sox only have a 5% chance of winning the World Series, then their chances of winning the championship only jump to 6.3% after taking the first game, using my same assumed inputs.

Again using the same assumptions, if the Red Sox were 75% favorites to win it all, then a first game victory pushes them up to 79.5% favorites using the Bayesian Theorem.

Next Steps

If we want to follow the rest of the Red Sox playoff outcomes probabilistically, we’d take our revised prior – let’s say 18.5% after Game One – and come up with updated probabilities for variables Y and Z for Game Two.  To use new Y and Z variables effectively we would need new historical data to determine the conditional probability of a World Series victory based on Game Two results.

Continued iteration

Nate Silver would advocate applying this constant iteration, revising our probabilities and priors as new information arrives, for a wide range of complex phenomenon that defy prediction.  Will Mike Napoli’s beard change weather patterns inside Fenway?  Is it not Nate Silver, but rather Big Papi who is the witch? Will super-agent Scott Boras release a karma-bomb press release on another client like he did with A-Rod during the 2007 World Series, effectively marking the beginning of the end for A-Rod?  The probabilities change as the events unfurl.

Or not

Or conversely, we could just ignore all math, attach ourselves to one big idea, and never let go.

Because unrevised big beliefs, like sports fandom, do have their attractions.

Please see related post Book Review of The Signal and the Noise by Nate Silver


[1] Fie on you New York! Shaking my fist.  Arggh!

[2] Incidentally, that 53 minute 30-for30 video of “the Greatest Sports Victory of All Time” I linked to on Youtube is totally awesome.  Gives me the chils.

[3] I’m not a baseball stats geek with easy access to this kind of data, so I’m just making up numbers for the sake of illustration.

[4] Again, a stats geek could come up with the correct historical data to suggest a more accurate probability for the false hypothesis, but just work with me here a little bit on my completely made up numbers.

[5] And of course if my numbers were based on real data, rather than just picked out of the clear blue sky.

Post read (12373) times.

Book Review: The Signal And The Noise by Nate Silver

I took a mandatory course in high school[1] called “Theory of Knowledge,” meant to help us consider ‘How do we know things?”

“How do we know things?” turns out to be one of those big philosophical questions – dating from the time of Plato & Aristotle – irritating all of us for the last few millenia.

What Nate Silver addresses more than anything in The Signal and The Noise: Why So Many Prediction Fail – But Some Don’t  is how we know things – in particular how we use and misuse information to understand and make predictions about complex phenomena such as baseball performance, political outcomes, the weather, earthquakes, terrorist attacks, chess, Texas Hold ‘em poker, climate change, the spread of infectious diseases, and financial markets.

I’ve written before that it’s Nate Silver’s world, and we just live in it.[2] The Signal and The Noise offers a 21st Century answer to the question of ‘how do we know things?’  Because most of us, and most media, do not yet think this way, Silver implicitly criticizes everything I hate about the Financial Infotainment Industrial Complex.

Big Ideas vs. Small Ideas

Silver argues effectively that we frequently go wrong in many areas when we adopt a single model or approach to a problem, when an evolving, flexible, multiple-input, probabilistic approach would serve us better.

The problem of political pundits

Silver repeatedly returns in The Signal and the Noise to criticize political pundits on a TV show called The McLaughlin Group, on which commentators from the left and the right appear to make bold political predictions.  Silver – among the most widely admired public forecasters of political outcomes – eviscerates this type of ‘prediction,’ citing data that shows these commentators make accurate predictions no more often than would a random coin toss.

But television rewards ‘bold stances’ and ‘big ideas’ of the type The McLaughlin Group traffics in, while largely ignoring more thoughtful approaches.

Silver labels and criticizes the “Big Idea” mindset that passes for political commentary on television in favor of a more modest, probabilistic, and empirical “Small Idea” mindset.  Small ideas, nuanced, uncertain, and modest, however, make for poor television ratings.

But Silver does have a Big Idea himself

For complex, hard to predict phenomena[3], Silver explains his preferred method, based on a probability theorem attributed to an 18th Century English minister Thomas Bayes.

No doubt Silver thinks many more of us should become familiar with this branch of probability and statistics math. [4]

Beyond the Bayesian theory, however, Silver encourages us to adopt a probabilistic world-view.   His big idea is for us to move away from “I have the explanation and I know what’s going to happen,” to a different way of understanding the world characterized by “I can articulate a range of outcomes and attach meaningful probabilities to the possible outcomes.”

Over time, as we refine our data gathering and multifaceted models, we can move our small ideas forward and become ‘less wrong’ about the world.

In the investment world the former style of traders – the one’s with big ideas and certainty – may have a good run of success, but generally get flushed out when markets turn.  The best traders I’ve ever worked with think and speak in the latter way, considering new possibilities as markets evolve.

Some parts of this remind me of Nassim Taleb

The habits of mind Silver’s book encourages are not dissimilar to Nassim Taleb’s empirical skepticism, although they differ greatly in style and in points of emphasis.  Taleb tends to be aggressively critical of everybody else’s models, whereas Silver more generously praises other theorists’ models and critiques his own.

Both Taleb and Silver share, however, a restless dissatisfaction with the inputs into our understanding right now.  Both would say we do not know enough. We have not considered enough factors to explain whatever phenomenon we purport to explain. Our models need improvement and perpetual skepticism.  The best we can do is to think probabilistically about future events.

Both encourage a learned humility about what we can know or patterns we think we observe in the world.

How does this relate to investing?

I’d estimate only about ten percent of Silver’s book explicitly addresses investing.  As I mentioned, The Signal And The Noise is really a “Theory of Knowledge” book rather than in investing book.

