推荐到广播
13202 人聚集在这个小组
(Memento Mori)
(~(￣︶￣)~)
(~(￣︶￣)~)
(Samantha在伦敦)
(~(￣︶￣)~)
第三方登录：不久前一个好朋友打算转金融，想做“宽客”。一直对宽客这群靠数学模型分析金融市场的物理学家和数学家非常感兴趣，在 Quora 和知乎上搜索了一些相关问题，感兴趣的读者可以瞄瞄看 :)本文中绿底黑字都是链接，请点击“阅读原文”（或者 read more）获取。From QuoraWhat is quant trading?What is it like to be a quant?How do I become a good quant?What should I study if I want to get a quant position at some of the top finance firms?What are the top professional and personal qualities or traits of a successful quant developer and analyst?What are the most challenging mathematical topics to master before one can become a good quant?How do I become a quant after graduation from a machine learning PhD?To become a quant trader, which research areas should a machine learning PhD focus on?How do quants with PhDs view quants with master's in financial engineering or financial math?What are the best quant hedge funds?How do I prepare for a quant internship at Two Sigma, Jane Street etc.?How does one set up a quant trading business in R?What are the best interview questions to evaluate a quant?Will I make more money if I become a Quant?Is it possible build a trading strategy/software for private use?来自知乎Quant 通常需要什么样的教育背景和知识结构？Quant 这个职业在国内的前景怎样？P Quant 和 Q Quant 到底哪个是未来？什么性格的人适合 Quant 这个职位？Quant 一天的生活是怎样的？Quant 和程序员到底有什么差别？Stochastic Calculus 在现实的 Quant 世界中到底可以干什么？纯CS（machine learning) 背景转quant 需要学习些什么？对于 Quant 来说， Financial Modeling 和传统的机器学习方法有什么联系和区别？对 Quant 而言 Python 的需求高吗，除 C++ 外还有哪些流行的编程语言？Quant 应该学习哪些 Python 知识？Quant 如何运算百万行的数据？风险管理 (Risk Management) 和 Quant 的区别是什么？所需知识和技能都有何不同？Google 这样的公司为什么不涉足量化对冲或高频交易领域？用人工智能计算股票的涨和跌可行吗？如何看待纽约大学教授 Marco Avellaneda 的言论：Pure Quant 叱咤风云的时代已经终结？可以获得国内股票和期货tick级别历史数据的数据库有哪些？MRWHY(MRWHY37)　
　文章为作者独立观点，不代表微头条立场
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This is the study note of "Shaping up with Angular.js", a set of videos teaching angular.js.《异类》，作者：格拉德威尔。
