{"id":3623,"date":"2017-09-05T10:25:57","date_gmt":"2017-09-05T14:25:57","guid":{"rendered":"https:\/\/www.bu.edu\/questrom-magazine\/?page_id=3623"},"modified":"2019-11-14T12:33:46","modified_gmt":"2019-11-14T17:33:46","slug":"questroms-toughest-class","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/questrom-magazine\/fall-2017\/questroms-toughest-class\/","title":{"rendered":"Lessons from Questrom’s Toughest Class"},"content":{"rendered":"

The welcome page<\/a> for the Questrom MS in Mathematical Finance greets would-be students with a stark warning: the 17-month program is \u201cnot for the faint of heart.\u201d<\/strong><\/p>\n

On their journey to becoming traders, risk analysts, financial software developers, industry regulators, or quants\u2014the in-demand financial analysts specializing in math and statistics\u2014students will have to crack some of finance\u2019s most complex, often arcane, theories: stochastic mathematics, algorithmic trading, derivatives modeling.<\/p>\n

But first, they have to get in. The average quantitative GRE of the latest\u00a0class<\/a> is around 168 (the maximum possible score is 170) and the only \u201cbasic\u201d thing in the prerequisites list is basic computer programming skills; from there, things spiral through the fundamental theorem of integral calculus to multivariate optimization. Although 1,335 mathematicians, engineers, physicists, economists, computer scientists, and others applied to join the Questrom MS in Mathematical Finance Class of 2019, just 30 percent were accepted, making it the School\u2019s most competitive master\u2019s program.<\/p>\n

Despite the welcome page admonition and the dauntingly high bar for entry, the program is increasingly popular. Founded in 1999 with just three students, it now has more than 200, including 15 doctoral candidates, and 9 full-time faculty.<\/p>\n

\u201cIt\u2019s the right program at the right time,\u201d says Allen Questrom Professor and Dean Kenneth W. Freeman<\/a>, who notes that the faculty is the highest rated of any at Questrom. \u201cThe need for sophisticated mathematical modeling has never been higher. Our graduates have the tools to make an immediate impact.\u201d<\/p>\n

The theories might seem inscrutable, but the applications aren\u2019t. Mathematical finance underpins your pension and mortgage, helps banks figure out the risks of big deals, and could save us from another stock market crash. You might never need to know an eigenvalue from an eigenvector, but your business might not function without someone who does.<\/p>\n

\"dice\"
\nLessons from Questrom\u2019s Toughest Class<\/span><\/p>\n

Luck vs. Skill<\/h3>\n

For those hoping <\/strong>mathematical finance has conjured a formula for getting rich, Eric Jacquier, clinical professor of finance, has some bad news. \u201cStudying finance, and mathematical finance in particular, will probably talk you out of trying to be a Gordon Gekko,\u201d he says. \u201cIt will teach you that the world is far too efficient and the people who do end up being disproportionately wealthy or successful are more likely to arrive in those states due to chance rather than superior skill\u2014much of it is just luck.\u201d<\/p>\n

Marcel Rindisbacher, faculty director of the math\u00a0finance program, offers a sliver of light for those looking for an algorithm for success. He\u2019s in the middle of a major research project to quantify the timing skills of hedge fund managers. Previous research has suggested that making a well-timed trade is all about luck, but after \u201cdeveloping more powerful statistical tests for timing skill,\u201d he\u2019s found that \u201csmall amounts of skill are more prevalent than people think.\u201d Even if those skills\u2014whether in reading market trends or spotting mispriced assets\u2014are still relatively small factors in the overall chances of making a successful deal, \u201cyou could be a little bit better than others and make some money out of that.\u201d<\/p>\n

