Monthly Archives: March 2014

Carry on CAPE

Posted on 06/03/2014 | 2 comments

The debates on the cyclically adjusted PE (CAPE), developed by recent Nobel Prize winner Robert Shiller, as a market valuation indicator continue to rage. My last post on the subject is indicative of where I left the arguments.

A variation on Jeremy Siegel’s arguments against CAPE was put forward in an interesting post on the blog Philosophical Economics (hereinafter referred to as PE), centred on the failure of CAPE to mean revert through the ups and downs of the past 23 years and the need for a consistent measure for earnings in the PE calculation across historical reference periods.

The first point essentially relates to the time period over which CAPE is relevant for today’s global economy. Shiller uses available S&P reported GAAP earnings dating back to the 1936 and has supplemented them with his own GAAP earnings calculations from 1936 back to 1871. PE makes the point that historical periods which are “distorted by world wars (1914-1918, 1939-1945, 1950-1953), gross economic mismanagement (1929-1938), and painfully high inflation and interest rates (1970-1982)” may not be the most appropriate as a reference period for today (as reflective of the changed macro-economic period covering the so called great moderation). In essence, PE is saying that structural changes in the economy and investor sophistication may justify a shorter and more relevant time period (yes, PE admits it is a flavour of the “this time it’s different” argument!).

On earnings, PE repeats many of Siegel’s arguments. For example, the point is again made about asymmetric accounting changes to intangible write downs from FAS 142/144. In addition, PE also highlights the lower dividend payouts of 34% over the past 18 years compared to 52% over the 40 year period between the mid-50s and mid-90s. PE argues that lower dividend yields indicate higher investment by firms and therefore support the argument that historical comparisons may not be as relevant.

PE uses Pro-Forma (non-GAAP) S&P earnings from 1954 as reported by Bloomberg for earnings (as opposed to Siegel’s use of National Income and Product Accounts (NIPA) earnings for all approx 9,000 US corporations) and stresses that these earnings may not necessarily be more applicable but they are at least consistent. PE shows that using these earnings since 1954 the market (as at December 2013) was only modestly above the geometric mean and further supports the use of Pro-Forma earnings by back testing this metric against CAPE as an indicator of value through the financial crisis.

A counter-argument (in a December paper) from Bill Hester of Hussman Funds centred on differences in the Bloomberg Pro-Forma earnings used in PE’s calculations, arguing that from 1988 to 1998 the earnings reported by Bloomberg are a mixture of reported & operating earnings and that from 1998 they are akin to operating earnings. The argument highlights the problem of data quality in many databases which are commonly used in the market creating a source of systematic risk. [As an aside, on an individual stock basis, I have found issues with data from commonly used databases and that is why I always take my historical figures from published accounts – not that they are without any issues, just try reconciling some of AIG’s historical financial statements given the almost annual restatements!]. On earnings, Hestor uses work done by Andrew Smithers in his book “The Road to Recovery” which suggests that executive compensation tied to short term results has been a factor in earnings volatility.

PE counters Hester’s counter argument in another post that after adjusting the Boomberg data pre-1998 and applying an adjustment for the change in dividend payout ratio the ProForma earnings based CAPE still signals a less overvalued market that Shiller’s CAPE. PE also rubbishes the contention from Smithers that volatility is as a result of executive remuneration saying that low volatility is in the executive’s interest to maximise their options which vest over time and that investment is currently low due to the uncertainty around unprecedented macro- economic risks.

PE cites arguments similar to those of other bulls such as Siegel who content that US corporate profits as a percentage of GDP (or GNP) is high compared to historical levels due to increased foreign contributions to profits, lower corporate taxes and a higher S&P concentration of globalised technology and energy firms with fatter profit margins. PE points to stability in statistics such as S&P 500 net profit margins for non-financials (excluding energy & technology) produced by BoA Merrill Lynch and analysis of David Bianco from Deutsche Bank on firms with a high level of foreign sales showing higher profit margins (see graph reproduced below). To be fair to Bianco, he recently maintained his year-end 2014 S&P500 target of 1850 and warning of volatility in 2014 stating “buy the dips, but I’m also saying in advance, wait for the dips“.

click to enlargeDeutsche Bank Foreign vrs Domestic Profit MarginsIn a December note, the extremely bearish John Hussman stated that “in recent years, weak employment paired with massive government deficits have introduced a wedge into the circular flow, allowing wages and salaries to fall to the lowest share of GDP in history, even while households have been able to maintain consumption as the result of deficit spending, reduced household savings, unemployment compensation and the like”. In another note from John Hussman out this week on foreign profits, based upon a range of valuation metrics (see graph reproduced below) he puts the S&P500 at a 100% premium to the level needed to achieve historical normal returns (or indicating a negative total return on horizons of 7 years or less). He also rubbishes the higher contribution from foreign profits, saying they have been decreasing since 2007 and that they “do not have any material role in the surge in overall profit margins”.

