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Some predictions for 2012:

1. The housing market will bottom out (50%).  If you need some property, get the best deal you can, but buy it.  If you want to speculate, buy it only at exceptional value.
2. There will be a mild recession in the second half (60%).  Things have been slowing down; a lot of economists have been predicting recession or even saying that one has happened already.  I don’t think there is a lot to be concerned about, but a mild recession should not be surprising to anyone.
3. Mitt Romney will be elected President of the United States (70%).  Credible models have predicted that he will the the Republican nominee, and credible models have predicted that a Republican will be elected President this cycle.
4. Republicans will gain majority control of the Senate (90%).  There are more Democrats retiring than Republicans, and there are more Democrats than Republicans in contested seats.  Combine that with a weak economy under Democrat control, and you can see that Republicans will almost certainly take over the Senate.
5. The Euro will survive (90%).  Brad DeLong has said straight out that the Euro is going to survive.  Cullen Roche has said that failure of the Euro is almost “unfathomable.”
6. The Occupy movement will gain some credibility as a political force, not on par with the Tea Party but more on the level of the Teamsters.  Some would say that this has already happened.
7. Natural gas consumption will rise modestly (70%).  Natural gas is cheap and can be used as a gasoline substitute.  Plus several coal-fired power plants are being retired by the Obama administration.
8. Gasoline prices will fall modestly (60%).  This is due in large part to expanded use of natural gas.
9. The U.S. will suffer a domestic terrorist attack (70%).  We haven’t had a high-profile domestic terrorist attack in a while.  It is only a matter of time.
10. Apple’s market share will wane (60%).  With Steve Jobs gone, Apple is like a headless chicken.  History has shown us that Apple without Jobs is a greedy brand with strict technology controls, poor innovation, and high prices.
11. There will be no significant additional legislation placed on high-frequency trading or any other exchange trading (80%).  Republicans and Libertarians won’t allow it.  Some rules may even be relaxed.

In the past month and a half, I made on social networks six short term predictions.  I was correct about only one of these, the survival of the Euro.  Fortunately, I did not lose any money on trades or embarrass myself all that much. Since the probability of using a coin flip to making predictions with equal or lower accuracy is 0.0625, I concluded that I was probably doing something wrong.  After some reflection, I concluded I have been making two mistakes:

1. Excessive Skepticism of Authority–Some amount of skepticism is healthy, even towards society’s most trusted sources.  I don’t believe everything that comes out of Ben Bernanke’s mouth–or Paul Krugman’s.  But really, I have been doubting authority more than is justified in most circumstances.  Once in a while, even the most prophetic of prognosticators makes a mistake.  But more often than not, individuals who are considered to be “experts” or “authorities” make correct predictions when undertaking the task.
2. Insufficient Skepticism in Adversarial Situations–It seems obvious, but when faced with highly adversarial situations such as those seen in currency trading, it is essential to take any opinions, even of so-called “experts,” with a grain of salt.  Where people have an economic incentive to lie, to mislead, to spread disinformation, some proportion of people will do exactly that.  When market movers can gain material advantage by moving the market counter to expectations, they will do so.  Skepticism alone will not shield you from all conceivable adversarial actions, but it does provide some armor against them.

Of course, it is always possible to avoid being wrong by hedging your statements (or at least almost always).  By “hedging” I mean that you can suggest that there is some possibility that the most likely conclusion is not the one that will occur.  One common way to do this is to use the statements of others to make your points.  An often stronger way is to clearly and fully state your belief, detailing circumstances under which the most likely outcome might not arise.  Of course, you can always just present the evidence that leads to your belief and let observers reach their own conclusions.

So, in summary:  Trust authority.  Be cautious in adversarial environments.  Hedge.

See you in the new year!

I am testing Facebook integration. Please disregard this post.

According to a new Verizon report focusing largely on credit card thefts, criminals are stealing data more frequently but are stealing lower quantities of it.

Report: Data Breaches Rise, But Stolen Data Drops | Threat Level | Wired.com.

I just finished watching Wall Street: Money Never Sleeps.

The movie was awful.

