NFO Analysis: DSP Quant Fund
With the onset of the digital era, tech-enabled tools employing artificial intelligence and machine learning will take most of the decision currently made by fund managers. Quant funds are one such category of fund that uses this technology to make portfolios. In a step towards this, DSP Mutual Fund has launched a mutual fund scheme called DSP Quant Fund.
The fund will invest based on multi-factor, which historically have given better risk-adjusted returns over the long term. Factor strategies also known as smart beta strategies combine active and passive investing models. Some of the factors that have worked earlier in various markets including developed and emerging market are low volatility, growth, quality and value. The portfolio of stocks will be selected, weighed and rebalanced using stock screeners, factor-based scoring and an optimisation formula which aims to enhance portfolio exposures to factors representing ‘good investing principles’ such as growth, value and quality within risk constraints.
Among this three broader factor, the fund will invest in stocks selected from a universe of S&P BSE 200 (TRI), which will be balanced on a half-yearly basis. The reason it has been selected as the universe is that BSE 200 consists of a group of reasonably liquid, well-researched companies. This means the fund will resemble more like large and mid-cap funds.
According to the backtested data provided by the fund house, the portfolio selected on the above factors has provided a better return than its benchmark in the long run. However, there are phases when they have underperformed, especially when the market has given higher positive return such as during 2016 and 2017. Hence, the fund is generating a better return by containing its losses in years when the market has fallen drastically such as 2008 and 2011.
Should You Invest?
There are very few ‘quant fund’ present in India, Reliance Quant fund is one such fund that operates like a quant fund and the portfolio is managed purely on a rule base. The fund has not been able to beat its benchmark in any time duration. For example, in the 10-year period, it has generated a return of a mere 10.75 per cent compared to 12.81 per cent generated by its benchmark. One of the reasons for the underperformance of such funds is the failure to imitate the back-test result because it has been tested on historical data. This data that has been generated by the factors (macro and micro both) which may not converge in the same way going ahead and hence may fail to repeat its performance.
Human intelligence still has an upper hand over machine intelligence atleast in the financial world and has been proactive in spotting opportunities to invest. Hence, it will be wise if you could wait and let the fund goes through a couple of springs and establishes a track record before investing in the fund.