Can You Accurately Project Expected Mutual Fund Performance?
We Decided To Find Out.

By: Neet Shah
Nathan Johnson

Mutual funds are enjoying a moment in the sun. Some of their intrinsic features – like built-in diversification across multiple assets, relatively low initial investment amounts, and the management being left to the professionals – make them an attractive option for first-time and veteran investors alike.

Mutual funds have had these features for a while, though, so their recent surge in popularity as an investment category can’t be attributed to this alone. Instead, consider that mutual fund fees, expense ratios, and accessibility are better than ever. [1] At their inception, investing in mutual funds usually required opening an account with the fund manager. This was often time consuming, hurt liquidity, and created potential counterparty risk.  Things are different now, however, when getting started with mutual funds can be as simple as opening a brokerage account, gaining access to potentially thousands of funds, often with overnight liquidity. [2]

FIRST INDIA, THEN THE WORLD

This growth has been especially prominent over the last 2-3 years within the India market; if you enter “mutual funds popular” in Google, the autocomplete will offer “in India” as the second most common search. Dig further into the details and it’s not hard to understand why. As a whole, the Indian mutual funds industry has grown an average of 12.5% over the last ten years, far outstripping the growth rates reported by Europe and the Americas. In the last year alone, total assets managed by Indian mutual funds jumped to $330 billion, a 17.33% increase from the previous year. [3] Some key social and political drivers deserve partial credit, including efforts by the government to increase banking coverage, recent demonetization, aggressive awareness campaigns, and increased financial literacy.

Historically, individuals in India invest in mutual funds through the advice of their brokers and mutual fund distributors. This tendency increases the risk of investors receiving biased advice driven by criteria mostly favoring the brokers and distributors rather than the investors (where the broker will get the most commission, recent performance of the mutual fund, or just “gut feel”). FinTechs have begun to offer the option of direct, online investments in mutual funds, including PayTM, ORO Wealth, Bharosa Club, and Clearfunds. [4]

DECISION FATIGUE FOR BEGINNING INVESTORS?

But the downside to that staggering number of choices is the inevitable decision paralysis besetting a lay person trying to make an informed choice. How do you choose between equity large cap, mid cap, or small cap? What about debt-liquid or debt-credit risk? Balanced/hybrid funds, sector specific funds? And those are just the categories, subdividing further into even more options. It’s death by a thousand tiny decisions.

What would simplify this decision-making process for the average mutual fund buyer? What if the buyer’s decision could be guided toward a specific investment with a higher likelihood of positive ROI, rather than asking them to make an arbitrary decision with no experience and hope for the best?

In short, what if there was a way to predict which mutual funds would do well over a specific window of time? This question eventually prompted us to create a robust, data driven recommendation engine to identify funds that will “outperform” their benchmarks over the next 2 years.

Fig. 1 – Investment Decision Tree Based on Quintile Performance

UNDERSTANDING THE “TERMS AND CONDITIONS”

At the outset, it’s important to realize that the return profile for each category of funds is unique, implying that each category requires its own model. The Equity large cap segment was an ideal starting point due to its substantial pool of funds with sufficiently long track records.

Our population had to be restricted to funds that had existed over multiple investment cycles. We weren’t trying to predict the expected return for the fund; if successful, this process would predict the likelihood that the fund would be in the top quintile within its category over the subsequent two years.

We believed the universe of variables affecting the performance of a fund category to potentially be quite large. So instead, we tried to identify funds within the category likely to perform the best. Experienced wealth managers in India advised planning for a 2-3 year horizon for the Equity large cap category.

 We ultimately settled on a combination of three metrics – historical percent change, drawdown, and standard deviation and identified a subset of funds with a 30% greater likelihood of being above the 60th percentile and a 50% lower likelihood of being below the 20th percentile of future performance (Fig. 1).

Using a combination of publicly available data sources, we created a data set to develop and test our model following a few key principles:

  1. Look for consistent predictive power across funds and across business cycles
  2. Ensure each variable selected makes intuitive sense.
  3. Avoid “overfitting” by restricting the number of independent variables finally selected
  4. Ruthlessly examine correlations amongst variables. This is particularly important in this model, because many variables are derivatives of the returns
  5. The funds recommended should be consistent. The model should not change its predictions too frequently

Fig. 2 – Cumulative FJ Model Performance vs. Nifty (2011-2017)

GIVING A PERFORMANCE REVIEW

We tested the model out of time and out of sample and continue to monitor in-market. The model is consistently identifying funds that are likely to be top quintile performers and has very rarely chosen funds that fall below the second quintile.  For example, between 2012 and 2017, our Equity Large Cap recommendations outperformed the Nifty by 12.0%, with similar performance over subsequent periods (Fig. 2). The model has been operating in a live production environment at a leading India FinTech firm for the last 3 years and continues to perform as per expectations as of the date of this publication.

This approach was applied to the Indian mutual fund market, but it can potentially be used in other markets and contexts. As mutual funds continue to grow in popularity as an investment category, newcomers will be looking to enter the world of mutual fund options – many of them without a clear-cut way to decide on the best point of entry. This approach forms the basis for a consistent method of clarifying a buyer’s decision, building on the increase in accessibility within the mutual fund market, with the added benefit of a greater likelihood of positive ROI for customers.

Are you leveraging a quantitative approach to aid your customers’ mutual fund investment decisions?

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