我认为这本书最有价值的地方在于挖掘机遇对于一个人成功的重要性，诸如出生日期、出身阶级、社会群体。但是除开机遇这种不可控的因素，第二章谈论的“10000小时”，才是自身能把握的那部分。动画的作用绝不仅仅是润色，它还能：引导注意力；包含层级；预告下一步动作；减轻加载焦虑；带来惊喜感。新兴市场网络昂贵、网速慢等特点，使得在这些地方进行的用户研究方法受到限制，因此需要采用不同的方法来做用户研究。本文介绍了四种在新兴市场进行用户研究的方法。Well caz u know for no matter what you can keep this smiley face and move on.没想到两天时间内阅读量过千...《移动互联时代的商机》是辻村清行在 2012 年出版的一本书，距今已经有 5 年。想一想 5 年前的时候，滴滴打车刚刚开始创业，拼车还没有出现；智能家居 / VR / 智能医疗还没有火热。但是这些当下的热点在 5 年前已经在这本书中出现了。做 VALSE 2016 网站的时候，没有设计师的参与。在这种情境下，只好考虑从功能性出发写网站，这个过程中，开始思考前端工程师的工作起点到底是不是设计稿，并以此看到了让前端工程师脱离设计师直接写网站的可能性：从功能性出发的网页构建。不久前一个好朋友打算转金融，想做“宽客”。一直对宽客这群靠数学模型分析金融市场的物理学家和数学家非常感兴趣，在 Quora 和知乎上搜索了一些相关问题，感兴趣的读者可以瞄瞄看 :)播客快要流行咯，我列了 4 个播客为啥会火的原因，外加一些高质量播客推荐。CV Dazzle，这个在 2013 年左右引起 New York Times、DIS 杂志追捧的“人类对战计算机视觉的胜利”，放在 3 年后的今天看更像是精神麻醉剂。人类该怎样在计算机视觉下隐藏？更悲观地说，人类能够在计算机视觉下隐藏吗？今天参加2016武汉开发者峰会，感觉武汉互联网大佬基本上来齐了。，苹果召开春季发布会（Apple Special Event 2016），发布包括 iPhone SE、iPad Pro 等新产品。跟以前看的发布会感觉很不一样，这次发布会苹果给我的感觉是：刷豆瓣书评看米兰昆德拉的新书《庆祝无意义》，没有得到很好的分数。评论里面有人说特别好，有人说特别差——
就是这样的书最吸引我。因为没有评论可以左右，能有自己的判断。（那你还刷豆瓣书评 - - ）
得到的结论是：是一本好书。唠一唠移动前端基础知识~带着没有抢到敬业福的怨气，一起吐槽春晚那只（tuo）吉祥物。韩大师粉丝请点击左上角返回，又轻又快又温柔~MRWHY37WangHongYang 的个人订阅号，纯粹个人兴趣——人工智能、WEB产品、前端技术、杂谈吐槽......乱七八糟的什么都有。热门文章最新文章MRWHY37WangHongYang 的个人订阅号，纯粹个人兴趣——人工智能、WEB产品、前端技术、杂谈吐槽......乱七八糟的什么都有。目前国内好像没有这个职业，主要都在香港，但随着国内金融业发展，quant是否会逐步兴起呢？
谢邀。我曾在国内一家主要的投行的Quant组工作过一段时间，我结合我自己的经历和同朋友交流的见闻谈一谈看法。简单而言：目前Quant在国内状况一般，但是长期肯定是有很大的发展的。国内其实是有这个职业的，每年都会有一个国内证券公司的数量分析组排名，2010年我记得国信证券是第一名。然后卖方JP Morgan在北京是有Quant组的，Citi在上海也有Quant组。另外中金、中信也有对应的Quant部门。在香港的话就更多了，Credit Suisse，Morgan Stanley，JP Morgan，Nomura我都是确定有Quant组的，而且规模不算小。高盛目前还没在亚洲提供Quant职务。买方也有很多Quant职务，比如富国基金的Beta类基金很多都是Quant设计的；上投摩根也是明确招Quant的。在国内一般Quant做什么内容呢，如果广义上来讲，一部分Quant是在做Risk Management，随着投行风险控制与国际接轨，这部分对于Quant人才的需求是比较大的，因为交易数量级越来越大，风险估计精度要求越来越高，传统的Risk人才已经不能满足需求了。这部分人员pay和待遇算Middle Office水平把，一般。另外一部分，所谓传统意义上的Quant，我个人的感受是，在卖方投行里面，大部分在做建模（Factor Model之类），结构产品定价（Structure Product）以及宏观模型（Macro Economics）。极少一部分中国的卖方Quant在做市场微结构（Market Micro Structure），高频交易（High Frequency Trading）和统计套利（Stats Arbitrage）。后面三种工作，大量出现在香港工作的Quant身上，或者买方Quant。总体而言，外资的Quant待遇比内资好（视具体情况不绝对），香港的Quant过得比内地好。内资里面，Quant的待遇介于Middle Office和Front Office之间，很多人想跳去做Equity Research或者Sales & Trading，安分干Quant的不多。新人年薪能够到30万算很不错的了。外资香港的Quant与国际接轨，Global Pay，入职差不多50-70万港币是可以期待的。外资的内地Quant很难说，我只知道JP Morgan北京Office的Pay不如香港，其它的因为人不多，很难给出靠谱的估计。买方的待遇差异非常大，看雇主，也看资历，总体内地买方Pay不算高，一个年近30的PhD去了Pay 50万这种情况也算很正常的。