My biggest gripe with the movie was that the characters used a poor definition for “moral hazard,” a phrase used approximately 50 times throughout the movie at random intervals. As any student of finance knows, a moral hazard is a situation that arises from information asymmetry where one party can take advantage of another. For example, a bond-rating agency wishes to sell bonds, so it may have an incentive to give high bond ratings, decreasing efficiency in the bond market, ultimately leading to fewer bonds being made available and fewer people buying them. It’s a complicated situation. But I digress…

The central theme of the movie involved various scams being pulled by individuals, most of whom were apparently bad guys, but it wasn’t terribly clear whether the good guys were pulling scams also or whether the writer considered pulling scams a nefarious activity (pulling scams is definitely nefarious).

Generally speaking, the movie was insipid, boring, hard to follow and morally loose. The movie had little swearing, violence or sex but was clearly not appropriate for young children.

I’ll give this one a 2/5. Frankly, I’d like to rate it even lower, but I think that’s fair.

Definition of Remembruary :

(rĭ-mĕm’brü-ĕr’ĭ)

1. (n.) The relative last month, or reoccurrence of the first month of the year in which people forget to transition into the following year. Often this period ends relative to the person’s psychological ability to progress into the new year. This month is known to last as little as an hour and as much as several years in some rare cases.

Origins: From Later Latin rememorari, and from Latin Januarius, January.

Example: “It’s January 14th, and I’m still signing my checks with last year’s date, it takes me a few weeks to get past Remembruary!”

Source: http://www.unwords.com/unword/Remembruary.html

Greetings, gentle reader.

People who know me well know that I am interested in local politics. For the most part, when I want to blog about something political, I just write on someone else’s blog. Usually this works well; most bloggers are highly ethical, but not all of them are, and there are few things more irritating than seeing one’s blog comment edited to reflect one in a negative light or to misrepresent one’s opinion. While I will readily admit to being extremely foolish from time to time, I would hope that most bloggers would have sufficient journalistic ethics to ensure that published blog comments accurately reflect their original contents. I am a huge believer in free speech, but I recognize that there’s a big difference between saying, “President Obama’s social policies are lacking,” and “President Obama is a deranged pederast hell-bent on imposing his twisted views and perversity upon us hard-working citizens.” Everyone has the right to exercise free speech, but common sense dictates exercising some restraint.

I am considering starting my own blog to cover political topics. That seems like a better solution than blogging about politics on a blog called ‘ixvivxi’–doesn’t it? I mean, sure, ‘ixvivxi’ is a fine name for a market data blog, but for political topics–that would be a little pretentious I think. I’m not sure what a good name would be… But if you’ll indulge me for the moment, I’ll state some of my political beliefs, as they apply to economics.

My core philosophy not all that different from libertarianism, but I have a metaphysical disagreement: The core libertarian philosophies state that the universe is non-deterministic, and it’s actually deterministic. This is really a rather minor disagreement. In the absense of evidence to the contrary (and sound reasoning) libertarian philosphy should prevail. Evidence is never perfect, and there are limits to what reason can accomplish. Does this not make sense?

Generally, governments should do what is most popular, but on rare occasion, governments must take unpopular action to serve the common good. In a perfect world, where everyone were well-educated and altruistic, that wouldn’t be necessary, but we don’t live in a perfect world. Fortunately, voters are really quite competent at sorting out issues relating to economic freedom and social justice, and usually it’s better if the voters seek to serve themselves anyway.

Hume had some great theories, but the gold standard has gone the way of the dodo. Smith had some great theories, and so did Ricardo. Keynes wasn’t so bad, although the particulars of some of his theories defy good sense, and usually it’s the public sector that causes a need for government intervention, not the private sector. On rare occasions when I have written about Marx, I generally have looked quite unfavorably upon his conclusions, but not everything Marx wrote was a pile of schizophrenic drivel. The economics of the early Soviets were really not as bad as often portrayed (though their policies were awful), but there’s a simple Ricardian explanation as to why communism ultimately failed. Maybe in a hundred years, the world will have sufficient knowledge and social structures in place for a fully planned economy. I’m doubtful that the world will be ready for it in our lifetimes, and it might even be several hundred years before communism can beat the free markets, but I’m pretty sure that someday it will happen. Maybe in a hundred years. Maybe in a thousand years. Maybe in ten thousand years. Until that day comes, classical (a.k.a. liberal) economics is the bill of the day. I like free markets and competition, equality, and for the most part, non-intervention.

