Steve Jobs: The Most Important Thing in Life

via Farnam Street

Well said, now its up to you to take the first step….

When you grow up, you tend to get told that the world is the way it is and your life is just to live your life inside the world, try not to bash into the walls too much, try to have a nice family life, have fun, save a little money.

That’s a very limited life. Life can be much broader, once you discover one simple fact, and that is that everything around you that you call life was made up by people that were no smarter than you. And you can change it, you can influence it, you can build your own things that other people can use. Once you learn that, you’ll never be the same again.

And the minute that you understand that you can poke life and actually something will, you know if you push in, something will pop out the other side, that you can change it, you can mold it. That’s maybe the most important thing. It’s to shake off this erroneous notion that life is there and you’re just gonna live in it, versus embrace it, change it, improve it, make your mark upon it.

I think that’s very important and however you learn that, once you learn it, you’ll want to change life and make it better, cause it’s kind of messed up, in a lot of ways. Once you learn that, you’ll never be the same again

Steve Jobs Solved the Innovator’s Dilemma

Nature Physics: Complex Networks in Finance

Complex networks in finance

The 2008 financial crisis has highlighted major limitations in the modelling of financial and economic systems. However, an emerging field of research at the frontiers of both physics and economics aims to provide a more fundamental understanding of economic networks, as well as practical insights for policymakers. In this Nature Physics Focus, physicists and economists consider the state-of-the-art in the application of network science to finance.

While I haven’t gotten my hands on a copy of the issue yet, it sounds like there is some very interesting research going on into using network, control and ecology theory to analyze and improve financial networks.

For example, from the Physics of Finance blog:

-“The power to control,” by Marco Galbiati, Danilo Delpini and Stefano Battiston, looks at research extending control theory to complex networks.
-“Reconstructing a credit network,” by Guido Caldarelli, Alessandro Chessa, Andrea Gabrielli, Fabio Pammolli and Michelangelo Puliga, looks at “theoretical ecology, where it was once thought (40 years ago) that more complexity in an ecology should generally confer stability. Later work suggested instead that complexity (at least too much of it) tends to breed instability. According to a number of recent studies, the same seems to be true in finance”

Wired also did an interesting article on preventing network failures:

When damage to a system reaches a “critical point,” Stanley, Havlin and their colleagues find that the failure of one more node drops all the network clusters to zero, instantly killing connectivity throughout the system. This critical point will vary depending on a system’s architecture. In one of the team’s most realistic coupled-network models, an outage of just 8 percent of the nodes in one network — a plausible level of damage in many real systems — brings the system to its critical point. “The fragility that’s implied by this interdependency is very frightening,” Stanley said.

I wanted to also mention that Coursera is offering a course on social network analysis for those that want to get an introduction to network theory.

Social Network Analysis

In the first week we visualized our facebook networks:

Emerging Markets (EEM): Pullback to breakout point?

This looks like an interesting setup. EEM has pulled back to its previous breakout point which has acted as support. It might be worth a trade with the stop. On the other hand, MarketCompass did some analysis recently saying the long term trend in emerging markets has deteriorated enough to be cautious. It all depends on your risk management.

Silver looks interesting here too, coiled like a spring.

More behavioral quotes from Quantitative Value

Continueing with my earlier post, there were a few more quotes worth mentioning.

Also check out CXO Advisory’s A Few Notes on Quantitative Value

On self managed vs managed account returns:

What happened? The self-managed accounts, where clients could choose their own stocks from the preapproved list and then exercise discretion about the timing of the trades, slightly underperformed the market. An aggregation of all self-managed accounts for the two-year period showed a cumulative return of 59.4 percent after all expenses, against the 62.7 percent performance of the S&P 500 over the same period. The aggregated professionally managed accounts returned 84.1 percent after all expenses over the same two years, beating the self-managed accounts by almost 25 percent (and the S&P by well over 20 percent). For a two-year period, that’s a huge difference. It’s especially so since both the self-managed accounts and the professionally managed accounts chose investments from the same list of stocks and followed the same basic game plan. People who self-managed their accounts took a winning system and used their judgment to eliminate all the outperformance and then some. Greenblatt has a few suggestions about what caused the underperformance, and they are related to behavioral biases.

