Importance of a Robust Recommendation Mechanism

Choosing the right mutual fund is one of the most important and difficult decisions that an advisor must make.

By selecting the right fund, the advisor can:

  1. Generate sustainable, long term wealth.
  2. Avoid backing short term shooting stars.
  3. Reduce the downside risk.
  4. Lowers overall execution expenses (including exit loads & tax leakages)

Also, there are some other benefits in making the right selection, such as:

  1. Ability to demonstrate value add to clients.
  2. Positive bias towards new ideas shown to clients
  3. Higher referrals from satisfied clients

The question then arises as to how difficult is it to make this selection? Given that the mutual fund data is available in a transparent manner with no information asymmetry i.e. everyone has the same information.

Unfortunately, Hindsight bias, which makes past events look more predictable than they actually are, focuses on short term past performance and most recommendations are made based on that – simply put funds with higher returns garner more flows.

Fund flow analysis of large cap funds

Source: ACE MF, Fintso Research Category as AMCs classification; More than 100% inflow indicates outflows from some of the other schemes in the category

This method, however, does not result in predicting future performance.

On average, only 24% of Top Quartile large cap funds based on 3Y performance were able to retain their Top Quartile status for next 1 year

Source: Fintso Research

What makes Selection so difficult?

Simply put, the number of factors that are involved:

  1. Fund Manager’s style.
  2. Investment process of various AMCs.
  3. External market conditions

Though we are surrounded with the best MF distributor software, the task of predicting which fund will generate a superior performance in the future, is an extremely complex, and difficult task to do; there is no foolproof approach to do so.

In addition to this complexity, there are:

  1. 41 AMCs
  2. 410 open-ended equity funds
  3. 33 years of NAV data available

Add to this humongous data set, analysis of each fund starting with historical performance, risk-adjusted ratios, up-side/down-side capture ratio, and several other factors makes the process of fund selection challenging.

This is the reason that most people do not give the attention that is required, and end up being distributors, rather than active advisors, for their clients. Therefore, it is essential to keep track of the development of premium mutual fund software for IFAs to steer us out of difficult times.  

As rightly said by a famous American businessman, investor, author & public speaker, Robert Kiyosaki, “Before you invest in something, invest the time to understand it”. To face the complex data challenges and provide analytical approach for recommending funds, we rely on technology.

The Recommendation Process

A Deeper Dive Into The Metrics Used For Analysis

Quant Analysis – Returns based

Rolling Returns

Measures consistency in beating the benchmarks over longer time horizons.

Hit Ratio

Measures the % times the fund generated outperformance vis-à-vis respective category benchmark on a rolling basis.

Up Capture Ratio

Measures the percentage of market gains captured by the manager when markets were rallying.

Quant Analysis – Risk based

Downside Volatility

Measures downside risk, takes negative returns into account and thereby does not penalize the fund for positive deviations.

Active Risk

Measure of risk in the portfolio that is due to active management decisions made by the fund manager.

Down Capture Ratio

Measures the percentage of market losses endured when the markets were falling.

Bottom-Up Analysis

Batting Average

Measures the manager’s ability to find companies that contribute positively to the overall fund performance, and get more decisions right than wrong.

Slugging Ratio

Measures whether the good decisions offset the poor ones. It is a ratio of alpha generated by the portfolio to the alpha loss because of bad.

Position Sizing

Measures the proficiency of the manager to make each investment the appropriate size to ear.

About Us

Fintso is a fintech platform that provides solutions to Financial entrepreneurs to address their needs, of research, advisory, product access and client engagement.

The team at Fintso has deep domain expertise on the Indian Investment space, Wealth management and cutting edge Technology.

Meet the Investment Team :

Rajan Pathak

Co-Founder and MD

Rajan was a member of team who launched India’s 1st Multi-Manager and Fund of Funds AMC concept in India. He had also launched India’s 1st Multi Asset, Multi Product “WRAP” Accounts with online action capabilities. These WRAP accounts were Ranked with a 4 Star by Value Research for process and performance. With more than 2 decades of experience in financial markets, Rajan’s accomplishments include establishing successful B2B businesses supporting the financial entrepreneurs. His vast experience & deep understanding of advisor’s need help us build a strong framework of actionable insights.

George Mitra

Co-Founder & CEO

George was part of the Investment Committee and has been instrumental in designing the algorithms for products and advisory in his previous firm. George had helped design the Financial planning software and created the models for Asset Allocation using Efficient frontiers way back in ’00 while in Deutsche Bank. With over 25 years working with Ultra-HNI clients, George has a deep understanding on designing solutions for the end clients.

Kumarpal Jain

Head – 3rd Party products

Over the past 8-years in financial markets, Kumar has developed an intrinsic understanding on different asset classes and built an excellent product knowledge. This in-depth knowledge on markets and products would help us bring best of the advisory to investors to facilitate informed investment decisions. He was a core member of the team that helped create the processes that were used for Fund selection in both his previous organizations.

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The information herein is meant only for general reading purposes and the views being expressed only constitute opinions and therefore cannot be considered as guidelines, recommendations or as a professional guide for the readers. The document has been prepared based on public available information, internally developed data and other sources believed to be reliable. The directors, employees, affiliates or representatives (“entities & their affiliates”) do not assume any responsibility for, or warrant the accuracy,completeness, adequacy and reliability of such information. Recipients of this information are advised to rely on their own analysis, interpretations & investigations.

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