一名优秀的 Quant 都需要具备哪些职业素养和技能?

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Adrian WANG   2018-9-22 11:43   80480   11
也可以说说你看到的一些优秀 Quant 都具有哪些职业素养和技能?
因为不同 Quant 的原因,技能可能要求不同,所以还望多说说素养:)
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2#
陈皇宇 Renco  4级常客 | 2018-9-22 11:43:48 发帖IP地址来自
首先是对这个行业的兴趣,很多人可能一开始觉得quant很酷炫就开始往这边挪,但是对行业不了解,对需要克服的困难没有清晰的认识,最终打了退堂鼓,所以一个持久的兴趣很重要。

然后说一些会学到用到的东西:
  1. 编程
  2. 数学/统计
  3. 金融

金融工程其实就是这三者的交叉学科。

编程是重中之重,没有谁能不编程就说自己是quant的,只不过需要负责的编程任务不同罢了。对于quant来说,你可能需要学会很多种不同的语言,你需要会C++,并不是说你一定会用到它,而是你对C++的知识让潜在雇主知道你能很快学会其他的语言。你需要会Matlab/R,这样你可以做research, 跑simulation,然后制定策略等等,你还要回SQL,因为金融是一个高频大数据的行业。其它根据不同岗位还会有不同需求。

到底是用数学还是统计,往往取决于你是个Q quant还是P quant,但是金融时间序列和布朗运动为主的difussion process是每个人都必须要会的,之后根据职业特点和个人兴趣会有不同。Q quant会进一步学到各种change of measure, change of numéraire, jump/levy process等等。而P quant则会进一步学习各种数据挖掘算法,滤波方法,贝叶斯统计等等。

最后是金融,这个每个人的看法不一样,有的人说他完全不关心市场是什么样的,他只关心Data,我不太赞成这种看法。很多经典的模型被人诟病,批评别人的Model是很容易的,但是一个Model永远只是现实的一种逼近,关键是这个模型能不能给你基本的直觉指引。很多人说CAPM没用,但是CAPM起码告诉了一个很简单的道理,你不可能在降低风险的同时提高收益,起码从期望上不能做到这个的,很多人做了半天model,最后把这个忽略了。你可能觉得APT很可笑,但是很多fund其实每天就干这个,找factor,然后hedge到各种risk。你可能觉得市场微观结构没意思,但是如果你要开发做市策略的时候这就是重中之重。你可能觉得utility function没意思,但是他能帮助你理解人性。

关于金融,这取决于你怎么看待quant这份工作,你觉得你是在做模型,只不过有人做流体你做金融,那你就会觉得金融理论很没意思,但我会觉得金融学的知识是最重要的,也是最有意思的。

如果把市场比作一个水面,在正常的时候,有很多涟漪,你说你能发现其中的规律,然后你模拟了这个波动,你赚到了钱。但是如果你从来不去思考这个涟漪为什么会出现,那你就不会知道有一个巨浪要来了。也许你10年都在赚钱,但是一天亏到底。

对风险的敬畏是每一个金融业者应有的素养,quant也不能忽略,甚至要多面临两层风险:模型风险和统计估算风险。


知道model什么时候fail比知道model好在哪里要重要的多。


At the end of the day, it's the thing that you don't know you don't know that makes you lose all you pennies.
3#
腾天  6级职业 | 2018-9-22 11:43:49 发帖IP地址来自
扎实的数学基础。我面试的时候都会故意问一些问题让临时看wiki的货暴露马脚。

基础的程序设计能力,根据工种细分不同对程序设计能力要求不同。高频的要求高一些,但就算是衍生品矿工和中低频矿工,写个script的基础的能力必须有。

耐心、耐心、耐心,细心,细心,细心。我以前是个马大哈,几年工作下来耐心大大的,不会放过任何小数点后8位以内的差距。开始磨死人,现在习惯了反而受不了别人马大哈了。

好奇心和不气馁的精神。好奇心帮你发现问题,不气馁的精神让你跟问题死嗑。最终哪怕只是在在一定程度上解决它,都会给你的公司带来利润。

交流能力和虚心精神。矿工是个需要紧密团队合作的工作,一个人再聪明再努力也对付不了市场上一群人都在拼命的聪明人。

最后就是用于面对错误的精神。人人都会犯错误,如果不敢于承认、面对错误,对团队、对公司都是很大的隐患。另外,矿工是个不进则退的行业,不敢于面对自己错误的也会限制自己的成长,这样迟早会被淘汰。

