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

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

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.

• The market has gone nowhere over the last three trading days despite what was being construed on bubblevision as unrelenting good news (home prices, house sales, consumer confidence, durable goods orders, Bernanke’s reappointment) — any other time in the last five months, these “green shoots’ would have turned the equity screens green.  Could be a sign that a lot of good news is already being discounted.
• While it is often reported that over 70% of S&P 500 companies beat their 2Q earnings estimates, only 46% did so meaningfully.  Not only that, but only 23% significantly beat their top-line revenue projections.  See page C2 of the WSJ (The Rally Revenue Forgot).
• Leading stocks have been seeing reduced trading volumes of late.
• VIX futures and the put/call ratio on the S&P 500 have shot upwards in the past few sessions.
• The ECRI leading economic indicator fell 0.4% in the latest week, the first decline in six weeks and only the second falloff in the past eighteen.
• Sentiment is far too bullish — to an extreme level.  A sentiment index quoted in today’s NYT business section is now 89% bullish, the same as it was in October 2007; at the March lows, it was sitting at 2%.  See Some Once-Bullish Analysts See an End to Market Rally on page B1 of the Monday NYT.
• Corporate insiders sold nearly 31 times more stock than they bought in August (TrimTabs data) — the long run average is 7x and it was 2x at the lows (apparently a heck of a buying opportunity at that time).
• Small-cap stocks are down for back-to-back weeks and Chinese equities are on a four-week losing streak.  Finally, the market has turned in the precise same 50% advance over the same 117 time period that it enjoyed coming off the 1929 lows — that rally ended despite all the hype at the time and the market lost more than 50% in the ensuing year.
• Of course, there are the negative seasonals too — since 1950, the S&P 500 is down 1% in September, on average, and has declined twice as often as it has rallied during the month.
• The H1N1 flu is a clear obstacle.  This is a time when psychology becomes a factor — a USA Today/Gallup poll shows that over one-third of adults are now worried about an outbreak, doubling since May.
• Commercial real estate defaults loom very large on the outlook and have emerged as a top priority now for the Fed — see page A2 of the WSJ.
• Geopolitical risks — see the editorial comment on page A12 of today’s WSJ (Israel, Iran and Obama).
• Protectionist sentiment is another and you may want to circle September 17 on your calendar because that is the deadline for Obama’s first true test on this score — to rule on the ITC’s recommendation that the White House slap on a 55% tariff on imports of Chinese-made tires.

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.

Translating the slippery stuff of human language into binary values will always be an imperfect science, however. “Sentiments are very different from conventional facts,” said Seth Grimes, the founder of the suburban Maryland consulting firm Alta Plana, who points to the many cultural factors and linguistic nuances that make it difficult to turn a string of written text into a simple pro or con sentiment. “ ‘Sinful’ is a good thing when applied to chocolate cake,” he said.

The simplest algorithms work by scanning keywords to categorize a statement as positive or negative, based on a simple binary analysis (“love” is good, “hate” is bad). But that approach fails to capture the subtleties that bring human language to life: irony, sarcasm, slang and other idiomatic expressions. Reliable sentiment analysis requires parsing many linguistic shades of gray.

“We are dealing with sentiment that can be expressed in subtle ways,” said Bo Pang, a researcher at Yahoo who co-wrote “Opinion Mining and Sentiment Analysis,” one of the first academic books on sentiment analysis.

To get at the true intent of a statement, Ms. Pang developed software that looks at several different filters, including polarity (is the statement positive or negative?), intensity (what is the degree of emotion being expressed?) and subjectivity (how partial or impartial is the source?).

For example, a preponderance of adjectives often signals a high degree of subjectivity, while noun- and verb-heavy statements tend toward a more neutral point of view.

As sentiment analysis algorithms grow more sophisticated, they should begin to yield more accurate results that may eventually point the way to more sophisticated filtering mechanisms. They could become a part of everyday Web use.

“I see sentiment analysis becoming a standard feature of search engines,” said Mr. Grimes, who suggests that such algorithms could begin to influence both general-purpose Web searching and more specialized searches in areas like e-commerce, travel reservations and movie reviews.

Ms. Pang envisions a search engine that fine-tunes results for users based on sentiment. For example, it might influence the ordering of search results for certain kinds of queries like “best hotel in San Antonio.”

As search engines begin to incorporate more and more opinion data into their results, the distinction between fact and opinion may start blurring to the point where, as David Byrne once put it, “facts all come with points of view.”

By ALEX WRIGHT of the NYT