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Quantitative investing refers to investment strategies in which buying and selling of shares/investments is done primarily on the basis of a process-driven analysis of numerical data. One of the biggest advantages that quant investing brings to the table is elimination or reduction of cognitive human bias in investing decisions. Further, volatility in markets can trigger human emotions of greed and fear and influence decision-making. Quant strategies aim to bypass these pitfalls by sticking to a backtested and validated investing/trading process based on quant models, and minimising human intervention.
When compared to the total size of equity MF schemes in India, quant MF schemes, with assets of about ₹3,100 crore, are still a minnow at just 0.25 per cent of the industry. Thus, they are still at a very nascent stage in India compared to the western world where they have an outsized share in investing/trading.
While events such as the ‘quant quake’ of August 2007, which had ripple effects across global markets, led to some apprehension about the industry’s prospects, it has evolved significantly since then and has continued to grow. Strategies have got even more sophisticated with the advent of artificial intelligence and its use in investing strategies.
Performance
Given the penetration in developed markets, one could argue the space in India too may have a long runway of growth. However, while numbers rule the quant world, it is always performance that rules the investing world. In that aspect, quant mutual funds in India are yet to pack a punch.
Performance analysis based on data sourced from ACE MF indicates that these funds are yet to fix the code when it comes to outperforming the benchmark. Their AUM remains relatively low, and lack of performance may be a factor.
Out of the 7 equity funds in this space, 4 have been in existence for more than a year – DSP Quant, ICICI Pru Quant, Nippon India Quant and Tata Quant. Two of them, DSP Quant and Tata Quant, have significantly underperformed the benchmark, S&P BSE 200 TRI, in the last year, while ICICI Pru Quant and Nippon Quant have marginally outperformed the benchmark.
In general one year is not a long enough period to conclude on performance. However, given that the recent phase has seen very high volatility and swings in markets since the benchmark hit its all-time high in October, it’s an opportunity to assess whether the discipline of machines has outclassed the human mind or passive investing during such periods of turbulence. So far the verdict is not in favour of machines.
When it comes to long-term performance, Nippon Quant, which is the only fund with more than a five-year track record, has an underwhelming performance. Its 5-year returns CAGR is at 13 per cent (absolute returns of 86 per cent) versus the benchmark’s 16 per cent CAGR (absolute returns of 106 per cent).
What ails
Quant funds, in general, try to identify the important factors that drive stock performance, and then arrive at the appropriate combination/weightage of factors that help generate consistent alpha. While Axis Quant strategy works on a combination of three factors – fundamental, technical and liquidity – the DSP Quant fund has a strategy that is built on eliminating highly leveraged companies and then picking stocks based on high ROE and earnings potential.
Tata Quant uses artificial intelligence and machine learning to pick stocks while Nippon India Quant follows a quality-cum-momentum strategy. ICICI Pru Quant Fund’s process includes selecting stocks based on a combination of macro, fundamental and technical factors.
Thus. while their strategies are varied, and all of them came with impressive backtested results at the time of launch, none of these has made a mark yet.
While the jury is still out, some factors that quant funds need to look at in the Indian context are aspects such as variety & quality of data (example: frequency and depth of financial reporting, depth of analyst coverage when using forward estimates) and size of portfolio versus size of benchmark. In general, since quant strategies do not involve detailed fundamental analysis, more high quality quantitative inputs across the board must make up for less deep insights to reduce risks of unknown factors and outliers.
When it comes to variety and quality of data, the US market is at more advanced levels and this may be a differentiating factor why the quant space is still evolving in India.
Similarly, more metrics as opposed to a combination of just three or four factors that some of these funds use can also make a difference.
Finally, most of these funds have positions in 30-50 stocks versus the benchmark’s 200 stocks. When it comes to long-only quant strategies, spreading out positions across more stocks in the benchmark and attempting to outperform by underweighting/overweighting positions relative to benchmark may also be a better strategy.
Investors need to assess these factors when newer quant funds hit the road.
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