Quant AMC is launching an NFO, which is set to open for subscription from April 13th, 2021 and closes on April 27th, 2021.
Investment Objective: To generate wealth over the long term, suitable for investors having a very high risk appetite who are also comfortable with high levels of volatility.
Investment Strategy: The fund emphasizes on selecting stocks by building a strong algorithm driven model based on various quantitative metrics and analytic pointers. The focus is to create returns which can outperform the the traditional human based fund management by relying more on algorithms and computer models for stock picking. In simple terms, the portfolio management is largely automated as algorithms are in place by reducing judgement made by humans.
Fund Manager: Ankit Pande, Sandeep Tandon, Sanjeev Sharma and Vasav Sahgal
Benchmark: Nifty 500 TRI
Minimum Lumpsum Investment Amount: Rs 5,000
Minimum SIP Investment Amount: Rs 1,000
Fund Management Process:
- The fund is semi-passively managed.
- Stocks are filtered from Nifty 500 along with an upper limit of 20% for International stocks.
- An algorithm is built by incorporating various factors such as
- Predictive Analytics Indicators(A tool built by screening various indicators)
- Mathematical Modelling
- Machine Learning and Artificial Intelligence
4. The output from the algorithm follows another round of filtering. It is a standardized process practiced by this AMC, known as VLRT:
- V - Valuation Analytics
- L - Liquidity Analytics
- R - Risk Appetite Analytics
- T - Timing
5. Fund Managers help in designing the final model and constantly monitor, review and update on a periodic basis
Based on our analysis, we have observed the following pros and cons
- Fund Manager decision making is largely minimized.
- Various cognitive biases like anchoring bias, recency bias, confirmation bias, loss aversion etc does not play a part in the investment process.
- Analyzing large amounts of data with relative ease as it is assisted by programs.
- Faster Investment decisions.
- Low Cost as it is driven by pre-set models.
- Too much reliance on data.
- Qualitative aspects of fund management is completely not taken into account.
- Past performance may not necessarily indicate strong results in the future. Models do not differentiate between numbers.
- Cannot take active calls like holding cash in a portfolio.
- Works purely on the ability of the algorithm to pick stocks, which can often miss out on quality growing companies as they may not fit into the selection criteria, given the checks and filters.
This fund has a semi passive fund management, focusing more on the strengths of algorithms for construction of the portfolio. This style of algo-model based investing has tremendous potential to outperform the traditional actively managed mutual funds, but at the same time, could face periods of underperformance as it is driven by purely quantitative metrics and not managed qualitatively.
Investors can consider allocating a small amount of their portfolio towards this fund if they are comfortable with an automated machine powered approach backed by intricate data points and filters. It is of utmost importance that the fund should be discussed with your financial advisor and then ascertain whether it is suitable to invest. Always read the scheme documents fully before investing.