Tell us about your MF recommendation model. How is it unique?
Moving away from the beaten track of choosing mutual funds purely based on schemes' absolute performance for periods ending on the decision making day, we started working on outperformance over Nifty in rolling periods. We also paid attention to the sigma of such outperformance to help us screen out volatile performers. It is our stated belief that a scheme under a smart fund manager remains efficient and continues to outperform indices unless the other factors that are essential to produce performance change. The other factors could be forced change in his philosophy, change of fund manager or his own deviation from policies setup by him to produce outperformance.
We came up with models to help us spot the possible slippage in performance of a scheme ahead of time. After back testing more than 12 years of data, we built a model that lists out the best possible outperformers over next 12 months compared to Nifty. Because investments held beyond 12 months are exempted from capital gains tax, we chose to work on this time period.
Now, investors desirous of testing the efficacy of the system are given an interface through which they could see what would have been this product's choices on any given day in the past 10 years based on a proprietary criteria set by our model. The same criteria is applied uniformly cross all time zones. One will find that most funds outperformed Nifty with outperformance being significantly large. Where underperformance is seen, such underperformance is often small.
Your model prompts you to shift from one MF to another at regular intervals. Does it make sense for investors to churn their portfolio so frequently? How do you take care of exit loads and short term capital gains tax which can be a drag on returns?
The impact of exit load, when MF is exited in less than one year, was taken into account while building our model. Imagine a case where Scheme A and Scheme B (out of the 10- scheme portfolio) are worth exiting for possible underperformance. If both the schemes are likely to result in short term capital gains tax, the decision is deferred to save on tax implication as well as exit load. If one of them is resulting in loss and the other in profit, investment in profitable schemes will be exited to the extent of compensating the loss suffered in losing scheme so that no short term capital gains tax ever arises for the client.
Backed by these safeguards, this model helped investors earn an average of 4% additional CAGR over Nifty every year. In a portfolio of 10 schemes, this model may at most require switch of one scheme on average. So, it is not correct to assume that there will be too much churn happening to earn additional income.
What are the parameters for shortlisting best funds?
Outperformance over Nifty is the foremost parameter considered for short listing the best fund. Next is the issue of quantum of outperformance. The model also looks at the volatility of each scheme in latest 12-month period. Possible outcome of returns and ranking of schemes are arrived at taking all these factors into consideration. Further to these factors, we build month end portfolios of schemes based on announced composition of stocks by mutual funds. Our model has been extremely effective in setting possible ranges of any properly diversified portfolios in near time frame. Using this service, our model checks the fair value of portfolio and compares with NAV to determine the possible upside. A combination of all these factors helps us identify strong and weak schemes ahead of time.
Many investors invest based on looking at the past returns of a fund. How does your model predict which funds will perform well in future?
What we do is to let our model identify top funds according to the logic set and check how they performed after 375 days.
In contrast, what most retail investors do is to check the performance of past one year and invest. In this context, we found that only 48% of the funds selected (based on past performance) have done better than Nifty. At such a percentage probability of success, it is no better than flipping a coin to decide on investing in a fund. Further, if we churn all these top funds after one year and avoid capital gains tax, the net outperformance on investments was found to be negative.
Outperformance should never be confused with absolute performance. When the sentiment is good, almost all schemes do well and produce positive return. But almost all investors wish to have a return that is better than Nifty over time. If your goal is not to earn more than Nifty, you may as well go with ETF where expense ratio is minimal and by going with direct plan there will not be any commission payout to your distributor.
Having said that, each prudent investor must aspire to earn more than Nifty at all times. Our model strives to spot such schemes that are likely to outperform Nifty in the next one-year time frame. This model has been tested rigorously over the last 11 years and it managed to produce a significantly large outperformance over Nifty. Please bear in mind, the returns are higher than Nifty in good times and losses are less than Nifty in bad times.
