Pension funds, insurance companies and sovereign wealth funds increasingly apply factor investing to equities in their portfolios. And now institutional investors would also like to apply this concept to credits. A research study ( ‘Factor investing in the corporate bond market’) shows that factor strategies can be attractive in credit markets. How does this work when applied to corporate bonds? Read more and watch the video.
The hunt for alpha is leading traditional investors toward more innovative strategies and operating models. And, significantly, one potential source of inspiration for long-term investors has come from an unconventional place: the community of sovereign development funds (SDFs).
In this paper academics from Stanford University provide readers with analytical frameworks that can assist in the establishment of SDFs, which they define as publicly-sponsored commercial investment funds that combine financial performance objectives with development objectives.
The paper can be accessed below
SDFs – Designing High-Performance Strategic Investment Institutions
About 11 years ago, the Towers Watson’s Thinking Ahead Group came up with the concept of investors appointing managers for 10-year mandates. The consulting arm then started talking to clients about it in 2004/05 and the early mandates have now matured. So did it work? Do longer-term mandates produce outperformance, better behaviour and more security? Amanda White spoke to the founder and chief executive of the Thinking Ahead Group at Towers Watson, Tim Hodgson.
The answer, at least in concept, is yes 10-year mandates produce better results.
But probably the biggest learning, and benefit, from the Towers Watson experience is that there are still behaviours, and relationships, that are constraining the success of long-term mandates.
Tim Hodgson, who heads up the Thinking Ahead Group, says that Towers Watson put together a model portfolio of long-term managers, a paper portfolio that performed very well.
“The performance of that portfolio has been very good, and this long-term mandate model portfolio proves you can find good long-term managers,” he says.
“But what we learnt this first time round is that in practice clients usually appointed one manager, not a portfolio of mangers, so they got a more volatile experience.”
He says that in the lead up to the GFC the relative performance of some long-term mandates was painful, because growth and momentum was strong.
“But if clients stuck to it then value bounces back,” he says.
“Over 10 years there is an incremental advantage. But what we’ve learnt is that not all clients are able to stick to the course, this is usually for the normal reasons like decision making, governance, accountability.”
Hodgson and his colleagues are taking these learnings and writing a paper, Long-term mandates 2.0, which outlines for investors the pillars for success.
“We have learnt from 1.0 that we still believe in long-term mandates but we need to learn how to deliver a better experience – part of that is to have a portfolio of long-term mandates.”
He says there was a very large spread between the best and worst managers in the model portfolio, but as a portfolio it has done very well, performing an average 2 per cent per annum above a world index.
The three pillars of success, Hodgson says, are:
- Laying the foundations. This includes setting up the fund for success and adequately addressing the mindset, governance, processes, and work necessary to ensure the decisions can be stuck out.
- Designing and negotiating the mandate
- Monitoring and living with the mandate
“We learnt that yes everyone signed up to a CPI plus x per cent but when it comes to living with it they want to compare it to a world index. It is an absolute return concept but people still want to do the usual quarterly checking.”
The Thinking Ahead Group is also assessing the design of the mandate. For example does a long-term mandate have to be pure listed equities? Do you allow listed equities mandates to include bonds? Can a mandate be redesigned to include illiquid mandates and listed equities? Would a multi-asset product be appropriate? How do you treat cash?
In addition Hodgson believes that if the design of a long-term mandate is on the drawing board then time diversification should be part of the conversation.
“Anyone who thinks good long term returns are about chain linking above average annual returns doesn’t understand the problem,” he says.
The thinking incorporates VUCA – and the existence of volatile, uncertain, complex and ambiguous conditions.
“You can’t be excellent in every period and link them. You could allow for very big swings. Just because you set up as long term animal doesn’t’ mean you can’t act in the short term.”
The hope is that version 2.0 of long-term mandates, incorporates room to move on fee negotiation.
The concept is that the mandate has longer lockups so managers should discount their fees. This didn’t work the first time around, in part because the lockups turned out to be more theory than practice. Not every asset owner could go the duration of the mandate.
In addition Towers Watson had to dip into a different pool of managers outside the usual institutional investment management pool.
“We went to the family office managers, who were used to managing a private pool of money where a client says ‘I’ve worked hard to build the business and do not lose my money’.”
Despite the teething problems encountered with 10-year mandates, Hodgson believes they will succeed.