工作的感觉是国内的Quant小日子还是挺舒服的，比美国这边的Front Desk Quant轻松很多，偏研究性质，毕竟直接做Strategy去交易赚钱的不多。压力也不算大，经常一个project你玩命拖没人管你。而且绩效管理混乱，不如IBD，Equity Research或者Sales & Trading那么好衡量。哎，一个业务，凡是涉及短期（获利）和长期（研究）的冲突，就很难做绩效考核了。其实国内的Quant现在算朝阳产业，现在说真的，不算一个特别风光的职业。有人说华尔街的Quant是街上的摇滚巨星，中国连娱乐圈的摇滚巨星都不多，遑论金融圈。。。。其实这都是有原因的，根本还是中国的市场在高频交易、对冲、市场开放度等方面跟国外差距甚大，很多Quant的传统领域或现在的前沿领域无法应用，大量的公司储备Quant其实是在储备研究结果，现在无法发挥威力。很多数量交易在中国的问题我在这个回答里面：有讨论。你可以参考。长期，如果市场健全了，我个人觉得Quant在国内有很大的发展。首先可以做的东西很多，国外过去30年的成果基本可以照搬来试验效果，其次国内的市场属于高度不有效市场，套利机会非常多，Quant可以在天朝的热土赢来黄金时代。最重要的是，在一个市场由散户主导向机构主导转变的过程中，数量化策略及数量化风险控制的兴起是必然趋势。冯仑在《野蛮生长》里面写，要在国内找商业机会，就看看美国50年前流行什么就行了。这句话很对，你看看华尔街30年前在搞什么，很可能就是国内市场未来几年的热点。美国也经历不靠谱的庞兹骗局横行时代，经历了庄家风行，经历了投机者英雄主义时代，最终发展到今天。我个人相信，的这段Quant黄金时代也会在中国重现。
&div class=&highlight&&&pre&&code class=&language-text&&1.quant是做什么的？
quant的工作就是设计并实现金融的数学模型（主要采用计算机编程），包括衍生物定价，风险估价或预测市场行为等。所以quant更多可看为工程师，按中国的习惯性分类方法就是理工类人才，而不是文科人才，这个和金融有一定的区别(当然金融也有很多理工的内容)。
2.有哪几种quant？
a. desk quant
desk quant 开发直接被交易员使用的价格模型. 优势是接近交易中所遇到的money和机会. 劣势是压力很大.
b. Model validating quant
model validating quant 独立开发价格模型,不过是为了确定desk quant开发的模型的正确性. 优势是更轻松,压力比较小. 劣势是这种小组会比较没有作为而且远离money.
c. Research quant
Research quant 尝试发明新的价格公式和模型,有时还会执行blue-sky research(不太清楚是什么). 优势是比较有趣(对喜欢这些人来说),而且你学到很多东西. 劣势是有时会比较难证明有你这个人存在(跟科学家一样,没有什么大的成果就没人注意你)
d. quant developer
其实就是名字被美化的程序员,但收入很不错而且很容易找到工作. 这种工作变化很大. 它可能是一直在写代码,或者调试其他人的大型系统.
e. Statistical arbitrage quant
Statistical arbitrage quant 在数据中寻找自动交易系统的模式(就是套利系统). 这种技术比起衍生物定价的技术有很大的不同, 它主要用在对冲基金里. 而且这种位置的回报是极不稳定的.
d. capital quant
capital quant 建立银行的信用和资本模型. 相比衍生物定价相关的工作,它没有那么吸引人,但是随着巴塞尔II银行协议的到来,它变的越来越重要. 你会得到不错的收入(但不会很多),更少的压力和更少的工作时间.
人们投资金融行业就是为了赚钱, 如果你想获得更多的收入,你就要更靠近那些钱的&生产&的地方. 这会产生一种接近钱的看不起那些离得比较远的人的现象. 作为一个基本原则, 靠近钱比远离钱要来得容易.
3.quant工作的领域
FX就是外汇交易的简写. 合同趋向于短期,大量的金额和简单的规定.所以重点在于很快速度的建立模型.
b.Equities
Equities的意思是股票和指数的期权. 技术偏向于偏微分方程(PDE). 它并不是一个特别大的市场.
c.Fixed income
Fixed income的意思是基于利息的衍生物. 这从市值上来说可能是最大的市场. 他用到的数学会更加复杂因为从根本上来说他是多维的. 技术上的技巧会用的很多. 他的收入比较高.
d.Credit derivatives
Credit derivatives是建立在那些公司债务还清上的衍生产品.他发展的非常快并有大量需求,所以也有很高的收入. 尽管如此,他表明了一些当前经济的泡沫因素.
e.Commodities
Commodities因为最近几年生活用品价格的普遍涨价,也成为一个发展迅速的领域.
