Trading in quantitative hedge funds (for earning to give)

What is this career path?

Hedge funds trade money on the markets on behalf of wealthy investors, in exchange for fees and a share of the profits. Quantitative hedge funds use strategies based on algorithms to make money. Jobs in this path involve using statistics, mathematical modelling and programming to devise, implement and manage these strategies.

Immediate impact

Earnings potential

This is likely to be one of the highest earning paths available.

Compensation starts at $100,000-$500,000 and grows rapidly in the first 5-10 years. Based on current salary levels and ignoring the chance of firm failure, we estimate the median lifetime earnings for a new entrant are $250,000-$750,000 per year, with a mean over $1 million per year. This ignores prospective salary growth or contraction. Overall, we recommend using an expectation value somewhat below these figures.

This path can also offer the chance to reinvest your earnings at above market rates, further boosting lifetime income.

For these reasons, this path has the potential to let you donate large amounts, which could make a big positive difference.

Direct impact of the work

There are some concerns that quantitative trading, or finance in general, might have negative impacts on society.

There are many types of quantitative trading, which likely have different impacts.

We’d encourage anyone considering entering this path to reflect on the potential negative social effects of the work they would be doing, and to compare it to its potential positive effects and the value of their donations.

We wouldn’t generally recommend undertaking work that you believe causes significant harm ‘for the greater good’ — as we write about in our article on whether it’s ethically permissible to pursue a harmful career.

That aside, what can we say about the impact of quantitative trading on society?

If you’re planning to enter this path, we’d encourage you to form your own view — but we did do a brief review of the economics literature about quantitative trading’s social impact. Here are some key points that stood out to us:

• The main concern we found is that there are some reasons to think that there is too much trading in society relative to what’s needed for price discovery, i.e. making sure financial assets have prices that correctly reflect economic fundamentals. This would mean that marginal trading is zero-sum, rather than that it has a negative impact. In other words, traders are moving money among themselves, such as in a game of poker. If true, it would imply that we should discourage people from entering the industry who don’t donate, because it’s not helpful. But if they’re donating to effective charities, the overall impact is positive.

• Quantitative trading may also have positive effects, including price discovery and providing liquidity, which makes it easier for people to trade — this then reduces the cost of capital in society, since if it’s easy to sell an investment, people are willing to accept a lower return for it. Price discovery may actually be undersupplied by the market, because the economic rewards of discovering new information about a firm (from trading profits) are usually less than the social value of the information. Quantitative trading can also be thought of as automating a much larger number of human traders, which frees up those people to do something more productive.

• There are some additional concerns about high-frequency trading, which is a minority of quantitative trading. Since high-frequency trading is unprecedented in the history of financial markets, there are concerns that it might create additional systemic risks that didn’t exist in the past — e.g. the conditions that led to the 2010 Flash Crash. It’s hard to estimate the size of these risks, but we would discourage someone from running a style of trading that might increase systemic risks in the financial sector. Fortunately the ‘Flash Crash’ hasn’t been repeated, which suggests operators may have figured out how to design their algorithms in such a way that they don’t cause large arbitrary price swings.

• Note that quantitative trading and hedge funds in general are not implicated in the 2008 financial crisis, which seems to have been driven by investment banks.

If the social impact of quantitative trading is negative, there is still an important question about whether the size of the negative effects is large enough to outweigh the potential good done by your donations. This involves considering the indirect effects of your actions (e.g. influencing social norms) and some more complex ethical questions. We explore these in our article on whether it’s permissible to take a harmful job to do more good, which includes a case study about finance in particular (though more focused on investment banks than hedge funds).

To learn more:

• Listen to our podcast with Alex Gordon-Brown, which includes a discussion of this topic.

• We found two academic review articles: “The Growth of Modern Finance” by Greenwood and Sharfstein (2013), and “Finance: Function Matters, not Size” by John Cochrane.

• There have also been interesting discussions on this topic on the economics blog Marginal Revolution — see all the articles on the topic. Noahpinion also has two interesting posts here and here.

Advocacy potential

This path is not particularly good for building connections and doesn’t give you a public platform, although you will have wealthy, intelligent colleagues, so there may be some opportunities to promote effective philanthropy or financial reform.

Building your long-run potential

Career capital

You’ll have opportunities to develop strong technical knowledge and skills, such as machine learning, programming and statistics. You’ll also develop most of the major transferable skills.

On the other hand, this career is not particularly useful for developing a broad network, and the firms may offer less prestige than alternatives in academia or technology. The only common exit option is into academia.

How difficult is it to enter?

If you’re capable of finishing in the top third of your class in mathematics at an Ivy League university, then you have a shot at entering this path. Slightly weaker mathematical ability can be compensated for with strong programming ability and rationality (for instance, skill at poker). You’ll generally need qualifications in mathematics, physics, computer science and so on to be considered, though not always. Many jobs require a PhD, though some can be entered directly from undergraduate. Applying to internships is one of the best ways to test yourself out.

For someone with the necessary mathematical skills, our impression is that this option offers unusually good pay and job satisfaction relative to its difficulty.

Job satisfaction

Many people find the work interesting and engaging. The culture often seems more supportive than finance in general (though it depends on the firm). Your colleagues will be very smart, but the pace is faster than academia. You’re expected to work 50-60 hours per week – considerably better than investment banking or tech startups.