Rajat Gupta’s Insider Trading Conviction: One Network to Bring Them All and in the Darkness Bind Them

Former McKinsey & Company head and Goldman and Procter & Gamble board member Rajat Gupta has been convicted of securities fraud and conspiracy for giving away confidential information from boardroom meetings. This makes him the latest in a string of convictions related to the insider trading scandal centered on hedge fund Galleon Group. The court cases since the scandal have revealed a pattern of information collection from board directors followed by quick and profitable trades, with involvement all the way to Galleon’s top. Its founder Raj Rajaratnam was convicted in October 2011, and many Galleon traders have also been convinced, as have their information sources.

An irony of Rajat Gupta’s conviction is that he appears not to have profited from the inside information he passed on. Rather, he passed on information, and Galleon traded on it, without any trades on his own behalf or (traceable) kickback. Indeed, prosecutors were quoted saying that his information leaks were “motivated not by quick profits but rather a lifestyle where inside tips are the currency of friendships and elite business relationships.” This quote sounds familiar to me, because along with Gerald Davis I have studied how boards of directors form their own norms of conduct, as reflected in the type of governance innovations they adopt. We found that they indeed determine norms through interacting with other board members, though we did not study anything illegal such as violating confidentiality.

But the investigation and conviction has another irony as well. A recent Wall Street Journal article has a map of the network of individuals implicated in the Galleon trades, as well as their current status in the court system. Of particular interest are those with status pleaded guilty (cooperating witness), because they were used (along with wiretaps) to break the case open. I study networks, and one glance at the map is enough to be impressed with the investigation. The central person they targeted, Galleon head Raj Rajaratnam, is surrounded by cooperative witnesses, and his closest associates are also nicely bracketed by cooperating witnesses. Clearly the investigators were able to follow the network of individuals involved through traces like suspicious trading patterns, phone call records, and eventually wiretaps, and once they knew who was spilling information to whom they went to work turning some witnesses in order to get at the main targets of the investigation. And hedge fund traders and leaky board members appear to be easy to turn cooperative compared with other organizations that the FBI has dealt with in the past. In Rajat Gupta's case they did not even need a direct wiretap or cooperative witness because the other evidence from phone records and suspicious trades was so compelling.

So, this incident really deserves the Tolkien headline: One network to bring them all and in the darkness bind them.

Davis, G. F. & Greve, H. R. 1997. Corporate elite networksand governance changes in the 1980s. American Journal of Sociology, 103(July): 1-37.

Bray, C. Rothfeld, M, and R. Albergotti. 2012/6/15. Insider Case Lands Big Catch. Wall Street Journal. Interactive graphic (may require subscription):

http://online.wsj.com/article/SB10001424052702303822204577468470878668722.html?KEYWORDS=rajat+gupta#articleTabs%3Dinteractive%26project%3DGALLEON0211_timeline

WaMu: The Mutual Bank that De-Mutualized and Melted Down

Wall Street Journal contributor Kirstin Grind has written a book on the downfall of WaMu, the second largest bank failure in the US (so far) and the name that has come to symbolize risky mortgage lending in the USA. I expect to enjoy reading it when it comes out, and learn more about the inside story. Here is some of what we know already.

WaMu started as a mutual bank, meaning that it was owned by its depositors. Mutual Banks come in different forms, but traditionally they are focused on savings deposits and mortgage loans rather than loans to businesses. They usually have low interest rates on loans and instead select their borrowers carefully to have low losses. Mutual banks are a traditional, one might even say old-fashioned, form of bank that is often heavily committed to the local communities and avoids having many branches. Hayagreeva Rao and I are doing research on such community organizations, and we have found that they can have a big and positive impact on the development of their communities.