--
安利一个我的live
理工生如何进投行交易部门
4#
董可人  4级常客 | 2018-9-22 11:43:50 发帖IP地址来自
我心目中 Quant 所需要的素养也就是一名科学工作者所应该具备的素养。

第一是要有怀疑精神。别人给你讲了一个思路,或是一项技术,也许那个人是你的老板,或是你敬仰的业内大牛,那么你就会信之不疑全盘接受吗?你从事某项工作多年,比如说一段固定模式程序的写法,很多年都那样写了,从来没出过问题,那么这种方法就一定是完全正确或是最优的吗?你新加入了一家公司或是团队,那里的人看起来都很聪明且有高大上的学历和工作背景,那么他们的做事方法就是无可置疑,你只需照葫芦画瓢吗?在我看来,一个优秀的 Quant,第一步要做的就是独立运用自己所学对要面临的任务和问题进行分析,不轻信其他人(包括自己的历史经验),对任何自己存疑的地方都积极调查,即便有时这会让你看起来像个傻瓜。当然,做到这一点有一个前提,就是你要确保自己并不真的是一个傻瓜,对于给出的质疑要有充足的理由和确凿的证据。

第二是要有纪律性。对于既定的纪律要坚决遵守,比如提交程序之前先跑测试,运行一个系统 之前先做例行检查,写程序的时候保证符合规范。即便有些时候一些纪律看起来很蠢,但如果你知道不遵守它们会导致什么后果,那就绝对不要抱有侥幸心理,严格执行。

第三是能在抽象层面进行工作。这个抽象并非是指数学上那种程度抽象,而是说对一项技术或是产品,能够深入理解其原理,进行有创造力的工作。 强调这一点,是因为我的确见过有些人虽然技术水平不错,但是只能刻板的照做别人交待好的事情,一旦遇到一些别人没有解释过需要自己想办法的问题,就会做的一塌糊涂。就其原因,就是因为对深入的概念和原理缺乏理解,只能浮于表象。

可以看出这三条没有一条是讲如何做交易的,甚至也不涉及具体的数学或是计算机技术。但是我相信能做到这三点的人,教他具体的业务或是技术细节绝非难事。而这三点其实恰恰是在任何科研工作中都需要的。虽然的确有些天才少年可以无师自通,但是对普通人来说,要磨练这些素养,读一个理工科 PhD 经受正规的科研训练是最有效的途径,不论是数学物理还是计算机。我觉得这正是为什么这一行如此偏爱 PhD 的原因。
5#
宋思源  4级常客 | 2018-9-22 11:43:51 发帖IP地址来自
数学得好,一般都对数理背景的很有好感。这里我很感谢我本科的数学系
上面也说了,基本三方面混合的
数理逻辑+金融+编程
我个人方面编程能力不算强
金融的知识方面,其实学金融的时候就一个感觉,这不就是什么和什么加一加就完事了嘛
说到底好多东西,金融方面的,也是可以找到数学的逻辑在里面的。

还有一点就是个人性格等方面的,抗压能力与是否能够静的下来还是很重要的。
有些时候欲速不达,需要静下心来,一点点一点点从头开始做,每一步不能有问题,逻辑得反复推演,要不容易出现没有覆盖的情况。
还有个人方面的感觉就是魄力吧,是否敢担事的能力。
还有从失败中吸取教训的能力~
说这么多也相当于是自我总结了,估计再过一段时间可能还会有更多的感受吧
6#
卢旺杉  3级会员 | 2018-9-22 11:43:53 发帖IP地址来自
一是对各类事物尤其是数理方面有极强的好奇心和热情。好奇心和热情不光使得工作时的效率提高,还会自愿增加处于工作和学习的状态中的时间。