“It is a more intelligent frame of reference,” he says. “Certain behaviour might be good for profits this quarter but long-term mandates force the consideration of whether there are long term implications.”
Systematic equity investment strategies – so-called smart beta strategies – are usually marketed on the basis of outperformance. However, it is important to recognise that performance analysis is typically conducted on backtests that apply the smart beta methodology to historical stock returns. Concerning actual investment decisions, a relevant question, therefore, is how robust the outperformance is, writes Felix Goltz.
In general, robustness refers to the capacity of a system to perform effectively in a constantly changing environment. For smart beta strategies, we distinguish between ‘relative robustness’ and ‘absolute robustness’.
A strategy is assumed to be ‘relatively robust’ if it is able to deliver similar outperformance in similar market conditions. Single factor indices aim to achieve this kind of robustness.
Absolute robustness is the absence of pronounced state and/or time dependencies and a strategy shown to outperform irrespective of prevailing market conditions. Multi-factor indices often aim to improve absolute robustness.
Causes of lack of robustness
Factor fishing and model mining risks
Investors who wish to benefit from factor premia need to address robustness when selecting a set of factors. Recent research (Harvey, C., Y. Liu, and H. Zhu, 2015 …and the ‘Cross-Section of Expected Returns’, Review of Financial Studies, forthcoming) documents a total of 314 factors with a positive historical premium, but shows that many factors may be a result of data mining: strong and statistically significant factor premia may be a result of many researchers searching through the same dataset to find publishable results.
To avoid exposure to data-mined factors, a key requirement for investors to accept factors as relevant in their investment process is that there is a clear economic intuition as to why the exposure to this factor constitutes a systematic risk.
Failure to recognise a suitable proxy for the rewarded factor will harm the relative robustness of the strategy.
Model mining risk is the risk of having an index construction methodology that results in a good track record in backtesting, without any persistence going forward. For example, many value-tilted or dividend-focused indices include a large set of ‘ad-hoc’ methodological choices, opening the door to model mining.
Exposures to specific risks
All smart beta strategies are exposed to unrewarded strategy-specific risks. Specific risks correspond to all the risks that are unrewarded in the long run, and therefore not ultimately desired by the investor.
In line with portfolio theory, among the unrewarded risks we find specific financial risks (also called idiosyncratic stock risks) that correspond to the risks that are specific to the company itself.
It is this type of risk that asset managers are supposed to be the best at knowing, evaluating and choosing in order to create alpha, but portfolio theory considers it to be neither predictable nor rewarded, so it is better to avoid it by investing in a well-diversified portfolio.
Specific risks can also correspond to important financial risk factors that do not explain, over the long term, the value of the risk premium associated with the index. The academic literature considers for example that commodity, currency, and sector risks do not have a positive long-term premium. For example, value strategies often lead to pronounced tilts towards financial sector stocks. During the financial crisis of 2008, exposure to the financial sector proved to be a major determinant of performance of these strategies. It should be noted that the tilt towards the financial sector may not be desired, but it came as a by-product of holding value stocks.
Model-specific risks that are specific to the implementation of the diversification model are also a form of unrewarded risk. As per Modern Portfolio Theory, every investor should optimally combine risky assets so as to achieve the highest possible Sharpe ratio. Implementing this objective, however, is a complex task because of the presence of estimation risk for the required parameters, namely expected returns and covariance parameters.
In practice, the costs of estimation error may entirely offset the benefits of optimal portfolio diversification.
Dependency on individual factor exposures
Systematic risks come from the fact that smart beta strategies can be more or less exposed to particular risk factors, depending not only on the methodological choices guiding their construction (implicit), but also on the universe of stocks supporting this construction scheme (explicit).
For example, fundamentals-weighted portfolios typically have a value tilt and minimum-volatility strategies exhibit a low-beta tilt.
Each weighting scheme exposes investors to implicit risk factors that may or may not be consistent with their risk objective. It is important to note that periods of poor performance in all factors are common throughout long-horizon historical tests, and the underperformance occurs at different points in time.
Therefore, investing in a single factor is not a robust approach in absolute terms, as the performance will vary greatly over time across different time periods.
Improving robustness
Avoidance of data or model mining through a consistent framework
Establishing a consistent framework for smart beta index creation limits the choices – while providing the flexibility needed – for smart beta index creation. Consistency in the index framework has two main benefits.
