A Fundamental Confusion About the Nature of Wealth

Via The Economist:

People nevertheless use the stockmarket as a barometer of economic health. So a rise in equity markets can be (and has been) seen by governments and central bankers as evidence that the economy is headed in the right direction. That can lead to policy mistakes, such as a lax monetary stance, and further irrational exuberance.

Housing is more complicated than the stockmarket since people get utility from their homes (shelter, relaxation) while simultaneously treating them as assets. Even so, a rise in house prices that outpaces GDP growth does not make a society richer. Instead, all that is achieved is a transfer of wealth from first-time buyers to retirees exiting the property market.

In theory house prices can rise faster than GDP for a while if citizens decide to devote more of their incomes to housing services (for example, they may prefer a bigger flat to a bigger car). In practice it is hard to disentangle such structural shifts from the speculation that is prominent in all property booms.

It is the link between speculation and asset prices that explains this crisis. The ability to borrow money to buy assets fuelled the rise in asset prices. And the wealth effect of higher prices persuaded those in English-speaking countries to borrow money to sustain consumption.

Not long ago the BBC transmitted a programme about credit-card use. One man said he felt “wealthier” because he was given a credit-card limit of £5,000 ($8,000). Of course, once he used the card he was poorer. Not only did he have to repay the £5,000, but he had to service a double-digit interest rate as well. Similarly those who buy an overvalued asset with borrowed money have not made themselves richer but poorer.

Read the full article here

Trust Behavior: The Essential Foundation of Securities Markets

H/T to Simoleon Sense:

Here’s the most important lesson from this paper (via ssrn):

“This suggests another fundamental difference between rational expectations investors and trusting investors. Where the former look to “the shadow of the future,” the latter care about “the shadow of the past.” Put differently, a rational expectations investor expects others to exploit her whenever possible. Accordingly she will always be forward-looking, trying, just as a chess player might, to anticipate other players’ opportunistic future moves. In contrast, trusting investors look to the past. If someone or something has always behaved in a particular way in the past, trusting investors assume that that person or thing will continue to behave similarly in the future, without worrying too much about understanding what drives the behavior in question.”

Click Here To Learn About The Role Of Trust In Securities Markets

Abstract (Via SSRN)
Evidence is accumulating that in making investment decisions, many investors do not employ a ‘rational expectations’ approach in which they anticipate others’ future behavior by analyzing their incentives and constraints. Rather, many investors rely on trust. Indeed, trust may be essential to a well-developed securities market. A growing empirical literature investigates why and when people trust, and this literature offers several useful lessons. In particular, most people seem surprisingly willing to trust other people, and even institutions like ‘the market,’ in novel situations. Trust behavior, however, is subject to history effects. When trust is not met by trustworthiness but instead is abused, trust tends to disappear. These lessons carry significant implications for our understanding of modern securities markets.

Great Introduction (Via SSRN)

Burt Ross graduated from Harvard University in 1965. After working several years as a stockbroker, he ran for and was elected mayor of Fort Lee, New Jersey. Then Ross turned to commercial real estate. In 2003, he decided to sell some of his buildings and invest the proceeds, which amounted to more than five million dollars. Ross thought he was prepared for retirement. At least, he thought he was prepared until December 11, 2008, when he learned that his nest egg–which he had invested almost entirely in funds managed by the now-infamous Ponzi schemer Bernard Madoff– was gone. (Pulliam, 2008)

Read the Paper Here

Wall Street’s Math Wizards Forgot a Few Variables

From the NYT:

IN the aftermath of the great meltdown of 2008, Wall Street’s quants have been cast as the financial engineers of profit-driven innovation run amok. They, after all, invented the exotic securities that proved so troublesome.

But the real failure, according to finance experts and economists, was in the quants’ mathematical models of risk that suggested the arcane stuff was safe.

The risk models proved myopic, they say, because they were too simple-minded. They focused mainly on figures like the expected returns and the default risk of financial instruments. What they didn’t sufficiently take into account was human behavior, specifically the potential for widespread panic. When lots of investors got too scared to buy or sell, markets seized up and the models failed.

That failure suggests new frontiers for financial engineering and risk management, including trying to model the mechanics of panic and the patterns of human behavior.

“What wasn’t recognized was the importance of a different species of risk — liquidity risk,” said Stephen Figlewski, a professor of finance at the Leonard N. Stern School of Business at New York University. “When trust in counterparties is lost, and markets freeze up so there are no prices,” he said, it “really showed how different the real world was from our models.”

In the future, experts say, models need to be opened up to accommodate more variables and more dimensions of uncertainty.

The drive to measure, model and perhaps even predict waves of group behavior is an emerging field of research that can be applied in fields well beyond finance.

Financial markets, like online communities, are social networks. Researchers are looking at whether the mechanisms and models being developed to explore collective behavior on the Web can be applied to financial markets. A team of six economists, finance experts and computer scientists at Cornell was recently awarded a grant from the National Science Foundation to pursue that goal.

“The hope is to take this understanding of contagion and use it as a perspective on how rapid changes of behavior can spread through complex networks at work in financial markets,” explained Jon M. Kleinberg, a computer scientist and social network researcher at Cornell.

Read the full article here

Ritholtz: Beware of Naive Contrarianism

Barry Ritholtz has a great post on being contrarian:

One thing you should consider when betting against the crowd: They tend to be right most of the time. There are a several things I disagree with in Surowiecki’s The Wisdom of Crowds, but the basic idea that crowds can determine outcomes is undeniable.

Indeed, markets are essentially the net result of the behavior of crowds. When asked why stocks were going down, the old trading desk joke is “More sellers than buyers.” That is as good a definition of a crowd as I’ve seen.

To better explain contrary thinking, I like to describe Wall Street and Markets as a sports stadium filled with fans. The better the team does, the louder the crowd cheers. The louder they cheer, the better the team does. Hence, markets have a large degree of self-fulfilling prophecy in the way they respond to crowd behavior.

Call it what you like — sentiment, reflexivity, feedback loop — for most of the time, the crowd not only determines market direction, IT IS market direction.

The secret to being a true contrarian is identifying when this excited (but orderly) crowd of cheering fans becomes a an unruly mob; Determining the point at which the fanatics become hooligans. Not throwing paper cups on the court, but overturning cars; When the Wisdom of Crowds becomes the Madness of Crowds.

That is when you short a raging bull market, buy into a crash. You hold your nose and make the purchase.

You can read the full post here.

Sentiment Analysis – Mining the Web for Feelings, Not Facts

From NYT

Computers may be good at crunching numbers, but can they crunch feelings?

The rise of blogs and social networks has fueled a bull market in personal opinion: reviews, ratings, recommendations and other forms of online expression. For computer scientists, this fast-growing mountain of data is opening a tantalizing window onto the collective consciousness of Internet users.

An emerging field known as sentiment analysis is taking shape around one of the computer world’s unexplored frontiers: translating the vagaries of human emotion into hard data.

This is more than just an interesting programming exercise. For many businesses, online opinion has turned into a kind of virtual currency that can make or break a product in the marketplace.

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