二是严谨和科学精神。严谨是各种事情不马虎,不得过且过,专注细节,实事求是。科学精神是不会盲从(包括不盲从所谓的科学知识),具备怀疑精神,什么事都会问为什么,什么事都不会十分笃定的精神。

这两点其实都跟大五人格中的开放性(经验开放性)有关,从前我们招聘的时候还会专门测这一项。
7#
LIKE  2级吧友 | 2018-9-22 11:43:54 发帖IP地址来自
之前的大神回答都已经很好了。
加多一些,扎实的微观市场知识。比如:
港交所分成的strict limit order和enhanced limit order的区别,会对市场造成什么的影响,发去交易所以后会吃点多少active order对市场的order book有什么影响,etc。

最基本的一些意义比如limit order provide liquidity 和market order consume liquidity。

市场的变化,东京交易所从一开始的五个tick level到现在的八个tick level对你的算法会有如何冲击?

延迟的敏感,一个best bid offer在你的系统收到有多大延迟,发出去一个order有多大延迟?如果做并发计算我可以做出什么样的事儿保证在用的是最fresh的data...?
8#
天降正义  1级新秀 | 2018-9-22 11:43:56 发帖IP地址来自
数学、金融、计算机,进入这个行业,有以上其中一项技能即可,但要做的好,挺难的。多学多练,多向高手请教,虽然核心策略没有人会告诉你。但一些方法论和注意事项还可以学习的。
优矿目前推出了两千万实盘FOF基金的投资经理征选,入选后直接发实盘产品,还可以得到中信证券千里马训练营入场券,直接进入量化行业核心哟,报名地址:优矿

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量化分析师的Python日记【第1天:谁来给我讲讲Python?】

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量化分析师的Python日记【第3天:一大波金融Library来袭之numpy篇】

量化分析师的Python日记【第4天:一大波金融Library来袭之scipy篇】

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量化分析师的Python日记【第6天:数据处理的瑞士军刀pandas】下篇

量化分析师的Python日记【第7天:QQuant 之初出江湖】

量化分析师的Python日记【第8天Q Quant兵器谱之函数插值】

量化分析师的Python日记【第9天Q Quant兵器谱之二叉树】

量化分析师的Python日记【第10天 Q Quant兵器谱 -之偏微分方程1】

量化分析师的Python日记【第11天 Q Quant兵器谱之偏微分方程2】

量化分析师的Python日记【第12天:量化入门进阶之葵花宝典:因子如何产生和回测】

量化分析师的Python日记【第13天 Q Quant兵器谱之偏微分方程3】

量化分析师的Python日记【第14天:如何在优矿上做Alpha对冲模型】

量化分析师的Python日记【第15天:如何在优矿上搞一个wealthfront出来】

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首先是股票量化相关。

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3.5 暴涨暴跌

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4.5 CCI

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4.6 RSI

4.7 DMI

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4.8 EMV

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4.9 KDJ

4.10 CMO

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4.12 Chaikin Volatility

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4.15 成交量

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4.16 K线分析

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五 量化模型

5.1 动量模型

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  • 《[策略]基于胜率的趋势交易策略》
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  • 《Contrarian strategy》
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5.2 Joseph Piotroski 9 F-Score Value Investing Model

  • 《基本面选股系统:Piotroski F-Score ranking system》
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5.3 SVR

  • 《使用SVR预测股票开盘价 v1.0》
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5.4 决策树、随机树

