Quant Research in Tokyo: How to Join the Machines

By; Brian DeChesare

If you work in sales & trading, will you be replaced by a machine? 

Maybe, but there are ways to reduce the risk of that happening.

One of the best is to learn to program and move into a quantitative role.

No matter how much artificial intelligence advances, computers probably won’t be able to program themselves effectively for quite some time.

Learn how to code, and you’ll be well-positioned for trading jobs, quant hedge funds, and even tech companies.

Our reader today got into quant research a bit more randomly than that, so I wanted to get the full story straight from him:

Breaking In: Taking the Red Pill

Q: Can you walk us through your story?

A: Sure. I’m originally from a “European country,” and I initially studied accounting in university because I thought it was the most practical option.

But I became more interested in finance, did an internship in corporate banking, and then completed an equity research internship.

I liked the experience, but I was more drawn to quantitative analysis.

After that, I had the chance to visit Silicon Valley and see a lot of young people starting companies and launching technical projects.

So, I taught myself programming, did a few data science projects, and spent time applying my new programming skills by implementing and back-testing trading algorithms.

Then, even more randomly, I studied abroad in Japan, and after graduating, I asked the company I had previously interned at if I could return and work in the quant team.

They were hesitant since I didn’t have a math or science degree, but my side projects and experience in the previous internship convinced them to hire me.

I also got to stay in Tokyo, which has its advantages and disadvantages.

Q: That is quite a story, but I’m assuming that most people don’t get into quant research groups like that.

What types of candidates are these groups usually looking for?

A: The biggest myth about these roles is that you’re doing rocket science math or inventing new fields of math or physics.

But the work is much simpler than that: We mostly back-test algorithms and see how well strategies in research papers would have worked in past market conditions.

You need to know programming, and it helps to have a background in trading and finance; many people on our team have more traditional technical degrees.

Overall, large banks care more about your formal background for these roles, while asset management firms and independent trading firms don’t care as long as you can do the work.

I won my role mostly because of my side project, where I implemented a momentum trading algorithm described in a research paper.

The strategy had worked well in the U.S. and most of Asia, but not Japan, and I used my project to demonstrate the reasons why it didn’t work so well there.

Build a portfolio of projects like this, and you’ll go a long way toward signaling your skills and determination to quant groups.

Q: Which programming languages are the most useful for this work?

A: I used a lot of R and Python in school, as well as MATLAB.

My current boss is fairly open about languages because speed is not a big issue: We don’t do high-frequency trading, so we don’t need to use lower-level languages such as C to maximize speed.

Q: OK. And what did they ask about in interviews, considering that you had already worked there?

A: I spoke with the senior people on my team and the Chief Investment Officer of the firm.

Most of the questions were about data manipulation and SQL, and they threw in brainteasers and algorithmic problems.

They didn’t ask me to code anything, but they did show me an algorithm and asked me to find the bugs (it was incorporating biases in implementing a certain strategy).

They did not ask any of the usual “fit” questions about strengths/weaknesses and leadership, but that’s probably because I had interned there before.

Quant groups care far more about the technical side, but you’ll still get a few qualitative questions if you’re new to the firm.

Quant Research… in Tokyo and Around the World

Q: You mentioned there are advantages and disadvantages to being in Tokyo.

What are they? And what’s the industry there like?

A: The biggest advantage is that there are very few qualified, bilingual professionals here.

If you’re in a quant research group here, you will be contacted by a lot of headhunters recruiting for banks, tech firms, and other companies.

The biggest downside is that Tokyo isn’t the best place for this work: New York, London, and Hong Kong all have more positions and bigger teams.

If an asset management firm has a few thousand people worldwide, its Tokyo office might have only a few hundred, and only a small percentage will be in quant research.

The large international banks, Japanese banks, and asset management firms (e.g., BlackRock), all have quant research groups, but we focus on automating much of what Portfolio Managers (PMs) at traditional funds do.

Actively managed funds have struggled to justify their fees, given their performance, and clients often prefer automated strategies because they’re cheaper and easier to explain.

Q: OK. So, let’s say you’re trying to automate a long/short equity PM.

How would you approach this task?

A: We would start by looking at research papers written by firms such as MSCI, a leading tools provider for index and portfolio analysis. Large banks also send us many of these papers.

Then, we would implement the algorithm, apply it to historical data (“back-testing”), and see how it performs.

We do not spend much time generating brand-new ideas, but you might do more of that in the U.S. or Europe.

We don’t focus on new ideas because the Japanese market tends to lag behinddevelopments in the rest of the world, so strategies from 4-5 years ago in the U.S. might be innovative here.

Once we get an algorithm working reasonably well, we spend a lot of time tweaking it and figuring out ways to rebalance portfolio holdings automatically.

Q: Why is the Japanese market so different?

A: One explanation is that “the mentality” is different: Right around the time Lehman collapsed and the financial crisis struck, the Japanese were still looking into subprime mortgages!

Major geopolitical events and crises make an impact here, but some people have argued that there’s more of a “cushion.”

Also, the economic picture has been quite stagnant for the past ~20 years, and aside from Abenomics, there hasn’t been much excitement in the markets.

Q: I see. What are the work environment and culture there like?

A: The quant research group is much less intense than the equity research group I worked in.

In an average day, I’ll start by back-testing some algorithms, tweak them a bit, and then show my results, along with some analysis of what worked and what didn’t work, to the PM or sales team.

That person will then show the results to the client, and the client might come back to us with some concerns – and I’ll have to make further tweaks.

But each cycle often lasts a week or more. It’s not like IB where everything must be done immediately and where you go through 113 turns of a presentation.

I don’t think I’ve ever stayed past 11 PM, and I usually leave much earlier than that.

Q: It sounds pretty nice.

Are you planning to stay there for the long term?

A: No, probably not. I’m satisfied with my current role, but I don’t think the asset management industry necessarily has a bright future.

We’ve seen a big squeeze on fees, and even with automated trading and algorithms, the lower fees will hurt us.

Plenty of recruiters have contacted me asking about my interest in tech companies, but I’m more interested in opportunities that combine tech and finance.

People in quant research tend to stay here for a long time because the money is good, the lifestyle is fairly relaxed, and, in a place like Tokyo, it’s tough to be fired.

Q: Great. Any other tips for students and professionals who are interested in this area?

A: I strongly recommend reading this primer on quant trading.

It’s geared toward automating strategies rather than quant research, but at least 50-60% of it applies to quant research as well.

There’s also a useful guide to back-testing algorithms here.

If you want to work in this area, do what I did and develop side projects implementing algorithms and trading strategies, get some experience at an existing finance firm in another area, and use your side projects to move into quant research.

Q: Thanks for your time!

A: My pleasure.

Brian DeChesare: http://www.mergersandinquisitions.com/quant-research-jobs/