click to enlargeHussman S&P500 Valuation March 2014The (only) slightly more cheerful folk over at GMO also had an insightful paper out in February by James Montier on the CAPE debate. One of the more interesting pieces of analysis in the paper was a Kalecki decomposition of profits which indicate that the US government deficit is a major factor in replacing reduced investment since the crisis (see graph reproduced below). As we know, this US deficit is in the process of being run down and the knock on impact upon profits could result (save a recovery in investment or a significant re-leveraging of households!). The Kalecki composition also seems to support the larger contributions from foreign earnings (albeit a decreasing contribution in recent years).

click to enlargeGMO Kalecki DecompositionDepending upon whether you use the S&P500 PE, the Shiller PE or the NIPA based PE since 1940 the market, according to Montier, is 30%, 40% or 20% overvalued respectively.  Using a variety of metrics, Montier estimates that the expected total return (i.e. including dividends) for the market over the next 7 years ranges from an annual return of 3.6% from Siegel’s preferred method using NIPA to a negative 3.2% per annum from a full revision Shiller PE (using 10 year trend earnings rather than current trailing 10 year earnings).  The average across a number of valuation metrics suggests a 0% per annum return over the next 7 years!

Ben Inker also has a piece in the February GMO letter on their strategy of slowly averaging in and out of the market. Inker calls it slicing whereby you take account of historical forecasts as well as your current “spot” view of valuations. Their research shows that you capture more value through averaging purchasing or selling over time by benefiting from market momentum. GMO currently are in selling mode whereby they “are in the process of selling our equity weight down slowly over the next 9 to 12 months”.

So, where do all of these arguments leave a poor little amateur investor like me? Most sensible metrics point to the S&P500 being overvalued and the only issue is quantum. As I see it, there is validity on both sides of the CAPE arguments outlined above. Earnings are high and are likely to be under pressure, or at best stable, in the medium term. I am amenable to some of the arguments over the relevant timeframe used to calculate the mean to assess the mean reverting adjustment needed (I do however remain wedded to mean revision as a concept).

To me, the figures of 20% to 40% overvaluation in Montier’s note based on calculations back to 1940 from different CAPE calculations feel about right. A 30% overvaluation represents the current S&P500 to a mean calculated from 1960. The rapid bounce back in the S&P500 from the 5% January fall does show how resilient the market is however and how embedded the “buy on the dip” mentality currently is. GMO’s philosophy of averaging in and out of the market over time to take advantage of market momentum makes sense.

In the absence of any external shock that could hit values meaningfully (i.e. +15% fall), the market does look range bound around +/- 5%. Common sense data points such as the Facebook deal for WhatsApp at 19 times revenues confirm my unease and medium term negative bias. I have cut back to my core holdings and, where possible, bought protect against big pull backs. In the interim, my wish-list of “good firms/pity about the price” continues to grow.

They say that “the secret to patience is doing something else in the meantime”. Reading arguments and counter argument on CAPE is one way to pass some of the time…….

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The imperfect art of climate change modelling

Posted on 03/03/2014 | 3 comments

The completed Group I report from the 5th Intergovernmental Panel on Climate Change (IPCC) assessment was published in January (see previous post on summary report in September). One of the few definite statements made in the report was that “global mean temperatures will continue to rise over the 21st century if greenhouse gas (GHG) emissions continue unabat­ed”. How we measure the impact of such changes is therefore incredibly important. A recent article in the FT by Robin Harding on the topic which highlighted the shortcomings of models used to assess the impact of climate change therefore caught my attention.

The article referred to two academic papers, one by Robert Pindyck and another by Nicholas Stern, which contained damning criticism of models that integrate climate and economic models, so called integrated assessment models (IAM).

Pindyck states that “IAM based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading”. Stern also criticizes IAMs stating that “assumptions built into the economic modelling on growth, damages and risks, come close to assuming directly that the impacts and costs will be modest and close to excluding the possibility of catastrophic outcomes”.

These comments remind me of Paul Wilmott, the influential English quant, who included in his Modeller’s Hippocratic Oath the following: “I will remember that I didn’t make the world, and it doesn’t satisfy my equations” (see Quotes section of this website for more quotes on models).

In his paper, Pindyck characterised the IAMs currently used into 6 core components as the graphic below illustrates.

click to enlargeIntegrated Assessment Models

Pindyck highlights a number of the main elements of IAMs which involve a considerable amount of arbitrary choice, including climate sensitivity, the damage and social welfare (utility) functions. He cites important feedback loops in climates as difficult, if not impossible, to determine. Although there has been some good work in specific areas like agriculture, Pindyck is particularly critical on the damage functions, saying many are essentially made up. The final piece on social utility and the rate of time preference are essentially policy parameter which are open to political forces and therefore subject to considerable variability (& that’s a polite way of putting it).

The point about damage functions is an interesting one as these are also key determinants in the catastrophe vendor models widely used in the insurance sector. As a previous post on Florida highlighted, even these specific and commercially developed models result in varying outputs.

