With constantly changing market conditions, static investment approaches have become overpowered by adaptive investment algorithms. By constantly analysing the financial markets, the risk exposure is dynamically and automatically adapted to the prevailing market conditions.
Traditional asset management relies heavily on subjective decisions. This might lead to irrational conclusions, especially during market turbulences, with potential severe financial consequences. By using quantitative methods relying on objective data and statistical analyses, the decisions are rational and not biased by emotions.
Investment strategies relying entirely on humans imply a low level of automation leading to potential inconsistencies and implementation errors. The use of automated algorithms is allowing to bypass those issues by ensuring a strict and systematic implementation.
Traditional asset managers mainly focus on returns rather than risk management. However, risk management is crucial since the gain required to recover from losses is exponential (e.g. a loss of 50% requires a gain of 100% to recover). Quantitative methods are leveraged to recognize risky situations, reducing short-term downside and thus increasing long-term performance.