DSP Quant Fund: Rigorous quantitative approach takes biases out of investing

There are many examples of once top performing mutual funds underperforming for years together. The underperformance that we are witnessing now is not related to market cycles because cyclical underperformance which is short term in nature. If underperformance persists for several years, then it is caused by errors in judgement of the fund managers. Even though fund managers are professional investors / investment experts, they may also make irrational decisions e.g. holding on to an underperforming PSU bank stock for very long hoping for a recovery. They may justify this strategy by saying that they are sticking to their conviction, but in reality they might simply be averse to booking a large loss and taking a hit to their accumulate profits reserve. This greatly hurts the long term prospects of the investors.

Fund managers are also humans like us and may find it difficult to remain unemotional while investing. However, mathematical models are devoid of emotions and quantitative investment strategies greatly reduce errors in judgement caused by human biases. Quant (shortened form of Quantitative) funds based on quantitative or algorithmic investment strategies are very popular in the West, especially in the United States. Nearly a quarter of all stock trades in the US comprise of quantitative or algorithmic investments. In India quant investing is still relatively new concept. Only a few Portfolio Management Services (PMS) offer quant products, that too only for High Net worth Individuals (HNIs). There are a few mutual fund schemes which follow quant strategies and artificial intelligence, but in many such schemes it is unclear whether there is a human intervention.

Why invest in Quant Funds?

In our country, one or two quant mutual funds have been around for a long period of time but quant funds are yet to make an impression with average Indian retail investors. However, we believe that quant funds will play a big role in the future. There are several reasons:-

• Many investors and even financial advisors continue to base their investment decisions on past performance of schemes and fund managers. Investors should realize that, as our capital markets mature and technology increasingly play a bigger role, past performance of even star fund managers will no longer be indicative of future returns. Investors should instead focus on fundamentals of investing to get superior risk adjusted returns in the long term. Quant Funds build their portfolios by simply following fundamental rules of investing using mathematical algorithms with little human interference.
• We have seen in the past, scheme performance suffering when fund managers change. New fund managers need some time to settle in their roles and implementing their own ideas, but in the interim period investors may see relative underperformance. Investors have no control over fund managers seeking career change opportunities. Since Quant Funds require little human interference, they are mostly unaffected by key personnel changes.
• Last year (2018), SEBI asked all AMCs to use Total Returns Index (TRI) to benchmark scheme performance. TRI includes all income, including dividends and interest, not just price changes. Since the introduction of TRIs as benchmarks, we have seen many schemes struggling to beat their benchmark indices and create alphas for investors. Since quant funds are based on mathematical models, they can back – test their models / algorithms to see how many times they were able to beat TRI benchmarks across different market conditions
• Total Expense Ratios (TERs) of quant funds are usually much lower than typical actively managed diversified equity funds. As our stock market matures and becomes more efficient, it will get increasingly difficult for fund managers to create alphas. We have seen this in the mature markets. In the US, most actively managed funds struggled to beat S&P 500 in the last 20 years. When alphas get more and more squeezed, expense ratios will become important in long term returns attribution. Investors should understand that 100 bps (1%) lower TERs can result in a huge difference in absolute returns for investors over long investment horizons due to compounding.
• Some investors confuse Quant Funds with index funds and ETFs. Quant funds are not index funds. Their models are designed with the objective of beating the benchmark index unlike index funds which aim to track the benchmark index. However, quant funds are usually low cost schemes like index funds. Their expenses are relatively higher than index funds but substantially lower than typical diversified equity funds. Quant funds combine the best of both worlds.

DSP Quant Fund

DSP Mutual Fund launched the DSP Quant Fund earlier this year. Till date (31stOctober 2019) they have Rs 143 Crores of assets under management (AUM). Their expense ratio is 1.23%. Anil Ghelani is the fund manager of this scheme.

The scheme has a rigorous quantitative approach to stock selection and portfolio construction. Beginning with the universe of companies from the S&P BSE 200 (the scheme benchmark), the scheme’s quant model filters these companies using a 3-step process:-

• Companies with high debts, inefficient capital allocation or stocks that indicate high volatility are removed in an unbiased way.
• Companies are evaluated on factors such as Quality (Return on Equity, Earnings etc.), Value (Dividend Yield, Free Cash Flow Yield etc.) and Growth (Estimated earnings growth etc.) and the best ones are chosen.
• Each selected stock is weighted appropriately to reduce stock & sector concentration and liquidity risk.

How robust is the Quant model of the scheme?

There is a saying in the world of algorithm and quantitative finance, “you are only as good as your algorithm or model”. Fortunately in the world of quant funds investors can evaluate the investment strategy of the scheme in an objective way by back-testing. Since quant funds follow a set of mathematical rules in buying / selling stocks and portfolio management, you can analytically test how the model would have worked in different market conditions in the past. There is no subjectivity involved; you simply need to feed in the data in your computer and see the results.

• In 10 out of the past 14 calendar years, the model of DSP Quant Fund outperformed its benchmark
• 100% of time the scheme model outperformed its benchmark over any 5 year, 7 year or 10 year periods
• 100% of time the scheme model delivered >12% average annual returns (CAGR) over any 7 year or 10 year period
• A long-term SIP in the DSP Quant Fund model would have outperformed a similar SIP in the benchmark S&P BSE 200 TRI by 4% XIRR
• In both years when BSE – 200 TRI delivered negative returns (2008, 2011), the model still performed better

We must reiterate here that, historical performance may or may not be sustained in the future, but back-testing the model shows that it has the capability consistently generating alphas.

Who should invest in DSP Quant Fund

• Investors who have disciplined approach to investing and are not interested in making a fast buck
• Investors who are patient and can remain invested for 7 years or more
• Investors with moderately high to high risk appetites to digest high short term volatility