Are quant and algo funds suitable for retail investors?

Though quant and algo funds are different, they are used quite synonymously. The underlying principle that binds both these funds is that they both use some mathematic model-based backtests, future predictions, pattern recognitions and building models around it. While quant models are generally coupled with human intervention, algo models usually trade in a robotic manner, simply executing what the model’s output dictates.

As data becomes more accessible and accurate coupled with strong AI models, there is a growing belief that such investments, which are devoid of human greed and fear, would outperform the conventional fund manager, especially on the quant side. Algorithmic models rely, in addition to quant models, on the speed of execution.

Some visible examples of algorithmic trading in India are a) Traders trying to capture disconnects between prices of the same stocks and commodities in different exchanges, b) As the closing prices of stocks take weighted averages of the last 30 minutes, some traders try to benefit from potential opportunities arising from the stock prices deviating from these averages.

Algo trading has been used more by boutique traders and broking firms for their proprietary trading, generating investment ideas for their clients. Some individuals also use it to garner public attention for their social media profiles, especially on the options side.

Also read: Quantum MF opens small-cap fund after index rises 35% in one year; should you invest?

Human choices coupled with quant strategies

On the other hand, quant-based investing has become popular largely due to the exceptional performance of the Quant Mutual Fund, which uses the quant approach as its primary tenet. This approach is also used by other fund institutions like ICICI, DSP, Axis, and 360 One Asset Management (formerly IIFL). A quant-based PMS (portfolio management service) method is employed by True Beacon (owned by Nikhil Kamath, a co-founder of Zerodha). It also needs to be known that these strategies might be a hybrid of human choices coupled with quantitative strategies at its core.

Quant MF through its various strategies manages around Rs 50,000 crore across its schemes while such strategies in other funds are much smaller. The exceptional performance of such schemes can be attributed to momentum stocks performing well over the past 12 months. It remains to be seen how these funds will adapt to a different market texture.

The Quant Small Cap Fund has delivered around 70 percent returns over the last 12 months on the back of a stellar run in the small-cap space followed by 60 percent in the Quant Quantamental Fund. Other funds in this space have relatively lagged but have still delivered great returns like 360 One Quant Fund (59 percent), Nippon India Quant ( 41 percent) and ICICI Quant at around 31 percent.

However, these funds carry inherently higher volatility than their peers and the underlying index. For example, most quant funds are significantly higher in terms of standard deviation (a measure of volatility which defines the deviation from the mean returns). Quant small-cap scheme’s standard deviation is around 20 percent versus 18 percent for the BSE Small Cap index while the Quant flexi-cap fund has a standard deviation of around 18 percent versus only around 14 percent of the BSE 500 and also around 13 percent of the flexi-cap category.

Global experience

While the AUM of these funds in India is low as investors remain apprehensive about the sustenance of the funds' returns in bad markets, globally quant and algo funds manage large AUMs.

Some notable ones like Renaissance in the US have managed close to a whopping $170 billion AUM followed by Two Sigma and Millennial Management at more than $100 billion.

From an investment perspective, unfortunately, it would be difficult to spot a fund in either of these categories in India with a fairly long history that encompasses multiple market cycles. The past few years might not be a relevant indicator of whether these strategies work during long recessionary periods or stagnancy (such as 2008-2013).

Know the risks

Some of the intrinsic disadvantages of these funds, especially the large ones, include the high-frequency trading that these funds need to do based on the triggers generated by the underlying software. This leads to significant inefficiencies in costs and taxation, especially if it is not under a mutual fund wrapper.

Furthermore, the majority of these models—quant and/or algorithms—rely heavily on backtesting and occasionally disregard Black Swan events as outliers. Unfortunately, because the technology isn't designed to handle such circumstances, these outliers are more common than one may think. When they do occur, substantial losses result.

For instance, most of these funds (Algo and not quant) work on a stop-loss mechanism that helps cut losses at certain pre-defined levels whenever the price falls thereby limiting losses. However, in exceptional cases, markets when they open in the morning, could open significantly down and the stop losses could be rendered ineffective leading to significantly more losses than expected. (These happen due to sudden events impacting the markets or companies overnight at a time when the fund cannot place orders).

Considering all these factors, for retail investors in particular, it is important to understand how these funds operate and the risks they entail. The one thing that investors should remember is that all these funds come with their share of risks and possibly more than the usual diversified equity funds. Hence, these funds should not be part of the core investment allocation, and if at all should only form part of a small allocation basis one’s risk profile and asset allocation.