The author of this article works as a prop trader for a securities firm in an emerging market. With a Bachelor in Engineering, he started out as a financial engineer whose job was to develop VBA applications to back test trading strategies for a senior trader. Three months into the job, the trader left and the author was promoted to run his own trading book. He has been trading over a year since. Here is a day in his life. SHARE YOUR STORY

6.15 – Clock alarm sounds. Immediately press snooze button

6.30 – Hear the alarm again and get up. An extra 15 minutes of sleep has been gained.

7.00 – Leave the house for office downtown

8.00 – Have a breakfast at a small diner next to my office building

8.20 – Arrive at the office

8.30 – Check e-mail, read news on Reuters and Bloomberg, and check out what happen in the world last night

8.45 – Briefly discuss the markets with other traders

9.00 – Download data and run my own VBA trading application

9.10 – Check data feed for correctness. I find that some data are not published correctly, so I call IT department to fix it.

9.30 – Casinos open. Monitor current positions as well as wait for the trading signal from my model to open or close the position. Index arbitrage, ETF arbitrage, inter-market arbitrage, pairs trading, calendar trading, arbitrage between warrants and their underlying stocks are strategies that I currently employ.

10.00 – After I am confident that my trading application and the data feed run smoothly, I spend my time to either revise the current model or develop a new model. I highly recommend Ernest Chan’s Quantitative Trading: How to Build Your Own Algorithmic Trading Business

10.30 – I pause researching for a moment because my trading application issues an alarm for me to cut loss the position that I opened yesterday. Hesitate for few seconds before executing the cut loss order and subsequently think about changing my job to pursue a career that can give me more job stability and less stress. Note that, I execute the order by myself since we are still emerging here. Anyway, it is fine for me as I trade in low-mid frequency.

11.15 – Make bet (open a new position) on another strategy according to the model

12.00 – Go to lunch with my teammates, sadly, all of them are men

12.40 – Back to the office and check out what happen during lunch time and update the data on my trading application

13.00 – Telephone rings in the trading room. Our boss asks one of my teammates to meet him in his room.

13.20 – My teammate, who has been working as a junior discretionary trader for half years, comes back to the trading room after finish talking with the boss. He first says that he will not be here anymore and then reveals that the boss pressures him to move to another department in back office but my teammate refuses. In other word, he is sacked. Thrilled! Who will be the next? Again, think about my career choices.

13.30 – Casinos resume. I do mostly the same as the first part of the day, which is sometimes quite boring because I have covered most of trading strategies that can be utilized in my country, where they lack of sophisticated financial products. This may also mean that there is limited opportunity to make $$$.

13.31 – Quickly think about getting an MFE to fulfill my intellectual thirst. Meanwhile, the junior trader is collecting his personal stuff.

14.00 – My almost ex-teammate goes to talk with HR

14.30 – Fortunately, this afternoon I have closed the position with profit.

15.00 – Say good bye to my ex-teammate. Still, I don’t know how much he costs the firm.

16.00 – All casinos that I cover are now closed. Check out what happen in each regional market and how Europe markets open.

16.15 – Review all of my trading activities today and make sure that I book the correct position on my trading application.

16.30 – Submit a trading summary report and prepare for a quarterly meeting with management committee in the next 15 minutes.

16.45 – Attend the meeting, where a trading performance is discussed. Some market outlooks are exchanged.

17.30 – Finish the meeting. Before going home, I pick up my annual medical test results. I find that some results are not good as they exceed the normal limits. I think I should have exercised and relaxed more.

17.35 – Go home by bus. (I choose to go home by bus rather than a helicopter since I do not want to finish my story like Nero Tulip*)

18.00 – Think about pursuing an MFE while traveling on the bus. Ask my self the following:  Suppose I finish an MFE, can join a global top-tier investment bank/hedge fund, and obtain a lot of $$$, will it make me happier?

*Editorial note: Nero Tulip is a character in Nassim Taleb’s “Fooled by Randomness“. Some have suggested that Nero Tulip is the author’s portray of himself. References on “Nero Tulip” can be found through Google Books here.

