Sexual harassment cases are nothing new on Wall Street, but CNBC has uncovered new details of one of the most salacious cases to hit a big trading house in a long time.

The case involves a sexual harassment lawsuit filed by a Andrew Z. Tong, a former junior trader at SAC Capital, the powerful Greenwich, Conn., hedge fund, against one of SAC’s top producers, a trader named Ping Jiang.

A New York State judge has sealed the case and sent the lawsuit into arbitration, where both sides would battle it out in private. He even cancelled oral arguments that were scheduled for Thursday following an appeal by Tong’s lawyers, who want the case to remain in state court.

The judge said he sealed the details of Tong’s allegations contained in the lawsuit because it is not in the public interest to disclose the salacious nature of the complaints. CNBC has learned the suit includes the following allegations made by Tong against Jiang:

• After being hired at SAC, Tong alleges that Jiang came to him and told him he had a trading method in which his traders must not be too aggressive; that traders must be more effeminate and to do so, he directed Tong to begin taking female hormones.
• Tong says he then took the female hormones that he bought on the black market.
• Tong then alleges he suffered emotional and physical distress. The hormones, he says, caused him to begin wearing women’s clothes. He also could not perform sexually with his wife, who wanted to have a baby.
• Tong says the sexual harassment included sexual relations between the two men.

While the salacious details will certainly generate a lot of press, could it really be that giving your male traders estrogen Kool-Aid would actually improve their market performance? Let’s review the research.

In many contexts, men have been found to be systematically more overconfident than women. Among these is “Gender and Overconfidence” by Bengtsson, Persson, and Willenhag. The authors studied a Stockholm University economics exam which has an optional extra credit question, which only applies to a student’s grade if they did sufficiently well on the rest of the exam. While women overall are more likely to pass the exam, only 83.8% of them attempt the extra credit question, compared to 87.1% of men.

This type of overconfidence can induce many decision making biases which can depress investment returns. Among these are a tendency to trade too often, generating excessive trading costs, and attributing random market movements with one’s own predictive skill, which impairs learning. In a study of trading activity by 35,000 households with a large brokerage house, Barber and Odean report that due to overconfidence, men trade 45% more than women. This excessive trading reduces men’s average net returns by 2.65% a year, compared to a 1.72% reduction for women.

Let’s do a thought experiment. SAC Capital has about $14 billion in assets under management, and reportedly returned 34% in 2006. While the type of trading activity going on at SAC is far different from the household brokerage trades in the dataset studied above, let’s assume for a moment that SAC could capture the 0.93% male-female performance difference if only its traders all behaved more like women. This small performance edge would improve the fund’s returns by OVER A BILLION DOLLARS in five years.

Perhaps it’s no mystery then why a top trader would want his underlings to trade more like women. Maybe the real question then is why a hedge fund would think it was better off taking the substantial legal risk of forcing employees to take drugs against their will, as opposed to working to hire actual women — overconfidence, perhaps?

Read more:

• CNBC: Detail emerge in SAC Capital Sex Harassment Case
  
• Gender and Overconfidence, by Claes Bengtsson, Mats Persson and Peter Willenhag
• Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment, by Brad M. Barber and Terrance Odean

Earlier:

• Mars and Venus on Wall Street
• All posts tagged “gender”
• All posts tagged “overconfidence”

Many social programs focused on improving health in developing countries require active participation .Unlike a one-time vaccine, products ranging from condoms to insecticide must be used regularly in order to have any health benefit. The crucial role of household behavior in making such products work has led practitioners to search for new ways to ensure regular use among households receiving much-needed health products.

Shapiro went to Zambia to study these issues in the context of the sale and usage of Clorin, a home water purification product. Two distinct effects were studied. First, does charging a higher price target distribution at those who are willing to pay more because they intend to use the product more? Second, does the act of paying more itself induce people to use the product more, due to the sunk cost effect, which might cause people to psychologically justify the purchase price through increased use?

In order to study whether charging more for Clorin results in greater use, the authors designed an experiment that would separate two effects of prices on product use. On the one hand, charging a higher price may help target distribution of the product to those who intend to use it most. On the other hand, the act of paying—or the amount paid—may exert a direct influence on use if households feel they must use a product to make the best use of the money they spent. These two effects—which the authors respectively call the “screening” and “psychological” effects of prices—combine to determine the effect of prices on product use.

The authors find strong evidence that higher prices screen out less intensive users of Clorin. For a given transaction price, increasing the offer price by 10 percent results in a 3.6 percent increase in reported use among buyers.

…

Put differently, the authors find that the screening and psychological effects allow a firm or government to achieve the same level of Clorin use while charging a higher price. This, in turn, means that the Clorin program can produce greater revenue, which can in turn be reinvested in advertising to promote use, or can be redirected to other valuable social programs.

