In almost every field, technological advancements are reaching new heights. Artificial Intelligence (AI) and Machine Learning (ML) have become buzzwords worldwide, influencing every sector and changing our perceptions of the human-machine interface.

However, could you ever imagine the application of AI and ML in an industry like mutual funds that is so heavily driven by human decisions?

Automation is taking over the world today, and it has already had an impact on many high-paying jobs around the world, and money management may be no exception. Automation has spread across practically in almost every field, including mutual funds, and one illustration of this phenomenon is Quant Funds.

For the past decade, active investing has dominated the Indian market, with the fund manager’s key talent being the identification of high-performing equities. However, on the other hand, quantitative investment is gaining more popularity around the world. The mutual fund industry has also adopted automation in decision-making. Over the last few years, Quant-based mutual funds have increased in popularity in India, since fund managers have found it challenging to beat the benchmarks.

Let us understand more about this exciting yet lesser-known category of mutual funds.

What are Quant Funds?

Quant Funds are a type of mutual fund in which the asset allocation, including stock selection, is determined by a pre-specified set of rules. A quant-based mutual fund is an investment fund that makes investment choices and executes transactions using mathematical and statistical methodologies, as well as automated algorithms and advanced quantitative models.

Quant Funds, unlike active funds, rely on an automated algorithm to make judgments about investments and entry and exit timing. Investment selection and associated decisions are made without the need of human intelligence or judgement.

What is the Mechanism behind Quant Funds?

Quant Funds are dependent on an automated mechanism to make portfolio decisions. They are a hybrid of active and passive investments in which the fund manager makes the final investment decisions but is guided by a set of rules and investment constraints. The Quant Model is a set of rules and limits that work more like a computer programmed AI or an ‘Algorithm’ and are very objective with little space for judgement calls. The Model Portfolio is intended to be replicated on a regular basis (most usually on a monthly or quarterly basis).

Quantitative investing models use a variety of tools to forecast future share prices, including existing and creative financial models, algorithms, machine learning, AI, and Big Data.

The quantitative investment process is usually broken down into three essential stages:

1. Input System

At the input stage, stocks having undesirable characteristics, such as high volatility, a large debt burden, inefficient capital allocation, and other relevant issues, are excluded from the quantitative model. This is an initial screening mechanism used to remove the undesirable elements beforehand and leave companies that are more likely to generate alpha.

2. Forecasting Engine

The forecasting stage is where estimations for expected return, price, risk parameters, and other factors are generated. The evaluation of stocks is also done at this stage based on two important elements. One is the fundamental factors such as Return on Equity, P/E Ratio, P/B Ratio, Cash flow, Dividend, Earnings growth, etc. Another is the technical indicators like 52-week high, 52-week low, Relative Strength Index, Moving Average Convergence Divergence, etc.

3. Construction of Portfolio

At this point, portfolio composition and construction takes place. Optimisers or heuristic-based methods are used to create the composition. The quantitative approach creates an optimal portfolio by assigning appropriate weight to each stock in order to generate targeted returns while reducing risk to acceptable levels.

Quant Funds use computer-based algorithms to reduce the risks and losses associated with human fund management. Quant Funds, like any other investment fund, strive to outperform the market by strategically investing in liquid and publicly traded assets. The goal in terms of finance is to generate alpha (excess return).

Fund managers review the model annually and make tweaks if necessary. But a quant fund manager may not be entirely hands-off like an index fund manager. Fund manager often designs and monitors the quant model that throws up the portfolio choices.

While active fund investing is largely about fundamental research on companies & sectors and investing with a view on the future, quant investing or quantitative techniques are all about trend analysis, computing & statistical modelling on past data. The Smart-beta strategy is the most popular quant strategy.

The term “smart-beta” refers to investing in portfolios that combine both passive and active strategies. The smart-beta strategy is a combination of classic value investing and efficient markets theory. It is factor-driven and can be skewed towards one or more factors by reweighting benchmark indices to favour low-volatility stocks, which can produce better risk-adjusted returns than the benchmark.