Hit by Lightning

Nero Tulip became obsessed with trading after witnessing a strange scene one spring day as he was visiting the Chicago Mercantile Exchange. A red convertible Porsche, driven at several times the city speed limit, abruptly stopped in front of the entrance, its tires emitting the sound of pigs being slaughtered. A visibly demented athletic man in his thirties, his face flushed red, emerged and ran up the steps as if he were chased by a tiger. He left the car double-parked, its engine running, provoking an angry fanfare of horns. After a long minute, a bored young man clad in a yellow jacket (yellow was the color reserved for clerks) came down the steps, visibly untroubled by the traffic commotion. He drove the car into the underground parking garage—perfunctorily, as if it were his daily chore.

That day Nero Tulip was hit with what the French call a coup de foudre, a sudden intense (and obsessive) infatuation that strikes like lightning. “This is for me!” he screamed enthusiastically—he could not help comparing the life of a trader to the alternative lives that could present themselves to him. Academia conjured up the image of a silent university office with rude secretaries; business, the image of a quiet office staffed with slow thinkers and semislow thinkers who express themselves in full sentences.

Temporary Sanity

Unlike a coup de foudre, the infatuation triggered by the Chicago scene has not left him more than a decade and a half after the incident. For Nero swears that no other lawful profession in our times could be as devoid of boredom as that of the trader. Furthermore, although he has not yet practiced the profession of high-sea piracy, he is now convinced that even that occupation would present more dull moments than that of the trader.

Nero could best be described as someone who randomly (and abruptly) swings between the deportment and speech manners of a church historian and the verbally abusive intensity of a Chicago pit trader. He can commit hundreds of millions of dollars in a transaction without a blink or a shadow of a second thought, yet agonize between two appetizers on the menu, changing his mind back and forth and wearing out the most patient of waiters.

Nero holds an undergraduate degree in ancient literature and mathematics from Cambridge University. He enrolled in a Ph.D. program in statistics at the University of Chicago but, after completing the prerequisite coursework, as well as the bulk of his doctoral research, he switched to the philosophy department. He called the switch “a moment of temporary sanity,” adding to the consternation of his thesis director, who warned him against philosophers and predicted his return back to the fold. He finished writing his thesis in philosophy. But not the Derrida continental style of incomprehensible philosophy (that is, incomprehensible to anyone outside of their ranks, like myself). It was quite the opposite; his thesis was on the methodology of statistical inference in its application to the social sciences. In fact, his thesis was indistinguishable from a thesis in mathematical statistics—it was just a bit more thoughtful (and twice as long).

It is often said that philosophy cannot feed its man—but that was not the reason Nero left. He left because philosophy cannot entertain its man. At first, it started looking futile; he recalled his statistics thesis director’s warnings. Then, suddenly, it started to look like work. As he became tired of writing papers on some arcane details of his earlier papers, he gave up the academy. The academic debates bored him to tears, particularly when minute points (invisible to the noninitiated) were at stake. Action was what Nero required. The problem, however, was that he selected the academy in the first place in order to kill what he detected was the flatness and tempered submission of employment life.

After witnessing the scene of the trader chased by a tiger, Nero found a trainee spot on the Chicago Mercantile Exchange, the large exchange where traders transact by shouting and gesticulating frenetically. There he worked for a prestigious (but eccentric) local, who trained him in the Chicago style, in return for Nero solving his mathematical equations. The energy in the air proved motivating to Nero. He rapidly graduated to the rank of self-employed trader. Then, when he got tired of standing on his feet in the crowd, and straining his vocal cords, he decided to seek employment “upstairs,” that is, trading from a desk. He moved to the New York area and took a position with an investment house.