What did investors do wrong?

The investors reliably and systematically avoided the best performers. Greenblatt says that stocks are often depressed for reasons that are well known.

Second, the self-managed investors tended to sell after periods of bad performance—either the strategy underperformed for a period of time, or the portfolio simply declined (regardless of whether the self-managed strategy was outperforming or underperforming the declining market)—and they tended to buy after periods of good performance.

On using checklists to simplify decision making:

We increase the complexity in an effort to generate better results, but increased complexity is a double-edged sword. More steps in the model means more opportunities to make a mistake. How can we create a more complex investment process and expect to maintain discipline when investors have a hard time sticking to a simple strategy like the Magic Formula? We next introduce the concept of a checklist, which is a simple way to break a necessarily complicated process into manageable pieces that can be repeated without errors and with a high success ratio.

Very interesting stuff indeed. In a future post we will look at how personality type factors into your optimal investing method as well….

Bears attack Canada

There have been a few articles posted recently attacking Canada’s economic position.

Is it time to short canada?
why Canada is in trouble
Canada housing cloud cast over Carney

Bonddad Blog summarized the reasons for future economic trouble:

1.) They depend on energy exports to the US. With the US now ramping up production, we need less Canadian energy.
2.) A strong currency has curbed exports.
3.) The housing market is overvalued
4.) Unit labor costs in manufacturing are high
5.) Mexico is now the leading exporter to the US
6.) They have a large current account deficit.

I doubt the need for our energy will ever go away, but I am concerned about the housing market and the lack of competitiveness in Canada. The credit agencies have taken the step to downgrade Canadian banks who are at risk:

Worries about Canada’s house prices and rising consumer debt prompted Moody’s, the rating agency, to cut the credit ratings of six of the largest Canadian banks last month.

Let’s take a look at the problem, a large run-up in housing prices caused primarily by a large run-up in debt:

Source: CIBC

As housing prices have increased, the ratio of household debt to income has increased in lockstep. The question is whether or not this is sustainable and how vulnerable is the debt structure? These questions are not easy to answer given public information. Lets take a look at a longer view of Canadian house prices:

Source: The Economic Analyst / CREA

Here we see a run-up in the late 80’s followed by a period of flat prices throughout the 90’s and finally the large increase in house prices in the last decade. I wasn’t able to find the chart, but the Toronto market was the major cause of the run-up in the 80’s and subsequently corrected.

If there is a bubble and it does unravel, fast or slow, what can we expect in terms of economic repercussions?

Source: The Economic Analyst

As we can see residential construction makes up more economic activity in Canada than the US at the peak, and now there is huge gap between the two. It seems as though Canada is very vulnerable to a housing related slowdown.

Source: The Economic Analyst

A large percentage of the labour force is also tied up in construction activities tied to the boom.

Source: The Economic Analyst

The percentage is even larger for real estate related industries. Here we see the percentage of labour force in the FIRE economy. FIRE meaning Finance Insurance and Real Estate (incl Construction). FIRE economy sectors all thrive on asset-price inflation financed by debt. If the housing market corrects, much of this economic activity will be at risk.

How at risk are individual housing markets in Canada?

Source: CIBC

We can see here that the larger the % of non-conforming mortgages in a city, the larger the subsequent correction due to the markets vulnerability. CIBC has made the argument that because the percentage of non-conforming mortgages in Canada is lower than in the US, the Canadian housing market is less vulnerable to a correction of similar magnitude. The problem with this analysis is that definitions on non-conforming are inconsistent across borders.

I speculate that the correction (peak-to-trough drawdown) we saw in individual US cities was related to the run-up we saw in prices. I believe that this relationship is stronger than the non-conforming mortgage relationship or that you can predict whether a city has a large percentage of non-conforming mortgages from its price runup.

Here we see that there is a statistical relationship between the run-up in housing prices in a city and their subsequent correction. Unfortunately I don’t have the non-conforming mortgage data to test the full hypothesis. I also highlighted that New York, Boston and Washington had relatively small corrections given their run-ups.