Understanding how these effects work will be very important for organizations looking to maximize the effectveness of their public aid and wondering how much to charge for the services they provide.

Understanding the screening and psychological effects of prices is critical to resolving public policy debates over the appropriateness of user fees for access to social products and services.

Ashraf, Berry, and Shapiros findings have important implications for economics and psychology, as well as for private and public sector industries in which product use is an important consideration.

“Our findings offer a new way to think about the pricing controversy,” says Shapiro. “Charging higher prices for health products does have an obvious downside, which is that fewer people will get access, but the benefit is in targeting the distribution of the product to the people most likely to use it, as well as greater revenue for social programs. These issues need to be weighed against each other when making policy decisions about setting prices.”

Watch the video at the link below to hear Jesse describe his research in Zambia. He also discusses applications to media and advertising, in particular the question of whether giving a publication such as a newspaper away for free induces people to pay less attention to it, thereby reducing the value the audience has to potential advertisers.

Read more: The Economics of Pricing: Can Higher Prices Stimulate Product Use?

Since the Enlightenment, the main model of rational judgment in an uncertain world has been probability theory. The laws of probability, however, do not deal with the constraints in time, information, memory, and other resources that are characteristic of the decision making of actual humans (and machines). As a consequence, the underlying vision of rationality has been termed “unbounded rationality”—an omniscient and omnipotent fiction with little or no regard for the limitations in time, knowledge, and computational capacities that humans face. To make rationality more human than God-like, the concept of “bounded rationality” has been proposed. The key difference between unbounded and bounded rationality is the concept of limited search, to be defined by a stopping rule. The vision of bounded rationality, however, is not of one kind.

Rationality comes in many forms. The first split in Figure 2 separates models that assume the human mind has essentially unlimited demonic or supernatural reasoning power from those that assume we operate with only bounded rationality. There are two species of demons: those that exhibit unbounded rationality, and those that optimize under constraints. Optimization under constraints means optimization given various constraints, that is, limited resources such as attention, time, money, or information. The vision of constrained optimization is that minds would calculate the optimal trade-off between the benefits and costs of further search at regular time intervals, and stop search when the costs would outweigh the benefits. The rule “stop search when costs > benefits” sounds plausible at first glance, but a closer look reveals that this… can demand even more knowledge and computation than unbounded rationality.

There are also two main forms of bounded rationality: satisficing heuristics for searching through a sequence of available alternatives, and fast and frugal heuristics that use little information and computation to make a variety of kinds of decisions.

An example of an ignorance heuristic in action:

Let me illustrate the way this heuristic works with one example: Which US City Has More Inhabitants: San Diego or San Antonio? We posed this question to students at the University of Munich and the University of Chicago. The latter, who have a reputation for being among the most knowledgeable in the US, were correct 62% of the time. Yet 100% of the Germans got the correct answer 100% of the time. How did the Germans infer that San Diego was larger? All of the Germans had heard of San Diego, but many of them did not recognize San Antonio. They were thus able to apply the recognition heuristic and make a correct inference. The American students were not ignorant enough to be able to apply the recognition heuristic.

Read more: Fast and Frugal Heuristics: Simple, Adaptive Decision-Making Strategies Leverage Ignorance To Make Us Smart

The clever idea of travel sites seems to me not to discriminate on any information you provide but rather on information that you request and, crucially, how you request it. Specifically, they know if you have chosen to sort prices ‘lowest first’ or ‘highest first’. This information can be used to the companies’ advantage.

If you sort your search results so as to see the cheapest option first, you’re probably looking for a budget hotel and a good value break. To get your custom, the firm will have to be competitive at the lower end, with headline grabbing rates - ‘Prices from only £x!’. To get you to make them a little more margin, they’ll want to tempt you up the scale a little bit - ‘For only £10 extra you could upgrade to…’. So a pricing structure for this sort of customer would be cheaper for the same reason that student cinema tickets are cheaper: the customer is more sensitive to price.

Conversely, if you sort your search results ‘highest first’, you aren’t looking for a bargain break. You may still want a good deal, but you’ve already indicated that you’re willing to pay for a good holiday and money is not your main concern. So you’d expect prices here to be a little dearer, especially at the top end. Furthermore, you want these people to think that they wouldn’t be saving much going for a cheaper hotel, thus helping them rationalise their choice of a top hotel.

As retailers become increasingly sophisticated at leveraging the massive databases of customer information they already have, can more of these types of tactics be far behind? In many ways the internet is providing the laboratory that economists have never had–a tool for running experiments, which is giving a more thorough understanding of consumer behavior and a venue to gather empirical evidence for new and existing theories.

