Tag: investment

[Books] Wiser – how group decisions can let us down

I’m sure you can picture the scene. A group sits down to make a critically important decision. Much discussion follows. One person after another lays out their views, lots of points are made. There are some clear areas of agreement. Nods all round. The answer starts to become clear. With a few changes a consensus develops. The decision is made. Everyone feels good, confident. this is definitely the right decision. No doubt. We all agree.

But is it?

Many people will have experienced or heard about situations where things didn’t quite go to plan and it transpired the group blundered. The term groupthink has become relatively well know.

This excellent book written by Cass Sunstein and Reid Hastie starts with a simple observation and a simple question: In many fields we endow groups of people with the authority or responsibility to make key decisions.

Do groups usually correct individual mistakes?

 

The simple answer is that they do not, and they can even amplify mistakes. This basic insight has great relevant to pension funds, investment committees and all sorts of other groups tasked with making meaningful decisions in complex domains.

In this highly readable book the authors take us through a quick tour of the taxonomy of “bugs” within group decision making, however their approach is balanced – also laying out the ways in which groups might be thought to do better than individuals, and the circumstances in which they can.

Understanding how and why groups blunder is not staggeringly complex, but requires a focused and methodical examination of human nature and biases, with social influences playing a big role throughout. Unpacking some of the sources of group failure in this way starts to yield immediately actionable insights on how to correct for these issues. The authors also helpfully guide readers through a number of real-life experiments that support the points they make.

 

Individual and Group judgements

 

We as individuals use judgement heuristics (rules of thumb), and have biases. We can be overconfident and place too much weight on our own experience and opinions. These behavioural traits are well known on an individual level. When we get together to debate and make decisions in a group sense these can result in “garbage in garbage out”.

Individual confidence tends to increase after a group deliberation. Deliberative groups (those that deliberate before arriving at a view) can be overconfident and wrong, this can have serious consequences in government policy, corporate strategy and for institutional investors including pension funds (tasked with making the investment decisions for large pools of invested assets).

In Defence of Groups – Wise Crowds?

Surely groups ought to be:

  • At least as good as the most informed member: if that individual can make their case persuasively or clearly, others will realise their own errors and get behind the better informed viewpoint – eg “why are all manhole covers round?”
  • Groups ought to be able to aggregate information effectively to get a fuller picture than held by any individual – particularly if they contain no experts but a range of dispersed information
  • Synergy: the give-and-take of group discussion might lead the group to sift information in a way that uncovers insight that the individuals would not have reached by themselves.

Is there evidence that these dynamics function in practice?

In practice there are four key reasons why groups fail, and this is really the central insight of the whole book

 

  1. Groups fail to successfully aggregate info shared by members, then focus on information that is widely shared by the members rather than that known by only one or two members

  2. Groups become polarized: adopt a more extreme position than the average of the members pre-deliberation

  3. Groups fall victim to decision making cascades. Whereby early opinions excessively influence direction of decision

  4. Groups amplify the individual biases of group members

 

Let’s draw a distinction between different types of group and different types of problem:

 

Statistical v deliberative groups: statistical groups each independently contribute a point estimate of an unknown variable (eg, the temperature of a room). Deliberative groups discuss the answer to a particular problem. Most of the issues with groups occur with deliberative groups.

 

“Eureka” problems are ones where the true answer, once voiced is immediately obvious to the rest of the group (“why are manhole covers round?”). Problems with an outcome which is certain and measurable (eg the temperature of a room) are different to those where outcomes are uncertain and not immediately measurable (eg investment decisions).

 

It is clear that the decisions taken by investment committees and trustees frequently fall into the toughest category where group failures are most likely!

 

Information Sharing – the Common Knowledge Trap

Groups often risk falling into the common knowledge trap – common information that is held by multiple group members is given more weight than it ought to be, and significant information held by only one or two members can be ignored.

 

Self-silencing is a big threat to effective group decision making.

There can often be social pressure or subtle penalties to speaking out, especially if what the individual has to say is jarring or disruptive. In practice this effect can depend on the self-confidence, and subtly on the status of the individual involved meaning that men, women, minorities and certain occupations will all experience this differently

 

Polarization

Like minded groups, post deliberation can often get into a more extreme position than any of them started in pre-deliberation. This is most clearly visibile with respect to politiical affiliation. The authors cite interesting studies that show that groups of left-of-centre or right-of-centre individuals will tend to adopt more extreme positions post-deliberation than their average pre-deliberation, and will tend toward greater consensus in the more extreme position. Why does this happen?

