The New Yorker is one of the best sources of long-form journalism out there. George Packer recently authored an 11,000+ word article on the largest insider trading case in the history of insider trading cases: Raj Rajaratnam’s Galleon hedge fund. Rajaratnam created an elaborate and extensive network of outside consultants and executives at various publicly traded companies that fed him actionable insider information. Insider trading is everywhere you look:
In the language of hedge funds, Galleon’s strategy was to “arbitrage reality” with the consensus on the Street—to find information about a given company that diverged from Wall Street’s view, allowing Galleon to cash in when the company’s stock price rose or fell. At Galleon, this was known as “getting an edge.” The analyst or portfolio manager with the best read on a company was called the “axe” on that stock. The surest way to become the axe was to have a source who passed on information about a company’s earnings, upcoming deals, and other confidential matters. The ultimate edge was insider trading—the acquisition of nonpublic information about a company—and Rajaratnam was the king axe.
In a recent article in Slate, Duncan Watts asks why bubbles occur with “metronomic regularity” and whether investors can spot one in the making. His conclusion? Bubbles in the making involve a great deal of confusion on what outcome is the most likely one and only seem obvious in hindsight:
We all know, of course, that “hindsight is 20-20,” but when we say this we’re implying that our failure to predict the outcome resulted simply from not paying attention to relevant information. After a bubble has burst, that is, we can always find signs, and very often people, pointing out that it was a bubble; thus it always seems that investors could have known what was happening and just chose to ignore it.
Continuing with yesterday’s theme of gambling, I would like to present a framework for optimal gambling strategy at a casino.
Some Key Assumptions
First, I assume that all possible gambles you can make at a casino have negative expected value. I am not considering counting cards, controlled shooting, cheating, or games of skill like poker. Second, I assume that the player is risk adverse. I realize that this assumption may not be true in reality since some individual players may be risk seeking when it comes to small sums of money at a casino. Still, I think this assumption is reasonable, especially as the amount a player wagers increases. I also think that adopting a risk adverse mentality will lead to better gambling strategy as I explain below.
Which one of these two gambles is best?
$75,000 with probability 50%
$25,000 with probability 50%
$100,000 with probability 98%
-$1,000,000 with probability -2%
Sometimes as part of my job, I have to review the academic literature to find some support for an argument that we are trying to make. From time to time, I look at the top papers at the Social Science Research Network and see if anything catches my attention. A few days ago, I came across What Happened to the Quants in August 2007? , a paper with around 11,000 downloads, making it practically viral for the world of academia.