Hybrids是多于一个市场的衍生物市场,典型情况是利息率加上一些其它东西.它主要的优势在于可以学到多种领域的知识.这也是当前非常流行的领域.
4.quant一般在哪些公司工作
a.商业银行
(HSBC, RBS)
商业银行对你要求少,也给的少. 工作会比较稳定.
b.投行 (高盛,Lehman Brothers)
投行需要大量的工作时间但工资很高. 不是很稳定的工作.
总的来说, 美国的银行收入比欧洲银行高,但工作时间更长
c.对冲基金 (Citadel Group)
对冲基金需要大量的工作时间和内容,他们也处在高速发展同时不稳定的情况中. 你可能会得到大量的回报,也可能几个月后就被开除.
d.会计公司
大型会计公司会有自己的顾问quant团队. 有些还会送他们的员工去Oxford读Master. 主要的劣势在于你远离具体的行为和决策,而且厉害的人更愿意去银行,所以你比较难找到人请教.
e.软件公司
外包quant模型变得越来越流行. 所以你去软件公司也是一个选择. 劣势和会计公司比较类似.
5.成为一个quant需要看哪些书?
现在有非常多的关于quant的书.基础书籍包括
- Hull - Options future and other derivatives. 这本书被称为bible. 缺点是这本书的内容主要面向MBA而不是quantitative专家
- Baxter and Rennie – 主要介绍一些手法和诀窍,但主要面向原理而不是实际操作.
- Wilmott (Derivatives) – 对PDE介绍的非常不错,但其他方面一般
推荐其他几本原作者的书(广告啊...但的确很好,大牛来的)
- The concepts and practice of mathematical finance
这本书的目标在于覆盖一个优秀quant应该知道的知识领域. 其中包括强列推荐你在应聘工作之前看的一些编程项目.
- C++ design patterns and derivatives pricing
这本书是为了告诉大家如何使用C++来做quant的工作.
随机微积分虽然在第一眼看上去不是很重要,但的确非常有用的. 我建议大家先看一些基本理论的书,类似Chung’s books. 一些这方面我推荐的书:
- Williams, Probability with martingales. 一本很容易让人了解account of discrete time martingale theory的书.
- Rogers and Williams, particularly Volume 1.
- Chung and Williams
6. 成为quant,我需要知道一些什么?
根据你想工作的地方不同,你需要学习的知识变化很大. 在写着篇文章的时间(1996),我会建议将我的书全部学会就可以了.很多人错误的把学习这些知识看作仅仅看书而已.你要做的是真正的学习,就像你在准备参加一个基于这些书内容的考试. 如果你对能在这个考试里拿A都没有信心的话,就不要去面试任何的工作.
面试官更在乎你对基本知识的了解是否透彻,而不是你懂得多少东西. 展示你对这个领域的兴趣也很重要. 你需要经常阅读Economist, FT 和Wall Street Journal. 面试会问到一些基本微积分或分析的问题,例如Log x的积分是什么. 问到类似Black-Scholes公式怎么得出的问题也是很正常的. 他们也会问到你的论文相关的问题.
面试同样也是让你选择公司的一个机会. 他们喜欢什么样的人,他们关心的是什么之类的答案可以从他们的问题中得出. 如果问了很多关于C++语法的问题,那么要小心选择除非那是你想做的工作. 一般来说, 一个PhD对得到quant的offer是必需的.
有一个金融数学的Master学位会让你在银行风险或交易支持方面却不是直接quant方面的工作. 银行业变得越来越需要数学知识,所以那些东西在银行的很多领域都有帮助.
在美国, 读了一个PhD之后再读一个Master变得越来越普遍. 在UK这依然比较少见.
所有类型的quant都在编程方面花费大量时间(多于一半).尽管如此,开发新的模型本身也是很有趣的一件事. 标准的实现方法是用C++. 一个想成为quant的人需要学习C++. 有些其他地方使用Matlab所以也是一个很有用的技能,但没C++那么重要. VBA也用的很多,但你可以在工作中掌握它.