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    优矿

5.5 钟摆理论

  • 《钟摆理论的简单实现——完美躲过股灾和精准抄底》
    优矿

5.6 海龟模型

  • 《simple turtle》
    优矿
  • 《侠之大者 一起赚钱》
    优矿

5.7 5217策略

  • 《白龙马的新手策略》
    优矿

5.8 SMIA

  • 《基于历史状态空间相似性匹配的行业配置SMIA 模型—取交集》
    优矿

5.9 神经网络

  • 《神经网络交易的训练部分》
    优矿
  • 《通过神经网络进行交易》
    优矿

5.10 PAMR

  • 《PAMR: 基于均值反转的投资组合选择策略 - 修改版》
    优矿

5.11 Fisher Transform

  • 《Using Fisher Transform Indicator》
    优矿

5.12 分型假说,Hurst指数

  • 《分形市场假说,一个听起来很美的假说》
    优矿

5.13 变点理论

  • 《变点策略初步》
    优矿

5.14 Z-score Model

  • 《Zscore Model Tutorial》
    优矿
  • 《信用债风险模型初探之:Z-Score Model》
    优矿

5.15 机器学习

  • 《Machine Learning 学习笔记(一) by OTreeWEN》
    优矿

5.16 DualTrust策略和布林强盗策略

  • 《谁能够帮忙实现DualTrust策略和布林强盗策略(BollingerBandit)?》
    优矿

5.17 卡尔曼滤波

  • 《有没有朋友懂如何用卡尔曼滤波进行金融数据分析的?》
    优矿

5.18 LPPL anti-bubble model

  • 《今天大盘熔断大跌,后市如何——based on LPPL anti-bubble model》
    优矿
  • 《破解股市泡沫之谜——对数周期幂率(LPPL)模型》
    优矿

六 大数据模型

6.1 市场情绪分析

  • 《通联情绪指标策略》
    优矿
  • 《互联网+量化投资 大数据指数手把手》
    优矿

6.2 新闻热点

  • 《如何使用优矿之“新闻热点”?》
    优矿
  • 《技术分析【3】—— 众星拱月,众口铄金?》
    优矿

七 排名选股系统

7.1 小市值投资法

  • 《学习笔记:可模拟(小市值+便宜 的修改版)》
    优矿
  • 《市值最小300指数》
    优矿
  • 《市值最小300股票(筛选器版)》
    优矿
  • 《流通市值最小股票(新筛选器版)》
    优矿
  • 《持有市值最小的10只股票》
    优矿
  • 《10% smallest cap stock》
    优矿

7.2 羊驼策略

  • 《羊驼策略》
    优矿
  • 《羊驼反转策略(修改版)》
    优矿
  • 《羊驼反转策略》
    优矿
  • 《我的羊驼策略,选5只股无脑轮替》
    优矿

7.3 低价策略

  • 《专捡便宜货(新版quartz)》
    优矿
  • 《便宜就是 alpha》
    优矿

八 轮动模型

8.1 大小盘轮动

  • 《新手上路 -- 二八ETF择时轮动策略2.0》
    优矿

8.2 季节性策略

  • 《Halloween Cycle》
    优矿
  • 《Halloween cycle 2》
    优矿
  • 《夏买电,东买煤?》
    优矿
  • 《历史的十一月板块涨幅》
    优矿

8.3 行业轮动

  • 《银行股轮动》
    优矿
  • 《申万二级行业在最近1年、3个月、5个交易日的涨幅统计》
    优矿

8.4 主题轮动

  • 《快速研究主题神器》
    优矿
  • 《recommendation based on subject》
    优矿
  • 《strategy7: recommendation based on theme》
    优矿
  • 《板块异动类》
    优矿
  • 《风险因子(离散类)》
    优矿

8.5 龙头轮动

  • 《Competitive Securities》
    优矿
  • 《Market Competitiveness》
    优矿
  • 《主题龙头类》
    优矿

九 组合投资

9.1 指数跟踪

  • 《【策略】指数跟踪低成本建仓策略》
    优矿

9.2 GMVP

  • 《Global Minimum Variance Portfolio (GMVP)》
    优矿

9.3 凸优化

  • 《如何在Python中利用CVXOPT求解二次规划问题》
    优矿

十 波动率

10.1 波动率选股

  • 《风平浪静 风起猪飞》
    优矿

10.2 波动率择时

  • 《基于VIX指数的择时策略》
    优矿
  • 《简单低波动率指数》
    优矿

10.3 Arch/Garch模型

  • 《如何使用优矿进行GARCH模型分析》
    优矿

十一 算法交易

11.1 VWAP

  • 《Value-Weighted Average Price (VWAP)》
    优矿

十二 中高频交易

12.1 order book分析

  • 《基于高频limit order book数据的短程价格方向预测——via multi-class SVM》
    优矿

12.2 日内交易

  • 《大盘日内走势 (for择时)》
    优矿

十三 Alternative Strategy

13.1 易经、传统文化


接着是基金、利率互换、固定收益类

一 分级基金

  • 《“优矿”集思录——分级基金专题》
    优矿
  • 《基于期权定价的分级基金交易策略》
    优矿
  • 《基于期权定价的兴全合润基金交易策略》
    优矿