But because Silver thinks like the best financial traders, uses probabilistic math effectively, and writes more clearly than almost anyone, his ideas are worth applying to investing.

1. Attribution of success

Among people who invest their own or other people’s money, 99.5%[5] of us attribute successful outcomes to personal investing acumen, while attributing unsuccessful outcomes to circumstances beyond our control.

The noise surrounding our own success – misinterpreting a generally rising market as stock-picking skill for example – leads us to overestimate our ability to influence investment returns.  As a result, too many of us engage in security selection, or too many of us pay others to achieve superior investment results, despite the evidence that we’re overpaying.

2. Responsibility for failure

Conversely, our abdication of personal responsibility for losses – it must have been ‘the bad markets’ after all! – leads us to underestimate our own errors of judgment.

In both cases – success or failure – we’re prone to adopt an uncritical approach to the right level of responsibility for outcomes.

3. Efficient market hypothesis as an illustration of the Bayesian approach

Although Silver gives numerous examples of his Bayesian probabilistic approach to problems with numbers, one of his best examples is purely textual, on the efficient market hypothesis.  He lists seven increasingly accurate, yet also qualified and probabilistic statements, on what we know about efficient markets.

The series of increasingly accurate, yet ‘less bold,’ statements are not only a great illustration of his big idea but also the right lesson for us on investing, so I reproduce it in full here:

a)     No investor can beat the stock market.[6]

b)     No investor can beat the stock market over the long run.[7]

c)      No investor can beat the stock market over the long run relative to his level of risk.[8]

d)     No investor can beat the stock market over the long run relative to his level of risk and accounting for transaction costs.[9]

e)     No investor can beat the stock market over the long run relative to his level of risk and accounting for his transaction costs, unless he has inside information[10]

f)       Few investors can beat the stock market over the long run relative to their level of risk and accounting for their transaction costs, unless they have inside information[11]

g)     It is hard to tell how many investors beat the stock market over the long run, because the data is very noisy, but we know that most cannot relative to their level of risk, since trading produces no net excess return but entails transaction costs, so unless you have inside information, you are probably better off investing in an index fund.[12]

The first approximation – the unqualified statement that no investor can beat the stock market – seems to be extremely powerful.  By the time we get to the last one, which is full of expressions of uncertainty, we have nothing that would fit on a bumper sticker.  But it is also a more complete description of the objective world.

If you want a 21st Century theory of knowledge, teaching you ‘how to think’ about the major world problems of global warming, financial crashes, avian flu, and terrorism, as well as ephemera like poker, chess, sports betting and baseball, start with The Signal and The Noise: Why So Many Prediction Fail – But Some Don’t  by Nate Silver.

Please also see related post on Bayesian Probability and the Red Sox.

Please also see related post All Bankers Anonymous Book Reviews in one place!

The Signal and The Noise
The Signal and The Noise


[1] Readers who study at an International Baccalaureate (IB) high school will be familiar with the “Theory of Knowledge” course.  It’s a really great idea for a course, but I have yet to meet anyone who thought the experience of the course lived up to the idea that inspired it.

[3] Each chapter separately tackles baseball, political forecasting, weather, earthquakes, economic growth, infectious disease growth, sports betting, chess, poker, climate change, and terrorism – each in their own way posing a challenge of seeing into the future.

[4] The mathematics of Bayesian probability is relatively straightforward so I think I’ll try in a subsequent post to do it justice.

[5] I rounded down to be conservative, because that’s just good science.

[6] The original, powerful, efficient market thesis

[7] Because, clearly, some people sometimes do, for some period of time

[8] You can take some crazy stock-market risks and WAY outperform boring stodgy stocks much of the time.  We have to match up comparable investment risk levels.

[9] A theoretical ‘market-beating’ high volume trading strategy often looks less market-beating when you take into account the frictions of trading.

[10] Inside information sure is helpful, when trying to beat the market

[11] Maybe some can do it, like Warren Buffett, but it’s super rare.  Probably you can’t do it.

[12] So carefully hedged!  So qualified and full of doubts!  So true!

Post read (11247) times.

One More On Nate Silver

I don’t read Paul Krugman much because his column falls in the category of people-whose-politics-are-entirely-too-predictable, when it comes to financial or political analysis.  I have this weird aversion to reading (or listening to, for that matter) the thoughts of people about whom I can predict their stance even before the conversation begins.[1]

However, occasionally Krugman reminds me why he’s wicked smaht[2] and says what I was thinking before I even thought it (if that makes sense, which I’ll admit, it doesn’t.)

Krugman points to a National Review piece attacking Nate Silver – of Five-Thirty-Eight.com fame – for his bias toward Obama.  The gist of the National Review piece is that Silver’s methodology is flawed, intentionally, to support Silver’s Democratic agenda.

Krugman’s point, and I whole-heartedly agree, is that when good statistical analysis like Silver’s – and science for that matter – is attacked for political reasons, we lose something important.

Clearly, I’m a Nate Silver fan, because he’s cutting through the distracting media infotainment industry better than anyone right now.  So Krugman’s larger point resonates with me – that if you can discredit and reduce good data-driven analysis to a base level with the rest of the noise, you’ve given ignorance a fresh start.

[1] Diane Rehm is guilty of this.  I hated on Joseph Stiglitz’ book recently for that reason.  I can barely read Nicholas Kristoff’s poltical columns as a result.  While their Op-Eds can be useful, nearly every editorial in the Wall Street Journal is unreadable.  Unless they are unintentionally comedic, like this one.

[2] As we say in my hometown.

Post read (2092) times.