First, it prevents model mining by limiting the number of choices through which indices can be constructed. A uniform framework is the best safeguard against ‘post hoc’ index design, or model mining (i.e. the possibility of testing a large number of smart beta strategies and publishing the ones that have good results).
Second, analysis across specification choices is vital because the range of outcomes gives a more informative view than a single specification, which could always have been picked.
An index that performs well across multiple specification choices is more robust than an index that performs only in a single specification choice, which could very well have been by chance rather than because of the robustness of the strategy.
Pre-packaged indices do not allow investors to compare across specifications in order to obtain a view on the sensitivity of performance to index specification choices, thereby exposing investors to a risk of unintended consequences of undesired risks.
Another approach to the inconsistency of the conceptual framework is to look at the evolution, or change of methodology over time, for the same strategy or the same factor.
Some index providers have launched new factor indices when they already had factor indices for the same factor on the market. In this case, the new indices have the same objective as the old ones, but different construction principles.
This phenomenon has a striking resemblance to the practice of funds or asset managers of creating new funds – or changing the strategy of funds – in order to overshadow the poor track record of the old fund. Thus, an inconsistent framework over time is also a kind of model mining that allows the index providers to launch new indices with better track records.
Improving relative robustness by reducing unrewarded risks
Relative robustness can be improved by minimising the unrewarded risk as much as possible. There are numerous approaches to estimating risk parameters.
The sample estimator of a covariance matrix produces extremely high estimation errors when the ratio of universe size to sample size is large (sample risk).
One solution to this problem is to reduce the number of parameters to be estimated by imposing a structure on the covariance matrix.
Although this method reduces sample risk, its drawback is that the estimator is biased if the risk model does not conform to the true stock-return-generating process (model risk).
State-of-the-art estimators for risk parameters aim to achieve a trade-off between sample risk and model risk.
One serious concern with optimisation-based weighting schemes is that the stocks with the highest estimation error may receive the highest weight—a process commonly known as ‘error maximisation’ – which is detrimental to the relative robustness of the strategies. In practice, various kinds of deconcentration constraints are adopted to improve diversification.
For example, recent research introduces flexible constraints that put limits on the overall amount of concentration in the portfolio, rather than limiting the weight of each stock in the portfolio, thus leaving more room for the optimiser while avoiding concentration overall.
Even though different weighting schemes offer efficient diversification of stocks, there is a need for additional diversification of the weighting schemes to diversify away the strategy-specific risks – a concept called “diversifying the diversifiers” (see Timmermann (2006), Kan and Zhou (2007), Tu and Zhou (2011) and Amenc, Goltz, Lodh, Martellini (2012) on the benefits of combining portfolio strategies).
The combination of different strategies diversifies risks that are specific to each strategy by exploiting the imperfect correlation between the different strategies’ parameter estimation errors.
Thus, diversifying the model risks further reduces the unrewarded risks, and renders the weighting scheme more robust (in a relative manner).
Improving absolute robustness by diversifying across factors
As discussed before, investors who rely on single factor exposure take the risk of the likelihood of the underlying factor underperforming over short periods.
The reward for exposure to these factors has been shown to vary over time. While this time variation in returns is not completely in sync for different factors, allocating across factors allows investors to diversify the sources of their outperformance and smooth their performance across market conditions.
Felix Goltz is head of applied research, EDHEC-Risk Institute; Research Director, ERI Scientific Beta.
Although factor investing is becoming more popular, there are still plenty of questions surrounding the concept and its implementation. In Europe, the US and Asia, it is a widely debated strategy within the professional investment community. Investors are attracted by the prospect of greater diversification, higher returns and lower risk. What should investors know in order to maximize their factor investing performance? Read the full article or download the pdf.
Co-investment is often perceived as referring to institutional investors investing alongside their external asset managers in companies, but increasingly it refers to investment responsibilities being shared among peer investors, as long-term institutional investors are forming co-investment partnerships with the specific objective of deploying capital collaboratively into attractive investment opportunities.
These collaborative partnerships seem to be an important means of capitalising on an investor’s network.
This paper, by academics at Stanford, identifies and analyses examples of the new collaborative investment vehicles being employed by institutional investors to access private market assets.
In so doing, this paper tracks the initial development of a growing trend among institutional investors towards more efficient ways of long-term capital deployment.
To access the paper click below
Capitalising on institutional co-investment platforms