Shutterstock\/Everett Collection<\/p>\n<\/div>\n

Why You Should Pay Attention<\/h2>\n

For the majority of students joining Questrom\u2019s math finance program, the goal is to land a job in the financial services sector, working on the complex models that underpin much of modern finance. The models are encoded in computer programs that banks use to calculate the pricing and structure of things like derivatives, the contracts that allow businesses to hedge against future changes in prices or speculate on shares with an option to snap them up at an agreed price and date. Quants help banks to figure out a fair price for those deals\u2014and to understand the risks involved.<\/p>\n

They\u2019re also building and managing the programs making the majority of stock market trades. Today, 70 percent of all US stock trades are made by computers\u2014not people. Automated trading<\/a>\u2014where sophisticated computer programs make thousands of deals per minute using intricate mathematical algorithms\u2014accounts for anywhere from 50 to 85 percent of the daily volume of share dealing in the United States. In 2005, that figure stood at around 30 percent.<\/p>\n

\u201cThat\u2019s really relevant to anyone who has a mutual fund and wants to save for retirement,\u201d says Marcel Rindisbacher<\/a>, the faculty director of the math finance program.<\/p>\n

When mutual funds\u2014what Rindisbacher calls \u201cslow traders\u201d\u2014try to place bulk orders, they\u2019re spotted in advance by the high-frequency traders, algorithm-based competitors that cash in and force the share price up. Rindisbacher says that\u2019s why it\u2019s important to train and hire people \u201cwho know the risk exposure of these strategies; in order to do that, you need to know how these algorithms work.\u201d<\/p>\n

Enter the quants. All students in Questrom\u2019s program have to learn the fundamentals of finance and computer programming, then pick from electives focusing on asset management, quantitative analytics, risk management, or analytics and research. According to the Wall Street Journal<\/em><\/a>, it pays to have them on your side. \u201cIn the past five years,\u201d the paper reported in May 2017, \u201cquant-focused hedge funds gained about 5.1% a year on average. The average hedge fund rose 4.3% a year in the same period.\u201d<\/p>\n

Even smaller banks are beginning to see the importance of having someone on staff who can do complex number crunching.<\/p>\n

\n

the class of 2019<\/h4>\n

1,335<\/strong>
\nApplications
\n30%<\/strong>
\nAccepted<\/p>\n<\/div>\n

\u201cThis perceived need was probably initiated by increased regulations and the associated complications, but in the process, these smaller financial institutions may have appreciated the value of quants\u2019 rigorous risk modeling,\u201d says Eric Jacquier<\/a>, clinical professor of finance and a specialist in volatility forecasting and financial econometrics.<\/p>\n

As well as helping companies comply with oversight rules like those in the 2010 Dodd-Frank Wall Street Reform and Consumer Protection Act, quants can also stop businesses, not just banks, from getting ripped off.<\/p>\n

Pretend you\u2019re an oil refiner who wants to hedge the cost of your primary ingredient: crude. You call your banker in New York to ask about using a crude oil swap to lock in the price for your next 10,000 barrels. Your banker will quote you a series of prices at which you\u2019ll be able to buy crude out into the future\u2014at prices guaranteed today. How do you know if the price is right? \u201cYour quant might be able to tell you that,\u201d says Jacquier.<\/p>\n

Increasingly, he adds, the boundary between commodities traders and asset producers has begun to break down; the bankers are also becoming refiners.<\/p>\n

\u201cCommodities trading firms have started buying real assets; they\u2019ve started buying refineries, and they\u2019re buying refineries because they want information about market pricing for the spot price of those underlying commodities.\u201d<\/p>\n

Causing a Crash; Preventing Another<\/h2>\n

Not everyone holds mathematical finance in such high esteem, however. Some blame it for the subprime mortgage crisis that sparked the 2007 global recession.<\/p>\n

In a 2009 Wired<\/em> article, \u201cRecipe for Disaster: The Formula that Killed Wall Street,\u201d<\/a> financial journalist Felix Salmon charted the rise and fall of the Gaussian copula function, a formula for calculating risk. Wall Street bosses loved the formula because it allowed them to bundle \u201cjust about anything\u2026into a triple-A bond\u2014corporate bonds, bank loans, mortgage-backed securities, whatever you liked,\u201d wrote Salmon. At the time, plenty said the formula was flawed\u2014it assumed unpredictable, once-in-a-lifetime events would never happen\u2014but, with money to be made, plenty were prepared to use it.<\/p>\n