Individual opinions can turn more extreme when corroborated by others, and confidence can also increase once an individual learns their view is shared by others. Social pressures/forces will cause members to adjust, at least slightly, to the dominant position.

Polarization doesn’t always lead away from the right answer of course, if the members of the group are individually leaning toward the right answer then the group polarization is likely to produce a decisive swing to the correct view. However groups badly blunder when they polarize toward an incorrect answer, becoming more confident in the incorrect answer in the process.

Cascades.

The human being is at root a social animal, language may well be the most subtle and engaging social mechanism in the animal kingdom – and we are wired to synchronise with other humans from birth. Hence what others do or say will influence what we do or say. What can easily happen is that subsequent speakers may defer to the opinion of earlier ones, and later speakers, hearing two or more people state the same belief may assume these beliefs were arrived at independently (and therefore have higher reliability). The authors describe an interesting experiment where subjects consistently make obviously false statistical judgements, being influenced by what earlier subjects stated.

If consensus is prized, and known to be prized, then self silencing is more likely.

Amplification

Groups often amplify natural human biases such as availability (if something can be easily called to mind, it is considered more likely), representativeness (if someone superficially appears to fit a particular mould, we are likely to judge them as being more suitable) , framing and egocentric bias. The planning fallacy, overconfidence bias.

Why? Informational influences and social pressures are again at work.

Having understood the ways in which groups blunder, the authors guide us through ways we can make groups function better – I discuss this in part 2 here.

One minute guide to real-world AI implementation 

McKinsey just published an excellent and comprehensive paper covering how Artificial Intelligence (AI) can deliver real value for business.

tl;dr

The only issue – at 80 pages it’s a lot to read.

A lot of the use cases focus on retail, energy and education, one angle I find particularly are the read-across of these examples into service based and business-to-business environments. There are definitely some relevant points that could map to a services/B2B worlds: for example the automation of admin tasks for teaches, more targeted sales and marketing and more personalised customer service.

Here’s my take on the key points from the document:

1. No shortcuts: first data & digital, then AI

AI becomes impactful when it has access to large amounts of high-quality data and is integrated into automated work processes. AI is not a shortcut to these digital foundations. Rather, it is a powerful extension of them.

The firs thing firm’s need to do is come up with a real business case for AI that relates to the firm’s strategy, this requires separating the hype and buzz around AI from its actual capabilities in a specific, real-world context. It includes a realistic view of AI’s capabilities and an honest accounting of its limitations, which requires at least a high-level grasp of how AI works and how it differs from conventional technological approaches.
Each new generation of tech builds on the previous one – this suggests AI can deliver significant competitive advantages, but only for firms that are fully committed to it. Take any ingredient away—a strong digital starting point, serious adoption of AI, or a proactive strategic posture—and profit margins are much less impressive. This is consistent with McKinsey findings in the broader digital space.
Technology is a tool and in itself does not deliver competitiveness improvements.

2. Areas to focus on to create real value: project, produce, promote or provide 

To fulfil the expectations being heaped upon it, AI will need to deliver economic applications that significantly reduce costs, increase revenue, and enhance asset utilization.

Mckinsey categorized the ways in which AI can create value in four areas:(1) enabling companies to better project and forecast to anticipate demand, optimize R&D, and improve sourcing; (2) increasing companies’ ability to produce goods and services at lower cost and higher quality; (3) helping promote offerings at the right price, with the right message, and to the right target customers; and (4) allowing them to provide rich, personal, and convenient
user experiences

3. Data ecosystem & staff culture to the fore 

Firms must conduct sensible analysis of what the most valuable AI use cases are. They should also build out the supporting digital assets and capabilities. Indeed, the core elements of a successful AI transformation are the same as those for data and analytics generally. This includes building the data ecosystem, adopting the right techniques and tools, integrating technology into workplace processes, and adopting an open, collaborative culture while reskilling the workforce

4. Take a portfolio approach focused on use cases in short, medium and long term, be lean, fail fast & learn

A portfolio-based approach to AI adoption cases, looking at use cases over a one- to five-year horizon, can be helpful.

In the immediate future, McKinsey suggest a focus on use cases where there are proven technology solutions today that can be adopted at scale, such as robotic process automation and some applications of machine learning. Further out, identify use cases where a technology is emerging but not yet proven at scale. Over the longer term, McKinsey’s view is to pick one or two high-impact but unproven use cases and partner with academia or other third parties to innovate, gaining a potential first-mover advantage in the future. Across all horizons, a “test and learn” approach can help validate the business case, conducting time-limited experiments to see what really works and then scaling up successes. Fast, agile approaches are important.