二 基金分析

  • 《Alpha基金“黑天鹅事件” -- 思考以及原因》
    优矿

三 债券

  • 《债券报价中的小陷阱》
    优矿

四 利率互换

  • 《Swap Curve Construction》
    优矿
  • 《中国Repo 7D互换的例子》
    优矿

然后是衍生品相关

一 期权数据

  • 《如何获取期权市场数据快照》
    优矿
  • 《期权高频数据准备》
    优矿

二 期权系列

  • 《【50ETF期权】 1. 历史成交持仓和PCR数据》
    优矿
  • 《【50ETF期权】 2. 历史波动率》
    优矿
  • 《【50ETF期权】 3. 中国波指 iVIX》
    优矿
  • 《【50ETF期权】 4. Greeks 和隐含波动率微笑》
    优矿
  • 《【50ETF期权】 5. 日内即时监控 Greeks 和隐含波动率微笑》
    优矿

三 期权分析

  • 《【50ETF期权】 期权择时指数 1.0》
    优矿
  • 《期权头寸计算》
    优矿
  • 《期权探秘1》
    优矿
  • 《期权探秘2》
    优矿
  • 《期权市场一周纵览》
    优矿
  • 《基于期权PCR指数的择时策略》
    优矿
  • 《期权每日成交额PC比例计算》
    优矿

四 期权日报

主要是@李自龙的期权择时日报。

五 期货分析

  • 《Gifts from Santa Claus——股指期货趋势交易研究》
    优矿

现金奖励活动:优矿

各种福利:优矿

9#
room song  3级会员 | 2018-9-22 11:43:57 发帖IP地址来自
必备:热情,勤奋,小强精神。
加分:天赋
10#
建文帝  2级吧友 | 2018-9-22 11:43:58 发帖IP地址来自
一个优秀的 Quant 需要扎实的数学基础和良好的编程能力,这里的数学基础主要思维和研究的能力,各种模型可在实践中不断学习,编程能力主要体现在把自己的想法或模型进行测试或实践的能力。金融是人在参与,Quant需要面对的不只是数字和模型 ,只要是做投资的,我觉得都离不开对市场和参与者的了解,所以对财务知识、心理学以及行为学的研究也是很必要的。
11#
吃点什么好呢  1级新秀 | 2018-9-22 11:43:59 发帖IP地址来自
在quantnet上看到的,希望对各位有帮助,侵删。
How to Get a Quant Job, Advice from Wall Street Executives
Whether you're looking for your very first job, switching carers, or re-entering the job market after an extended absence, finding a job requires two main tasks: understanding yourself and understanding the job market. I received several emails asking me for advice regarding quant jobs and various companies that are hiring and what they are looking for specifically. I usually pass on the questions to someone I know and then reply back to them. Over the last month I have got an increased number of such emails. I decided to compile these questions and made some of my own and decided to ask some people who would be better suited to answer these questions.

Over the course of my blog I have had the pleasure to build some really good contacts. It has given me a chance to meet and talk to several Wall Street executives. I decided to send my questions to some of them to get answers to the most frequently asked questions. They are more than happy to provide guidance to members of quantnet.com in the condition that their names are not displayed due to their firm's policy.

