Asset Allocation & Models

In 2015, I wrote a small post introducing the Portfolio-Yoga Asset Allocator.

Introducing Portfolio Yoga – Asset Allocator

While the model in itself has undergone a change once, given the dichotomy with the current allocation and the market trends, I felt that a post was required to help better understand the model and how to use it as a guide for your investments.

I am a strong believer in systematic rule based models for these enable one to test out the nature of the idea before committing money or time on the same. Yet, the single point of failure for most models lies in the developer of the model himself. Models are build in two ways – one by way of data mining the past and coming up the optimal combination that seems to generate the best possible return.

The risk of such models is that the future is not a repeat of the past. As a wonderful quote goes, “History doesn’t repeat itself but it often rhymes,”. While human behaviour will ensure that we never really run out boom and bust cycles, the length of cycle will keep differing making even the most data oriented model but one that was trained on a different cycle length go repeatedly wrong.

Asset Allocation models aren’t really different – at the core, the philosophy is that you get the best “Risk adjusted Return”. That doesn’t mean one never goes wrong, but when one goes wrong, its better to err on the side of caution that on the side of Risk.

The model offered here is contaminated by the bias I bring to the table. While in my own investing, I was never conservative, owing to the experiences of self and clients, I have come to believe that its better to be safe than sorry.

The simplest asset allocation split is 60:40 in favour of equities. But when you adjust it for risk, you are actually looking at 90:10 and to me, that requires tremendous will power and experience to be able to row through a tough time with such a massive tilt.

Much of the literature on Asset Allocation comes from the United States but one that is uniquely different from the Indian markets. Interest rates in US have been low for a very long period of time and given the rising cost of living, especially Education and Healthcare, Investors are forced to risk more than what they should or rather would want to risk.

Since , Interest Rates in the United States have hovered close to the 0% mark and this means that Debt (Short Term Bond Funds) have yielded just around 3% for the end saver. Equity on the other hand has been in one of its longest bull runs with CAGR since the start of the rally in 2009 being to the tune of 17.75% {Total Returns}.

In India, Debt has yielded around 8% versus 16% for the similar period. Equity does deliver more, but that is also more a function of where we started at. If we had moved it back to just a few months back, January 2008, the Equity returns falls to 5% while Debt would be barely change much.

Investing in Equities is a game of timing. Invest in a good time and returns shine while on the other hand, investing in bad times can result in long periods of under-performance and even long term persistence may not change the end result by a great deal.

The highest correlation between factors of today and returns of tomorrow lies with Valuation which is a key input for most asset allocating algorithms. One of the better known valuation model is Robert Shiller’s Cyclically Adjusted Price to Earnings Ratio which tries to use a longer average of returns and hence avoid pitfalls of short term variation in yearly numbers.

The logic behind the Asset Allocator I update here is similar in approach. Since valuation is relative in the time space continuum, I use historical data to try and smoothen the curve. This ensures that the model is not binary in output.

Using the Asset Allocator Model

Asset allocation at its basic is about two things – Time and Return, Return being a product of time and valuation / growth. Both of these factors are hence the bulwark of the model.

Regardless of where valuation is, if you don’t have time on your hands, it make sense to have a Conservative Asset Allocation. For example, assume your kid will go to College in 3 year from now – would you want to risk the ability to pay his fees on the state of the market?

Conservative is also for folks who aren’t able to get through a major draw-down in their Networth without it impacting their way of life. Loss affect each one of us in different ways, but if loss – even we anticipate it to be temporary will make you worry, you are better suited to a Conservative allocation – it will provide a much lower return, but at least, you aren’t losing sleep over it.

Between the short term goals and long term goals, you will encounter what are medium term commitments – something which is at least 3 – 6 years away.  Here, you can take a bit more risk than with short term for you have a bit more time on your hands.

Finally there is the Aggressive – Aggressive is a mode for those risk takers who understand what they are doing as also applicable for those whose goals are years away – Retirement for example.

One of the primary complaints has been that the Asset model even at the Aggressive mode is way too Conservative. Currently for instance, this stands at 20% Equity and 80% Debt. As a friend who is ultraconservative recently commented, even he had more equity exposure than what the model seemed to argue for.

In software parlance, this is not a bug but a feature. When the going is good, it’s easy to mistake luck for skill and be overly aggressive in allocation versus what one is comfortable with. Those who haven’t yet seen a bear cycle fall into such traps for long bull markets make even the ordinary investor seem extra-ordinary when you look at his returns.

Historical Allocation and Draw-Down’s

Old timers remember how the markets fell post the Harshad Mehta scam. Yet, the bigger draw-down came in 2008. The fall of 2008 means that any model needs to account for a probability of 65% fall in the future. Accounting for a high level of draw-down has a direct impact on returns too given the correlation most models have with regard to Valuation, Return and Draw-downs.

Nifty is currently down just 5% from the peak. Assume that 2019 turns out to be similar to 2008 and Nifty goes down 65%. With a 20% allocation, you have the possibility of seeing a maximum draw-down of 16%.

The chart below showcases the average draw-down and maximum draw-down you could experience at various levels of exposure.

The above chart is based on the historical data of Nifty 50 from 1991 to 2019. The maximum is the same as what we saw in October of 2008. The deficiency if one can call if of the chart is that its path dependent.

Standard Deviation is close to the average, so you can at most times expect anywhere between double the average or close to new highs.

Finally, the model is for Investors who aren’t experienced the markets and would like to have an understanding of how much to risk at the current juncture. As you gain experience and go through cycles, you begin to get a better understanding of how much to bet on equity.

For sake of simplicity, I am ignoring all other asset classes out here. I don’t believe that given our fascination for Gold, it makes sense to bet even more by buying Gold backed financial assets.

Asset Allocation isn’t a one size fits all. Each person needs to evaluate his own requirements, his time frame of thought, his risk temperament among others. If you think you aren’t really capable of doing all that and there is no harm or shame in asking for outside help – preferably a qualified Financial Planner.

But do note that it’s finally your money and it’s very important you understand the risks and rewards for the weakest link in any strategy is bound to be you. Even the best advisor cannot be of help if you aren’t prepared to take his advise.