一个quant能赚多少? 一个没有经验的quant每年大概会挣到磅. 我所见过最低的是25000,最高的是60000加奖金. 如果你的工资超出这个范围,你要问自己why? 收入会迅速的增长. 奖金也是总收入中一个很大的组成部分. 不要太在乎开始的工资是多少,而是看重这个工作的发展机会和学习的机会.
9. 工作时间
一个quant工作的时间变化很大. 在RBS我们8:30上班,6pm下班. 压力也是变化很大的, 一些美国银行希望你工作时间更长. 在伦敦有5-6个星期的假期. 而在美国2-3个是正常的.
&/code&&/pre&&/div&
1.quant是做什么的？
quant的工作就是设计并实现金融的数学模型（主要采用计算机编程），包括衍生物定价，风险估价或预测市场行为等。所以quant更多可看为工程师，按中国的习惯性分类方法就是理工类人才，而不是文科人才，这个和金融有一定的区别(当然金融…
前不久刚向华夏基金的一位高管咨询过这个事，因为我也面临职业的选择。综合我接收到的全部信息，我最后决定毕业之后放弃quant，这个领域实在不适合我。&br&具体说，国内的quant岗位本来就少，加上金融产品太少，管制太多，quant可做的事情比较少。在另一个回答中我提过一个事儿，国泰君安的一位金融工程的主管，有次说：我在这个部门做了七年主管，终于迎来了股指期货。这是什么概念呢？没有股指期货，A股市场基本不能做空，量化交易实在多余。而对于sell side quant，同样是上述原因，产品少，quant的技术含量跟美国没法比，国内一些外国投行有很牛的quant部门，但是大多是为外国产品服务的。国内sell side的机构里quant太像码农了，反正我是不想做这种工作，何况，教码农金融要比教金融master或bachelor编程的成本低，所以卖方quant岗往往被编程能力强的人占据。&br&当人们说quant多么多么牛逼的时候，说得都是对冲基金里的quant，这种quant国内很少，短期内看不出有蓬勃发展的可能性。现在入行的人将来可能作为中国这个领域的元老级专家，会有不错的收入和地位，但是真难说要等多久，我国的对冲基金行业和欧美相差得有五十年。&br&综上所述，从择业的角度来说，在国内quant不是个好职业，发展空间也很小，如果你具备quant要求的编程能力，还不如去IT行业或者互联网行业；从产业发展的角度说，quant的前景肯定好，正常的金融市场发展必然经历这个大潮，但是从中国现在的发展速度来看估计要五十年后见了。国内唯一一个有希望短期内追上美国的金融工种是股权私募，为什么呢，因为这是个靠投资的大智慧和人脉获利的行业，主要核心不是技术而是人，国内的私募确实也是飞速发展，追英赶美的势头很强。
前不久刚向华夏基金的一位高管咨询过这个事，因为我也面临职业的选择。综合我接收到的全部信息，我最后决定毕业之后放弃quant，这个领域实在不适合我。 具体说，国内的quant岗位本来就少，加上金融产品太少，管制太多，quant可做的事情比较少。在另一个回答…
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想找quant方面的工作，搞金融衍生品定价，风险控制等。国内都有哪些公司有这样的需求啊，比如投行，基金公司等等什么的？不是特别清楚，希望过来人来个详细的解答，谢谢！！
载入中......
