Category: Investing Insight

Investing insight to make you a better investor.

Long-term averages are still being made

Value investing in the US has been under pressure recently, having underperformed growth and momentum over the last decade. The most popular explanations given for this are:

  1. Price-to-book, the most popular metric of value investors, stopped being a good measure of value.
  2. Value, as a strategy, got crowded after putting in a strong performance the prior decade.
  3. The value effect is the strongest in small- and micro-caps but scale prevents investment managers from being able to access it. Making large-cap value an over-fished pond.
  4. All anomalies, including value and momentum, have their ups and downs. Investors chase performance, thus preserving the anomaly.
  5. This time is different.

From a quantitative point of view, “value” is a way of ranking the universe of stocks and applying a cut-off on them. The cut-off is based on historical averages. But the problem with historical averages is that history is still being made. This point is driven home in newer markets, like India’s, where we cannot lean on 100-year back-tests but have to depend on data-sets that are, at best, 10- or 15-years old. And here’s how that last 10-year price-to-book of different indices looks like:
PB ratio

The averages are being made as we speak. This presents a moving target for value investors because value is all about mean-reversion. And something similar could be happening in the US – maybe a few decades from now, a test looking back at today will reveal that the high PB stocks were unusually cheap.

Related: Index Valuations

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Momentum vs. Low Volatility

Is the low-volatility anomaly overrated?

momentum vs. min-volatility

momentum vs. min-volatility

Investors may be giving up on the significantly higher returns of a momentum strategy in favor of slightly lower drawdowns of a low-volatility strategy. They maybe better off managing overall portfolio risk through a bond allocation rather than tilting away from momentum.

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No Silver Bullets

Most of the time, beta swamps alpha. Take the case of the Roubini Country Insights model, for example. It claims to “rank countries based on an analysis of over 2500 data points from institutions such as the Bank of International Settlements and the World Bank.” Also, “these data points cover each country’s demographics, quality of education, healthcare and ability to innovate, and will look at the country’s growth potential in political and social spheres, as well as its top-down macro-economic situation.” It sounds like it does everything that a smart investment manager with a long-only global equities mandate should be doing. And you would expect such a smart model to add significant alpha.

Thanks to Barclays, a bunch of equity indices based on this model have been available for a while now. We were curious as to how these performed vs. their corresponding plain-vanilla market-cap weighted cousins.

market-cap vs. maro-quant

Developed markets: MSCI World (black) vs. Insights (green)
Emerging markets: MSCI EM (red) vs. Insights (blue)

The value add from the smart-beta quantitative “Insights” model, roughly about 1% a year, seems skinny compared to all the work that must have gone into it. 2500 data points is a big dataset but it looks like most of them have no effect on equity returns. This also ties into the curse of dimensionality when dealing with complex adaptive systems – more data typically subtracts from the model.

As an investor, it probably would have been easier to stay invested in one of the cap-weighted indices, just accepting the beta, rather than reach for that 1% extra with fancy sounding strategies.

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Index Valuations, Part II

In Part I of Index Valuations, we showed how the relative PE (price-to-earnings ratio) and PB (price-to-book ratio) of the NIFTY 50 and NIFTY MIDCAP 50 indices have varied over time. What would a portfolio that weighted each of these based on the relative valuation ratio look like?

Backtest

Suppose, the relative ratio (R) = Ratio(MIDCAP)/Ratio(NIFTY)
Then, at the end of every month, re-weight the protfolio so that portfolio (S1) = R * NIFTY + (1-R) * MIDCAP, and
portfolio (S2) = (1-R) * NIFTY + R * MIDCAP

Ratio can either be PE or PB

PE based weights:
PE weights

PB based weights:
PB weights

It looks like:

  1. a portfolio with PB based weights is a lot less volatile than the PE based one.
  2. PB portfolio recovers much faster that the PE or plain-vanilla indices from deep drawdowns
  3. PB out-performs an equal weight portfolio

You can track and map this strategy to your portfolio using the PB weighted NIFTY/MIDCAP Theme.

Code and charts on github.

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