Nero specialized in quantitative financial products, in which he had an early moment of glory, became famous and in demand. Many investment houses in New York and London flashed huge guaranteed bonuses at him. Nero spent a couple of years shuttling between the two cities, attending important “meetings” and wearing expensive suits. But soon Nero went into hiding; he rapidly pulled back to anonymity—the Wall Street stardom track did not quite fit his temperament. To stay a “hot trader” requires some organizational ambitions and a power hunger that he feels lucky not to possess. He was only in it for the fun—and his idea of fun does not include administrative and managerial work. He is susceptible to conference room boredom and is incapable of talking to businessmen, particularly the run-of-the-mill variety. Nero is allergic to the vocabulary of business talk, not just on plain aesthetic grounds. Phrases like “game plan,” “bottom line,” “how to get there from here,” “we provide our clients with solutions,” “our mission,” and other hackneyed expressions that dominate meetings lack both the precision and the coloration that he prefers to hear. Whether people populate silence with hollow sentences, or if such meetings present any true merit, he does not know; at any rate he did not want to be part of it. Indeed Nero’s extensive social life includes almost no businesspeople. But unlike me (I can be extremely humiliating when someone rubs me the wrong way with inelegant pompousness), Nero handles himself with gentle aloofness in these circumstances.

So, Nero switched careers to what is called proprietary trading. Traders are set up as independent entities, internal funds with their own allocation of capital. They are left alone to do as they please, provided of course that their results satisfy the executives. The name proprietary comes from the fact that they trade the company’s own capital. At the end of the year they receive between 7% and 12% of the profits generated. The proprietary trader has all the benefits of self-employment, and none of the burdens of running the mundane details of his own business. He can work any hours he likes, travel at a whim, and engage in all manner of personal pursuits. It is paradise for an intellectual like Nero who dislikes manual work and values unscheduled meditation. He has been doing that for the past ten years, in the employment of two different trading firms.

Modus Operandi

A word on Nero’s methods. He is as conservative a trader as one can be in such a business. In the past he has had good years and less than good years—but virtually no truly “bad” years. Over these years he has slowly built for himself a stable nest egg, thanks to an income ranging between $300,000 and (at the peak) $2.5 million. On average, he manages to accumulate $500,000 a year in after-tax money (from an average income of about $1 million); this goes straight into his savings account. In 1993, he had a bad year and was made to feel uncomfortable in his company. Other traders made out much better, so the capital at his disposal was severely reduced, and he was made to feel undesirable at the institution. He then went to get an identical job, down to an identically designed workspace, but in a different firm that was friendlier. In the fall of 1994 the traders who had been competing for the great performance award blew up in unison during the worldwide bond market crash that resulted from the random tightening by the Federal Reserve Bank of the United States. They are all currently out of the market, performing a variety of tasks. This business has a high mortality rate.

Why isn’t Nero more affluent? Because of his trading style—or perhaps his personality. His risk aversion is extreme. Nero’s objective is not to maximize his profits, so much as it is to avoid having this entertaining machine called trading taken away from him. Blowing up would mean returning to the tedium of the university or the nontrading life. Every time his risks increase, he conjures up the image of the quiet hallway at the university, the long mornings at his desk spent in revising a paper, kept awake by bad coffee. No, he does not want to have to face the solemn university library where he was bored to tears. “I am shooting for longevity,” he is wont to say.

Nero has seen many traders blow up, and does not want to get into that situation. Blow up in the lingo has a precise meaning; it does not just mean to lose money; it means to lose more money than one ever expected, to the point of being thrown out of the business (the equivalent of a doctor losing his license to practice or a lawyer being disbarred). Nero rapidly exits trades after a predetermined loss. He never sells “naked options” (a strategy that would leave him exposed to large possible losses). He never puts himself in a situation where he can lose more than, say, $1 million—regardless of the probability of such an event. That amount has always been variable; it depends on his accumulated profits for the year. This risk aversion prevented him from making as much money as the other traders on Wall Street who are often called “Masters of the Universe.” The firms he has worked for generally allocate more money to traders with a different style from Nero, like John, whom we will encounter soon.

Nero’s temperament is such that he does not mind losing small change. “I love taking small losses,” he says. “I just need my winners to be large.” In no circumstances does he want to be exposed to those rare events, like panics and sudden crashes, that wipe a trader out in a flash. To the contrary, he wants to benefit from them. When people ask him why he does not hold on to losers, he invariably answers that he was trained by “the most chicken of them all,” the Chicago trader Stevo who taught him the business. This is not true; the real reason is his training in probability and his innate skepticism.

