Tag: diversification

Indian vs. US Mid-caps

There used to be a time when getting your kids through college was the final act before kicking them out of the house. But kids these days want their parents to fund their US education as well. And how about a gap year to travel through Europe? You can roll your eyes all that you want but 15-20 years from now, this will be the new normal for middle-class Indians. What can we do? We have always been an aspirational lot and it is bound to rub off on our kids. As much as we like our kids to be financially independent when they grow up, we also don’t want them to start their lives with a ton of student loans. However, given the potentially large dollar liabilities in the future, most Indian investors continue to keep all their eggs in the Indian rupee basket. If you think your Indian mid-cap mutual fund alone is going to fund your kid’s grad school, think again.

Not only have Indian Mid-caps trailed US Mid-caps over the last 25 years, they have done so with steeper and longer drawdowns.

Over the last 25 years or so, US mid-caps have out-performed Indian mid-caps. Indian asset managers would have you believe that “east or west, India is the best” but that is not what the numbers say. Here are the cumulative and annual returns of the MSCI India MC and MSCI USA MC indices:
MSCI India vs. US mid-cap indices
MSCI India vs. US mid-cap indices

Living in India, it is easy to get carried away with stories about how Indian equities present big opportunities. However, historical returns show that investors were not compensated for the additional risk that they took by investing in India. Also, the US equity market cap is 50% of the total world equity market cap. So even if you have bonds, gold etc in your portfolio, being 100% invested in India is not true diversification. Besides, the Indian rupee keeps depreciating, making your future dollar liabilities that much larger when priced in local assets.

We ran through different allocations between Indian and US mid-caps to get an idea of what the potential returns could look like:
Allocating between MSCI India vs. US mid-cap indices

Assuming a monthly rebalance, the 50/50 portfolio beats the “all in” 100/0 and 0/100 portfolios. And it does so with shallower drawdowns. So both from a diversification and returns point of view, it makes sense to allocate towards US mid-caps.

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Further reading: Funding Your Dollar Dreams

Asset Allocation

Introduction

How does an equity/bond 2-asset portfolio look like?
Read: Allocating a Two-Asset Portfolio

A three asset portfolio

Indian midcaps + bonds with Nasdaq-100 ETF. Is there a benefit to using portfolio optimization algorithms after taxes and transaction costs are taken into account?
Read: Allocating a Three-Asset Portfolio, Equal Weighted and Allocating a Three-Asset Portfolio, Optimized

Adding gold into the mix

Does gold have a role to play in a systematic, diversified portfolio?
Read: Allocating a Four-Asset Portfolio

Investing in a systematic, diversified portfolio

A ready-to-invest Theme, the EQUAL-III, that takes care of keeping track of everything.
Read: The EQUAL-III Theme

Expected Returns

What are the range of expected SIP returns under prudent asset allocation schemes?
Read: SIP: Expected Returns

The EQUAL-III Theme

Our recent series on asset allocation walked through how different investment decisions affect portfolio returns and risk.

  1. Number of assets: Three is better than two and four.
  2. Rebalance threshold: Allowing a single asset to drift upto 80% reduces transaction costs and taxes.
  3. Weighing scheme: Equal weight is better than portfolio optimization methods.

You can read through the posts and the various factors that went into the analysis in order:

  1. Allocating a Two-Asset Portfolio
  2. Allocating a Three-Asset Portfolio, Equal Weighted
  3. Allocating a Three-Asset Portfolio, Optimized
  4. Allocating a Four-Asset Portfolio

For investors looking to gain from such a portfolio, we have setup a ready-to-invest Theme, the EQUAL-III, that takes care of keeping track of everything. It maintains an equal-weight portfolio of the M100 (Midcap-100 ETF,) N100 (Nasdaq-100 ETF) and the RRSLGETF (Long Term Gilt ETF.)

Questions? WhatsApp us +91-80-2665-0232

Allocating a Four-Asset Portfolio

Our previous posts showed how various allocation decisions impact optimized and equal-weighted three-asset portfolios. Here, we add a fourth asset – gold – and run it through the same scenarios.

Picking the Assets and Allocation

The assets we selected previously – MIDCAP, 0-5yr bond and NASDAQ-100 – were based on low observed historical pair-wise correlations. Most investors tend to add a fourth asset – gold – to their portfolios. Not only is gold not correlated with the other three, it has the added benefit of being priced internationally but traded locally. This allows it to benefit from rupee depreciation even if international gold prices remain flat. Observe how, at times, gold has a negative correlation to other assets:
correlations between gold, SPY, QQQ, MIDCAP and BONDs

The results

In the cumulative return and drawdown chart below, A1 is the MIDCAP index, A2 is the 0-5yr bond index, A3 is the QQQ and A4 is gold. A tax drag of 10% and an STT of 0.1% is applied at every rebalance. The rebalance threshold is set at 20%. The light-blue lines are the resulting portfolio returns. In the case of optimized portfolios, assets are allowed to have a weighting between 10% and 40% during the optimization process.

