In the fast-paced world of finance, the need for complex mathematical and statistical modeling has exploded. The experts who develop these models are known as “quants” – short for quantitative analysts. But with advancements in artificial intelligence and automation, some have wondered if the era of the quant may be coming to an end. Let’s take a deeper look at the current state of demand for quants.
What is a quant?
A quant is a specialist who applies mathematical and statistical methods to financial problems. Some of the responsibilities of a quant include:
- Developing complex financial models to analyze markets and assets
- Designing algorithms to automate trading strategies
- Performing risk analysis on portfolios
- Predicting movements in stocks, derivatives, and other securities using quantitative techniques
Quants come from rigorous academic backgrounds, often holding PhDs in mathematics, statistics, physics, computer science, or engineering. They combine their advanced technical skills with deep understanding of financial theory and practice.
The rise of the quant has tracked the increasing complexity and data-dependence of finance over the past few decades. Advanced quantitative methods have become integral to derivatives pricing, risk modeling, algorithmic trading, and portfolio optimization.
Quant roles and salaries
Quants are highly sought after in a variety of finance sectors:
- Investment banks: Quants build models for pricing securities and structuring complex derivatives products. They also develop trading algorithms.
- Hedge funds and asset managers: Quants research new quantitative trading strategies and design models for constructing optimal portfolios.
- Data analytics firms: Quants mine big data sources to identify lucrative trading opportunities or develop sentiment indicators.
According to data from Wall Street Prep, starting salaries for quants at top investment banks can exceed $150,000. Experienced quants earn $200,000 to $400,000, with some managing directors earning over $1 million.
Given the potential for high compensation, competition for quant roles has traditionally been fierce. Banks and funds seek “quants with coding” – those with advanced programming abilities to implement complex models and strategies.
Have quants been automated away?
With the rise of artificial intelligence and machine learning, some argue that many quant functions will be automated. Tasks like data analysis, modeling, and trading strategy development could potentially be handled by AI.
But many in the industry believe humans will still play an indispensable role. AI lacks human judgment, intuition, and flexibility. Quants also spend much of their time communicating with business leaders and translating model insights – soft skills AI cannot replicate.
Rather than replace quants, AI will be a tool that allows them to focus on higher value work. Quants may rely on AI for faster data processing and model prototyping, but still direct the strategy and ensure rigorous statistical validity.
Market demand for quants
Quant roles remain highly sought after amid massive growth in data and computing power in finance. A report from Coalition Greenwich found 87% of financial firms planned to increase quant headcount in the next 3 years, especially in data science and machine learning.
Large fund managers like Two Sigma and D.E. Shaw have grown astronomically, powered by armies of quants. In 2019, Two Sigma managed $60 billion in assets with over 1,500 employees – up from just $5 billion and 250 employees in 2009.
Banks also continue to seek quants, even as trading revenue has declined. Quants develop new products and optimize operations across divisions – for example, building models for loan risk analysis.
The table below shows total job openings for quant roles at top financial firms over the past 5 years:
| Year | Quant Job Openings |
|---|---|
| 2019 | 1,200 |
| 2018 | 1,100 |
| 2017 | 950 |
| 2016 | 850 |
| 2015 | 780 |
Demand has risen steadily, emphasizing the continued need for human quants even with improving AI capabilities.
How has the quant role evolved?
While core modeling and data analysis skills remain essential, the quant role has evolved in recent years:
- More coding: Quants must be fluent in programming languages like Python and C++ to implement statistical models and trading algorithms.
- Specialization: Subject matter expertise in specific products like mortgage-backed securities is increasingly valued.
- Collaboration: Quants interact more with technologists to productionize models and translate them into applications.
- Communication: “Hybrid” quants who can explain technical concepts to business partners are in demand.
Some view these trends as diluting the traditional quant skillset. But most see them as complementary evolution that builds on mathematical expertise.
How can quants stay in demand?
For individual quants, these tips can help remain employable as the field evolves:
- Learn high-demand programming languages like Python and R.
- Keep up with latest advancements in data science, machine learning, and AI.
- Consider specializing in a domain like derivatives or credit risk.
- Develop communication skills to collaborate with technologists.
- Stay on top of new financial products and markets.
- Be flexible and willing to adapt as responsibilities shift.
Quants who embrace new skills while retaining their quantitative core will continue to thrive.
Conclusion
Far from going extinct, quants appear more essential than ever in finance. Demand remains robust across banks, hedge funds, and asset managers as data and technological complexity grow.
AI and automation will transform aspects of the quant role, but not eliminate the need for human expertise. Quants may leverage new technologies as tools, while retaining strategic oversight and communicating insights.
Quants who stay flexible and expand their skillsets will be poised to take advantage of the rich new opportunities in their field. The era of the quant is far from over.