The route into a hedge fund used to be straightforward. Earn your stripes on the trading floor of an investment bank - ideally in a proprietary trading role - develop a demonstrable 'track record' and then make the switch across.
But investment banks have closed their prop trading desks, which traded the banks' own money, after the regulatory crackdown under Dodd-Frank. Hedge funds have complained about the lack of talent emerging from the sell-side as a result and, increasingly, have taken it upon themselves to train graduates up from scratch. Formal training programmes in hedge funds - once a rarity - have become more commonplace, along with internships and insights into an industry previously masked in mystery.
Man Group has been running its graduate training programme for a number of years and takes on between 4-6 people annually.
Roland Beunardeau joined its programme in 2014, having graduated with a Masters in Management from HEC Paris. He interned in M&A at Rothschild in Paris, as a private equity analyst at Apax Partners in France and then at Goldman Sachs' private equity division in London.
Junior hedge fund jobs are highly-coveted and only a select band of graduates end up with the role - Man Group receives around 1,000 applications for its positions. But what does it really involve? Roland gives us the inside view.
I’ve just finished my graduate training at Man Group, which involved three rotations around its key business areas. In my case, after some time in sales, I spent most of my two years within its quant arm, Man AHL and Man GLG, its discretionary fund management division [where investment decisions are taken by a portfolio manager and supporting investment team]. These are very different roles.
As a junior in GLG, you’re essentially an equity analyst and the job entails looking deep into the financials of existing portfolio companies. So, you look into its business model, update the financial models, look at the cash flow of the company and generally perform some fundamental analysis and come up with some investment ideas for the portfolio manager.
This is important, simply because although the portfolio manager knows the companies well, they might not have had time to conduct such in-depth analysis recently, so they listen carefully to your recommendations and, after further research, can often act upon them.
In the long run, the job is really about developing an investment style and process for yourself – it’s actually a pretty personal process. You learn about your strengths and weaknesses, as well as how to pitch ideas and really understand how companies make money and what constitutes a good investment.
Obviously, it’s different – not just in terms of the job, but also the way you work. It’s very project-based and you’re working for long periods of time relatively autonomously. One project can last months, and it’s more about looking for patterns and ideas that might reproduce themselves in financial markets, and conducting a deep analysis of a broad range of securities, looking for statistically significant patterns.
It’s a very flexible way of working, and also – despite the quantitative nature – quite creative. Gaining an edge by analysing a huge dataset can be very hard to find, so you have to dig a lot deeper.
On the GLG side of the business, the workflow is quite flexible and you have to be pragmatic in the way you react to market developments. How you use your time in terms of analysis is pretty much up to you – aside from the meetings with the portfolio managers, you’re really looking to see how you might be able to create alpha [outperforming a financial index] on an investment independently.
You also have to be prepared to have your day disrupted – you can be all set to prepare a financial model for one thing, and then a piece of market news can break and you have to be able to react to it.
AHL is much less cyclical and, aside from a few meetings to update your manager, most of the time you’re working out how best to analyse and input the data.
If you’re doing fundamental analysis for a discretionary hedge fund, you need to be able to make sense of a large amount of data and understand which numbers are important. In a competitive space, it’s sometimes a small number that can make a big difference to performance and it’s up to you to trust your judgement on what’s important. This really comes with experience.
For the systematic side of the business [which uses computer models for most its trades], creativity is important, but you have to be really thorough and rigorous in your use of statistics. It’s really easy to over-fit the model to the data, only to find it doesn’t work at all. Coding skills also help – here we use Python, so understanding that programming language is important.
I’m very analytical, and by that I mean that I have the desire to dig deeper than the competition. You’re pitting yourself against people with similar information and a similar mindset in terms of their analysis of that data – you have to work that little bit harder to get an edge.
Also, I focus a lot on risk and this is incredibly important. It’s essential that you understand what is driving your strategy and ideas, and assess which risks to take at the appropriate time.
I had experience both within the financial sector generally, but also specifically of investing. People often talk about their ‘passion’ for investment, but if you’ve never actually invested in the market, it can be difficult to prove this.
My educational background was very statistical, so I was able to demonstrate the quantitative skills needed, but it’s also important to demonstrate good interpersonal skills, which are important because you’re working as part of a team.
I’d also interned in both M&A and private equity, so I’d developed my analysis skills and also financial modelling techniques, even if it was in other parts of the financial sector. I had well-rounded experience, which definitely helps.
I’m going back to GLG full time and I’ll be doing a quantitative research job here. I believe that’s really the way the industry is evolving anyway – discretionary fund managers are increasingly relying on quantitative tools. In five years, I think a lot of discretionary people are going to be combining deep fundamental analysis with systems based around data construction and analysis.