The incorporation of Artificial Intelligence (AI) and Machine Learning (ML) is changing the way we think about various industries, including the mutual fund sector, which has traditionally been driven by human decisions. Over the years, the Indian market has primarily relied on active investing, where the ability of a fund manager to pick top-performing stocks and sectors was of utmost importance. However, with the growing difficulty in outperforming benchmarks, quant funds have gained popularity in India. To learn more about this innovative yet underexplored category of mutual funds, continue reading.
Quant funds are investment vehicles that rely on mathematical and statistical methods, along with automated algorithms and sophisticated quantitative models, to choose securities in the portfolio and execute trades. Pre-established models are formulated using historical data analysis, which the algorithms then use to continuously evaluate new data and determine the most appropriate investments to hold or sell. This eliminates the role of human judgement in the decision-making process and also provides insight into past performance, which is not attainable through discretionary investing.
How is quant investing different from other investing strategies?
Traditional active investing is considered more of an art, based on the skill and intuition of the fund manager whereas quant investing relies on trend analysis, computing and statistical modelling of past data rather than fundamental research on companies and sectors. This approach is seen as more scientific and mathematical, with the goal of producing predictable and replicable results.
Quant investing take a different approach than passive investing by employing a more active method for stock selection and portfolio construction. Instead of tracking the performance of a benchmark index, quant investing uses a data-driven model to filter and select stocks. This allows for unbiased market capitalization and potentially capturing performance from smaller companies. The portfolio construction methodology used in quant investing is thoroughly tested and refined over time, giving it an edge over purely passive investment strategies. Hence, quant investing can be considered a "passive plus" strategy that offers the benefits of passive investing with additional elements of active management.
How do quant funds work?
Quant Funds are a unique type of investment that combines aspects of both active and passive investing. They are managed by a fund manager, but their decisions are based on a set of rules and investment restrictions, known as a quant model. This model operates like a computer program, guiding the manager's actions and resulting in a templated portfolio, which is replicated periodically. The fund manager's role is limited to monitoring the performance of the fund and ensuring that the portfolio aligns with the model's outcomes.
Initially to begin the investment selection process, a broad investment universe such as Nifty 500 companies is selected and then the quant model is applied as a screener to narrow down the list of companies to a smaller number such as 30. These companies are then ranked based on the strength of the quant factor to create a model portfolio.
Quantitative funds in India utilize either single factor or multi-factor models to filter their investment universe and build a model portfolio based on specific factors. Single-factor models rely on ratios such as price-equity ratio, price-to-book value ratio, dividend yield ratio, fundamentals such as Return on Equity (ROE) and Return on Capital Employed (ROCE) and statistical phenomena such as standard deviation and beta, to build models around various factors unique to each company. Only the top companies are selected based on the ranking of one of these factors, resulting in a final model portfolio. On the other hand, multi-factor quant models use a combination of two or more factors, which leads to improved outcomes in terms of better returns and lower risk. These models are built based on the availability of quality market data, and fund houses conduct extensive testing to ensure that these factors work together. As more data emerges, these models are continuously monitored and fine-tuned to achieve better results. Quant models undergo rigorous testing and refinement before they are launched and constant monitoring is required to ensure that they remain relevant and effective.
Quant portfolios are developed through extensive research of statistical models and markets. As a result, they come with both benefits and drawbacks.
Advantages of quant funds:
Predictability and cost efficiency: One advantage is that the portfolio outcomes are usually predictable and easy to understand since they follow a standardized process and model, unlike traditional actively managed mutual funds that rely on the investment manager's market views. Their passive and consistent strategy also results in lower management fees, making them cost-effective for investors.
Absence of human intervention: Quant funds are designed to be free from human biases, making their approach and results highly objective. This minimizes the possibility of behavioural biases affecting their investment decisions.
Scalability: Quant funds are designed with a scientific approach and are thoroughly tested before they are launched, making them highly scalable. The design is based on existing market conditions and is constructed in such a way that it can easily adapt to market changes.
Better risk management: Quant funds have machine learning capabilities and can draw insights by analysing large amounts of real-time data to compute asset allocation. Instead of investing in a preferred asset class, these funds focus on actual sources of returns, leading to better risk management for the fund.
Better decision making: Quant funds through algo trading use algorithms to make investment decisions and execute trades automatically. This results in high volume trading that happens at a fast pace and exploiting gains from thin price differentials more effectively, which would not be possible for a human trader. However, not all quant investing strategies require automatic execution, for example, strategies that only trade once every three months can be executed manually.
Limitations of quant funds:
Empirical bias: These models are based on past data and statistical models that have been proven over time, but they may not always be a reliable predictor of future events. This means that if there is a market disruption or an occurrence of unexpected circumstances such COVID 19, it can result in unexpected change in variables, such as irregularity in capital flows or incorrect data inputs and the models may not perform as expected and deliver unfavourable and potentially unexplainable results. Besides, the quant models may be based on too many assumptions, some of which may not hold in a changing environment, resulting in undesirable trading decisions.
Risk of Black Box approach: The secrecy surrounding quant funds' models can sometimes make them seem like "black boxes" to investors, as fund managers often guard their models closely and rarely share their design or workings. This lack of transparency can sometimes be even greater compared to traditional mutual funds.
Should you invest in quant funds?
Quant funds are more suitable for long-term investors because of their data-driven and systematic approach to investing which eliminates the influence of personal biases of fund managers. These funds may take time to provide returns, so investors should consider them as part of a diversified portfolio. To invest in quant funds, investors must understand the degree of mathematics and programming involved. Quant funds are an interesting and innovative approach to investing, leveraging the latest advancements in technology, data analysis and machine learning. However, like any other investment, they also come with their own set of risks and challenges and investors must be fully informed before making any investment decisions. Back tests that are undertaken by quant funds are not a guarantee of future performance and should not be relied upon as the sole basis for investment decisions. They are just one of the many tools that investors can use to help assess the potential of a strategy. It is always wise to consider other factors such as the track record of the fund manager, the volatility of the strategy, the risk involved and the investor’s own risk tolerance and investment goals before making a decision. It would also be wise to seek the advice of a financial advisor before making any investment decisions.
Conclusion
The use of quant funds in India is still relatively new and most are found in private investment vehicles such as Portfolio Management Services (PMS) and Alternative Investment Fund (AIF), making it challenging to assess their performance over an extended period. Quant funds are widely accepted globally and manage a significant amount of assets and in India too, the launch of new quant funds is gaining momentum, offering investors a range of choices. The availability of large amounts of data and technological advancements has become the new standard. Quantitative methods are rapidly spreading across the investing world and are likely to become the norm. While the nature of data may change in the future, it is clear that using data to make sense of the markets is the way forward.
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