Can you please tell us a bit about your company and the department you work in?
  • Managing Director 1 (MD 1): Market risk, major investment bank
  • Managing Director 2 (MD 2): The company I work for is an International Bank involved Equities, Fixed income, FX and Commodities trading in addition to their IB activities. I work in the commodities trading division of the bank running a commodity index portfolio.
  • Capital Management Firm Partner: I work in a capital management firm. The fund is a Hong Kong based corporate finance firm that provides wealth management and risk hedging advisor services. I worked in equity and derivatives trading desk before, and right now I am working in strategist department with some talents, give advice to other traders and analysts. Plan and make future trading ideas and decisions. I do most of the hiring for the equity division. We also have a relatively new and growing operation in North America with operations in Toronto, Chicago and San Francisco.
  • CEO: My company creates quantitative investment strategies and sells them to high net worth, institutions, pension funds, endowments, etc.
What are the typical jobs that you interviewed candidates for over the course of your tenure as a hiring manager at your firm?
  • MD 1: Desk risk management, model review, market risk methodology, market risk reporting, market risk quant analysis, head of model review, risk intern, risk analyst, administrative officer, treasury capital analyst, IT project manager
  • MD 2: I generally interview candidates for various types of positions: Analyst, Trading or Quant positions. As an Analyst your job is to provide support on the desk, analysis of the markets, etc. Trading role means that you will, from the start, be involved on the trading side of the business and may be given your own book to manage risk and client flow. As a Quant you are expected to operate on a more analytical level and be able to understand the various models used for pricing the various products traded on the desk.
  • Partner: Traders and analysts.
  • CEO: I’ve interviewed potential researchers, programmers, strategists, and traders, over my career.
There are several MFE/MQF/MSCF/etc programs mushrooming all over the world. How do you distinguish the good from the bad and the ugly?
  • MD 1: Anecdotal based upon the people I’ve seen. I had one person from a top-rated program turn out to be a real dud. That has biased me against that program. I’ve also had a great person from a poorly-ranked program who leads me to grant the benefit of the doubt. On the other had, I have found CMU and Wharton people to be consistently excellent.
  • MD 2: I generally distinguish the good programs from the bad ones, when I find that the candidates coming out of the good programs have the right balance of practical and theoretical knowledge around quantitative finance. Candidates that have the analytical background and are able to quickly implement models and demonstrate their relevance to the business. I consider the “bad programs” the ones that just immerse candidates with enormous amount of theoretical information with very little hands-on or practical training.
  • Partner: Depends on the skills of candidates, not depend on the programs.
  • CEO: Honestly, I really have not focused on where they came from as long as it sounded nerdy and I’ve heard of it before.
Before, it was possible for MS, PhD in non-finance subjects like Statistics, Computer Science, etc to get quant positions. Is an MFE or an MFE type program a bar for those positions now? If there were two candidates, one MS in a non-finance subject and another with an MFE and all other credentials were at par, would the MFE have an obvious upper hand?
  • MD 1: Not at all. MFE’s tend to be light in statistics, strong in programming.
  • MD 2: I generally do not really have any sort of bias when it comes to considering a candidate with an analytical background, whether they have a non-finance degree or a pure MFE degree. What I generally look for is the ability to think “outside the box” or be able to withstand the stress of a trading environment. I also look for candidates that can potentially turn into good “risk managers” on the desk. I also look for a sense of passion to learn and to continue expanding their knowledge base. (Generally I find candidates with MBAs to be a little more obstinate in their adherence to looking at things from the Market Efficient Theory side).
  • Partner: Actually, depends on the interview. I can not make a decision only depends on the degree but not the skill. We need some one really can do the job and get the profit. But if you compare MS or MFE, I may choose MFE, but if you choose MS from MIT or a MFE from a university and I never heard about it, I may consider about MS more than MFE. However, the final decision depends on interview results and skills, not programs.
  • CEO: Doesn’t matter to me. I look for high GPAs for one. It says that the candidate took school seriously, then I look for something special about the resume, like first place in Math Olympics etc. social things like class President not as interesting to me.
What mathematics topics do you believe are essential to quantitative positions?
