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  • Writer's pictureAkshay Nayak

Advisors And India's Evolving Retirement Challenge

The exercise of retirement planning is undergoing significant evolution around the world. Retirement has gone from being defined as a specific age to being defined as a specified sum of money, on attaining which the individual can safely retire. In India too, such a shift in thinking around retirement planning is beginning to manifest itself. More and more individuals are beginning to show a desire to hang up their boots between their late 40s and early 50s. The normal retirement age, especially for most working in the corporate sector in India is reducing from 60 or 65 and tending towards 55.

Portfolios of most Indian investors have witnessed a significant shift from traditional fixed income assets to equities and other market linked assets. There is now a need for a clear strategy not just during the accumulation phase of retirement, but more importantly the distribution phase post retirement. Therefore, the way in which financial advisors and planners in India think about and approach retirement planning needs to evolve significantly. Therefore, today I am going to highlight the ways in which financial advisors and planners must prepare themselves for the evolving changes within the retirement planning exercise in India.

One of the most critical nuances of retirement planning is to test an available retirement corpus for resilience. This involves checking whether the corpus would be able to generate a reasonable level of income irrespective of market conditions, given the needs of the client. Advisors can test a corpus for resilience by simulating the performance of the corpus using data on return sequences for reasonable periods of time (at least 20 years) in the past, for various asset allocation combinations. This is where a Monte Carlo simulation may generally be of use. A Monte Carlo simulation can provide a reasonable estimate of the amount of income that the client’s portfolio can generate in retirement. An example of a Monte Carlo simulation where amounts are denominated in $1000 is given in the graphic that follows.

Clearly, the example here shows the individual can safely withdraw between $161,000 and $195,000 in portfolio income post retirement. But it must be borne in mind that the results of a Monte Carlo simulation may not always be accurate. So advisors must remember not to rely on them too heavily when creating and recommending a retirement plan to the client. Also changes in the real world circumstances of the client may see a change in the level of income they require post retirement. The test for resilience of the corpus must therefore be carried out whenever there is such a change in the income needs of the client post retirement.

The results of a Monte Carlo simulation may then be used as a starting point to decide an ideal portfolio withdrawal rate post retirement. Given that the typical post retirement period today ranges between 30 to 35 years (assuming retirement at age 55 and a life expectancy of 85 to 90 years of age), reliance on thumb rules such as the 4% rule would not be realistic.

Therefore advisors have to work with clients to define a withdrawal rate that allows them to meet their annual spending needs while ensuring that the portfolio does not run out completely. This may involve having to define an upper spending limit for each year of a client’s post retirement period. An illustration is given below :

Available corpus = Rs 700 lakh (Rs 7 crore)

Expected years in retirement = 30

Upper spending limit for the year = 700/30 = Rs 23.33 lakh

The size of a client’s retirement corpus would also dictate the degree of risk that they can afford to take with their corpus. An advisor recommending a high equity allocation post retirement to a client who has a corpus worth Rs 10 crore when they need Rs 5 crore for their needs post retirement is understandable. But recommending the same to a client who has Rs 6 crore when they need Rs 5 crore is fraught with risk. This is because a sustained sequence of negative portfolio returns over the course of a few years at any time post retirement would drastically reduce the value of the client’s retirement corpus.

And this may leave the client having to lead a lifestyle that is highly compromised compared to their envisioned ideal. Advisors must therefore define a threshold limit for the size of a retirement corpus below which they recommend a highly conservative asset allocation strategy post retirement. Doing this would mean that the client who has Rs 6 crore needing Rs 5 crore may possibly be recommended such a strategy. The threshold limit for the value of the corpus may be set as per the advisor's discretion. Clients who are recommended a highly conservative strategy may be advised to park their corpus in instruments such as bank deposits, annuities and low duration debt mutual funds.

Every client would have a few scenarios that they would not want to face at any point of their post retirement lives. For some clients it could be the prospect of facing a shortfall in the size of their retirement corpus or income therefrom in and around retirement. For others it could be the prospect of retiring into a bear market that is followed by a prolonged sideways market. Advisors would need to sit down with the client and figure out what constitutes a negative extreme for the client in retirement. They would then need to explain the likely consequences of being faced with such situations.

Finally both parties would need to work on mapping out strategies that would help the client navigate those extreme scenarios effectively. For instance, a few strategies to bridge a shortfall in the value of the retirement corpus and/or portfolio income in and around retirement may include :

1. Postponing retirement for a few years

2. Increasing contributions to tax advantaged avenues for retirement savings such as EPF, PPF, NPS

3. Building a source of passive income.

The risk of retiring into a bear market can be effectively managed by employing the Retirement Bucket Strategy post retirement. Progressively reducing equity allocation in the client’s portfolio as the client’s intended retirement age draws closer is another option.

But the biggest challenge most advisors would face is the lack of practical experience when it comes to providing advice in respect of retirement planning. This is especially true in case of devising strategies for portfolio withdrawals post retirement. Financial planning, and more so retirement planning is a discipline that is almost entirely practical in nature. But advisors in India gain a primary understanding of financial and retirement planning through a course of study that is predominantly theoretical in nature. It is mainly when advisors actually work with clients over a period of time that they gain an understanding of practical aspects of the discipline.

And given that the assumptions and nuances of each client’s financial and retirement plans would vary, there can be no standardised solution to the problem. Advisors would have to understand what works best for the client and incorporate those elements into plans prepared for the client. And this would only be possible for advisors with superior insights and increasing experience. Advisors must therefore learn to have as little reliance as possible on standard academic sources for education. They must instead be a lot more open to learning from a wider variety of avenues and experiential learning.

It is clear that retirement planning is going through a paradigm shift in India, and this will continue into the future. While it is unlikely that the reliance on traditional investment avenues for retirement such bank deposits and defined contribution schemes will go away completely, the share of market linked assets in retirement portfolios is almost certain to increase. This means that the way retirement portfolios are designed, built and managed would all need to change. Advisors must therefore develop the skills required to be able to guide their clients through their retirement years safely and smoothly.

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