Quant Funds 101 – Part 2

Now that we have a sense of what quant funds are (refer: https://pirimidtech.com/quant-funds-101-introduction/), we can look at their various forms, their advantages, and the risks that the fund managers undertake.

Types of Quant Funds

Black Box

In such kinds of funds, disclosure is not provided for the method and logic of investment to investors. Past performance is the only barometer for judging the particular fund.


The portfolio in such kind of investment is churned less frequently. This method is passive in nature. The investment mandate is based on pre-defined rules which are known to investors.

Factor Models

Contrary to index funds, these funds are managed actively. The investment methodology is based on factors that could drive stock performance.

Benefits of Quantitative Investing?

1. Rule-Based Investing

You have standardized rules for 1) asset allocation, ii) stock selection, and iii) timelines for entry and exit. Once the fund managers decide the rules, the process of investment becomes easier. The algorithm performs the buying and selling based on pre-defined rules.

Assume you are a pilot who is piloting an Aircraft. Suddenly, mid-air you lose one of your engines. As the human brain is prone to panic & extrapolation, it may make errors at this crucial juncture that may result in a devastating end. To avoid this situation, airlines provide a checklist to the pilots on what they should do in such a situation. It barely matters if the pilot has decades of experience or is an amateur who has just started flying, they are supposed to follow the same set of checks to ensure the plane lands safely. In other words, they stick to pre-defined rules that are written based on historical experiences and resulting actions. Thanks to the rules, pilots barely panic and can pivot through even the most complicated situations. Quantitative Funds are very similar!

2. Not influenced by human-emotions

The decisions of quant funds are based on an algorithm or a computer model. Human beings do not influence the algorithm. Fear and greed of human beings drive investment decisions. Further, there are also biases towards buying or selling a particular security.

Quant funds avoid such situations and take advantage of such inefficiencies or irrational decisions made by other investors in the market.

3. Wide-coverage

A small team of quantitative analysts can cover a wide range of securities by either eliminating or selecting securities based on a pre-selected filter. Quantitative investing also allows fund managers to track new markets, regions, and countries without hiring analysts. They can merely customize the investment algorithm suiting to that market.

4. Investment strategies can be tested

Quant funds allow the fund managers to backtest the algorithm used for securities selection back in time. With backtesting, the quant fund managers can evaluate the fund performance in trying times like the global economic meltdown (2008) or taper tantrum (2013).

5. Cost-Effective Strategy

Quant funds require a lower involvement of analysts and fund managers. Mathematical models handle all the hard work. Further, quantitative funds do not require meetings with the management, which reduces the overall cost of decision-making.

6. Lower expense ratio – Investor attraction

Further, investors, these days are also attracted to funds with lower expense ratios. Therefore, savvy investors prefer quant funds which beat the benchmarks with a low expense ratio.

7. Combat the information overload

Given the sheer volume of data on the web, it is humanly impossible to sift through millions of results and 2.5 quintillion bytes of data to find a single search word. Quantitative fund eases the process and makes investing easier by sifting through or highlighting relevant information for investors.

Risks of Quantitative Funds

1. Black swan events

Quant fund strategies use historical data. Therefore, it may not respond well to black swan events like a coronavirus-led crash or a global financial crisis. As per Kaustubh Belapurkar, Morningstar India, “Since these (Quantitative funds) are driven by quantitative models, there could be some blind spots which may be otherwise picked up by fund managers through a more qualitative lens.” While a strong quant team constantly adds new aspects to the models to predict future events, it is impossible to predict the future every time.

Quant funds can also become overwhelmed when the economy and markets are experiencing greater-than-average volatility. The higher buy & sell signals can come so quickly that high turnover can create high commissions and taxable events soaring transaction costs.

2. Reliance on past data

Since quantitative models rely on past data being fed into artificial intelligence (AI) and machine learning (ML) models, they might face another hurdle. As per Tarun Birani, TNBG Capital Advisors, “If the machine has not witnessed an event in the past or has neither been fed with information about it, it will not be able to account for the same in its future predictions”.

For instance, Long-Term Capital Management (LTCM) was one of the most famous quant hedge funds run by respected academic leaders & Myron S. Scholes and Robert C. Merton, two Nobel Memorial Prize-winning economists. They generated good returns for their investors for a very long time. However, their models did not include the possibility that the Russian Government defaulting on some of its debt. This one event triggered events, and a chain reaction magnified by leverage created havoc. LTCM was so heavily involved with other investment operations that its collapse affected the world markets, triggering dramatic events.

3. Backtesting past results may not work for future

Quant fund managers backtest their models on the past data, there is a high possibility that it may not perform the same in the future. Gaurav Rastogi, Kuvera cautions, “Do not invest just because the backtest looks good. Investors have burnt their hands chasing such fictitious returns quite often. So, investors with a certain level of sophistication in evaluating and understanding statistical significance should invest in quant funds”.

4. Lack of subjective data accounting

Quantitative funds do not take subjective factors into account while decision-making. For instance, the managers of quant funds do not interact with the investee’s management. While quantitative investing does not require meeting management, fund managers have to be careful before deploying funds in companies with weaker corporate governance. The quant fund may fail to detect companies with weak corporate governance. In situations where the weak corporate governance of such a company comes to light, the fund may suffer losses. Therefore, the mathematical quant fund may pick securities without performing due diligence.

This is not true for all quant funds, and new data sources are now being used to create models that generate alpha in the short term. However, most quantitative funds are unable to take such subjective factors into account and there is a scope for improvement.

5. Large Holdings dilute percentage return

As quantitative models and strategies are based on an expected return distribution and probabilities, a relatively large number of holdings is required. Such holdings can dilute percentage returns.

Drop a comment or write us an email at info@pirimidtech.com for any feedback about this article. Do check out our portfolio in building Robo advisory, Large Scale Trading Systems, Algo Trading, Stock Sentiments, Price Trends Forecasting, Backtesting frameworks, Credit Model, Open Banking, etc. on our website. Connect with us to discover how our Fintech expertise can help you build cutting-edge solutions powered by AI/ML.

This is a guest post by Yash Surana, who is a CA and an MBA (Finance) from SPJIMR, Mumbai, and loves writing about finance, strategy, startups & Personal Finance.













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