Read more: Price discrimination in online travel firms

via Marginal Revolution

I have seen in Richard Dawkins’ work many references to the difficulty people have, when looking at an animal, in accepting that it is not the product of a top-down design, but the result of a random process — more exactly the upper bound of a random process, in which (roughly, and only roughly) the most successful mutations tend to make it. Yet my problem is that when those who accept the evolutionary argument look at a computer, at a laser beam, at a successful drug, at a surgical technique, at the spread of a language, at the growth of a city, or at an commercial enterprise, they tend to fall for the belief that its discovery or establishment partook of some grand design. And, in hindsight, some “explanation” will be given as to why it happened: there was a plot — it could not have been an accident.

Alas, we are victims of the narrative fallacy — even in scientific research (but while we learned how to manage it in religion, and to some degree in finance, we do not seem to be aware of its prevalence in research). The pattern-seeking, causality producing machine in us blinds us with illusions of order in spite of our horrifying past forecast errors. I hold that not only discoveries are also largely the result of a random process, but that their randomness is even less tractable than, and not as simple as, biological evolution. While nature might produce milder form of stochasticity, the environment for manmade discoveries is governed by a far, far more severe, wilder form of processes, those called “fat tailed”.

Against what one might expect, this makes me extremely optimistic about the future in several selective research-oriented domains, those in which there is an asymmetry in outcomes favoring the positive over the negative — like evolution. These domains thrive on randomness. The higher the uncertainty in such environments, the rosier the future — since we only select what works and discard the rest. With unplanned discoveries, you pick what’s best; as with a financial option, you do not have any obligation to take what you do not like. Rigorous reasoning applies less to the planning than to the selection of what works. I also call these discoveries positive “Black Swans”: you can’t predict them but you know where they can come from and you know how they will affect you. My optimism in these domains comes from both the continuous increase in the rate of trial and error and the increase in uncertainty and general unpredictability. I have seen in Richard Dawkins’ work many references to the difficulty people have, when looking at an animal, in accepting that it is not the product of a top-down design, but the result of a random process — more exactly the upper bound of a random process, in which (roughly, and only roughly) the most successful mutations tend to make it. Yet my problem is that when those who accept the evolutionary argument look at a computer, at a laser beam, at a successful drug, at a surgical technique, at the spread of a language, at the growth of a city, or at an commercial enterprise, they tend to fall for the belief that its discovery or establishment partook of some grand design. And, in hindsight, some “explanation” will be given as to why it happened: there was a plot — it could not have been an accident.

Alas, we are victims of the narrative fallacy — even in scientific research (but while we learned how to manage it in religion, and to some degree in finance, we do not seem to be aware of its prevalence in research). The pattern-seeking, causality producing machine in us blinds us with illusions of order in spite of our horrifying past forecast errors. I hold that not only discoveries are also largely the result of a random process, but that their randomness is even less tractable than, and not as simple as, biological evolution. While nature might produce milder form of stochasticity, the environment for manmade discoveries is governed by a far, far more severe, wilder form of processes, those called “fat tailed”.

Against what one might expect, this makes me extremely optimistic about the future in several selective research-oriented domains, those in which there is an asymmetry in outcomes favoring the positive over the negative — like evolution. These domains thrive on randomness. The higher the uncertainty in such environments, the rosier the future — since we only select what works and discard the rest. With unplanned discoveries, you pick what’s best; as with a financial option, you do not have any obligation to take what you do not like. Rigorous reasoning applies less to the planning than to the selection of what works. I also call these discoveries positive “Black Swans”: you can’t predict them but you know where they can come from and you know how they will affect you. My optimism in these domains comes from both the continuous increase in the rate of trial and error and the increase in uncertainty and general unpredictability.

Taleb’s optimism about the value we can derive from uncertain environments has parallels with the use of real options to value and analyze the payoffs from different potential courses of action. We know from the Black-Scholes model in financial theory that the value of a financial option (e.g. a call or a put on a stock) increases with the volatility of the underlying equity. Real options are essentially investments which give one the opportunity, but not the necessity, of pursuing further courses of action down the road. Similarly to a financial option, a real option increases in value the more uncertain, or random, an environment we are operating in. It has become cliche at this point to describe the current global business environment as increasingly rapid and complex, but what we can learn from these generalizations is that the value of “keeping your options open” is ever increasing.

Real options sit at a rich crossroads between financial theory and decision theory; expect more discussion of real options here soon!

Read more: Nassim Taleb: The Birth of Stochastic Science

Trust plays a key role in many economic transactions, from buying a secondhand car to choosing a college. In the simplest version of the trust game, one player gives some money to another player, who invests it on his behalf and then decides how much to return to him and how much to keep. The more the first player invests, the more he stands to gain, but the more he has to trust the second player. If the players trust each other, both will do well. If they don’t, neither will end up with much money.