For much more coverage on the Canadian housing bubble check out Ben Rabidoux’s blog The Economic Analyst . One of his presentations, courtesy of the LePoidevin Group, is embedded below:

A few notes on 'Quantitative Value'

I’m just reading Quantitative Value by Wesley Gray and Tobias Carlisle. It’s very interesting and I hope to explore some of the results, but for now I thought I would highlight a few quotes pertaining to behavioral biases and modelling.

On investors chasing returns in the best performing mutual fund:

In the decade to December 31, 2009, the Wall Street Journal reported that the best-performed U.S. diversified stock mutual fund according to fund-tracker Morningstar was Ken Heebner’s CGM Focus Fund. Over the decade, the fund had gained 18.2 percent annually, beating its closest rival by 3.4 percent per year, which is exceptional. The typical investor in Heebner’s fund, however, lost 11 percent annually. Investor returns, also known as “dollar-weighted returns,” take into account the capital flowing into and out of the fund as investors buy and sell. The investor returns were lower than the fund’s total returns because investors bought into the fund after it had a strong run and then sold as it hit bottom. Heebner’s fund surged 80 percent in 2007, and then investors poured in $2.6 billion. The following year, the fund sunk 48 percent, and investors yanked out more than $750 million. Said Heebner32: “A huge amount of money came in right when the performance of the fund was at a peak. I don’t know what to say about that. We don’t have any control over what investors do.” This behavior caused the investor returns in Heebner’s fund to be among the worst of any fund tracked by Morningstar. Amazingly, this means that the worst investor returns were found in the decade’s best-performed fund. We are each our own worst enemy.

On experts under-performing simple quantitative models:

The reliance on heuristics and prevalence of biases is not restricted to laymen. Experts are also subject to the same biases when reasoning intuitively. In his book, Expert Political Judgment,36 Philip Tetlock discusses his extensive study of people who make prediction their business—the experts. Tetlock’s conclusion is that experts suffer from the same behavioral biases as the laymen. Tetlock’s study fits within a much larger body of research that has consistently found that experts are as unreliable as the rest of us. A large number of studies have examined the records of experts against simple statistical model, and, in almost all cases, concluded that experts either underperform the models or can do no better. It’s a compelling argument against human intuition and for the statistical approach, whether it’s practiced by experts or nonexperts.3

On simple models continuing to outperform the judgments of the best experts, even when those experts are given the benefit of the outputs from the simple model:

In many disciplines, simple quantitative models outperform the intuition of the best experts. The simple quantitative models continue to outperform the judgments of the best experts, even when those experts are given the benefit of the outputs from the simple quantitative model. James Montier, an expert in behavioral investing, discusses this phenomenon in his book, Behavioral Investing: A Practitioners Guide to Applying Behavioral Finance.….. Goldberg found that his model applied out-of-sample accurately predicted the final diagnosis approximately 70 percent of the time. He then gave MMPI scores to experienced and inexperienced clinical psychologists and asked them to diagnose the patient. Goldberg found that his simple model outperformed even the most experienced psychologists. He ran the study again, this time providing the clinical psychologists with the simple model’s prediction. Goldberg was shocked. Even when the psychologists were provided with the results of the model, they continued to underperform the simple model. While the performance of the psychologists improved from their first attempt without the benefit of the model, they still didn’t perform as well the model did by itself. Montier draws an interesting conclusion from the results of the study: “[As] much as we all like to think we can add something to the quant model output, the truth is that very often quant models represent a ceiling in performance (from which we detract) rather than a floor (to which we can add).”39

This effect happens across multiple fields:

How can it be that simple models perform better than experienced clinical psychologists or renowned legal experts with access to detailed information about the cases? Are these results just flukes? No. In fact, the MMPI and Supreme Court decision examples are not even rare. There are an overwhelming number of studies and meta-analyses—studies of studies—that corroborate this phenomenon. In his book, Montier provides a diverse range of studies comparing statistical models and experts, ranging from the detection of brain damage, the interview process to admit students to university, the likelihood of a criminal to reoffend, the selection of “good” and “bad” vintages of Bordeaux wine, and the buying decisions of purchasing managers.

Even if we know our biases, it doesn’t help our decision making:

• Montier says, “Even once we are aware of our biases, we must recognize that knowledge does not equal behavior. The solution lies in designing and adopting an investment process that is at least partially robust to behavioral decision-making errors.”

Also check out TurnKey Analyst’s series on the book