They were willing to ‘em a chance, but no more. Read more »
In Wake Of Exec “Accidentally” Stabbing A Cab Driver, Morgan Stanley Insists You Ask, “What Would The Post Say?”By Bess Levin
A year ago this Friday, a Morgan Stanley banker named William Bryan Jennings attended a couple holiday parties, drank a few Coors Lights, and around 10:30PM hailed a cab and asked the driver, Helmy Ammar, to take him home to Connecticut. On the way, a hungry WBJ requested they stop at G&G Deli off 10th Avenue, where he bought “a 20 oz. bottle of Aquafina water, a sandwich and some Burger King cheesy fries.” As the cab entered approached Jennings’ hometown of Darien, a dispute reportedly broke out as to what the fare for the ride would be. Ammar claims that they’d agreed on $204 before leaving Manhattan, but once in Connecticut, Jennings said he’d only pay $50. Jennings claims that Ammar jacked the price up to $300 and was unhappy when the banker offered $160. Another matter of he said/he said is whether or not Jennings started shouting racial slurs at Ammar and told him, “I’m going to kill you. You should go back to your country!” (Jennings denies this happened and says that Ammar locked the doors and wouldn’t let him out of the cab.)
The one aspect of the story that is not in dispute is that as tensions flared, WBJ whipped out a pen knife he had in his pocket. For those of you reading from Morgan Stanley, this is where the teachable moments occurs: if you ever find yourself in a situation wherein you’re winding up to stab a cab driver in the hand, stop and ask yourself, “Is this going to look bad in the Post tomorrow morning?” Jennings did not and now this is happening: Read more »
For the last year or so, Morgan Stanley CEO James Gorman has sent a simple message to employees grumbling about compensation: STFU or GTFO. Now, according to Charles Gasparino, the bank may be telling a few employees to GTFO regardless of whether or not they’ve been bitching about pay. Read more »
If you’re looking for a cheerleader, go bark up another tree.
“Say you want to be out ahead of it and give a lot of speeches and talk about all the good we’re doing,” Gorman said today at an industry conference in New York. “And then some trader does some stupid thing like this guy at UBS did and he’s in jail and all bets are off,” Gorman said. He was referring to Kweku Adoboli, the UBS AG trader convicted of fraud this month in the largest unauthorized trading loss in British history…Traders at New York-based Morgan Stanley had too much latitude in the past, “what I call having an outsized sandbox,” Gorman, 54, said at the conference, which was sponsored by the Securities Industry and Financial Markets Association. “Until we can be really confident we’ve got discipline around the sandboxes, I think you have to be really careful not to be holier than thou,” Gorman said. “We’re going to be in the doghouse for a while.”
Guy Who Was Fired By Goldman Sachs For Amassing “Inappropriately Large” Position Welcomed With Open Arms At Morgan StanleyBy Bess Levin
Back in December 2007, things weren’t going so well for Matthew Marshall Taylor. He’d just been fired from Goldman Sachs and not only was he out of a job, but his prospects for finding a new one didn’t look so hot, on account of the fact that Goldman planned to put a note in his file detailing the reason he’d been let go– “for building an ‘inappropriately large’ proprietary trading position”– and it seemed unlikely anyone at the firm would be open to serving as a reference for him moving forward. Three months later, however, one bank told MMT that there was room for him at their inn. Morgan Stanley, apparently having decided the incident at Goldman was but an asterisk in what would be a long and fruitful career, told Taylor to come on down, employing him for over four years until he left in July of his own accord and not because of any legal issues relating to his work at Goldman Sachs. Read more »
A value-at-risk model basically works like this. You have some stuff, which is worth X today. Tomorrow it will be worth X + Y, where Y ranges from more or less negative infinity to positive infinity. Y is a function of a bunch of correlated random variables, rates and credit and stock prices and general whatnot. You look at a distribution of moves in those variables and take (usually) a 2-standard deviation daily move; if 95% of the time rates move by -10 to +10 basis points, your VaR model will assume a -10bp or +10bp move, whichever is bad for you. You take the 95%-worst-case, taking into account correlation etc., and tot up how much you’d lose in that case. Then you write that number down and feel a bit better, since you’ve sort of implicitly replaced “we have $X today and will have some number between negative and positive infinity tomorrow” with “we have $X today and will have some number between ($X – VaR) and positive infinity tomorrow,” though of course the first statement is true but unhelpful and the second is not true and also unhelpful.
But that aside! You get your VaR from a distribution of your variables, but the obvious question is what distribution. A good answer would be like “the distribution of those variables over the next three months,” say, for quarterly reporting, but of course that is only a good answer because it begs the question; if you knew what would happen over the next three months you would, one assume, always end those three months with more than $X and this VaR thing would be moot or moot-ish.1
So instead you look at things that you think will allow you to predict that future distribution as accurately as possible, which is epistemically troubling since VaR is a measure of how inaccurate your predictions might turn out to be. Anyway! You pick a distribution of variables based on the sort of stuff that you always use to estimate future distributions in your future-distribution-estimating business, which could mean distributions implied by market prices (e.g. option implied vol) but which seems to mostly mean historical distributions. You look at the last N days of data and assume that the world will be similarly distributed in the following M days, because really what else is there to do.
Picking the number of days to use is hard because, one, this is in some strict sense a nonsense endeavor, but also two, the world changes over time, so looking back one year is for instance rather different from looking back four years. Here is how different: Read more »