  • MD 1: Calculus, linear algebra
  • MD 2: A solid foundation in Stochastic Calculus is a must. Also , having backgrounds in areas like “Information Theory”, “Game Theory”, and pattern recognition or signal processing are huge pluses.
  • Partner: For trading, the basic mathematics should be a given. Usually I prefer if they have a strong grasp on bond mathematics and basic financial mathematics too but most of it is just about understanding the markets.
  • CEO: Minimum requirements, statistics, probability, econometrics, time-series, calculus, etc. Just the basic core stuff.
What technical skills do you believe are essential to quantitative positions?
  • MD 1: SQL, inference, spreadsheet expertise, EDA, common sense
  • MD 2: Knowing how to program a complex quantitative model efficiently and accurately. Sometimes I come across quants that are brilliant analytically but have very little programming skills, which means I need to hire an additional person to be the programmer. I highly recommend having several modern programming languages in your tool bag.
  • Partner: C++ VBA, Modeling skills.
  • CEO: Good programming skills in R, Matlab, C, or C++.
What do you believe are the top 5 credentials that you look for when interviewing a candidate for a quantitative position? Programming? Strong Mathematics? Good communication? Brand name University? Etc.
  • MD 1: Communication is perhaps the most important. There are gazillion quants who can’t express themselves clearly. Having them on your staff is like having a 1 million horsepower engine that has no transmission to harness its power. Brand name school helps. It means there’s a higher probability that the person is smart. Not programming. If I want a programmer, I’ll hire one. We can train good people with all the programming skills they’ll need. For anything other than model review or model development, I don’t need a PhD in math.
  • MD 2:
    1. Strong Communications Skills
    2. Strong Mathematics
    3. Strong programming skills
    4. Ability to think out-side the box type of mentality.
    5. Good interpersonal skills.Don’t really pay much attention to the brand name of the school they went to.
  • Partner: Good Communication , passion, Knowledge and skills (not only in programming or mathematics, but also cover some other area), imagination ( think about use different method to find the solution) , and the most important thing is HE Really like math, programming and this job.
  • CEO:
    1. High GPA, tells me they took their studies seriously,
    2. “The Fit”, will the candidate fit in with the others in the group, or will he/she be too difficult to assimilate into corporate life,
    3. Past accomplishments and anything that took initiative,
    4. Charity work, or mentoring shows maturity and selflessness
    5. University is the last thing, although I notice that I’m kind of partial to places that I’ve been to. (but that shouldn’t really matter, as long as (1) is satisfied.)
What are the most common misconceptions of people seeking this line of work?
  • MD 1: Soft skills are as important as hard skills. WE DON'T CARE IF YOU HAVE A PRM/FRM/CFA THOSE REPRESENT MEANS AND NOT ENDS.People think it’s the fast track to big bucks. It’s not. It’s the fast track to mediocre bucks combined with high stress and long hours. Do this if you find it inherently interesting. Otherwise you’ll be a miserable, overworked, geek bouncing from firm to firm chasing the money.
  • MD 2: That they will be moved into a trading role right off the bat. You have to earn this privilege over time.
  • Partner: Hmmm there are lots of misconceptions and I am not really sure which one is most common.
  • CEO: You just have to be smart. You also need some soft skills so people want to work with you. I think every hiring manager is looking for someone smarter than him/her and nicer than him/her. If you can show that you can do the most mundane tasks without complaining, and can master the most difficult tasks without getting a big head, then you will get many offers.
How important is having previous internship experience for an entry level job? How do students with no finance experience show they are worthy of the jobs too?
  • MD 1: An internship helps, but isn’t crucial. Inexperienced people should do their homework. Don’t tell me you’re interested in fixed income analytics and equity research and foreign exchange modelling. Which one? Why? Prove to me you know something about the business.
  • MD 2: Far more important for me is their technical and quantitative background, the business side of things can always be learned on the job. Much easier to teach a candidate about the business than to teach them about Stochastic Calculus.
  • Partner: It is really important to have a internship experience for the future jobs. Students need to show their skills to connect math with real market, and actually sometimes head of quantitative department would like to hire someone without finance experience, but really like mathematics and find solutions of puzzle.
