Part 1: Why Most Investors in Publicly-Traded Securities Tend to Avoid Overlaying Quantitative Portfolio-Management Tools

In the current financial market landscape, quantitative investing has continued its emergence in sophistication and scale at harnessing the power of data, technology, and complex algorithms to guide investment decisions. However, implementing quantitative investing within a real-life pooled-investment vehicle requires unique skills, advanced technology, and robust infrastructure. This article aims to explain why traditional investment managers seldom deploy even the most basic quantitative strategies to assist them in generating alpha for their clients.

Quantitative Investing Requires Additional Rigor

The allure of quantitative investing lies in its data-driven, emotion-free approach. It relies heavily on mathematical models, utilizing numerous variables such as price, volume, volatility, and various economic indicators to predict market behavior. However, the implementation of these strategies in a limited partnership is far from straightforward.

1. Technological Infrastructure: Quantitative investing involves processing vast amounts of data in real-time, which requires advanced technology, including high-performance servers, sophisticated data processing tools, and high-speed internet connectivity. The challenge of setting up and maintaining this infrastructure is significant and involves a hefty investment.
2. Data Sourcing and Cleaning: Accurate, high-quality, and real-time data forms the bedrock of quantitative investing. However, sourcing this data from reliable sources and cleaning it to remove discrepancies or outliers can be time-consuming and complex. It also requires a strong knowledge of various data sources, their reliability, and their relevance to the chosen investment strategy.
3. Algorithm Development: Building a successful algorithm requires deep knowledge of financial markets, advanced mathematics, and machine learning. It's a rigorous process, involving backtesting on historical data, refining based on performance, and continuous updates to accommodate changing market dynamics.
4. Risk Management: Quantitative investing, like any other investment strategy, carries risk. An essential component of managing a quantitative fund is understanding, quantifying, and mitigating this risk. This includes addressing concerns such as model risk (the risk that the model itself is flawed) and operational risk (the risk of failure in day-to-day operations).

Skill Sets of A Quantitative Team Differ From Traditional Research

For most investment managers, their investment team looks considerably different than a team able to implement quantitative strategies, accurately track each strategy's performance and continue to improve such strategies.

Specifically, in quantitative research, deep understanding of mathematics and software platforms are very helpful, yet neither are crucial for success in traditional securities research. (Obviously basic math and software skills are necessary in most industries.) Further, embracing automation in day-to-day functions such as order routing, portfolio accounting, portfolio rebalancing and management can add further obstacles to quantitative investing.

In Conclusion

In a world inundated with data combined with the nascent AI industry, quantitative investing will take more prominence in the coming years. Individual investors will be hard-pressed to keep up with developments, and traditional investors who fail to embrace the advanced tools will continue to face ever-increasing headwinds.

Note: Although I am a registered investment advisor, this is NOT INVESTMENT ADVICE. It's simply my observations on a major hurdle facing the traditional investment managers.