It’s pretty clear that exponential growth eventually outstrips polynomial growth. Malthus discussed some potential ramifications; it’s really a pretty interesting set of theories. Of course, polynomial growth can closely approximate exponential growth over some span, as can sigmoidal growth. Growth experiences many shorter term fluctuations, and humankind isn’t running out of food any time soon.

Though many of his theories were not complete, John Stuart Mill had some fantastic writings. I also like Von Neumann, who invented game theory and the modern computer, and I try to keep up with new economics research.

Most of the time (but not always), when government intervention in the economy is required, it’s a result of prior government intervention in the economy. There are a few exceptions, of course. In particular, government should break up monopolies. Government should police markets. For the most part, it’s better for the government to stay out of the economy, but government can play an important role in overcoming negative externalities and capturing positive externalities.

Well, that’s about all you’re likely to see about politics on this blog, but look forward to more on the markets, in the future.

Researchers commonly find that the best way to swim through a sea of data and find practical answers is to use statistical modeling. Some of the more interesting statistical models include a class called estimators. An estimator is a statistical model that produces an estimate for a quantity. Estimator qualities often desired by researchers include:

1. Efficiency – the quality of an estimator that best estimates the desired quantity with the data available. An estimator which requires more data to estimate the desired quantity than another estimator is said to be less efficient. An estimator which produces the most precise estimates possible given the data is said to be sufficient. A related concept is completeness, the quality of an estimator that makes the best estimates possible given the data. The difference between complete and sufficient estimators is subtle; for example, a complete estimator may be more accurate, while a sufficient estimator may be more precise. In many, if not most, circumstances, complete estimators are sufficient estimators and vice versa.
2. Consistency – the capacity of the estimator to correctly estimate the quantity of interest, given sufficient data (this may require limitless quantities of data).
3. Robustness – capability to avoid bias from outliers and dispersion. Note: robust estimators are often biased.
4. Unbiasedness – freedom from bias. There are numerous types of bias. Generally, an estimator may be said to be unbiased if the expectation of its estimates is equal to the quantity of interest and the distribution is symmetric. Some estimators are asymptotically consistent yet biased in finite sample sizes. Failure to meet model assumptions may induce bias. Some estimators are biased by shrinkage. Systematic model selection can induce bias without great care. Robustness assumptions can in some cases lead to bias. The bias of an estimator can often be estimated theoretically or empirically; unbiased estimates of estimator bias may enable bias-corrected estimation. Bias is not *always* a bad thing–in some cases, an estimator may be biased yet produce more precise estimates than a similar unbiased estimator–but unbiasedness is certainly a lofty goal.
5. Precision – while this term is rarely formally defined with respect to estimators, it generally denotes that quality of an estimator that gives it low probable error.

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Generally speaking, investors are interested in achieving a balance between risk and return. Different investors have different attitudes towards risk, different goals and different quantities of assets–and thus require a different balance of risk and reward.

One technique commonly used in decision theory to quantitatively model attitudes towards risk is to construct a utility curve. According to Howard Raiffa, utility curves can be constructed from individual subjective beliefs on probability and reward. For example, as an individual, you might consider the maximum you would be willing to pay for an investment with a 1/10 chance of returning $100,000 and a 9/10 chance of returning nothing. With no risk aversion (nor risk premium), such an investment would rationally be worth $10,000. Of course, most people have some degree of risk aversion and thus would not be willing to pay that much for such an investment. Indeed, two perfectly rational individuals with different attitudes towards risk would likely not pay the same price. One might pay $5,000; another might pay $9,000. Yet another might be willing to pay virtually nothing for the same investment. You could similarly consider how much you would be willing to pay for an investment with a 2/10 chance of returning $100,000 and an 8/10 chance of returning nothing, 3/10, 4/10 and so on up to 10/10. Of course, it can be difficult to obtain accurate estimates of subjective beliefs of others in this manner; structured interviews based around the notion of even swaps can help to overcome this difficulty.

Once you have formalized your subjective beliefs in such a manner, based on present circumstances, you can fit a utility curve which reflects your attitudes towards risk and reward. There are various methods that can be used for this purpose, and there is some debate over which is best. Exponential utility has some favorable mathematical properties, but alternatives are worth considering.

The ideal portfolio, based on an individual’s attitudes towards risk, would be that which maximizes the value obtained by integrating across potential returns and his utility curve.