Jay Dweck and his wife, Shoshana Dweck

Jay Dweck, global head of strategies and technology at Morgan Stanley, left the investment bank on Wednesday, following a three and half years stint at the firm. After an anonymous member tipped us off on our blog yesterday, we contacted our sources who confirmed Mr. Dweck’s departure on the condition of anonymity because the bank has not made an official announcement.

The bank sent out a memo to selective individuals on Wed following Dweck’s departure, sources familiar with the situation told us. Request for comment has not been returned by Morgan Stanley’s spokesperson at press time.

Jay Dweck, 55, joined Morgan Stanley on August 2007 as a Managing Director and Global Head to oversee and help build the Strategic Analytics, Modeling and Systems Group, which will focus on creating and developing models and systems designed to maximize effectiveness in sales and trading. Mr. Dweck also worked closely with the Information Technology Department. In his role, Mr. Dweck oversees quants who design and maintain computer systems and analytic models for sales and trading businesses.

“We are delighted that Jay has decided to join the Firm,” said Zoe Cruz, then Co-President of Morgan Stanley. “He brings a wealth of knowledge and experience in analytical modeling and business solutions, which will help us grow our sales and trading businesses.”

Zoe Cruz was fired a few months later in Nov 2007 by John Mack, then CEO of Morgan Stanley. In a feature article detailing the rise and fall of Zoe Cruz, NYMag quoted Mr. Dweck as saying “At Goldman, this isn’t happening. When they say get out, they get out. At Morgan Stanley, when Zoe says get out, people start negotiating.”

Mr. Dweck was previously at Goldman Sachs where he worked since 1994, most recently as Head of Equities Strategies. During his time at Goldman Sachs, he was a Participating Managing Director and held several senior positions, including Head of Core Strategies.

According to the firm’s press release at the time of his hiring, Mr. Dweck earned B.S., M.S. and Ch Eng degrees in Chemical Engineering and a B.S. in Math from the Massachusetts Institute of Technology.

Jay and his wife, Shoshana Dweck are members of the Co-Chairs’ Council of The Perlman Music Program which offers musical training for students, ages 12 to 30, who play the violin, viola, cello, bass and piano.

Picture credit: http://www.perlmanmusicprogram.org/

This is the second year the workshop is offered by the Mathematics in Finance Workshop and Conference Center at Courant. According to an email sent to Quantnet in November, the workshop’s intended audience is “buy-side practitioners (portfolio managers and risk managers), sell-side practitioners (traders, financial engineers, quantitative analysts, research teams), and academics will deepen and broaden their understanding of the recipes they implement everyday and will learn the most cutting-edge techniques.”

The event’s materials list the following topics:

• Financial market microstructure for the practitioner and the mechanics of trading
• How to work with high frequency data
• Common trading strategies
• Estimation of transaction costs and market impact models
• Portfolio construction with the Black-Litterman model and robust optimization
• Portfolio optimization with transaction cost
• Simulation techniques
• Back-testing strategies
• Multi-period dynamic portfolio optimization with transaction costs

Lee Maclin, an adjunct professor at Courant Institute and a founding partner of Pragma Financial Systems, presented in detail the mechanics of trading and many of its available tools such as limit and market orders, the two-way double auction, direct market access pipes, etc. He described how they work, as well as the effect they have on the market and how it could influence our positions and decisions. He continued with ways of working with high frequency data and what types of data set can be constructed and used.

Petter Kolm, clinical associate professor at Courant, had the chance to introduce to the audience the calculus of variations and a couple of market impact models. He discussed the Almgren-Chriss model and the capabilities it has along with the the efficient frontier of optimal execution and models that could utilize public data.

The rest of the day included Petter Kolm and Farshid Asl, an adjunct professor at Courant Institute. Dr Kolm gave an interesting presentation of the Black-Litterman model and discussed robust portfolio optimization techniques. Farshid Asl, was up next giving a basic overview of financial time series modeling. He also explained filtering in statistical arbitrage and applications of Hidden Markov Models.

