Mutual Funds

Tata Quant Fund NFO: An analysis

Yoganand D | Updated on January 11, 2020 Published on January 11, 2020

The scheme suits investors who have an appetite for high risk

Tata Mutual Fund has launched a new fund offer (NFO) — Tata Quant Fund. Its subscription closes on January 17, 2020.

As an open-ended equity scheme, the fund will also be open for continuous purchase, shortly after the NFO closes.

Being a thematic-category fund, it generally comes with high risk.

Investment strategy

Tata Quant Fund employs a proprietary quantitative model (Quant Model) on select stocks.

The fund is benchmarked against the S&P BSE 200 TRI.

It will be investing in equity and equity-related instruments selected based on a quant model.

The scheme will invest in stocks which form a part of S&P BSE 200 or are from the equity derivative segment.

When the predicted returns are negative, it uses derivatives to hedge the gross long-equity position held previously. The portfolio is rebalanced monthly, on the basis of the latest economy and market information. The scheme implements artificial intelligence (AI) and machine learning (ML) modules in the investment process so that the factor selection framework evolves as the market changes.

The fund will be managed by Sailesh Jain.

Portfolio strategy

Tata Quant evaluates the factor strategy (alpha, value and quality) that has worked in a macro scenario and selects an optimal portfolio with a combination of top-ranked stocks, within the factor strategy.

The alpha strategy is an alpha score calculated for all securities forming part of the scheme universe, over the Nifty 50 Index over a period of one year.

Based on the stock’s score on return on capital employed (ROCE), price-to-earnings (P/E), price- to-book value (P/B) and dividend yield (D/P) parameters, a value portfolio is created.

Apart from these, a quality portfolio is created, which is a combination of stocks with high scores on ROE, D/E ratios and earnings per share (EPS) growth variability, over the previous five years.

The alpha, value and quality portfolios are called factor scores. Based on the calculated factor score, stocks are selected and those with the highest scores have the maximum weight in the portfolio. After which, the fund undergoes an ‘invest or hedge’ decision.

It evaluates whether the macro conditions and the various strategy scenarios favour factor strategy investment or may lead to a fall in the portfolio performance.

Based on the evaluation, the model makes the buy or hedge (current portfolio) decision.

Pros and cons

Machine learning avoids the pitfalls of emotional or behavioural biases in investment decision-making. However, elimination of human bias does not automatically guarantee high returns.

The machine learning- based investment model has the flexibility in selection of stocks, and learns from new economy and market conditions to make its own decision. But it needs to be seen how the removal of subjectivity works in a market such as India, where information asymmetry helps in generating alpha.

Other quant funds

Quant funds are a nascent category in India, and there are only two other quant funds in the market — Nippon India Quant and DSP Quant. Nippon India Quant is a pioneer in this category with over 10 years of track record.

It has delivered returns of 8.3 per cent and 9.8 per cent over the past one- and three-year periods, respectively, but these returns have underperformed the benchmark S&P BSE 200 TRI returns of 11.5 per cent and 14.5 per cent.

In the past six months, Nippon India Quant has gained 4.8 per cent.

DSP Quant was launched in June 2019, and the fund has gained 14.4 per cent in the past six months. These two funds have delivered diverging returns in the past six months.

 

 

Published on January 11, 2020
  1. Comments will be moderated by The Hindu Business Line editorial team.
  2. Comments that are abusive, personal, incendiary or irrelevant cannot be published.
  3. Please write complete sentences. Do not type comments in all capital letters, or in all lower case letters, or using abbreviated text. (example: u cannot substitute for you, d is not 'the', n is not 'and').
  4. We may remove hyperlinks within comments.
  5. Please use a genuine email ID and provide your name, to avoid rejection.