Much of what you say seems like valid counterpoint. I’m getting out of my depth, insofar as I know very little about finance :-). I hope that other more knowledgable people will step in.
A professor in his 40s once told me that the department where he got his PhD had run the numbers, and financial success was inversely correlated with grades / research skills / intellectual ability; the top people got professorships and were middle class, whereas the people who were at the bottom of the class had left academia, and several of them made millions on Wall Street.
I don’t find this at all convincing, e.g.:
It’s only one department
He may have been misremembering the situation
“Bottom of the class” could reflect motivation rather than just intellectual ability.
It’s well known that academia doesn’t pay so well.
It seems to me to be weak support for the claim “if top tier math/physics talent went into finance, they could plausibly make billions, since fourth tier talent make millions.” (Fourth tier meant in the sense of this comment, but third tier might be more appropriate.)
There are also famous high-profile failures, though, such as Scholes at LTCM. (He did only win the Nobel prize in Econ, though, which is not a particularly strong predictor of math ability.)
Madoff fooled Simons for 10 years. He told Stony Brook to invest in Madoff in 1991. In 2003, Renaissance decided that Madoff was a fraud and pulled out. Simons told Stony Brook to pull out, but they left in 50%.
Many people investing with Madoff thought he was a fraud, just that he was defrauding someone else (eg, frontrunning his brokerage clients). It’s not clear that they pulled out because they realized he was a fraud or because they worried that Spitzer would investigate him. Their suspicions triggered a failed investigation of Madoff not because they complained to the SEC, but because when the SEC audited RenTech, it found emails discussing Madoff.
Oddly, the wikipedia article seems to be based on the first WSJ article I linked, not the Bloomberg one it cites.
It’s mild evidence against the “top level math talent will be able to identify bubbles before they burst” hypothesis, but only mild because I don’t think Madoff was transparent enough to be obviously unstable in the way that the housing market was.
How would we go about making it such that someone who contemplated perpetrating that sort of fraud would be very likely to conclude, “Nah, there’s no way that would work; if I tried that, I’d just end up in prison like Madoff”?
Considering that the wikipedia article about Madoff didn’t exist until the scandal was unveiled … probably. (I checked the Wikipedia history when I first heard about it in Dec 08, check the history for yourself. )
Hmm. I thought I mentioned Renaissance in this post, but for some reason it says Medallion, which is one of their funds. Apparently I derped earlier, fixing it now.
You’re a PhD in math with no specialist knowledge of finance. Do you think that you personally could do better financial analysis than, say, a Business major with a CFA and a decade or two of industry experience? If no, why do you think mathematicians are the way forward for the field?
I would expect the mathematician to be more likely to be a good coder, and more likely to lean on data analysis that she understood from first principles.
The closest thing to finance from first principles is just to assume the EMH, use the CAPM theory to constuct a market portfolio, and practice a policy of pure commission/tax avoidance. Finance from first principles is incredibly boring—the only way to make it interesting is to start getting into second-order effects.
The comment was “lean on data analysis”, as in “lean on data analysis to do finance”. Yes, the analysis itself is not “finance” per se, but most of the data analysis done in finance is simple math that gets used in complex ways. It’s easy enough to calculate P/E, but figuring out whether that implies you wanting to buy or sell is a much harder question. About the only way to use first principles to actually make buy/sell decisions is, as I said, to use the CAPM and just embrace your ignorance.
That seems a little extreme; presumably there’s a difference between using statistical tests as a heuristic you don’t understand, and using statistical tests in a well-understood way, even if you’re not deriving finance from first principles.
Also, CAPM isn’t actually true (i.e. assumptions never hold in the real world), whereas statistics is.
Much of what you say seems like valid counterpoint. I’m getting out of my depth, insofar as I know very little about finance :-). I hope that other more knowledgable people will step in.
I don’t find this at all convincing, e.g.:
It’s only one department
He may have been misremembering the situation
“Bottom of the class” could reflect motivation rather than just intellectual ability.
It’s well known that academia doesn’t pay so well.
It seems to me to be weak support for the claim “if top tier math/physics talent went into finance, they could plausibly make billions, since fourth tier talent make millions.” (Fourth tier meant in the sense of this comment, but third tier might be more appropriate.)
There are also famous high-profile failures, though, such as Scholes at LTCM. (He did only win the Nobel prize in Econ, though, which is not a particularly strong predictor of math ability.)
There’s Renaissance technologies making obscene returns consistently for decades.
According to Wikipedia, the founder advised people to invest with Bernie Madoff...
Madoff fooled Simons for 10 years. He told Stony Brook to invest in Madoff in 1991. In 2003, Renaissance decided that Madoff was a fraud and pulled out. Simons told Stony Brook to pull out, but they left in 50%.
Many people investing with Madoff thought he was a fraud, just that he was defrauding someone else (eg, frontrunning his brokerage clients). It’s not clear that they pulled out because they realized he was a fraud or because they worried that Spitzer would investigate him. Their suspicions triggered a failed investigation of Madoff not because they complained to the SEC, but because when the SEC audited RenTech, it found emails discussing Madoff.
Oddly, the wikipedia article seems to be based on the first WSJ article I linked, not the Bloomberg one it cites.
Hindsight bias?
It’s mild evidence against the “top level math talent will be able to identify bubbles before they burst” hypothesis, but only mild because I don’t think Madoff was transparent enough to be obviously unstable in the way that the housing market was.
Yeah, it’s a cheap shot. Madoff fooled a lot of people, and detecting fraud takes a different skill set.
How would we go about making it such that someone who contemplated perpetrating that sort of fraud would be very likely to conclude, “Nah, there’s no way that would work; if I tried that, I’d just end up in prison like Madoff”?
Trump up the trials, studiously avoid mentioning the obvious fact that there’s a bunch of people living large on top of a Ponzi scheme right now.
Considering that the wikipedia article about Madoff didn’t exist until the scandal was unveiled … probably. (I checked the Wikipedia history when I first heard about it in Dec 08, check the history for yourself. )
Hmm. I thought I mentioned Renaissance in this post, but for some reason it says Medallion, which is one of their funds. Apparently I derped earlier, fixing it now.
You’re a PhD in math with no specialist knowledge of finance. Do you think that you personally could do better financial analysis than, say, a Business major with a CFA and a decade or two of industry experience? If no, why do you think mathematicians are the way forward for the field?
I would expect the mathematician to be more likely to be a good coder, and more likely to lean on data analysis that she understood from first principles.
The closest thing to finance from first principles is just to assume the EMH, use the CAPM theory to constuct a market portfolio, and practice a policy of pure commission/tax avoidance. Finance from first principles is incredibly boring—the only way to make it interesting is to start getting into second-order effects.
Finance != data analysis.
The comment was “lean on data analysis”, as in “lean on data analysis to do finance”. Yes, the analysis itself is not “finance” per se, but most of the data analysis done in finance is simple math that gets used in complex ways. It’s easy enough to calculate P/E, but figuring out whether that implies you wanting to buy or sell is a much harder question. About the only way to use first principles to actually make buy/sell decisions is, as I said, to use the CAPM and just embrace your ignorance.
That seems a little extreme; presumably there’s a difference between using statistical tests as a heuristic you don’t understand, and using statistical tests in a well-understood way, even if you’re not deriving finance from first principles.
Also, CAPM isn’t actually true (i.e. assumptions never hold in the real world), whereas statistics is.