Courant workshop attendants discuss material with lecturer during break

The second day of the conference started with Eran Fishler, director of algorithmic trading at Pragma Securities, giving an overview of dark pools and how to trade using dark pools. He went on to include empirical performances and findings regarding dark pool performance. Dr. Maclin followed with an introduction to dynamic portfolio analysis and discussion of the dynamic portfolio framework

During the evening session Dr. Kolm presented the stochastic Linear Quadratic Gaussian (LQG) regulator, which is probably the most fundamental optimal control problem, and showed how to do dynamic portfolio analysis with transaction costs. The event ended with Joseph Cerniglia, CFA, senior investment manager at Aberdeen Asset Management, on a discussion on factor-based quantitative trading strategies and back-testing methodologies.

While the event concluded without a major hiccup, some attendants complained about name tags with wrong affiliations, lack of event staff, drinks that quickly ran out, and other inconveniences.

Several Quantnet attendants express disappointment with a lack of hands on, practical experience from the event. While they agree the materials are interesting, many feel it is simply a series of lectures, instead of a “workshop” which would benefit them more if there are citation sessions, coding samples so they can put to use what they just learned.

The cost of attending this two-day workshop runs from $900 for professionals to $550 for academics/students with group and affiliate discounts. The organizers also provided limited $99 registrations for full-time students studying in MFE programs.

The workshop’s website lists IAFE, QWFAFEW, SQA, Axioma, Quant Network as partners of this year’s event.

Billy Diomis contributed reporting and photo from NYC.

In this article, one of Quantnet members describes a typical day as a software contractor at a financial institution in London’s business district, Canary Wharf. SHARE YOUR STORY

5:45am – Alarm goes off, time to get up and get ready

6:30am – Head to bus stop

6:40am – Bus to train station.

6:55am – Arrive at train station, purchase coffee.

7:15am – Find seat on train

7:30am – Train heads off to London – usually read a book or the Economist or similar.

8:30am – Arrive at London Bridge train station

8:35am – Head to tube stop to grab the tube over to Canary Wharf

9am – Arrive at desk at bank (sometimes with Krispy Kream in hand)

9-noon – Work on software and meetings with stakeholders

Noon – Head down to canteen and grab some lunch – if it’s a Friday go for the Pie, mushy peas and chips.

12:15pm – Eat at desk whilst working. Yeah, I know what you are thinking. Eating at your desk isn’t much fun. However when you are sitting in an office with a lot of full time folks who know the contractors are on a lot more than them, you tend to feel like you are “taking the pi$$” of you stroll off for a long lunch – especially when they are bitching about other full-time team members who aren’t pulling their weight

One thing I did notice was that, with there being a number of consultants and contractors in our area, some of them get a frosty reception when they rocked the boat somewhat.

Also to add to that, we billed for every hour we worked, so over-time and short lunches made sense, especially if you wanted to head off early on a Friday.

12:30pm – Work on code, meetings etc. I was developing software that was going to be rolled out to the various teams in all the countries the banks had offices in. This meant TC’s with folks in Hong Kong and Mexico for example.

As a by-product of that, it wasn’t unusual to stay late to catch a call with folks in NYC for example. Our work days were pretty intense in that we had a lot of coding to do and testing. Thank god for my ipod, which thankfully we were permitted to use.

My specific role was integrating the new software into an existing delivery system. This in itself was fairly interesting although you soon come into contact with bank I.T bureaucracy which has a habit of slowing the job down somewhat.

5pm on Fridays – head home.

5:30-6pm – On very good days, I head out the door (maybe grab a pint on the way with other team members).

6:30-7pm – Head to tube station on most days but on busy days, I get off around 8:30pm.

Anywhere from 6-9pm – Grab train back home. The time depends on the day.

Anywhere from 7-10pm – Get to station and grab bus home

Anywhere from 7:30-10:30pm – get home.

Some Saturdays I’d be in the office from around 10am until 3pm. Working on a Saturday afforded me an excellent view of the Red Bull stunt plane event over the Thames one Saturday which was pretty good fun.

As for compensation the money was very very good and made the contract well worth it. I’d happily do seven months a year and take the other five off if it was guaranteed , but the bank certainly got its money worth.