Fehr and his collaborators divided a group of student volunteers into two groups. The members of one group were each given six puffs of the nasal spray Syntocinon, which contains oxytocin, a hormone that the brain produces during breast-feeding, sexual intercourse, and other intimate types of social bonding. The members of the other group were given a placebo spray.

Scientists believe that oxytocin is connected to stress reduction, enhanced sociability, and, possibly, falling in love. The researchers hypothesized that oxytocin would make people more trusting, and their results appear to support this claim. Of the twenty-nine students who were given oxytocin, thirteen invested the maximum money allowed, compared with just six out of twenty-nine in the control group. “That’s a pretty remarkable finding,” Camerer told me. “If you asked most economists how they would produce more trust in a game, they would say change the payoffs or get the participants to play the game repeatedly: those are the standard tools. If you said, ‘Try spraying oxytocin in the nostrils,’ they would say, ‘I don’t know what you’re talking about.’ You’re tricking the brain, and it seems to work.”

… and this…

The results provide further evidence that reason and emotion often compete inside the brain, and it also helps explain a number of puzzling phenomena, such as the popularity of Christmas savings accounts, which people contribute to throughout the year. “Why would anybody put money into a savings account that offers zero interest and imposes a penalty if you withdraw cash early?” Cohen said. “It simply doesn’t make sense in terms of a traditional, rational economic model. The reason is that there is this limbic system that produces a strong drive. When it sees something it likes, it wants it now. So you need some type of pre-commitment device to make people save.”

Laibson and Brigitte Madrian, an economist at the Wharton School, have studied one such “pre-commitment device” for 401(k) plans, which deduct part of an employee’s earnings each month and invest them in stocks and bonds. Because the plans are often optional, many people fail to join them, even when their employers offer to match a portion of their contributions. Laibson and his colleagues have called for people to be automatically included in the plans unless they choose to opt out. At companies that have adopted such a policy, enrollment rates have increased sharply.

Reforming 401(k) plans is an example of “asymmetric paternalism,” a new political philosophy based on the idea of saving people from the vagaries of their limbic regions. Warning labels on tobacco and potentially harmful foods are similarly intended to keep subcortical structures in check. Neuroeconomists have suggested additional policies, including warning buyers of lottery tickets that their chances of winning are practically nonexistent and imposing mandatory “cooling off” periods before people make big-ticket purchases, such as cars and boats. “Asymmetric paternalism helps those whose rationality is bounded from making a costly mistake and harms more rational folks very little,” Camerer, Loewenstein, and three colleagues wrote in a 2003 issue of the University of Pennsylvania Law Review. “Such policies should appeal to everyone across the political spectrum.”

The results provide further evidence that reason and emotion often compete inside the brain, and it also helps explain a number of puzzling phenomena, such as the popularity of Christmas savings accounts, which people contribute to throughout the year. “Why would anybody put money into a savings account that offers zero interest and imposes a penalty if you withdraw cash early?” Cohen said. “It simply doesn’t make sense in terms of a traditional, rational economic model. The reason is that there is this limbic system that produces a strong drive. When it sees something it likes, it wants it now. So you need some type of pre-commitment device to make people save.”

Laibson and Brigitte Madrian, an economist at the Wharton School, have studied one such “pre-commitment device” for 401(k) plans, which deduct part of an employee’s earnings each month and invest them in stocks and bonds. Because the plans are often optional, many people fail to join them, even when their employers offer to match a portion of their contributions. Laibson and his colleagues have called for people to be automatically included in the plans unless they choose to opt out. At companies that have adopted such a policy, enrollment rates have increased sharply.

Reforming 401(k) plans is an example of “asymmetric paternalism,” a new political philosophy based on the idea of saving people from the vagaries of their limbic regions. Warning labels on tobacco and potentially harmful foods are similarly intended to keep subcortical structures in check. Neuroeconomists have suggested additional policies, including warning buyers of lottery tickets that their chances of winning are practically nonexistent and imposing mandatory “cooling off” periods before people make big-ticket purchases, such as cars and boats. “Asymmetric paternalism helps those whose rationality is bounded from making a costly mistake and harms more rational folks very little,” Camerer, Loewenstein, and three colleagues wrote in a 2003 issue of the University of Pennsylvania Law Review. “Such policies should appeal to everyone across the political spectrum.”

Read more: Mind Games: What neuroeconomics tells us about money and the brain

The implication for marketing executives is that they should de-emphasize designing, making, and selling would-be hits and focus instead on creating portfolios of products that can be marketed using real-time measurement of and rapid response to consumer feedback.

