Types of Quantitative Investment Strategies
In a sense, the history of quantitative investing and its milestones is also the history of the modern market system. Just as economics began quantifying its models and methods in the post-World War II era, mathematical models were introduced into finance to identify undervalued investments. In 1952, Harry Markowitz laid the foundation for Modern Portfolio Theory, demonstrating the importance of diversification in reducing risk.
A little over a decade later, William Sharpe introduced the Capital Asset Pricing Model (CAPM), which posited that higher returns come with higher risks.
Then in 1973, Fischer Black, Robert Merton, and Myron Scholes developed the Black-Scholes model for option pricing— the first widely-used mathematical method for calculating the theoretical value of option contracts.
“News: The Royal Swedish Academy of Sciences has decided to award the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for 1997.”
These advancements, combined with the increasing computational power in the 1960s and 1970s, enabled financial analysts and econometricians (later known as “quants”) to create more complex algorithms and models. Financial firms could now manage risk and identify investment opportunities across a broader scope.
By the 1980s and 1990s, hedge funds began incorporating quantitative methods into their strategies, leading to the rise of firms such as Jim Simons’ Renaissance Technologies. This drew widespread attention to the potential of data-driven approaches to generate substantial profits.
After the 2008 financial crisis, focus shifted toward developing more robust models and accounting for tail risks—rare but extreme events—in quantitative investment frameworks. In recent years, with the integration of machine learning and artificial intelligence into the design and application of financial models, quantitative investing appears to be on the cusp of another revolution. These technologies have significantly improved the application of data analysis.
As algorithms become increasingly complex and autonomous, the associated strategies and techniques continue to evolve. Today, quantitative trading is a well-established and mature technology in the financial markets. Many investors have embraced quantitative strategies, with most of them achieving significant returns as a result.
The history of quantitative investing is rich and marked by innovation, risk, and evolving methodologies. Due to its growing significance in global financial markets, it continues to be highly regarded.