And then the unpredictable started happening: house prices began tumbling.<\/span><\/p>\n

\"monopoly
\nLessons from Questrom\u2019s toughest class<\/span><\/p>\n

Homeowners are Derivatives Traders<\/h3>\n

Derivatives are often perceived as obscure financial instruments,<\/strong> the preserve of well-trained financial whizzes. But your mortgage is chock full of them. When the bank tries to figure out how much to lend on a particular home, says Eric Jacquier, that\u2019s an exercise in derivatives. \u201cBanks are using complex derivative valuation when they price the mortgages they sell us or decide what rate we are going to pay on our mortgage,\u201d he says. \u201cThe decision to refinance or not refinance our mortgage can be modeled as a quantitative, derivative pricing exercise, whether we realize it or not and whether we understand it or not; in this case, a quant would give you the value of keeping the old loan versus the value of taking the new loan.\u201d<\/p>\n

Shutterstock\/Everett Collection<\/p>\n<\/div>\n

\u201cIn the world of finance,\u201d wrote Salmon, \u201ctoo many quants see only the numbers before them and forget about the concrete reality the figures are supposed to represent. They think they can model just a few years’ worth of data and come up with probabilities for things that may happen only once every 10,000 years.\u201d<\/p>\n

\u201cThere\u2019s a perception that mathematical sophistication has contributed to evil things, not prevented the evil things. But sometimes people use the models very blindly, without understanding the deeper consequences.\u201d
\n\u2014Marcel Rindisbacher, faculty director of the math finance program<\/span><\/p><\/blockquote>\n

The end of the housing boom turned out to be a concrete dose of reality. Homeowners began to default on their loans and suddenly those supposedly triple-A securities\u2014packed to the brim with mortgage debt\u2014didn\u2019t look so risk-free. Wall Street institutions, stuck with junk securities, were out of pocket in a big way.<\/p>\n

\u201cThere\u2019s a perception that mathematical sophistication has contributed to evil things, not prevented the evil things,\u201d says Rindisbacher. \u201cBut sometimes people use the models very blindly, without understanding the deeper consequences.\u201d Decision makers choose to ignore risks; mathematicians churn out complex models\u2014mathematical formulas\u2014without understanding the real world they operate in.<\/p>\n

\u201cThe crisis exposed the problems that many of these mathematical models have\u2014they are approximations that do not take into account possibly rare events and other market complications,\u201d says Jacquier. \u201cWhile they may work decently most of the time, they might be completely useless in some situations. The fundamental problem is complacency: people become comfortable with approximations. It takes a well-trained quant to understand the dangers.\u201d<\/p>\n

Rindisbacher calls the financial crisis a \u201cmotivation to do better next time\u201d and to work harder at informing people about \u201cthe limitations and the dangers\u201d of these complex financial instruments. \u201cIt\u2019s really important to educate people because the application of these instruments in the industry has not been reduced, they\u2019re still there.\u201d<\/p>\n

Most banks now see the benefit of having someone on staff with a mathematical finance background, but those at the top haven\u2019t always been great at listening to quants\u2019 advice. And quants haven\u2019t always been adept at giving it. Both need to change to help prevent future crashes. Rindisbacher hopes graduates of the Questrom program are equipped to argue their case, translating complex math into useful briefings that anyone can grasp.<\/p>\n

\"golf
\nLessons from Questrom\u2019s toughest class<\/span><\/p>\n

You Might Not Retire on Time<\/h3>\n

Allow yourself a moment to dream of retirement. <\/strong> Where are you? On the beach or sinking a putt on the 18th? Playing with the grandkids or volunteering for a good cause? Now, for a more important question: How confident are you that your retirement plan will help you get there?<\/p>\n

