Update: Ja jūs meklējat par akadēmisko izcilību, skatīt: Starpība starp riska un nenoteiktības. Šajā amatā zemāk, mēs kritizējam uzņēmumiem, ne mainīt savu praksi, neraugoties uz pēdējā neveiksmes viņu aplēsēm, metodikas, un modeļi. Protams, mēs domājam, ka abi ir vērts lasījumā.
Katru pirmdienas rītu, ka pēdējo dažu gadu laikā, mēs esam saņēmuši e-pastu no http://jobs.phds.org kurā uzskaitīti pieejamie amatus atbilstoši mūsu specifikācijām, , kas ir:
“Nosūtīt Nedēļas e-pasta vēstules, kas satur darba vietas…
…par zinātņu doktoru: Bizness / Finansēt / Ekonomika
…tipu: Līgums / Projekts / Temporary, Employee, Non-tenure-track faculty, Postdoctoral researcher, Tenure-track / tenured faculty
…in sectors: visi
…located: in United States
…with keywords: none
Parasti, there’s 20 – 40 positions listed each week, and most of those involve quantitative finance, usually in the NYC area. For the past year or so, we’ve been particularly interested to see if the job descriptions would change given the failure of many quantitative trading strategies, modeling techniques and risk measures. (Yeah, we know they didn’t actually “fail.” Recent results were just plain bad luck that no one could have predicted. The models worked perfectly, except when they didn’t.)
Diemžēl, our parenthetical sarcasm seems to be the implicit position of many financial firms–without the sarcasm, protams. We say because we haven’t observed any change in the posted job descriptions in the jobs.phds.org emails or any of the other ones that we receive from recruiters who regularly send similar descriptions.
Tagad, we’ve been meaning to write about this observation for a few months but were finally motivated to do so because of several other items we read this morning, including two opinion columns and one article.
Raksts, Computer-Trading Models Meet Match kas Wall Street Journal, describes how several algorithmic-based hedge funds have lost money recently because of “the recent high volatility.” Tāpēc, we guess their models aren’t flawless.
One of the op-ed pieces is ar L. Gordon Crovitz, and it is also in the Journal: In Finance, Too, Learning Entails Risk. Tajā, Mr. Crovitz attempts to relate “financial engineering” to other types of engineering, g, mašīnbūve, and he seems to imply that it’s still a young discipline; tāpēc, give it time, but we think that his argument ultimately fails and is unconvincing.
Tas ir tāpēc, “financial engineering” isn’t really engineering, which we’d define as the thoughtful application of science or technology to (or in) a well-understood, physical environment. Finance is a subset of a social “science.”
Mr. Crovitz writes in his last paragraph that: “The measure of innovators is not in the mistakes they make, but in the lessons they learn. We now know that our complex markets need better models, which should include more humility, acknowledging that some risks are still too uncertain to measure and should be avoided.” We’d argue with the “still too” in the last sentence as we doubt that such social uncertainty can be resolved or precisely measured. (Starp citu, we also disagree with his conclusion in that sentence that “some risks…should be avoided.” We have no problem with folks taking wild or uncertain gambles; Tomēr, we see no reason that we should subsidize their losses when those gambles go bad.)
To his main point, Tomēr, we don’t see much learnin’ goin’ gada. It seems to be business as usual at many firms and funds.
A much more critical op-ed piece is by Michael Barone, and it’s entitled ‘Formulas’ for certain failure, and his first sentence is “Beware of geeks bearing formulas.” He discusses (and criticizes) financial models, global warming/climate change models, and health-care models, and it reads much like our post from six months ago, Globālā sasilšana un Hipotēku krīze. Remember that this is Michael Barone, who is very well-known for using statistical data in the analysis of politics and demographics.
Kā parasti, Mēs punktu jaunus lasītājus mūsu eseja, Nenoteiktība Management, which details our perspective and philosophy on these issues as well as any number of related posts: see our blog archives. The main point is that not all uncertainty is measurable, i, ka izmērāmu nenoteiktību, vai riska, ir pareizi apakškopu neskaidrības un unknowing. (Citiem vārdiem sakot,, precīzas matemātiskas nosacījumi jāizpilda, lai nenoteiktību, kurām veiks riska. Tāpēc, nenoteiktība ir vispārīgāks termins, i, visu risku ir saistīta nenoteiktība, bet ne viss, kas ir skaidrs ir riskanti, jo ne visi nenoteiktība izmērāma, which a specific mathematical definition.)
As we read the evidence, many institutions and their ‘quants’ will continue to solve mis-specified riska problēmas, because they don’t know how to treat more diffuse and difficult uncertainty problems; tāpēc, they assume them away and treat them as risk problems. We’re clearly not underestimating the difficulties these folks face nor the necessity of making trade-offs, but we’re not sure if they understand the nature of the problem or trade-off. As we’ve written many times before, if they don’t understand them, then they are ignorant, and if they do, then they are cynical., g, Mūsu Mūžīgais jautājums: Cinisks vai Naivs? Neither charactistic is appealing or useful.
Ignoring the larger epistemological issues and the problem of induction, here’s a simple example of the difficulty of making inferences and finding useful information. Even when a distribution can be perfectly known, it’s moments–like the mean and variance–need not exist. (Look a Cauchy distributions and, plašāk, certain stable distributions. While one can calculate historical means and variances from a time series, tiem “aprēķini” may be nonsensical. (They can’t estimate something that doesn’t exist.) The arithmetic can be performed, but the notion is empty.
Kā mēs to redzam, too often if one has a (riska) hammer, then everything looks like a (riska) nail, and it’s easy to pound away, especially when the alternate is to admit that a solution doesn’t exist, which too often sounds like, “Es nezinu.” Tāpēc, while various numbers can be calculated–even calculated very precisely, earnestly, and diligently–to do so is to apply technology, but it’s not engineering nor is it very smart and it can be very harmful.