Author: shyam

Book Review: The Fish that Ate the Whale

The Fish that Ate the Whale (Amazon,) is a biography of Sam Zemurray, the Banana Man. The author, Rich Cohen, tracks the rise and fall and rise and fall of the banana company that Sam built through the late 19th and early 20th century.

The sheer magnitude of the rags to riches story that starts with Sam trading discarded bananas and ends with him running the United Fruit Company is completely breathtaking. Oh, and it also involves overthrowing a Latin-American government that was about to turn hostile to the banana trade. Incredible.

The book could easily be about any business in the perishable commodities business. We now take American apples and Australian kiwis in our supermarket for granted. However, there was a time before cool chain and cold storage when most produce had to be sourced within a certain geographic radius. Ships had to be unloaded, by hand, onto steam-powered trains. Most of the produce would rot and go to waste. Hemmed-in by these constraints, businesses had to work with what they had. If it involved bribing bureaucrats or overthrowing governments to ensure zero taxes and union-free labor, then so be it.

The book is also the story about most corporations. Even though, technically, a company could live on forever, most have finite lifespans. Founders who know the business inside-out inevitably age. They are replaced by “professional management” who may keep things going for a while. But eventually, most companies that die end up being run by bureaucrats far removed from the ground with very little of the spirit that shaped the company at the beginning.

Sam Zemurray’s life could very well be an exemplification of the American dream but the banana company that he built ended up being an exemplar of the worst of American business’ colonial instincts.

Recommendation: Read it now!

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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.

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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

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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.

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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.

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