Marcel Rindisbacher thinks mainstream retirement planning advice might be selling us all short. The problem lies with the defined contribution pension plans offered to most employees. \u201cThese plans give the employer the choice to select among a few types of mutual funds, which are composed of portfolios based on relative mispricings\u2014so-called anomalies\u2014within the cross-section of stocks,\u201d says Rindisbacher. Most pension funds \u201care not composed of optimal portfolios that finance the actual consumption plan the retiree may want to follow during retirement.\u201d In other words, they don\u2019t differentiate between the person who dreams of puttering in the yard and the one who plans to travel the world. Rindisbacher wants pension managers to design plans that keep these goals in mind using dynamic asset allocation and life cycle finance theory, a method of portfolio building that he says \u201cfocuses on local diversification benefits and incorporates hedging components against risk factors that affect the long-run risk-return trade-off.\u201d Rindisbacher says that \u201coptimal portfolio strategies are difficult to compute,\u201d but that recent advances in the field have made it a more accessible approach. He hopes they\u2019ll be used to build pension plans \u201ctuned to investors\u2019 risk profiles, investment horizons, bequest motives, and other individual-specific\u00a0factors like life expectancy and health.\u201d<\/p>\n

Shutterstock\/Everett Collection<\/p>\n<\/div>\n

\u201cA lot of quants have disappeared into back offices and might not be influential in decision-making processes,\u201d says Rindisbacher. \u201cIt was very visible during the financial crisis that a lot of the CEOs would continue to play games in the markets where things were very fragile and not based on good models. We want to elevate our students to the level where they become influential in the decision-making process. Some of the things that we have can be very dangerous, as the financial crisis showed, but it would be good to put that in the hands of financial engineers who are very conscious of the limitations of their models, but nevertheless help the decision makers.\u201d<\/p>\n

Many of Questrom\u2019s risk management students are increasingly policing those who can\u2019t exercise restraint, joining regulatory bodies like the Federal Reserve, the Securities and Exchange Commission, and the Comptroller of the Currency.<\/p>\n

Rindisbacher wants the lessons he and his colleagues impart in the classroom and test in research papers to filter through the industry, changing it for the better. The Class of 2016\u2014the latest to be surveyed\u2014is already doing its part, landing jobs at companies like Goldman Sachs, Moody\u2019s Analytics, Fannie Mae, and State Street Corporation, and pulling in an average starting salary of $84,000.<\/p>\n

Once his students have finished their classes, Rindisbacher has one final lesson to impart. He might be talking about financial models, but there\u2019s wisdom in it for the rest of us.<\/p>\n

\u201cWhat you\u2019re doing in terms of model building, don\u2019t believe that because it works at some moment in time, it will always work,\u201d he says. \u201cAlways try to question what is missing, what are the potential factors you didn\u2019t take into account? If you ever find something that looks good, don\u2019t believe that this is the final answer.<\/p>\n

\u201cYou have to have a lifelong learning approach. Just because you\u2019re now relatively state of the art because you have been through a very rigorous program, doesn\u2019t mean this is sufficient for your career. Things will change\u2014you have to be alert and place all that you have learned in a broader context.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

The welcome page for the Questrom MS in Mathematical Finance greets would-be students with a stark warning: the 17-month program is \u201cnot for the faint of heart.\u201d On their journey to becoming traders, risk analysts, financial software developers, industry regulators, or quants\u2014the in-demand financial analysts specializing in math and statistics\u2014students will have to crack some […]<\/p>\n","protected":false},"author":10779,"featured_media":0,"parent":3590,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"story.php","meta":[],"yoast_head":"\nLessons from Questrom's Toughest Class - Questrom Magazine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.bu.edu\/questrom-magazine\/fall-2017\/questroms-toughest-class\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lessons from Questrom's Toughest Class - 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