Photo credit: chrisjohnbeckett/Flickr

On Friday, November 19^th 2010, the Center of Applied Probability and the Center for Financial Engineering of Columbia University held its 17^th Annual Workshop on Financial Engineering: Quantitative Trading and Asset Management Workshop in Davis Auditorium. Along the many of industry-known speakers, many were also found in the audience. Peter Kolm, director of the NYU Math in Finance program, Emanuel Derman, director of the Columbia’s Master in Financial Engineering program, were among the many. Many MFE students from Baruch and Columbia had the chance to be part of this full day event.

Emanuel Derman of Columbia (center) pictured with Baruch MFE students

The workshop started with Brian Hayes from Morgan Stanley giving a presentation on Quantitative Equity Hedge Funds. The results of the model he presented suggested that herding of funds into the same stocks may not have been responsible for the August 2007 Quant Equity Crisis. He concluded that despite moribund returns, regime-switching models indicated the HFRI EMN Index has likely been in a “good” regime since spring 2009, although this probability has fallen recently.

Robert Engel of NYU

The workshop continued with Kent Daniel, Professor of the Columbia Business School talking about momentum strategies which have been shown to be profitable across numerous asset classes, and are now a standard tool of quantitative asset managers. Stephen Blyth from Harvard Management Co. continued with a presentation discussing forces that have been reshaping the quantitative financial landscape over the past three years, such as the empirical price action following the Lehman bankruptcy that challenged long-established logical arguments; a long series of government intervention, from the opening of the discount window through TARP and TLGP to QEI and II, and the looming impact of financial regulation following the passage of Dodd-Frank.

Robert Litterman from Kepos Capital

Robert Litterman from Kepos Capital was up next discussing how the appropriate price for carbon emissions today is far from the current actual price, which throughout the world is very close to zero. He argued that this discrepancy between the actual and the appropriate price is a disequilibrium situation. After lunch, Professor Robert Engel, recipient of the Nobel Prize for Economics in 2003, had the chance to present the Stern Systemic Risk Rankings, which are a market based measure of the relative risk of US Financial Firms.

Gregg Berman from Securities and Exchange Commission

One of the most interesting parts of the workshop were the presentations regarding the flash crash in May and the decision process in high  frequency trading. Gregg Berman, from Securities and Exchange Commission and Andrei Kirilenko, from Commodity Futures Trading Commission described the market structure of the bellwether E-mini S&P 500 stock index futures market on the day of the Flash Crash. They were followed by Douglas Borden, from Knight Equity Market, who presented a different approach to making High Frequency Trading decisions, making use of techniques from Stochastic Control Theory.

John Crosby, from UBS, concluded the workshop with a presentation on generalized variance swaps, which he argued, generalize and include, as special cases, popular derivatives such as variance swaps and gamma swaps.

Billy Diomis contributed reporting from New York City. Photo: Billy Diomis.

In a 2008 interview with Quantnet, Carr contributed “collaboration with some of the best minds in the business” to his successful career. When asked what he believes are the essentials of a successful financial engineer, Carr replied, “I think its being able to assimilate some deep ideas into the core of your being. One can reap financial rewards from a superficial understanding, but the real rewards lie deeper.”

Two years later, his belief is shared and validated.

The International Association of Financial Engineers (IAFE) and SunGard today announced that Peter Carr, managing director and global head of market modeling for Morgan Stanley, has been named the 2010 IAFE/SunGard Financial Engineer of the Year (FEOY). The award will be presented to Dr. Carr on February 10, 2011, at the New York Stock Exchange in New York City, during the IAFE/SunGard FEOY Award Gala Dinner. This year’s selection committee was chaired by Dr. Bob Litterman, an IAFE senior fellow and the 2008 FEOY award winner.

Brian Traquair, president of SunGard’s capital markets and investment banking business, said, ““Peter Carr has distinguished himself with a number of important contributions to financial engineering. In addition, his dedication to training the financial engineers of tomorrow makes it fitting that he is awarded the title of 2010 Financial Engineer of the Year.”

Peter Carr moved to Morgan Stanley in April after seven years as head of quantitative financial research at Bloomberg. A well-known figure among the quant finance community in New York City, Carr is well liked by many of his students and peers for his very down to earth personality.