  • CEO: To me, that’s not that important for junior positions, it just shows that you know how to behave in a corporate setting. If you have no experience, show me something you did. Show me a model you’ve built and what you know about back-testing, market microstructure, research design, about being creative. Show me one of your research working papers, when I read them I can tell what the candidate can and can not do. Resumes look very similar at the top-end of the spectrum.
Where do you think the largest job growth is within the quantitative finance industry ? Risk ? Structured ? Trading ? etc.
  • MD 1: Risk for secular growth. Growth in trading and structuring tends to be cyclical. Marginal people often get hired here and are the first to be fired.
  • MD 2: I think the largest job growth will be in Risk and in Trading. Given what’s happened in the world in the past few years, there will be more likely a movements towards better Risk management and Trading as opposed to the creation of more complex structured products.
  • Partner: Risk Management.
  • CEO: As regulation continues to gain steam, risk will always be a large employer of financial engineers. I personally like the trading part, creating new models and implementing them but I have found that this is not for everyone.
What is the best way for an entry level candidate to secure a job at the large investment banks? Through recruiters? Applying on the website? Campus recruitment? School Career Services?
  • MD 1: Through referrals. Network.
  • MD 2: The best way to secure an entry level type of position in the large investment banks is through a combination of the use of recruiters as well as campus recruitment events. Attending industry specific events are also an excellent way to meet industry experts that can provide guidance.
  • Partner: Relationship and networking. Not only from campus recruitment, but also from some other places, like IAFE events, meetings, or even church. Candidate needs to show something to prove skills and know how to manage relationship. It is the best way to get the interview. Sometimes campus recruitment or school career services maybe a choice but you need to make yourself stand out the line.
  • CEO: Network your alums, head-hunters can be a waste of time, but there are a few good ones. For entry-level jobs you can just Google quant-jobs and find a ton of listings, then just keep applying.
How important is networking for entry level candidates? What are some possible networking venues that you would suggest?
  • MD 1: Networking is crucial. GARP, PRMIA, conferences, newsgroups, blogs. School, too. Talk to your professors.
  • MD 2: Attending industry specific conferences I have always found to be excellent places to meet and network with people. These types of activities are normally used by industry participants as a means to recruit candidates. It also gives candidates a better understanding of the types of issues and problems that are being addressed in Risk Management and in Trading. From experience, I have always found these to be the best places to network.
  • Partner: As I said above, it is really important not only for entry level candidate, but also for other managers, bankers, even traders. For the best venues, I am afraid I do not have any good suggestions, but if you can ask your professor to go out and have a drink, you may find the answer
  • CEO: I think networking is important but probably for more senior level positions. Junior quants just entering the industry can find tons of open listing just using the internet.
Thank you for your time. I greatly appreciate it. Any last parting words that you would like to leave us with regarding securing a job at a company like yours?
  • MD 1: If you put it on your resume, be ready to explain it. I’ve dinged many people for stating they knew how to do Monte Carlo simulation, but who couldn’t tell me what a random walk was (for time series MC) or what a Gaussian Copula was (for a VaR or credit risk MC).
  • MD 2: Sometimes securing a job at a large IB may not be possible in a certain situation, but that does not mean that you many not take a job at a Technology company first and get an experience that help you later on to secure an even better job at an IB. It is always better to gain as much experience as possible in any type of job and work towards eventually getting the ideal job that you desire. The more experience you can get under your belt the better. From my own personal experience, before I landed the position I truly desired as a Trading Manager I went through various career changes along the way. These career changes have over the long run have helped me gain a better appreciation for managing people as well as managing risk on a trading desk. Spending time learning about the ‘soft” skills can be time well spent.
  • Partner: Study, network, networking.
  • CEO: Show me why you are better than everyone else that has a resume and test scores that looks just like yours. Show me one thing that makes you unique in all the world of quants. Good luck!
I hope this helped with any questions any readers had. Feel free to put questions below here and maybe I will do a Part 2 if there is enough demand. It will take a while as I do not want to pester all these busy people with questions.
#1Joy Pathak, 9/8/10
12#
青烟雨后  2级吧友 | 2018-9-22 11:44:00 发帖IP地址来自
我也想转这个,看看怎么入行吧
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