In the last couple of years, several fund houses in India have launched Quant-based mutual funds. Nippon India Quant Fund (earlier known as Reliance Quant Fund) is the first Quant based mutual fund scheme in India that was launched in 2008 by the erstwhile Reliance Mutual Fund, followed by DSP Mutual Fund in 2019, Tata Mutual Fund in early 2020, and ICICI Prudential Mutual Fund in late 2020. Quant Mutual Fund and Axis Mutual Fund both launched their Quant-based schemes in the first half of 2021, and the latest Quant Fund was launched by IIFL Mutual Fund in Nov 2021.

Here’s the list of Quant Funds in India and their performance so far:

Scheme Name Absolute (%) CAGR (%)
6 months 1 year 3 years 5 years 10 years
Axis Quant Fund -7.0285
DSP Quant Fund -10.0006 12.2822
ICICI Pru Quant Fund -5.8384 17.6421
Nippon India Quant Fund -4.1880 15.8129 14.6296 11.8122 11.7802
Quant Quantamental Fund 8.3220 21.5730
IIFL Quant Fund
Tata Quant Fund -6.3874 2.4478
Category Average – Diversified Thematic Equity Funds -4.7706 19.0659 18.0033 13.2321 14.1570
S&P BSE 200 TRI -3.0216 19.7618 16.5354 14.2159 15.0230

Data as on May 04, 2022
(Source: ACE MF)  

Should you consider investing in Quant-based Mutual funds?

Quant Funds are a novel concept in India, and while they are allowed as a thematic category in mutual funds by the SEBI, they are, predictably, a small category.

As a result, each of the quant funds described earlier by various fund houses has its own set of regulations. Before investing, investors should learn about each fund model and evaluate the benchmark for performance comparison.

Investors benefit from this new quantitative investing strategy because it eliminates human influence and provides an impartial perspective. One of the biggest benefits of investing in Quant Funds is that you don’t have to worry about the fund manager quitting, making mistakes, developing behavioural biases, or deviating from the fund’s objective. Quant Funds have lower management fees due to their passive and consistent strategy, making them cost-effective for investors. Automation allows for faster decision-making and the placement of orders, as well as the more effective use of thin pricing differentials. Quant models’ machine learning skills extract insights from massive volumes of data in real-time, and quantitative research tries to exploit market inefficiencies and gain alpha.

However, keep in mind that Quant Funds select stocks solely based on quantitative data; as a result, they may lose out on stock market movement due to qualitative information such as board efficiency, company ethics, and other intangible characteristics that are difficult to quantify. To guarantee that quant models continue to operate as predicted, they must be rigorously back-tested on a regular basis.

Some quantitative models fail to account for unforeseen conditions, which can lead to unfavourable outcomes in catastrophic events such as pandemics. Artificial Intelligence (AI) can lead to diverse quantitative models making the same judgements in tandem, thereby causing financial market instability. Various assumptions may exist, and they may not work if the dynamics of the market change suddenly.

Furthermore, these funds are very new, and most of them do not have a long performance track record to evaluate their effectiveness and efficiency. As a result, investors with a conservative or moderate risk appetite should avoid such Quant Funds. Aggressive investors with a high-risk appetite and a good understanding of markets, on the other hand, might consider investing a small percentage of their whole portfolio in Quant Funds as a diversification strategy only after determining their suitability.

Therefore, Quant Funds are only ideal for long-term investors, as the quant model’s strategy may take time to reap benefits from its full potential. With all of the risks and complexity that Quantitative investing entails, objectivity, a high level of transparency, and a reduced cost make for a strong case. However, one should evaluate their risk profile and conduct thorough research before investing.

We understand that not everyone is able to dive deep into analysing worthy investments or has financial expertise. Therefore to earn optimal-risk-adjusted returns, I suggest investing in a well-diversified portfolio of actively managed high alpha-generating equity funds.

You should consider well-managed and process-driven funds that hold a superior track record and are capable of beating the market by a substantial margin in the long run. By selecting schemes wisely, you will be able to add high-return generators to your portfolio and, at the same time, boost your portfolio returns.

This article first appeared on PersonalFN here

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