For WaMu, however, the period as a mutual ended in 1983. Mutuals can de-mutualize, and this is done both in banking and in insurance (many insurers are also mutuals). De-mutualization means that they turn into regular joint-stock firms, but often they keep their name. There is nothing preventing a non-mutual from calling itself a mutual. They can keep their business practices too, but most often do not. Mutuals are very efficient organizations as long as they don’t grow, but de-mutualization means that they can grow quickly, adding branches and getting new lenders. Shortly before failing, de-mutualized WaMu had tens of thousands of employees and hundreds of branches, and a strong electronic-banking presence. More worryingly, due to permissive loan practices and incentives for loan generation, it has a loan portfolio that was bloated and risky when compared to its capital. Losses on loans started to mount, triggering a depositor bank run that was fast and furious thanks to the ability to transfer money electronically: it is estimated that 16.7 billion dollar in deposits left WaMu in nine days.

What happened? WaMu had become a commercial bank with commercial bank ambitions but a shortage of commercial bank experience. It also had some bad luck: 2008 was not a good year for having a large mortgage loan exposure, because the subprime loan crisis was in full swing. Its failure shows some of the problems of changing from one form of organization to another. Strategies can change quickly, but the capabilities to back those strategies take longer to develop.

In the book she reports another side to the story too, however, and one that shows in a better way how WaMu did not totally outgrow its roots as a mutual organization. When WaMu started experiencing problems collecting loans, they sent out a team to investigate how they might improve payments from their borrowers. This team made interviews on video tape documenting how people had been blindsided by the fall of the housing market and found themselves unable to pay. When they showed this footage to the president of WaMu's Home Loans Group, David Schneider, he ordered a revision of loan collections to take a softer approach. In the end, it is hard for a firm with mutual in its name and history to escape the idea that mutuals are a way for people to help each other.

Greve, H. R. & Rao, H. 2012. Echoes of the past: Organizational foundings as sources of an institutional legacy of mutualism. American Journal of Sociology, 118(November).

Grind, Kirsten. 2012. A Bank on the Run: How WaMu's Demise Hit Home. Wall Street Journal, June 12 2012.
Grind, Kirsten. 2012. The Lost Bank. The Story of Washington Mutual, the Biggest Bank Failure in American History. Simon & Schuster.

Technology Races: To the Victor belong the Spoils

J.P. Eggers just published an article that looks at the outcome of the technology race between LCD (liquid crystal display) and plasma display technology in the flat screen market. We know the broad story of how the market has developed over time: both technologies were tried initially, then LCD became dominant, and then the tables turned so that plasma is now resurgent. What the paper adds is an interesting take on what happened to each firm. It is useful knowledge for managers interested in how to place bets on competing technologies, and it also tells a lot about how organizations make decisions.

Firms that bet only on plasma early on did more poorly in the technology race as LCD became dominant, as one would expect from them having bet on the wrong technology. Firms that developed both technologies at once also did more poorly than those that bet on LCD only, again a reasonable result given that specialization is better if it is in the right decision. But here is the big surprise: when plasma re-emerged as the better technology later, the firms that entered with plasma technology initially still did worse than those that entered with LCD.

How is that possible? If knowledge stays in the firm, they should have been able to use the early investment to recover. But what seems to have happened is that managers learnt too much from the initial negative experience with plasma. Not only did they learn that plasma was the wrong choice early on, but the experience of placing the bet on plasma created greater skepticism either against that technology (even as it improved), or even against the effectiveness of research and development in general. Clearly they were over-learning from this experience, and that held back the firm technology development.

The disturbing lesson is that choosing the right technology has consequences beyond the first technology generation, but it is still necessary to do so before the advantages are known (late entrants also did poorly). So the spoils really belonged to the victors, who not only enjoyed LCD dominance but were also able to change to plasma with greater ease than the original plasma firms.  

Eggers. J.P. 2012. Falling Flat: Failed Technologies and Investment under Uncertainty. Administrative Science Quarterly, 57 (1): 47–80.

What Kind of Company is Splunk? Categories and Organizations

Let me begin by answering the question in the title. Splunk (http://www.splunk.com/company) describes itself as a company founded to "make machine data accessible, usable and valuable to everyone." With machine data they mean not just any data on a computer, but rather the data generated by machines rather than humans; by accessible they mean that they are making programs to analyze data that would normally be in an unstructured form, so not in a database. Their software deals with system configuration and monitoring, security logs, servers, virtual machines, and so on. The name comes from spelunking, or cave exploration, because supervising machine data is like cave exploration when the software tools are poor.

