Why quant?


Quantitative managers have a unique value proposition to investors. They have a clear competitive edge in the areas of return, risk as well as liquidity.

At the heart of quantitative investing lies rigorous analytical discipline which subjects large financial data sets to the objective approach of mathematical analysis. The goal is to develop scientifically sound computer-based models that produce risk and return forecasts.

Based on these, portfolios that generate attractive risk-adjusted returns are constructed.

"Our goal is to develop scientifically sound computer-based models that produce risk and return forecasts."

Daniel Schild, Head of Computation



The fundamental law of active management has taught us that achieving high risk-adjusted returns is a function of skill, the number of independent investment decisions taken, and the translation of these insights into efficient portfolio implementation. Quantitative investing excels at all three.

Resorting to computer-based algorithms, quantitative managers are able to harness immense data volumes and cover vast amounts of securities. This generates a large and strongly diversified number of high-quality actionable investment insights. These are fed into a rules-based portfolio construction process. Due to the sheer amount of securities covered, quant investing achieves rare levels of portfolio diversification. In addition, behavioral biases of traders are kept at bay. In moments of crises, human emotions are difficult to control and might trigger rash trading decisions which may result in inefficient capital allocations at the most critical moments.

With quantitative investment strategies, human intelligence is essentially codified into a set of algorithmic rules. These produce returns that are replicable across multiple datasets and scenarios. Therefore, they are transferable from one person to another. Ultimately, this removes key man risk from the investment and portfolio management process.



Quant managers have a firm grip on risk management which is a core quantitative discipline. Investment risk is captured by abstract measures such as standard deviation, value at risk, and expected shortfall. These are inherently quantitative concepts that are clearly measurable and controllable. As such, they lend themselves to rules-based implementation mechanisms.

Quantitative investment tools keep these measures within clearly defined bounds without relying on a separate risk-management function. Moreover, the question of what to do when the market goes down and volatility goes up can largely be automated by using intelligent and agile algorithms that monitor the portfolio in real time. More importantly, quantitative analysis has the ability of separating well-rewarded sources of risk from unrewarded ones. Thereby, they steer clear of the pitfalls of risk exposures that carry no, or only limited, economic pay-off potential.

Lastly, this dissecting quality of quantitative precision comes in handy when striving for transparency in the investment process. Investment strategies implemented by computerized algorithms offer strong look-through qualities by allowing the investor to drill down into his or her portfolio’s mechanisms and exposures.



Big numbers are the quants’ home turf as they exclusively operate in highly liquid markets of great depth. These markets are characterized by factors such as a high number of market participants willing to trade, reduced market impact of trades placed and low transaction costs.

The investor welcomes these features as they allow him to remain agile and able to exit and enter into positions in a changing market environment. In addition, liquid strategies have high capacity. Strategy capacity is determined by how much capital a strategy can deploy before bringing down performance.

Computer-empowered quant strategies cover immense universes and invest at quick intervals. Therefore, they are remarkably scalable across markets and various time frames. This combination ensures that the effect of diminishing returns sets in later.