The aurhors recommend five measures for more effective marketing campaigns which take social network effects into account:

1. Increase the number of bets, and decrease their size
2. Focus on detection, measurement, and feedback
3. Follow through with flexible marketing budgets
4. Exploit naturally emerging social influence
5. Build flexibility into supply chains and contracts

Their results are based on academic work published earlier this year by Watts as well as Matthew Salganik and Peter Dodds: Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market

Read more: Marketing in an Unpredictable World, by Duncan J. Watts and Steve Hasker

UPDATE: I’ve heard from some readers that the Harvard Business Review link didn’t work for them. Here is an alternate link to the paper, hosted by Columbia: Marketing in an Unpredictable World

UPDATE 2: For discussion of a very similar “portfolio” approach to dealing with complex or uncertain environments, this time in the context of business strategy, see my earlier post Strategy in an Unknowable Universe

The premise of counter-trading by sophisticated investors “has been the main argument for why bubbles could not happen,” Nagel said in an interview. Yet, the study’s results importantly support recent theories of the limits of arbitrage. According to these theories, rational investors reasonably refuse to short or trade against even plainly overpriced securities if they believe most investors will continue to act irrationally, such that the security’s trading price will continue to rise. These, of course, are the very conditions of a market bubble.

“There is no evidence that hedge funds as a whole exerted a correcting force on prices during the technology bubble,” Nagel and Brunnermeier write. Indeed, “among the few large hedge funds that did resist the bubble], the manager with the least exposure to technology stocks—Tiger Management—did not survive until the bubble burst.” Nagel and Brunnermeier note in the study that Tiger Management was an example of a classically rational investor. Tiger declined to take major positions in technology stocks, believing them to be overpriced. While Tiger Management was proved right in the long run, its results fell far behind other funds that soared with the “irrational” approach of buying technology issues. Tiger was compelled to close up shop.

“The key to this is that if you feel you can predict what the irrational guys are doing, then it may be entirely rational to buy irrationally priced stocks,” Nagel said. In part, these possibilities arise because of time factors in hedging. Hedge traders generally are unwilling to hold short positions for a long period. Instead of betting on long-run reversal to fundamentals, they may prefer to follow short-run trends in the behavior of “noise traders,” as economists call them. “It seems that the hedge funds did exploit such a predictability during [the bubble],” noted Nagel.

The abstract in their own words:

The efficient markets hypothesis is based on the presumption that rational speculators would find it optimal to attack price bubbles and thus exert a correcting force on prices. We examine stock holdings of hedge funds during the time of the Technology Bubble on NASDAQ and find that the portfolios of these sophisticated investors were heavily tilted towards (overpriced) technology stocks. This does not seem to be the result of unawareness of the bubble: At an individual stock level, hedge funds reduced their exposure before prices collapsed, and their technology stock holdings outperformed characteristics-matched benchmarks. Our findings do not conform to the efficient markets view of rational speculation, but they are consistent with models in which rational investors can find it optimal to ride bubbles because of predictable investor sentiment and limits to arbitrage. Moreover, frictions such as short-sales constraints do not appear to be sufficient to explain why the presence of sophisticated investors failed to contain the bubble.

Read more:

• Stanford Research: Make Day Traders Act Rationally Rather Than Regulate Hedge Funds
• Hedge Funds and the Technology Bubble, by Markus Brunnermeier and Stefan Nagel

Treating Investors Like Customers

Seen from the perspective of the financial markets, a company’s ultimate product is its equity. So companies need to start applying to their shareholders the same kind of strategic disciplines they typically apply to customers. Treating investors more like customers does not mean employing misguided, and increasingly discredited, techniques for “managing earnings.” Nor does it mean that corporate executives should let investors determine business strategy any more than they should let customers determine product strategy. What it does mean: developing a detailed process for ensuring that a company’s strategy is informed by the perspectives and requirements of its investor base, and then working over time to create alignment between strategy and shareholders.

In marketing practice, customer segmentation is perhaps the cornerstone to creating any successful campaign. Segmentation is the process of surveying a pool of potential customers, segmenting them into manageable groups based on shared traits, analyzing those traits to understand what product attributes are important to each segment (e.g. style, convenience, price), and finally aligning your products and strategies with one or more of those segments. An extension of customer segmentation is that not all customers are equally valuable to a company. It is not uncommon for a minority of a company’s customers to generate the majority of their profits. What would it mean to apply the process of customer segmentation to one’s shareholders?

Just as some customers are more profitable than others, some investors are more attractive than others–whether because of their timeframe (long horizon, low churn), investment objectives (more in tune with future direction than past portfolio), or interdependence (insiders, employees, and alliance partners). Cultivating these aligned investors will help the company migrate toward an owner base that supports the long-term strategy and will reduce unnecessary volatility as short-term investors move into and out of the stock.

