In this article, we will cover quant-based mutual funds and how they eliminate human biases from investing decisions.

The story

Technology has come a long way, transforming how we live and work. Thanks to breakthroughs in computing power, machine learning, and AI, we can now process and analyze information at speeds unimaginable a few decades ago.

Investing has also evolved in this landscape, giving rise to Quant-Based Investing—a style defined by data-driven insights and algorithms.

Quant based investing

Quant (quantitative) investing uses complex mathematical models, algorithms, and computer systems to analyze a large set of data and make investment decisions. It follows predetermined rules to make investment decisions with no human involvement.

Think of it as a funnel where investment ideas are filtered based on various parameters, such as financial performance, valuation metrics, or market sentiment.

For example, imagine a fund manager analyzing years of historical data to find patterns. Let’s say a fund manager found that companies with a market value of ₹25,000 crores or more, zero debt, double-digit revenue, and profit growth, and at least 20% market share, have outperformed the Nifty 50 around 80% of the time. Now, this insight becomes the basis for setting up algorithms that a mutual fund, by default, will only invest in companies meeting all these criteria, screening companies and building a portfolio without any human bias or judgments.

While this is an oversimplified example, it gives you a sense of how quant mutual funds work: they rely on data-backed rules and algorithms to pick investments. It lets the numbers do the decision-making!

It is both–a part active and a part passive style fund. Fund managers play an active role in developing mathematical and statistical models to set up rules and algorithms while these rules act as ‘instructions’ the fund must follow.

Key Advantages of Quant-Based Investing

1. Objectivity:
Quant funds follow strict mathematical models, which means decisions are purely data-based and unaffected by emotions or personal bias.

2. Quick, Efficient Processing:
The power to analyze vast amounts of data at incredible speeds enables the analysis of substantial real-time data to uncover hidden insights in data.

3. Transparent and Tested:
Quant models are clear-cut, transparent, and can be tested on historical data. This gives investors a good idea of how the model might perform in the future.

Disadvantages of Quant-Based Investing

Model Risk:
The model performing well on past data could perform poorly when presented with new events. So these funds could struggle if market conditions suddenly change.

Data Quality:
The success of a quantitative strategy relies heavily on using high-quality, up-to-date data. Even small data errors can have a major impact on the strategy’s performance.

To conclude

The oldest fund in this category is the Nippon India Quant Fund, launched in February 2005. It has delivered 13.77% returns in the past ten years, while the BSE 500 Index (the benchmark) has delivered 14.26% returns for the same period.

Currently, in India, there are about 10 mutual fund schemes following quant-based investing, with nine of them launched only after 2018. Given the limited data, it’s still unclear if these funds could be a good addition to a portfolio at this stage.

Will it bring a significant change in the investing industry? Only time will tell.