Category: Investing Insight

Investing insight to make you a better investor.

Food: A problem of plenty?

fertilizer and food price chart

fertilizer, cereal, rice and wheat indices

PFERT: Primary Commodity Prices, Fertilizer
PCERE: Primary Commodity Prices, Cereal index
PRICENPQ: Primary Commodity Prices, Rice, Thailand
PWHEAMT: Primary Commodity Prices, Wheat
PFOOD: Primary Commodity Prices, Food index

If I am reading this chart right, when prices of food commodities (cereals, rice, wheat) go up, fertilizer prices go up. But when food prices come down, fertilizer’s stay up? Wheat prices are more-or-less where they were back in the 90’s. The rest have barely budged. No wonder I’ve spent most of my adult life hearing about farm distress.

Code and charts are on github.

Chart: Number of stocks going up

We often hear market commentators croaking about the number of stocks going up vs. those going down. There is even an advance/decline line filed under technical “analysis.” However, have a look at the statistical distribution of stocks going up in any given month and see for yourself if it makes any sense paying attention to it:

It is, at best, a historical artifact… something that is nice to lookup when someone says that they had a great/crappy month. When a lot of stocks have gone up, it presents a target rich environment for long-only traders. So a random selection of stocks would out-perform the index in that scenario. But good luck using it for market timing.

Code and chart are on github.

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

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.

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.