你具备什么条件，申请Quant可是要求很高的呀，那可是将来中国的最高端金领啊，也是我向往的职业。
去这上面看看,有一些专门要类似quant的岗位
They Tried to Outsmart Wall Street -3.10纽约时报科技版关于Quant 文章谈了《宽客人生》作者的看法，虽仍然赚了些钱，但认为物理学家是把灵魂都出卖了，为什么，自己去看，也谈了入行经历，应该是不好找工作，几个图形把老板忽悠了，然后得到了重用。谈了老巴和前线交易员的看法，贬低得不能再贬低了，不过做为直接交易的人员，说话的确有可信度。还谈了black scholes模型，说怎么怎么好，但后面又说得诺贝尔奖后一年，公司就倒闭了（老家伙为了得诺贝尔奖，证明自己理论正确才开个公司证明自己？得了奖了，公司就没有什么存在的意义了？）还谈了一些基本指标，弄得好像很专业的样子，其实就是波动率和波动率微笑。黑天鹅也露了露头，这也算Quant？最后引用了些人的看法，总体讲卖软件的、卖所谓的金融产品的，经过怎样合理定价的，一律吹嘘Quant多神，模型多好；实际操作交易的，没一个说好的；挂着Quant头衔儿的都忙着推责任，说自己比较客观公正，良心没让狗吃了，是迫于形势（就是说还是选择了赚钱不是？）。总结：搞物理的不好好研究原子弹跑到华尔街是为了赚钱，券商、投行为了卖产品鼓吹Quant和模型是为了向客户“骗钱”，投资者不管看得懂看不懂，只要被唬住了就往外掏钱，为的是钱生钱。顺便说一句，把次贷危机的部分责任归咎于Quant和冒充quant的还是有道理的。理论和模型都不管什么适用性就乱用一气。还是那句话，为的都是钱。原文如下：They Tried to Outsmart Wall Street NEW THEORIES After spending 20 years in the study of physics, Emanuel Derman applied his thinking to stock options. By DENNIS OVERBYEPublished: March 9, 2009 Emanuel Derman expected to feel a letdown when he left particle physics for a job on Wall Street in 1985.After all, for almost 20 years, as a graduate student at Columbia and a postdoctoral fellow at institutions like Oxford and the University of Colorado, he had been a spear carrier in the quest to unify the forces of nature and establish the elusive and Einsteinian “theory of everything,” hobnobbing with Nobel laureates and other distinguished thinkers. How could managing money compare?But the letdown never happened. Instead he fell in love with a corner of finance that dealt with stock options.“Options theory is kind of deep in some way. I it had the quality of physics,” Dr. Derman explained recently with a tinge of wistfulness, sitting in his office at Columbia, where he is now a professor of finance and a risk management consultant with Prisma Capital Partners.Dr. Derman, who spent 17 years at Goldman Sachs and became managing director, was a forerunner of the many physicists and other scientists who have flooded Wall Street in recent years, moving from a world in which a discrepancy of a few percentage points in a measurement can mean a Nobel Prize or unending mockery to a world in which a few percent one way can land you in jail and a few percent the other way can win you your own private Caribbean island.They are known as “quants” because they do quantitative finance. Seduced by a vision of mathematical elegance underlying some of the messiest of human activities, they apply skills they once hoped to use to untangle string theory or the nervous system to making money.This flood seems to be continuing, unabated by the ongoing economic collapse in this country and abroad. Last fall students filled a giant classroom at M.I.T. to overflowing for an evening workshop called “So You Want to Be a Quant.” Some quants analyze the stock market. Others churn out the computer models that analyze otherwise unmeasurable risks and profits of arcane deals, or run their own hedge funds and sift through vast universes of data for the slight disparities that can give them an edge. Still others have opened an academic front, using complexity theory or artificial intelligence to better understand the behavior of humans in markets. In December the physics Web site arXiv.org, where physicists post their papers, added a section for papers on finance. Submissions on subjects like “the superstatistics of labor productivity” and “stochastic volatility models” have been streaming in.Quants occupy a revealing niche in modern capitalism. They make a lot of money but not as much as the traders who tease them and treat them like geeks. Until recently they rarely made partner at places like Goldman Sachs. In some quarters they get blamed for the current breakdown — “All I can say is, beware of geeks bearing formulas,” Warren Buffett said on “The Charlie Rose Show” last fall. Even the quants tend to agree that what they do is not quite science.As Dr. Derman put it in his book “My Life as a Quant: Reflections on Physics and Finance,” “In physics there may one day be a Theory of E in finance and the social sciences, you’re lucky if there is a useable theory of anything.”Asked to compare her work to physics, one quant, who requested anonymity because her company had not given her permission to talk to reporters, termed the market “a wild beast” that cannot be controlled, and then added: “It’s not like building a bridge. If you’re right more than half the time you’re winning the game.” There are a thousand physicists on Wall Street, she estimated, and many, she said, talk nostalgically about science. “They sold their souls to the devil,” she said, adding, “I haven’t met many quants who said they were in finance because they were in love with finance.”The Physics of MoneyPhysicists began to follow the jobs from academia to Wall Street in the late 1970s, when the post-Sputnik boom in science spending had tapered off and the college teaching ranks had been filled with graduates from the 1960s. The result, as Dr. Derman said, was a pipeline with no jobs at the end. Things got even worse after the cold war ended and Congress canceled the Superconducting Supercollider, which would have been the world’s biggest particle accelerator, in 1993.They arrived on Wall Street in the midst of a financial revolution. Among other things, galloping inflation had made finances more complicated and risky, and it required increasingly sophisticated mathematical expertise to parse even simple investments like bonds. Enter the quant.