IAFE/SunGuard press release

In this article, one of Quantnet members describes a typical day in his former life as an Interest Rates Developer at a Major Investment Bank (MIB), FO and MO spreadsheets support. He is now employed at another MIB. SHARE YOUR STORY

4:50 AM – I do not want to wake up, can someone please break this evil clock alarm!!!

5:15 AM – I’m on the train and going to job

6:30 AM – Finally I’m here and first thing I check if all publishing systems are up. Looks like couple of indicator on my alarm monitor that I wrote in C# are red so I’m going in and investigate, looks like couple of Reuters’ spreadsheets which publishing data closed overnight and did not reopen, so I help them and we are good here. Time to drinkk some coffee (1st cup)

7:00 AM – First call from the trader that his model in excel showing N/As, investigating, looks like some dlls are missing; map them to the s/s and recalculate; looks good. I get back to the desk, my two coworkers already here and working on other issues.

7:30 AM – Answer for couple of calls from MO and a call from a trader who could not start his pricing application.

8:00 AM – MO can not figure out what the problem is and I’m going to them, after some time tracing the formulas in at least ten different s/s looks like that some MD (market data) that comes to the model for building the curve is not correct and analytical library failing to do the right calculations. Fixing the problem and come back to my desk.

8:45 AM – Trying to explain over the phone that we do not support Asia only US and Canada and there should be another number in the directory.

11:00 AM – Answered about 5 different issue calls, fixed couple of s/s problems time to drink a coffee (2-nd cup)

12:00 PM – Lunch, Good, I’m still alive and hungry. (3rd cup of coffee)

1:00 PM – 4:00 PM In the periods between support calls working on C++ application which interprets Perl commands and link them to the C++ MD API. (4th cup of coffee)

4:00 PM – Data for the one of the currency was published wrong and I have to republish it; telnet to UNIX server for that. Have to dig up to couple more calls from MO who started closing.

5:30 PM – Going to train

5:45 PM – In the train; no seat; good I will not fall asleep.

7:00 PM – Home; making espresso; it will be my fifth cup of coffee for the day

7:15 PM – 12:00 PM – Just a usual home activity

12:00 AM – Getting to sleep happy, tomorrow second shift from 12:00 pm until last MO closed, if I’m lucky 10:00 pm, but at least I will got a good sleep tonight. Last thought before fall asleep: “Time to look for the new job”

Photo credit: Teemu Mäntynen

SHARE YOUR TYPICAL DAY AT WORK WITH QUANTNET. Click HERE

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.

Boston skyline (image by Werner Kunz)

By DOUG REICH

If New York Is the Capital of Finance, there are lots of provincial towns…

A very quick bit of background. I entered the Baruch MFE in Fall 2008 as a full-time student, while continuing to do work for my previous employer. When my summer internship search was unfruitful, I negotiated some work at another hedge fund where I had a contact, located in the Boston area. For various personal reasons completely unrelated to the work (although I had an incredible time at this fund!), I found my summer residence extremely agreeable, and decided to seek employment only in the Boston area upon graduation. As luck would have it, shortly before graduation I received my perfect offer and all the pieces fell into place.

This article isn’t about that history, rather it is about some of the issues I had regarding moving to a new city, searching for employment, and how the two compare. Foremost, why would you bother leaving New York for another city? One good answer may sound tautological, but time spent in the workforce is much more valuable to your career than time spent out. While education and self-study have their places, it is immediately apparent in a person’s confidence and knowledge when they have work experience. Therefore, taking a job in another city or in a non-ideal job is going to make future hiring much easier. The corollary of this is that since many companies prefer to hire internally rather than externally, working at a large company – even doing what you don’t want for the time being – can be very advantageous. Consider that many members of my group moved up the ranks from within my company; they began answering phones, and then transitioned to becoming traders or analysts.

Just as in New York, employers can be found in several distinct areas. In New York, these areas are Downtown and Midtown; in Boston, it is Downtown (or the Financial District, or just “Boston”) and Back Bay. However, there are many smaller companies found in Cambridge or in more suburban locales.