The presence of this type of misalignment between shareholders and corporate strategy raises some troubling questions related to market efficiency. How would such a misalignment arise? We could guess that investors have a poor understanding of a company’s strategic direction, but this is unlikely in the case of institutional investors. A more likely explanation is what I’ll call “strategy drift”, where investors and corporate executives had the same objectives at the time of purchase, but over time the positioning of the company has changed. Various factors, behavioral and otherwise, could cause institutional shareholders to maintain their holdings, at least in the short run, despite the misalignment.

In the end, BCG argues that shareholder value can be created directly by resolving these misalignments where they occur. Companies need to understand who their shareholders are, what attributes of the company’s stock they value, and if these values conflict with corporate strategy, either the strategy needs to shift, the shareholder base needs to be “migrated” towards a better fit, or a combination of both. This is an intriguing approach to value creation, which seems to run counter to orthodox financial theory.

Read more: BCG Perspectives: Treating Investors Like Customers

Harvard Business School Working Knowledge interviews professor John Gourville about his research into these psychological aspects of price setting. Here’s an excerpt:

Q: You talk about sunk cost, the idea that people will use a product or service more right after they pay for it. How can companies make this work for them?

A: Sunk costs are a curious bit of psychology. Economists say that attending to sunk costs is not rational—when considering whether to go to a play or attend a football game, the amount of money you spent on the tickets should be irrelevant to the decision to go. The only things that should matter are the costs not yet incurred, such as cost and hassle of driving, and the benefits to be consumed, such as the enjoyment of the game. At the same time, almost everyone pays attention to sunk costs. We go to plays or concerts that, in retrospect, we’d rather not go to simply because we have a $50 ticket in the pocket.

Similarly, one of my colleagues describes a person who pays $300 to join a tennis club, only to come down with tennis elbow. Nevertheless, he continues to play in spite of the pain, reasoning, “I don’t want to let my $300 go to waste.” And some people even count on their own irrationality and buy season tickets to a play or symphony series, knowing that it will force them to get out of the house.

Is any of this rational? No. Is it a good thing? That’s tougher to say. If people are happier to attend to sunk costs than to let the money spent on an item “go to waste,” perhaps it’s a good thing. Can it be used by companies to influence consumers’ behavior? Absolutely.

Take your average health club. It faces two tasks: getting people to join and getting people to renew. (The churn at health clubs can exceed 50 percent.) Knowing that members are going to be more likely to renew in year two if they feel that they have “gotten their money’s worth” in year one, a club should look to encourage attendance. One way to do this is through the timing of payments. Many clubs demand payment in full at the start of a year-long membership. The result is that people work out a lot in the first month or two, while that payment is still fresh in their minds, but gradually stop going as the payment fades from memory. In this case, the sunk cost effect weakens the further one is from that initial payment. Now consider the member who makes payments monthly. For him or her, the cost of membership will always be vivid and they will feel obliged to work out on an ongoing basis. At the end of the year, who is more likely to renew? Clearly, the person who worked out regularly will have a higher likelihood of renewing his or her membership.

The same concept can be applied to health care. In its current form, most of us pay for blanket health care coverage that entitles us to a number of periodic services such as checkups, shots, mammograms, etc. The problem is that the costs of these benefits are not particularly clear. I don’t know what that annual checkup is costing me, so I don’t perceive it as a cost. If health care providers could make the costs of these procedures more salient—perhaps by sending me periodic reminders that these procedures are costing me, say, $50 each regardless of whether I use them—they would be able to tap into the sunk cost effect. Patients would be more likely to say, “I’m paying for it, I shouldn’t let it go to waste.”

Read more: Use the Psychology of Pricing To Keep Customers Returning

Do these emails have any real effect on the market? New research claims they do. Laura Frieder and Jonathan Zittrain compared a database of collected stock spam against historical market activity to examine the effects of spam on both market volume and price. They found that stock spam does make a significant impact on the market. From their abstract:

Based on a large sample of touted stocks listed on the Pink Sheets quotation system, we find that stocks experience a significantly positive return on days when they are heavily touted via spam, and on the day preceding such touting. Volume of trading also responds positively and significantly to heavy touting. Indeed, on a day when no tout has been detected in our database, the likelihood of a touted stock being the most actively traded stock that day is only 6%. On the other hand, on days when there is touting activity, the probability of a touted stock being the single most actively traded stock is 81%. Returns in the days following touting are significantly negative. The evidence accords with a hypothesis that spammers “buy low and spam high,” purchasing penny stocks with comparatively low liquidity, then touting them - perhaps immediately after an independently occurring upward tick in price, or after having caused the uptick themselves by engaging in preparatory purchasing - in order to increase or maintain trading activity and price enough to unload their positions at a profit. Selling by the spammer then results in negative returns following touting. Investors who respond to touting are losing, on average, 5.25% in the two day period following touting. For the quintile of stocks in our sample that are touted most heavily, this 2-day loss approaches 8%. These estimates are conservative, as they do not account for transaction costs.

