Author: shyam

Principal Component Analysis, Part II

This post is an update to Part I of applying PCA to NASDAQOMX India TR indices.

Daily returns tends to be noisy. One way to smooth things out is to use rolling returns over a certain period of days. Rolling returns also allows a bit of slack in terms of variable response times. We wanted to check if using rolling returns would help shake out any obvious regime shifts that daily returns could not.

To recap, we split the NASDAQOMX India TR Index (NQINT) into two regimes. One above SMA-200 (A SMA_200) and the other below (B SMA_200.) The idea was to use PCA on the component indices to see if we could develop a “good times” and “bad times” portfolio based on the regime we are in.

NQINT 200-day SMA chart:
NQINT SMA 200

Factor loadings of indices when NQINT is above its 200-day SMA:
loadings above 200-day SMA

Factor loadings of indices when NQINT is below its 200-day SMA:
factor loadings below 200-day SMA

And finally, factor loadings through out:
factor loadings

Unfortunately, neither using daily returns nor lagged rolling returns resulted in PCA being useful in chalking out a regime specific portfolio.

Code and more charts are on github.

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.

Allocating a Three-Asset Portfolio, Equal Weighted

Our previous post showed how various allocation decisions impact a two-asset portfolio. We started with two assets – equities and bonds – because it forms the foundation on which most allocation plans are built. However, the true impact of diversification is felt when you have uncorrelated assets in the portfolio. Here, we expand on the post by adding a third asset.

Picking the Assets and Allocation

As much as we would like equities and bonds to be uncorrelated, it is not always true. Indian equities and bonds are buffeted by the same storms at about the same time. However, what has historically shown to be a true diversifier are international equities. If you run the pair-wise correlations between the S&P 500, Nasdaq 100, the USD adjusted MIDCAP 100 and 0-5yr TRI, you see this:
correlations between SPY, QQQ, Indian MIDCAP and bonds

Given how strongly the S&P 500 (the SPY ETF) and the Nasdaq 100 (the QQQ ETF) are correlated, there is no point in adding both. We’ll go with the QQQ because it has an Indian analogue in the N100 ETF. Also, note how the SPY and QQQ are uncorrelated to the dollar adjusted MIDCAP and 0-5yr TRI indices. And finally, observe the light correlation between the MIDCAP and 0-5yr TRI indices.

So, to keep things simple, we will go with the MIDCAP 100 index, the 0-5yr TRI and the QQQ ETF (prices converted to INR) as the three assets that form our portfolio. And to get things started, we’ll keep an equal weight on all these three assets in the portfolio. The toggles that remain are the rebalance thresholds and tax impact. Here, we will use the same iterations that we employed in our two-asset portfolio example.

The results

In the cumulative return and drawdown chart below, A1 is the MIDCAP index, A2 is the 0-5yr bond index and A3 is the QQQ. A tax drag of 10% and an STT of 0.1% is applied at every rebalance. The rebalance threshold is set at 20%. The blue line is the resulting equal weight 3-asset portfolio returns.
NIFTY MIDCAP 100, 0-5yr bond and NASDAQ 100 equal weight portfolio

Take-away

  1. Lower risk: The equal-weight portfolio draws-down less than the MIDCAP index.
  2. Lower returns: The MIDCAP index out-performs the equal-weight portfolio (but with far greater volatility.)
  3. Needs discipline: The difference in returns between the MIDCAP index and the equal-weight portfolio can often be very large. Keeping still is a virtue.
  4. Tax-impact: The equal-weight portfolio has a tax and transaction cost angle that a MIDCAP buy-and-hold strategy doesn’t. Capital gains on international equities, bonds and domestic equities are all treated differently.
  5. Asset allocation is about delivering better risk-adjusted returns through diversification.
equal weight portfolio returns
Side note: setting the rebalance threshold is a trade-off between holding onto trending assets (potentially more profits) vs. booking profits and buying a lower-return asset (FOMO and tax incidence.) There is a wide range of theories on this topic. For now, we will restrict ourselves to just running through different scenarios.

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