One example of IAMs directly influencing current policymakers is those used by the Interagency Working Group (IWG) which under the Obama administration is the entity that determines the social cost of carbon (SCC), defined as the net present damage done by emitting a marginal ton of CO2 equivalent (CO2e), used in regulating industries such as the petrochemical sector. Many IAMs are available (the sector even has its own journal – The Integrated Assessment Journal!) and the IWG relies on three of the oldest and most well know; the Dynamic Integrated Climate and Economy (DICE) model, the Policy Analysis of the Greenhouse Effect (PAGE) model, and the fun sounding Climate Framework for Uncertainty, Negotiation, and Distribution (FUND) model.

The first IWG paper in 2010 included an exhibit, reproduced below, summarizing the economic impact of raising temperatures based upon the 3 models.

click to enlargeClimate Change & Impact on GDP IWG SCC 2010

To be fair to the IWG, they do highlight that “underlying the three IAMs selected for this exercise are a number of simplifying assumptions and judgments reflecting the various modelers’ best attempts to synthesize the available scientific and economic research characterizing these relationships”.

The IWG released an updated paper in 2013 whereby revised SCC estimates were presented based upon a number of amendments to the underlying models. Included in these changes are revisions to damage functions and to climate sensitivity assumptions. The results of the changes on average and 95th percentile SCC estimates, at varying discount rates (which are obviously key determinants to the SCC given the long term nature of the impacts), can be clearly seen in the graph below.

click to enlargeSocial Cost of Carbon IWG 2010 vrs 2013

Given the magnitude of the SCC changes, it is not surprising that critics of the charges, including vested interests such as petrochemical lobbyists, are highlighting the uncertainty in IAMs as a counter against the charges. The climate change deniers love any opportunity to discredit the science as they demonstrated so ably with the 4th IPCC assessment. The goal has to be to improve modelling as a risk management tool that results in sensible preventative measures. Pindyck emphasises that his criticisms should not be an excuse for inaction. He believes we should follow a risk management approach focused on the risk of catastrophe with models updated as more information emerges and uses the threat of nuclear oblivion during the Cold War as a parallel. He argues that “one can think of a GHG abatement policy as a form of insurance: society would be paying for a guarantee that a low-probability catastrophe will not occur (or is less likely)”. Stern too advises that our focus should be on potential extreme damage and that the economic community need to refocus and combine current insights where “an examination and modelling of ways in which disruption and decline can occur”.

Whilst I was looking into this subject, I took the time to look over the completed 5th assessment report from the IPCC. First, it is important to stress that the IPCC acknowledge the array of uncertainties in predicting climate change. They state the obvious in that “the nonlinear and chaotic nature of the climate system imposes natu­ral limits on the extent to which skilful predictions of climate statistics may be made”. They assert that the use of multiple scenarios and models is the best way we have for determining “a wide range of possible future evolutions of the Earth’s climate”. They also accept that “predicting socioeconomic development is arguably even more difficult than predicting the evolution of a physical system”.

The report uses a variety of terms in its findings which I summarised in a previous post and reproduce below.

click to enlargeIPCC uncertainty

Under the medium term prediction section (Chapter 11) which covers the period 2016 to 2035 relative to the reference period 1986 to 2005, a number of the notable predictions include:

  • The projected change in global mean surface air temperature will likely be in the range 0.3 to 0.7°C (medium confidence).
  • It is more likely than not that the mean global mean surface air temperature for the period 2016–2035 will be more than 1°C above the mean for 1850–1900, and very unlikely that it will be more than 1.5°C above the 1850–1900 mean (medium confidence).
  • Zonal mean precipitation will very likely increase in high and some of the mid-latitudes, and will more likely than not decrease in the subtropics. The frequency and intensity of heavy precipitation events over land will likely increase on average in the near term (this trend will not be apparent in all regions).
  • It is very likely that globally averaged surface and vertically averaged ocean temperatures will increase in the near term. It is likely that there will be increases in salinity in the tropical and (especially) subtropical Atlantic, and decreases in the western tropical Pacific over the next few decades.
  • In most land regions the frequency of warm days and warm nights will likely increase in the next decades, while that of cold days and cold nights will decrease.
  • There is low confidence in basin-scale projections of changes in the intensity and frequency of tropical cyclones (TCs) in all basins to the mid-21st century and there is low confidence in near-term projections for increased TC intensity in the North Atlantic.

The last bullet point is especially interesting for the insurance sector involved in providing property catastrophe protection. Graphically I have reproduced two interesting projections below (Note: no volcano activity is assumed).

click to enlargeIPCC temperature near term projections

Under the longer term projections in Chapter 12, the IPCC makes the definite statement that opened this post. It also states that it is virtually certain that, in most places, there will be more hot and fewer cold temperature extremes as global mean temper­atures increase and that, in the long term, global precipitation will increase with increased global mean surface temperature.

I don’t know about you but it seems to me a sensible course of action that we should be taking scenarios that the IPCC is predicting with virtual certainty and applying a risk management approach to how we can prepare for or counteract extremes as recommended by experts such as Pindyck and Stern.

The quote “it’s better to do something imperfectly than to do nothing perfectly” comes to mind. In this regard, for the sake of our children at the very least, we should embrace the imperfect art of climate change modelling and figure out how best to use them in getting things done.

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