For a nontechnical review of the paper, see Spammers Make a Sound Investment in Stocks. The original paper is here: Spam Works: Evidence from Stock Touts and Corresponding Market Activity.

It’s important to note that timing is everything when it comes to profit or loss from temporary market manipulations like these. The Spam Stock Tracker is a mock portfolio of penny stocks touted in spam received by the author. As of today, his portfolio has lost over $47,000 (on paper), based on an investment of $70,987.

Via kottke

UPDATE: Roger Ehrenberg at Information Arbitrage has an insightful post on the same topic: Stock Spamming for Profit - A Sucker Born Every Day

Mauboussin then makes the distinction between endogenous risk, which emerges from a complex system itself, and exogenous risk, which is forced on a system from outside. System dynamics that emerge endogenously, without being precipitated by any outside event, are behind many of the interesting phenomena we see in the markets and society at large today. While endogenous risk is just beginning to be understood, it’s a critical area of research. We’ve looked at some examples of endogenous system dynamics earlier this week with Exploiting the Herd, A Case Study and Exploiting the Herd: Case Study Two. Mauboussin presents some of the common frameworks used to understand endogenous risk:

The first framework is the wisdom of crowds, which writer Jim Surowiecki laid out well a couple of years ago in his book of the same title. The basic idea is simple and somewhat counterintuitive: if you get a diverse group of people together to solve a problem, the group’s answer will typically be better than that of any individual, even an expert. The wisdom of crowds is a more common way of describing a type of complex adaptive system–the heart of the Santa Fe Institute’s work–and is an apt description of the stock market.

The key is that the crowd is only wise under certain conditions. You need agent diversity, an aggregation mechanism, and some sort of incentives. When one or more of these conditions is violated, all bets are off. In human systems, diversity is the most likely condition to be violated. When you take away diversity, the complex system can become fragile and in some cases will lead to large-scale changes. Booms and crashes are good examples of diversity breakdowns in markets. Fads and fashions also illustrate the concept. And that leads to the second framework: diffusion theory.

Technologies, ideas, and illnesses tend to diffuse following an S-curve pattern. So, for example, a new technology will start with only a few adopters, and will grow at a relatively slow rate early on. The rate then accelerates, and the technology takes off. This field has been studied in detail, and is of prime interest to epidemiologists and technologists, just to name two groups. The key point is the growth rate is not stable: it’s low to start, rises, and then slows down again. Also important is that most technologies or ideas don’t diffuse–they simply sputter out.

The final framework is network theory, or how the individual nodes in a network are connected. Network theory bears on a wide variety of phenomena, including your network of friends, transmitters on the power grid, or the spread of disease. In recent years, scientists have made major advances in understanding the nature of networks. We now know that the structure of the network is important in understanding how things get transmitted over the network.

There are two features of these frameworks worth emphasizing. First, they are non-linear. For example, in the case of the wisdom of crowds you can reduce diversity, reduce diversity, and nothing happens. Then you reduce it a bit more and the system reacts violently–the proverbial straw that broke the camel’s back. Many of you know this idea as the tipping point.

That leads to the second feature: lack of proportionality. The size of the perturbation and the outcome are not always linked. Sometimes small perturbations lead to large outcomes, and vice versa. When you combine a lack of linearity with a lack of proportionality, it’s not hard to see that predictions are difficult and cause and effect thinking is often futile.

Read more: Mauboussin on Strategy: Interdisciplinary Perspectives on Risk

Previously:

• Michael Mauboussin: How Do You Compare?
• More Than You Know
• Mauboussin on Discounted Cash Flow Models
• Mauboussin on Strategy: Size Matters
• All posts tagged Michael Mauboussin

3. Does your study measure how well what people say they look for matches with what they actually look for?

Most of what I do is work on corruption in poor countries. If I want to know how much someone is paying in bribes, I’m not going to ask them, “How much did you pay in bribes last year?” I’m going to say, “The guy down the street from you, who looks pretty much like you, how much did he pay?” Similarly, in the speed-dating study we ask people, “What do you care about?” We also ask them, “The average man, what do you think he cares about?” But then we actually see how they behave in the game. And, not at all surprisingly, what they say the average man cares about lines up much more closely with what they actually reveal through their actions than what they claimed they cared about beforehand. In particular, everyone — both men and women — says they care less about physical attractiveness than the average.

4. Do you think speed-dating is more efficient than traditional search methods?

In some sense, it’s efficient: there are all these slice studies on how 10 seconds’ worth of observation is as predictive of your experience with a professor as a semester’s worth, and they’ve reduced it to 2 seconds and that’s just as good; and they’ve reduced it to just a photo and that’s pretty good, too. So you learn a lot in four minutes, perhaps as much in four minutes as you do in a much longer superficial interaction like, say, a date. So, this does meaningfully provide you with 20 rapid-fire dates, to the extent that we form as much of an impression in 4 minutes, or 10 seconds, as we do in 4 hours. The thing that’s left out of this neat decomposition of people into attributes, though, is actually learning to love someone. And that’s what I think is kind of missing. Focusing on people as a bundle of attributes almost makes people think about this decision in the wrong frame of mind.