Equal Weighted

after tax cumulative returns of 4-asset equal weighted portfolio

Variance optimized

after tax cumulative returns of 4-asset variance optimized portfolio

Expected Tail Loss optimized

after tax cumulative returns of 4-asset ETL optimzied portfolio

Pre- and Post-tax returns

before and after tax cumulative returns of 4-asset equal weighted portfolio
before and after tax cumulative returns of 4-asset variance optimized portfolio
before and after tax cumulative returns of 4-asset ETL optimized portfolio

Rebalance

The rebalance threshold ends up determining the frequency of rebalance events. For a variance optimized portfolio, contrast the difference between a 20% threshold and an 80% threshold:

4-asset portfolio at a 20% rebalance threshold
4-asset portfolio at a 80% rebalance threshold

Take-away

  1. Every time there is a rebalance, the tax-man cometh and taketh away. Trying to minimize taxes is equivalent to minimizing the number of rebalancing events.
  2. To minimize reblancing events, one could set the threshold of rebalance higher. But there is a point of inflection with regards to after-tax returns.
  3. Allowing a single asset to balloon in weight risks larger portfolio drawdowns if that asset deflates.
  4. A four-asset equal weight portfolio under-performs a 3-asset equal weight portfolio. Gold maybe a good diversifier, but it doesn’t appear to do any favors to the portfolio on the performance front.
  5. Equal-weight 4-asset portfolio containing gold (above) drew-down less than the equal-weight 3-asset portfolio during the 2008 carnage (~30% vs. ~40%, respectively.)

Adding gold to a portfolio does not look like a good idea when looked through the lens of asset allocation schemes discussed here. However, there is a strong case for owning gold and the Sovereign Gold Bond (SGB) Scheme makes a lot of sense. See our previous post regarding the case for owning gold in India here.

Code, charts and the complete result dataset are available on github.

Allocating a Three-Asset Portfolio, Optimized

Our previous post showed how various allocation decisions impact an equal-weighted three-asset portfolio. However, equal-weights are not the only way to go. Every time a rebalance occurs, we can use that opportunity to re-weight the assets to minimize expected risk while maximizing expected returns. In this post, we look at two ways in which risk and returns can be optimized.

Portfolio optimization and the efficient frontier

The intuition behind what we are going to do is quite simple: for a given set of assets, there is an ideal mix of them that perfectly balances risk with reward. Imagine a plot of risk and returns of each asset under consideration. Harry Markowitz showed back in the 1950’s that they form a parabola and at a particular tangent of the parabola lies the ideal mix. The goal of portfolio optimization is to find that point. Here are some links that explain this concept further:

For the purpose of this post, we will assume risk to either mean variance (var) or expected tail loss (ETL.) We will use portfolio optimization methods to minimize one of these risk metric and maximize expected mean returns below.

Optimized portfolios

Like before, to keep things simple, we will go with the MIDCAP 100 index (A1), the 0-5yr TRI (A2) and the QQQ ETF (prices converted to INR, A3) as the three assets that form our portfolio.

Here is how the optimized minimum-variance portfolio performs after-tax:
min-var 3-asset portfolio (NIFTY MIDCAP, 0-5yr bond, NASDAQ-100)

Asset weights after rebalance:
min-var 3-asset portfolio (NIFTY MIDCAP, 0-5yr bond, NASDAQ-100) asset weights

And here is how the optimized minimum-ETL portfolio performs after-tax:
min-etl 3-asset portfolio (NIFTY MIDCAP, 0-5yr bond, NASDAQ-100)

Asset weights after rebalance:
min-etl 3-asset portfolio (NIFTY MIDCAP, 0-5yr bond, NASDAQ-100) asset weights

Min-var portfolio returns

min-var 3-asset portfolio (NIFTY MIDCAP, 0-5yr bond, NASDAQ-100) returns

Min-ETL portfolio returns

min-var 3-asset portfolio (NIFTY MIDCAP, 0-5yr bond, NASDAQ-100) returns

Take-away

  1. All things equal, the optimized portfolios under-perform the equal-weight portfolio in terms of absolute returns.
  2. Optimized portfolios show lesser risk than the equal-weight portfolio. During the 2008 carnage, for example, equal-weight drew-down ~40% whereas optimized portfolios drew-down ~20%.
  3. Optimized portfolios over-weigh bonds. Hard limits were set on the maximum and minimum weights the assets can have in optimized portfolios. Toggling these will have a significant impact on portfolio risk and returns.

Code, charts and the complete result dataset are available on github.