To someone outside the computer industry, like me, most of this description is hard to follow. For those inside the industry, each function and piece of software may be easy to understand, but it is not necessary clear why a single company would be founded to take care of these services and not others. What would we call such a company, and who are its competitors? That is the question of categorization. Customers care about categories because they want to know which firms to approach for bids when there is a need for new software. Investors care about categories because they want to understand the strategy of a firm they want to back.

Usually when I look at a problem of categorization I want a clear answer. Green tea ice cream bothers me because a non-dairy product that is usually served hot is frozen and milky. There is something fundamentally wrong with three-wheel cars and four-wheel motor scooters. And, as Elizabeth Pontikes has found in recent research, I seem to be a typical consumer in that respect. If a firm is difficult to categorize because it belongs to a category that is hard to delineate or has significant overlap with other categories, then they assign a lower value to it.

The surprise came in how venture capitalists made the same judgment: they took the exact opposite tack and thought such companies were more valuable, not less. Why is that? The key seems to be that (potential) customers don't have any control over the firm, and are facing with a possibly unclear product or service that they have to take or leave. Venture capitalists, on the other hand, will invest in a firm and take a role in setting its strategy, and for them the unclear labeling may indicate novelty and opportunities to maneuver. So, it is not just the category that matters in how one reacts, but also the degree of control over it. For consumers, that still means staying with four-wheel cars. For entrepreneurs it means that the radical but still half-baked idea might be more interesting to investors than they imagine. Oh, by the way: When I looked today, Splunk was worth approximately 3 billion USD. 

Pontikes, E. G. 2012. Two Sides of the Same Coin: How Ambiguous Classification Affects Multiple Audiences' Evaluations. Administrative Science Quarterly, 57(1): 81-118.

The Late-Goal Effect: Risk-taking and Deadlines

Last week Manchester City became Premier League champions after a nail-biting match that had them lagging 1 to 2 by the end of regulation time, and so second in the Premier League after local rivals Manchester United, but then recovering with two injury-time goals to win the match and league. It was surely an effort to bring their fans to the edge of their seats, as well as the Manchester United fans. Lest we think that such late-game heroics are local the kind of football played in Europe (and, pretty much everywhere), fans of American football know that many games are decided by magical plays in the last two minutes of the 60-minute game time.

What is going on in those last few minutes, and why is it so different from the rest of the game? Looking at the data from American Football, Lehman and coauthors (2011) looked at the deadline-proximity effect. It starts with the idea that decision makers make changes and take risk if their performance is below a goal, or aspiration level. This is known in many situations, and is actually best documented for organizations making changes when their performance is low (Greve, 2003). But being below is worse just before a deadline, because then one has to do something truly dramatic and risky to catch up. And in fact, that is exactly what the football teams in the study were doing, because they were choosing a riskier play when they were behind late in the game then they were equally behind earlier.

Is such risk-taking good? For sports, it may be. Risky plays naturally have a chance of failing, but the extra risk could well be worth the chance of winning the game, and losing by more points is not such a great loss. The only problem would be the following. Risk in sports and in management is unfortunately more similar than we would like it to be, because there is the same sense of winning when getting just above a target level in management, even when the targets are arbitrary and the deadlines (usually by accounting convention) are arbitrary as well. We do not know yet, but it is reasonable to expect that some late-period risk taking occurs also in firms. Does the level of risk taking by managers and others in organizations matter enough for us to care about whether it is done consistently over time? A good person to ask that question would be J.P. Morgan Chase CEO James Dimon, who is now looking at losses estimated to $3-5 billion inflicted on the bank by some unusual trading positions.

Greve, H. 2003. Organizational Learning from Performance Feedback: A Behavioral Perspective on Innovation and Change. Cambridge University Press.

Lehman, D. W., Hahn, J., Ramanujam, R., & Alge, B. J. 2011. The Dynamics of the Performance-Risk Relationship Within a Performance Period: The Moderating Role of Deadline Proximity. Organization Science, 22(6): 1613-1630.

What if Our Best People Join the Competition?