5. Do you think people become unwilling to commit because of all the choices dating services enable?

Yes. And the way that you can make these choices — just the very fact that it’s set up in this way — distorts the way people choose. There was an article in the New York Times on a backlash against Internet dating, and I wonder to what degree that’s at least partly as a result of these sorts of realizations.

6. The results of your speed-dating studies, particularly with regard to intelligence and physical appearance, seem to reinforce gender stereotypes. Why do you think this is?

Well, they are stereotypes for a reason. However, it’s not as simple as, “I avoid all women who are ambitious or intelligent.” It’s about, “Intelligence and ambition is OK until it supersedes my own.” It’s also worth mentioning that these are average effects — there are surely men who do not have this property. I like to think I’m one of them: my significant other is definitely a lot smarter than I am. When her grandmother heard about me, she said, “I told your mother this, and now I’m going to tell you: never let a man think you’re smarter than he is. Men don’t like that.” Everyone laughed and thought this was so anachronistic, but it shows up in our data. Grandma’s views on dating aren’t so dated after all!

Read more: Dating Data: Economic Theory and the Search for a Mate

The most likely answer is overconfidence. We all think that we know better than simple models. My own confession at the start of this note is a prime example of such hubris. The key to the quant model’s performance is that it has a known error rate, whereas our error rates are unknown.

And furthermore:

Grove and Meehl suggest many possible reasons for ignoring the evidence presented in this note; two in particular stand out as relevant to the discussion here. Firstly, the fear of technological unemployment. This is obviously an example of a self serving bias. If, say, 18 out of every 20 analysts and fund managers could be replaced by a computer, the results are unlikely to be welcomed by the industry at large.

Secondly, the industry has a large dose of inertia contained within it. It is pretty inconceivable for a large fund management house to turn around and say they are scrapping most of the processes they had used for the last 20 years, in order to implement a quant model instead.

Another consideration may be the ease of selling. We find it ‘easy’ to understand the idea of analysts searching for value, and fund managers rooting out hidden opportunities. However, selling a quant model will be much harder. The term ‘black box’ will be bandied around in a highly pejorative way. Consultants may question why they are employing you at all, if ‘all’ you do is turn up and run the model and then walk away again.

It is for reasons like these that quant investing is likely to remain a fringe activity, no matter how successful it may be.

Read more:  Painting By Numbers: An Ode To Quant

Earlier:

• James Montier: Quantitative Strategies Rule
• All posts tagged James Montier

I happened to be riding to work with an exec from one of the major studios this morning, and he mentioned that the studios are increasingly making deals with theaters to inflate opening numbers. In particular, they will give the theaters very high revenue share for the first X days of the movie (he mentioned 100% for the first 3 days), incentivizing the theater to maximize the number of screens the movie’s shown on, inflating opening numbers.

The particular example of Superman and Pirates were actually the ones he brought up—that Superman’s decline was partially due to the theaters’ incentive period running out.

I have no idea how true or prevalent this is, but something you might want to look into. This would be done for movies which the studio considers potential “hits”, increasing discrepancy between them and normal movies.

To me, 100% of the take even for three days sounds ludicrous, but I could certainly imagine the promise of inflating the real figures by 25% for an additional 5 or 10% of the short-term take. I’ve always assumed the reported box-office figures were pretty much made up, anyway. But, as widely as these figures are reported these days, essentially serving as the best marketing a movie could hope for, I would think there’s plenty of value in paying to drive up those first-week numbers as high as possible. It’s hard to imagine anyone in Hollywood objecting, really: studios and producers all want to look more successful than they are, and actors and directors get to negotiate their next deal based on inflated figures.

There is reason to believe that the box office¹ profit function for a movie is nonlinear; hits tend to become even bigger hits, while the viewership of an unpopular movie tends to fall off quickly. This gives Hollywood executives strong incentives to do what they can to boost the numbers in the first week, and especially the all-important first weekend of a movie. Those efforts might come in the form of perfectly legitimate marketing campaigns, or in the form of greyer strategies like that discussed above–incenting the individual theaters to inflate their numbers. Like the proverbial flap of a butterfly’s wings, small differences in early viewership can lead to large differences in overall viewership.

Read more: Box-Office Payola?

Previously: Exploiting the Herd: A Case Study

[1] I’m specifically talking about theater numbers here–I think the markets for movie rentals and DVD purchases exhibit somewhat different dynamics.