Evolution of quant Evolution of quant http://www.federatedhermes.com/us/static/images/fhi/fed-hermes-logo-amp.png http://www.federatedhermes.com/us/daf\images\insights\article\programmers-small.jpg July 2 2025 July 2 2025

Evolution of quant

A brief history of quantitative investing.

Published July 2 2025
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Over the past century, quantitative, or quant, investing has evolved from a purely theoretical concept to a practical approach to investing in financial markets. Ideas that were once confined to the world of academia have been implemented by numerous investment strategies, some with remarkable success. Quant models have undergone a significant evolution in recent years, with advances in computing power and an extraordinary abundance of data allowing today's quant managers to achieve insights that were previously unimaginable. Early quant strategies were typically based on simple models that incorporated a few key factors such as valuation, momentum and earnings growth. But, in August 2007, the market experienced a major event known as the Quant Quake. Heavy losses at a large quant fund forced it to sell down its holdings rapidly to meet redemptions. Other funds with similar strategies tended to be not only invested in the same securities, but also highly leveraged, which made them especially vulnerable to contagion when one of their peers had to unwind its positions at pace. This event highlighted the need for more robust and resilient strategies, prompting a reevaluation of existing approaches and spurring further innovation in the field. Since the Quant Quake, incremental developments have driven considerable evolution in quant investing. Improvements in the quality and quantity of data have been crucial, enabling more sophisticated models that can better predict market movements and identify investment opportunities. The integration of machine learning has been particularly transformative, allowing models to continuously learn and adapt to new information. These machine learning models became more experienced over time, much like traditional bottom up active managers, enhancing their predictive accuracy and decision making capabilities. The evolution of quantitative investing reflects a shift from simple, factor based models to sophisticated, data-driven approaches that leverage machine learning for enhanced performance. This adaptability and continuous learning have made quant strategies more resilient and effective, providing investors with a powerful tool to navigate market environments and pursue uncorrelated alpha.
Tags Markets/Economy . Equity . Fixed Income .
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Views are as of the date above and are subject to change based on market conditions and other factors. These views should not be construed as a recommendation for any specific security or sector.

Alpha shows how much or how little return is generated, given the risk a portfolio takes. A portfolio with an alpha greater than 0 has earned more than expected given its beta—meaning the portfolio has generated excess return without increasing risk. A portfolio with a negative alpha is producing a lower return than would be expected given its risk.

Investing in equities is speculative and involves substantial risk.

Investing involves risks including possible loss of principal.

The quantitative models and analysis used by MDT may perform differently than expected and negatively affect performance.

The value of investments and income from them may go down as well as up, and you may not get back the original amount invested. Past performance is not a reliable indicator of future results. 

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