Readers of the business press have been watching the slow decline of the famous New York law firm Dewey and LeBoeuf, which took on too much debt and is reported to be investigating financial misconduct by one of its leaders. Most troubling for a law firm, which lives on the expertise and customer networks of its leading partners, it has experienced an increasing stream of defections by its partners to other law firms. To many observers, the doubling of departures in four months (8 in February, 22 in March, 40 in April, and 81 in May, according to Wall Street Journal) is a signal that the end is near.

What if it had not been a rush like the one we are currently seeing, but instead fewer exits? We might think that a firm would withstand a smaller stream out, as job movements are a common sight on most industries. But if these are key personnel, a case can be made that even a smaller number of exits is dangerous. So which is it? In fact, Wezel and coauthors have looked at exactly this issue (for accountant firm partners), and found some interesting effects.

First, departures do harm the firm - a plausible first result. Second, departures of individuals were much less damaging than departures of groups. This is not just because groups are "bigger", but also because their experience working together means that they add up to more than the sum of the individuals. Third, reasonably, the effect is worse when they depart to a nearby competitor. Fourth, and perhaps most surprising, the damage to the firm that they depart is greater when they start a new firm than when they join an existing one. What makes this surprising is that existing firms are often stronger competitors to begin with, and adding more expertise to current competitors seems to create a greater threat. But a startup is worse than a greater threat; it is a new threat.

This may not quite be news that rescues Dewey, which seems to have a bigger set of problems than the departure of a few partners. But, for other firms it may be useful to know that retaining key employees is not just good one’s own competitive strength; it also avoids strengthening existing competitors or creating new ones.

Spector, Mike and Jennifer Smith. 2012. "Bankruptcy Specialist at Dewey Heads for Exit." Wall Street Journey, May 13 2012.

Wezel, F. C., Cattani, G., & Pennings, J. M. 2006. Competitive implications of interfirm mobility. Organization Science, 17(6): 691-709.

Network Theory and Drone Attacks

Chances are that you will find a news story about a drone attack on militants within a week of reading this blog post. They have become common in Pakistan, and are now used in other places such as Somalia and Yemen. Drone attacks are expensive, with the missile alone costing more than USD 50,000, plus the cost of operating the drone. The expected payoff of drone attacks is to weaken insurgencies such as the Taliban. But do they work?

David Siegel has an article on repression and social networks that provides some answers on when such tactics are effective. The reason social networks are relevant is that people decide to join insurgencies based in part on what their friends are doing, because the actions of friends are helpful for learning what actions are considered good, what causes to join, and what the risks and benefits are. As a result, insurgencies spread through social networks, and they will spread faster when these networks have many connections and few places that are isolated from each other.

Thinking about insurgencies as social networks helps us understand what drone attacks do: they remove individuals from the networks, and when one individual disappears the connections that individual has also disappear. A significant number of attacks can tear the network into small pieces and stop an insurgency completely, but that requires a scale of attack much greater than anything currently seen. Instead, what the current pace of (approximately) one attack a week does is to pick of some parts of the network, leaving much of it unchanged.

Based on network theory, will that work? If individuals are equally eager to join the insurgency, and equally well connected, such a strategy has only a remote chance of stopping an insurgency, because there are too many connections that need to be severed. If some individuals are (much) more influential and connected, and the attacks hit those individuals more than others, they can be effective, especially if these individuals occupy central positions in the network. Because drone attacks target leaders or assemblies of central persons, this is in fact the theory behind them. They remove those who are most central in the network, and through that seek to remove those who are the strongest drivers of the insurgency, and break the network apart.

But what if friends of the people who are removed (well, killed) by the attacks react by getting angry and joining the insurgency? In that case, the attacks not only lose their effectiveness, they can in fact become counter-productive and can fuel the insurgency. This backlash effect is true under one, ironic, circumstance, however. Attacks are counter-productive when they hit actual leaders with many ties to others who can become angry. If they instead hit innocent people, the backlash will be less because few people in a society of villages have enough network contacts to fuel much anger.

Siegel, David. 2011. “When Does Repression Work?: Collective Action and Social Networks.” Journal of Politics 73 (4): 993-1010.