“Bonds have a price and a stream of payments — a lot of numbers,” said Dr. Derman, whose first job was to write a computer program to calculate the prices of bond options. The first time he tried to show it off, the screen froze, but his boss was fascinated anyway by the graphical user interface, a novelty on Wall Street at the time.Stock options, however, were where this revolution was to have its greatest, and paradigmatic, success. In the 1970s the late Fischer Black of Goldman Sachs, Myron S. Scholes of Stanford and Robert C. Merton of Harvard had figured out how to price and hedge these options in a way that seemed to guarantee profits. The so-called Black-Scholes model has been the quants’ gold standard ever since.In the old days, Dr. Derman explained, if you thought a stock was going to go up, an option was a good deal. But with Black-Scholes, it doesn’t matter where the stock is going. Assuming that the price of the stock fluctuates randomly from day to day, the model provides a prescription for you to still win by buying and selling the underlying stock and its bonds.“If you’re a trading desk,” Dr. Derman explained, “you don’t care i you still have a recipe.” The Black-Scholes equation resembles the kinds of differential equations physicists use to represent heat diffusion and other random processes in nature. Except, instead of molecules or atoms bouncing around randomly, it is the price of the underlying stock.The price of a stock option, Dr. Derman explained, can be interpreted as a prediction by the market about how much bounce, or volatility, stock prices will have in the future.But it gets more complicated than that. For example, markets are not perfectly efficient — prices do not always adjust to right level and people are not perfectly rational. Indeed, Dr. Derman said, the idea of a “right level” is “a bit of a fiction.” As a result, prices do not fluctuate according to Brownian motion. Rather, he said: “Markets tend to drift upward or cascade down. You get slow rises and dramatic falls.”One consequence of this is something called the “volatility smile,” in which options that benefit from market drops cost more than options that benefit from market rises.Another consequence is that when you need financial models the most — on days like Black Monday in 1987 when the Dow dropped 20 percent — they might break down. The risks of relying on simple models are heightened by investors’ desire to increase their leverage by playing with borrowed money. In that case one bad bet can doom a hedge fund. Dr. Merton and Dr. Scholes won the Nobel in economic science in 1997 for the stock options model. Only a year later Long Term Capital Management, a highly leveraged hedge fund whose directors included the two Nobelists, collapsed and had to be bailed out to the tune of $3.65 billion by a group of banks. Afterward, a Merrill Lynch memorandum noted that the financial models “may provide a greater sense of sec therefore reliance on these models should be limited.” That was a lesson apparently not learned.Respect for NerdsGiven the state of the world, you might ask whether quants have any idea at all what they are doing.Comparing quants to the scientists who had built the atomic bomb and therefore had a duty to warn the world of its dangers, a group of Wall Streeters and academics, led by Mike Brown, a former chairman of Nasdaq and chief financial officer of Microsoft, published a critique of modern finance on the Web site Edge.org last fall calling on scientists to reinvent economics.Lee Smolin, a physicist at the Perimeter Institute for Theoretical Physics in Waterloo, Ontario, who was one of the authors, said, “What is amazing to me as I learn about this is how flimsy was the theoretical basis of the claims that derivatives and other complex financial instruments reduced risk, when their use in fact brought on instabilities.” But it is not so easy to get new ideas into the economic literature, many quants complain. J. Doyne Farmer, a physicist and professor at the Santa Fe Institute, and the founder and former chief scientist of the Prediction Company, said he was shocked when he started reading finance literature at how backward it was, comparing it to Middle-Ages theories of fire. “They were talking about phlogiston — not the right metaphor,” Dr. Farmer said.One of the most outspoken critics is Nassim Nicholas Taleb, a former trader and now a professor at New York University. He got a rock-star reception at the World Economic Forum in Davos this winter. In his best-selling book “The Black Swan” (Random House, 2007), Dr. Taleb, who made a fortune trading currency on Black Monday, argues that finance and history are dominated by rare and unpredictable events.