In the second of three articles looking at scheme funding, Con Keating considers behavioural influences.
Economists have begun to respond to the many disconcerting and reproducible findings of behavioural economics that challenge their old workhorse, the rational agent, homo economicus; this relatively young and rapidly growing body of work passes under the rubric “Motivated Reasoning” and sometimes “Motivated Beliefs”. It is quite distinct from the sociologists’ intrinsic and extrinsic motivation which figures prominently in the analysis of trust.
Here, people still pursue their self-interest but may have multiple and sometimes conflicting goals. To quote Epley and Gilovich: “People generally reason their way to conclusions they favour, with their preferences influencing the way evidence is gathered, arguments are processed, and memories of past experience are recalled. Each of these processes can be affected in subtle ways by people’s motivations, leading to biased beliefs that feel objective (to them)”.
As Tim Taylor has pointed out: “The crucial point is that the process of gathering and processing information can systematically depart from accepted rational standards because one goal – a desire to persuade, agreement with a peer group, self-image, self-preservation – can commandeer attention and guide reasoning at the expense of accuracy.”
There is more: “A person who recognises that a set of beliefs is strongly held by a group of peers is likely to seek out and welcome information supporting those beliefs, while maintaining a much higher level of scepticism about contradictory information.”
This adds a new line of criticism to the concept of ‘high conviction’ investing. Edwards and Lazzara, of S&P Global, recently published an excellent study of this style, Fooled by Conviction: “In order to improve performance, advocates of active management have begun to argue that managers should focus exclusively on their best ideas, holding more concentrated portfolios of securities in which they have the highest confidence. In contrast, we argue that if it becomes popular, such ‘high conviction’ investing is likely to:
• increase risk,
• make manager skill harder to detect,
• raise asset owners’ costs, and
• reduce the number of outperforming funds.”
Market capitalisation indices themselves tend to be highly concentrated; in many developed markets the largest 10% of listed companies account for 5% or more of the total. If the motivated reasoning school is correct, we need to add another problem to this list: a tendency to hold on to allocations far beyond their ‘sell-by’ dates.
For those of us active in the Transparency Taskforce, the conclusion of Edwards and Lazzara’s final thoughts is relevant: “The challenge for an asset owner is to distinguish genuine skill from good luck. The challenge for a manager with genuine skill is to demonstrate that skill to his clients. The challenge for a manager without genuine skill is to obscure his inadequacy. Concentrated portfolios will make the first two tasks harder and the third easier.” (emphasis in original)
Smart beta has taken the investment management world by storm in recent years. These strategies rely upon indices constructed to reflect aspects of securities markets other than market capitalisation and which are believed to deliver superior performance.
In Smart Beta, Scrabble and Simian Indices, Nick Motson, of Cass Business School, investigates the long-run historic performance of eight such approaches. His early findings are that: “We have eight alternative indices all of which have a higher Sharpe ratio than the market-cap over a 46-year sample period. Three have statistically different Sharpe ratios than market-cap. Nearly all have CAPM alpha, none have Fama-French alpha.”
He then proceeds to construct simian portfolios, the output of the legendary monkeys with darts. Though he considers 10 million repetitions of the random selection process
spanning the 46-year period, it is worth considering the magnitude of the sample problem. There are 1.73 x 1030 unique portfolios containing one or more of a universe of just 100 securities, and that would just be the presence or equal weights case.
Notwithstanding this sample size reservation, from my own experience and work, his conclusions appear robust: “The back-tested historical risk-adjusted returns of ‘smart beta’ indices look good when compared to a market cap-weighted index. The outperformance can be explained by exposure to value and size factors. There have been periods of sometimes severe underperformance for all of the ‘smart beta’ approaches. 99.82% of random (or simian) indices would also have beaten market cap over the same period BUT (original emphasis) ’smart beta’ generally beat over 90% of the monkeys.”
It is a pity that the eight strategies investigated did not include dividend weighted indices; for which there is a growing body of evidence of outperformance relative to market cap weighted indices, and indeed a growing range of ETFs and passive funds available.
Income focused portfolios are of particular interest to long-term investors as the income yield dominates the total return achieved, which contrasts markedly with the short-term where changes in price dominate returns, and the Beebower, Brinson et al. result that asset allocation is responsible for over 90% of return variability holds.
In another illustration of motivated beliefs, almost all investment advisers and pension trustees cling fervidly to this Beebower result and spend the majority of their time and effort on asset allocation, even those who profess to be long-term investors.
The use of simulated portfolios suggests that one extremely common portfolio management technique, that of market cap index-relative weighting, may not deliver the benefits expected even when the deviations are ‘prudently’ extreme.
Market cap benchmarks are widely used in the assessment of manager performance, an extremely important piece of information for the decisions of trustees. The argument for the use of these benchmarks is that they capture the mandate’s available investment universe.
However, the objective is to assess the manager’s value-added arising from their decisions and selections, and the correct counterfactual comparator for that should not be the market cap weighted index, but rather an average of simulated randomly selected portfolios, constrained to portfolio mandate guidelines.
Doubtless this issue will reappear in arguments over portfolio transaction cost disclosure, where many have already argued that these costs only arise from value added management activity, and should not be accounted for separately. The counterfactual for that is a much simpler simulation; the performance of the start period portfolio had it been held unchanged over the period under analysis.
A caution on causality and the counterfactual, due to Elster, belongs here: “The truth of the counterfactual statement is neither necessary nor sufficient for the truth of the causal statement,” and “More generally, any attempt to define causal notions in terms of counterfactual statements involves putting the cart before the horse.”
Con Keating is head of research at BrightonRock
* Friedrich Nietzsche in Beyond Good and Evil, trans Helen Zimmern, The Works of Friedrich Nietzsche ed Manuel Komroff
