Tag: Arxis

The Master of Science in Finance – Quantitative Track (MSFQ) students from Olin Business School trekked to Boston and New York in October to visit some of the top investment firms in the industry. The trip was organized by the Weston Career Center (WCC). During these company visits, the students got a chance to meet with experienced professionals working in the complicated world of finance powered by quant analysis and algorithms.

Day 1 – Boston:


We gathered in the lobby of Wellington Management–a firm with nearly $1 trillion of client assets under management. The most interesting thing about the firm was that it is organized as a collection of teams and a spirit of entrepreneurship is encouraged within teams.

The most important advice the students received from the speakers was to choose and specialize in any one of the asset classes early in their career instead of trying to be a generalist.

It is easier to apply skills mastered to other asset classes later.

numeric-presentationThe next stop was Numeric Investors, part of the Man Group. Numeric follows a bottom-up and very systematic quant approach in investing, but based on fundamentals. Numeric also has a special day each year when employees work on whatever they like for one day and deliver the results. In the past, employees have come up with innovative outcomes like applying machine learning to quant trading.

fidelity-logoOur day in Boston ended with a visit to Fidelity Investments. The students got a chance to meet with two MSFQ alumni working here, Michelle Hoerber, MSFQ’14 and Chloe Sun, MSFQ’15. After a detailed explanation of what Fidelity does, the alumni discussed their stories about how they chose Fidelity. The students connected well with the alumni, as they saw them as individuals who were recently in their shoes, but clearly successful now. After the long day in Boston, the group boarded a train to New York City, excited to meet the firms there.

Day 2 – New York City

arxis-groupThe first stop in New York was Arxis Capital, an electronic market making firm. Everything at Arxis centered around having the latest cutting-edge technology. For Arxis, speed is the name of the game. The team took pride in the fact that their algorithms are capable of matching orders in a mere 10 to 15 microseconds! It is achieved by gathering market news from all the venues, building consolidated order books, and then generating trade signals. The value proposition includes tightening spreads, aiding price discovery, and reducing liquidity risk by providing liquidity. It was exciting to observe the trading floor in action!

highbridge-board-room-viewHighbridge Capital Management was the next stop. The group was taken to Highbridge’s
conference room on the 33rd floor with beautiful view of Central Park. The students interacted with the statistical arbitrage team of the firm. The team provided deep insight into what goes into making statistical arbitrage work. They made sure the students got a feel for the city, not just by giving them insights of the finance industry in the city, but by arranging a delectable New York-style pizza lunch for the group.

de-shaw-logoThe last visit of the trek was exciting. This firm has been described by Fortune magazine as the most intriguing and mysterious force on Wall Street. The firm was D.E. Shaw & Company. The students were quick to notice that employees at the firm were dressed in smart casual attire as opposed to formal clothing. The firm employs highly quantitative strategies which are based on core strategies, like maintaining a good Sharpe Ratio and very low correlations. The core belief of the firm is that the markets are efficient over long periods of time but the opportunities arise in short term anomalies. The visit ended with a long and interesting Q&A session.

The two-day trek helped students see the industry up close. The most important takeaways from the trek were:

  • Keep mathematical models simple and understandable.
  • Have faith in your models but do not fall in love with them. It has to be accepted that models can fail.
  • It is not enough to just extrapolate from the past. It is important to be creative.
  • Be relentless and never get bored of working with large data.
  • It’s not just math; presentation skills matter!

Guest Blogger: Nishant Vaishampayan (MSFQ 2017)