“Every trader will tell you that every risk manager is a fraud,” he said, and options traders used to get along fine before Black-Scholes. “We never had any respect for nerds.” Dr. Taleb has waged war against one element of modern economics in particular: the assumption that price fluctuations follow the familiar bell curve that describes, say, IQ scores or heights in a population, with a mean change and increasingly rare chances of larger or smaller ones, according to so-called Gaussian statistics named for the German mathematician Friedrich Gauss.But many systems in nature, and finance, appear to be better described by the fractal statistics popularized by Benoit Mandelbrot of IBM, which look the same at every scale. An example is the 80-20 rule that 20 percent of the people do 80 percent of the work, or have 80 percent of the money. Within the blessed 20 percent the same rule applies, and so on. As a result the odds of game-changing outliers like Bill Gates’s fortune or a Black Monday are actually much greater than the quant models predict, rendering quants useless or even dangerous, Dr. Taleb said.“I think physicists should go back to the physics department and leave Wall Street alone,” he said.When Dr. Taleb asked someone to come up and debate him at a meeting of risk managers in Boston not too long ago, all he got was silence. Recalling the moment, Dr. Taleb grumbled, “Nobody will argue with me.”Dr. Derman, who likes to say it is the models that are simple, not the world, maintains they can be a useful guide to thinking as long as you do not confuse them with real science — an approach Dr. Taleb scorned as “schizophrenic.” Dr. Derman said, “Nobody ever took these models as playing chess with God.” Do some people take the models too seriously? “Not the smart people,” he said.Quants say that they should not be blamed for the actions of traders. They say they have been in the forefront of pointing out the shortcomings OF modern economics.“I regard quants to be the good guys,” said Eric R. Weinstein, a mathematical physicist who runs the Natron Group, a hedge fund in Manhattan. “We did try to warn people,” he said. “This is a crisis caused by business decisions. This isn’t the result of pointy-headed guys from fancy schools who didn’t understand volatility or correlation.”Nigel Goldenfeld, a physics professor at the University of Illinois and founder of NumeriX, which sells investment software, compared the financial meltdown to the Challenger space shuttle explosion, saying it was a failure of management and communication.Prisoners of Wall StreetBy their activities, quants admit that despite their misgivings they have at least given cover to some of the wilder schemes of their bosses, allowing traders to conduct business in a quasi-scientific language and take risks they did not understand.Dr. Goldenfeld of Illinois said that when he posted scholarly articles, some of which were critical of financial models, on his company’s Web site, salespeople told him to take them down. The argument, he explained, was that “it made our company look bad to be associating with Jeremiahs saying that the models were all wrong.” Dr. Goldenfeld took them down. In business, he explained, unlike in science, the customers are always right. Quants, in short, are part of the system. “They get paid, a Faustian bargain everybody makes,” said Satyajit Das, a former trader and financial consultant in Australia, who likes to refer to them as “prisoners of Wall Street.”“What do we use models for?” Mr. Das asked rhetorically. “Making money,” he answered. “That’s not what science is about.” The recent debacle has only increased the hunger for scientists on Wall Street, according to Andrew Lo, an M.I.T. professor of financial engineering who organized the workshop there, with a panel of veteran quants.The problem is not that there are too many physicists on Wall Street, he said, but that there are not enough. A graduate, he told the young recruits, can make $75,000 to $250,000 a year as a quant but can also be fired if things go sour. He said an investment banker had told him that Wall Street was not looking for Ph.D.’s, but what he called “P.S.D.s — poor, smart and a deep desire to get rich.”He ended his presentation with a joke that has been told around M.I.T. for a long time, but s “What do you call a nerd in 10 years? Boss.”
国内误区不小，加上搞培训的，高校一起忽悠，很容易失真。
感觉国内和国外很不一样吧
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