Another, less existential reason for looking for work in Boston (or Chicago, or San Francisco) is that a certain type of company can be found there; Boston has a huge concentration of institutional money managers, whereas Hartford has insurers, Chicago has commodities traders, and New York has hedge funds, sales and trading and prop traders. Looking for work outside your home town can have a very good diversification element to it. These towns also have different working environments. While I don’t have a job in New York to compare it to, my impression is that the intensity is lower in Boston than at New York companies, however this difference in atmosphere may parallel the difference in lines of business.

Attending school in New York and looking for jobs in Boston was a little challenging. The first thing I did when putting resumes out to employers was put down a Boston address (I used a friend’s — don’t just make one up, since when I filled out the employment application they started sending important mail to this address!). This way, inter-city hiring seems less of an impediment; say even that you are in the process of relocating to remove the question of relocation costs (especially if you are looking for a junior role); in my case it was also true that I spent a lot of time in Boston, and was, in fact, planning on moving to there. Buying a Boston Red Sox hat is not required at this point.

How does Boston differ from New York? It’s smaller, flatter, less intense, has more young people (and a different variety of young people), has many more colleges and college students, prefers beer to mixed drinks (as compared to New York), has cooler weather, has a less visible and smaller immigrant population, has less frequent mass transit, likes the outdoors more, and costs about 25% less to live in (this is a rough measure). Some of these things can be virtues, but are also generalizations; you have to go there and spend some time to really get a feel. The advantage is that since there are so many schools, chances are that you may know someone who is at college there, or someone from your past who graduated but never left Boston. Regardless, it’s good to have friends or at least acquaintances when starting in a new place.

As a born-and-bred New Yorker, the biggest adaptation to Boston was its relative quaintness. Getting around in New York always has a rough-and-tumble feel, with constant activity on the dirty subway platforms and noisy trains; Boston trains (the “T”) are clean and pleasant by comparison, without any panhandlers or performers on the cars (I still haven’t figured that one out). Bars close at 2 am on weekends and 1 am during the week, both of which are times after which the subway system has closed (if you live on the right line, you may be able to catch a 1 am train). There are not delis on every corner; those delis that exist do not furnish the same unlikely combination of coffee, candy, junk food, milk and fruit that you find in New York. You can probably walk between any two points in an hour, and if cars are your thing, a pound of flesh is not their price.

On the other hand, it’s still big-city living. You can get someone else to do your laundry; there are plenty of fast food lunch places and fancy restaurants in downtown; there are pockets of bars; a theater district; tourists; mass transit meets your needs; great walkability.

And for some, the best thing about Boston is that it is only a few hours and less than $50 from New York by train, car or bus.

AQR Capital Management
Aviva Investors
Bank of America
Bank of New York Mellon
Barclays Capital
BlackRock
Bloomberg Financials
BNP Paribas
Bogle Investment Management
Bridgewater
Chicago Trading Company
Citadel Group
Citi
Clinton Group
Commerzbank
DEShaw
Deutsche Bank
DFG Investment Advisers
DRW Trading Group
Fidelty International
FINCAD
First Quadrant
GBAR
Glenwood Capital Investment
Goldman Sachs
Houlihan Lokey Howard & Zukin
Infinum Capital Management
ING
Investec
J.P.Morgan
Janes Street
Jump Trading
Knight Capital Group
M&G Investments
Mitsubishi UFJ Securities International
Moody’s
Morgan Stanley
Nomura
Numeric Investors
Numerix
Nuveen Investment
Optiver
QVT Financial
Royal Bank of Canada
Royal Bank of Scotland
SIG Susquehanna
Spot Trading
Societe Generale
Standard & Poor’s
Standard Chartered Bank
State Street
TD Securities
Tower Research Capital
Two Sigma Investments
UBS
Walleye Trading

These investment banks, hedge funds, quant shops cover all asset classes and for these quant internships and graduate hires, the firm will sometimes offer the opportunity for rotational programs in sevaral of these areas to give you a feel for what each area does.

Know a firm that should be on this list? Broken links? Leave us a comment!