December 18, 2011

How I became a Quant. Summary

The purpose of this post is to summarize the various stories mentioned in this book. This write up is a result of a marathon writing on this weekend. Actually it took more time to write than read the entire book. In the process of summarizing, I have tried listing down some of the statements/remarks/suggestions(under the heading, learning’s) made by these quants that are worth pondering upon.

Success in finance would require one to acquire much more intuition and insight into the basic problems faced by investors and to build up a toolkit for analyzing those problems.

My approach to quant finance was very different from what a formal education in this area would involve. Basically, I tried to master specific areasâlike interest rate optionsâand then branch out and master new areas, through new projects. It was a patchwork quilt approach. I hoped I could build enough sufficiently large patches that they would eventually form a substantial quilt.

Is quant revolution over. Certainly much of the relevant theoretical underpinnings are in place. At the same time, there exist even today significant issues in need of rigorous quantitative analysis. Present-day debates over liability-driven investing prove that fundamental issues remain unresolved among investment professionals Other areas worthy of further research include hedge funds and, more generally, optimal leverage and shorting. Between these remaining fundamental issues and the never-ending competition amongst active managers for out performance, the need for quants has never been greater. There is plenty more to do in this challenging, interesting, and rewarding field.

This is the story of a quant who doesn’t want to be called a quant. Quant is sometimes used pejoratively on Wall Street as in, `He is ONLY a quant’. After graduating from Princeton with a PhD. Berman realized that he was not cut out for academics. His first few jobs were at hedge funds where he wrote automated trading strategies for commodity futures and trades those strategies.In the first few years he was so fascinated by coding that he programmed even on 2 hour train daily commute. The years at hedge fund helped him understand markets, psychology of trading, the type of statistics that can be used, etcdots He traded futures for a couple of years and did not particularly enjoy it during a bear market phase. He felt that trading was an activity where luck played a significant role in the success and his academic bent of mind, where hard work and smartness fetch results, was not accepting the vagaries of trading. He remarks,

I also found that that I had less of a stomach for the huge ups-and-downs of trading than I had previously believed. This was a hard lesson for me to acknowledge. After all, many people claim that if they just had some money to start with they could use it to make a fortune. Well, I had millions in potential capital at my discretion and was asked to do just that. But I couldn’t. I very much wanted to, but I just couldn’t come up with any systems or strategies that would make fortune in the commodities markets. The more I wracked my brain, the more I realized I liked the detailed analytical parts of my job much more than the trading parts

The big change in the career came when he moved to RiskMetrics and did a lot of quant work at JPMorgan. At JPMorgan he donned quit a few hats and became a true quant in Wall Streetish sense. The big takeaway from Berman’s story is that quant is not a job but an attitude that one must develop, to do well in finance, and more so in today’s tech driven finance.

This is the story of a quant, Evan Schulman, whose belief in efficient markets got strengthened in his initial years of work at a trust company analyzing pension accounts. After developing quant tools that helped portfolio managers focus on their stock selection. he came to believe that quantitative skills can improve trade processes, if not generate alpha. He subsequently joined Keystone group at Boston to develop quant stuff like `Implementation shortfall ‘models.

After his stint at Keystone, he moved to Batterymarch and that was a key step in his career. At Batterymarch, the entire firm was working hard to lower commissions and to keep the market impact of trades to a minimum.Schulman executed the first program trade while at Keystone Funds. Lot of people on the Street know Schulman because of this first program trade thing. He not only helped computerize the firm-front, middle and back office-but also introduced an innovative trading system at Batterymarch. Schulman let brokers access its orders via computer to trade. He left Batterymarch to start Lattice Trading, a firm that offered an electronic trading product that was a forerunner to today’s algorithmic trading.

What was Lattice core idea. It was multi-broker system to allow for the fact that institutions tended to use several brokers in the conduct of business. Lattice became very popular and was bought by State Street. Evan then left to start another firm. Upstream technologies in 1999.In his earlier firm, the work was mostly for the institutional side. At Upstream, he and his co-founder Mark Hoffman decided to apply the tools, discipline, quality control to individual accounts, even the small ones. Upstream uses quant stuff to work on individually managed accounts that dominate mutual funds. As Evan remarks

Optimization is ideal for the accounts of individuals.

Today, Upstream technologies is a success story on Wall Street.

Learning’s:

• A quant who believes in efficient markets can still add tremendous value to improving the investment process itself, be it on the institutional side or the retail side.
• Compound returns equal a function of the average return less half the variance. Meaning, the longer the time horizon, the greater the difference between arithmetic return and geometric return on investment

• Not all problems can be solved by throwing more bodies in to a mix. The performance and productivity of quants varies a lot. Sometimes just one or two quants can bring in radical changes to the business. I am reminded of Dragan Skoko and his tremendous contribution to the success of Batterymarch

Leslie Rahl graduates from MIT in 1972 with a degree in Electrical engineering and realizes that she has no aptitude for circuit boards, electricity, or the mechanical facets of electrical engineer’s trade. In a totally random combination of events, she lands up in Citi and spends 19 years doing trading and risk management. Starting from a job of trading options for a prop book, she starts handling interest rate trading book and eventually swap business. In 1994, she leaves Citi to start her own company, Capital Markets Risk Advisors and since then has grown her company to a decent size on Wall Street.

Her belief is that models are useful for trading and not valuation. This comes from a quant who has traded for 19 odd years. so there could be a bias in her statement. What surprises me is that she starts a risk management company where you are typically valuing something under extreme events. So, how is that she has spent the last 15 years on something she doesn’t believe. Anyways, when asked how she became a quant. she replies ,`I was born a quant’ and adds that being a quant helped her not only with solving quantitative problems, but has taught her an analytic framework for problem solving that applied equally well to non quantitative problems.

Learning’s:

• It is better to run your own business / work for a smaller company/ startup than working in a big firm. When you count everything, Positives outnumber the negatives .

Thomas Wilson, a graduate from Berkley and Stanford is not sure whether he would consider himself a quant or not. He believes in `asking the right question’ as being crucially important than any mathematical technique. You ask the right question and then go about looking/ creating/developing/copying the mathematical techniques to solve the question. He remarks

I put on the other end of the continuum individuals whose contributions were driven more by âtheâ question, or the intuitive interpretation of the observed economic and financial phenomena, rather than by the quantitative techniques that were used to represent their intuition. For individuals at this end of the spectrum, phrasing the question seemed more important than the techniques used to find the answer. In this camp, I put such individuals as Akerlof, Stiglitz, Lucas, Diamond, and Dybvig, individuals whom I judge to have contributed more through the intuition behind the question then the actual quantitative techniques they used.Who can argue that the intuition and insights behind Akerlof âs market for lemons outshadows the relative simplicity of the algebra usedf to prove the point?

He is extremely honest in confessing that he has lesser mathematical expertise than the quants who get cited in finance papers, media. In his career, he has stumbled on three questions and has learnt/developed mathematical tools to answer the questions. In this book, Thomas Wilson lists some of the most important questions he has answered in his career.

Market Risk Era ( Early 1990’s)

Is it possible to calculate VaR for nonlinear portfolios from local risk information verb#(e.g. deltas and gammas)#, which are readily available on the trading floor?

How many independent factors are practically required to capture the risk of a multicurrency fixed income trading book?

What happens to the tails of our VaR calculations if we have only estimates of volatilities and correlations and not their exact values?