5. Don’t be a hammer in search of a nail … 

To ensure a focus on the most valuable use cases, AI initiatives should be assessed and co-led by both business and technical leaders. Given the significant advancements in AI technologies in recent years, there is a tendency to compartmentalize accountability for AI with functional leaders in IT, digital, or innovation. This can result in a “hammer in search of a nail” outcome, or technologies being rolled out without compelling use cases. The orientation should be the opposite: business led and value focused. This business-led approach follows successful adoption approaches in other digital waves such as mobile, social, and analytics.

McKinsey graphics on AI:

2016’s Most Popular 

My most-read blog posts of 2016 were: 

1. Why Anthony Hilton is wrong about DB pensions 

This post, responding to the misguided (in my view) viewpoints of London Evening Standard journalist Anthony Hilton in September garnered by far the most views of any of my blogs this year (around 1400 views). 

An extended version of this article was also featured in Professional Pensions, and also became their most viewed opinion piece of the year

The article must have stuck a chord with readers in the pensions world. We do of course live in pretty challenging times for DB pension funds, with several strong macro-economic headwinds making it harder to deliver the benefits that have been promised. This year saw a vigorous debate around what should, or should not be done to the DB pensions system. This debate was further catalysed by the high-profile cases of BHS and British steel, and the debate looks set to run on into 2017. 

Given the importance of the DB system to the retirement prospects of millions of members I believe a solid debate on some of these important issues is to be welcomed, and look forward to continuing the debate productively in 2017.

2. No Ordinary Collision – the Future of Asset Management

Powerful forces of change are at play in many industries, and asset management is certainly one of them. Technological and demographic shifts will shape the future of the asset management industry, in this piece (April 2016) I discussed some of  the intersecting forces, drawing on a wide body of existing research on the future of work and finance. 

Here are my six key takeaways: 



3. Consulting firms reply to the Work & Pensions Select committee 

In the wake of the BHS pensions story, the W&PSC issued a green paper calling for views on the future of the DB pensions system in the U.K. Given the prominence of this debate and the considerable air-time it’s received this year the responses from the main actuarial & investment consulting firms were considered and insightful. What was also interesting was the diversity of views. Will most schemes pay the benefits promised? Should the role of TPR change? Should there by wholesale change to the system? Will small changes be effective? Is consolidation feasible? These were all questions on which the consulting firms gave insightful, but often differing answers. 
I hope you’ve enjoyed my blog posts this year. I look forward to sharing more in 2017. Sign up to receive updates on new posts.

Grim Summer for DB Pensions

It’s not been a good summer for the financial health of UK DB pension funds.

Data released by the PPF in September underlined a summer of bad news for schemes and scheme sponsors.

A continued trend of falling gilt yields have seen liability values increase dramatically – adding more than £200bn to the aggregate deficit compared to the start of the year. The aggregate funding level (on the PPF basis) fell to 76% at the end of August, the lowest value in the 10-year history of the series. For context the funding level on this basis was close to 100% at the end of 2013.

Long-dated gilt yields have fallen from around 2.5% at the start of the year to around 1.2% by the end of August.

The result of the referendum vote certainly added impetus to the move lower in gilt yields, as “lower for longer” became a more likely scenario, and this was re-enforced by the BoE’s announcement of renewed bond purchases (quantitative easing) and a cut in interest rates to 0.25%.

While asset markets have generally performed positively – especially overseas equities in £ terms, these good results have been insufficient to keep pace with unhedged liabilities.

It seems likely that actuarial valuations as at 30 June or 30 September are likely to contain bad news for corporate sponsors, prompting tough conversations such as those happening at plastics manufacturer Carclo, and negative headlines such as those at Associated British Foods. These are likely to become more and more common as we move forward, as highlighted by LCP in their Accounting for Pensions report.

august-2016-a-decade-of-db-pensions

 

Two Days Among Actuaries

Just back from a couple of great  (sunny!) days in Edinburgh for the institute of actuaries conference which importantly this year incorporated pensions, investment and risk components together for the first time.

Quite a lot of interesting takeaways and observations from around the industry. As Marian Elliott said in the first session  “To the worm that lives in horseradish, the whole world is horseradish”. The point being it can be helpful to get a different perspective on things from the one you usually have.

Here are the big 3 things I took away:

 1. Integrated Risk Management (IRM). The pensions regulator continues to stress the importance of IRM in the latest funding statement here, and the IFoA working group has made progress thinking about the issues.

My  takeaway was that this remains a work in progress in that no-one has (yet) put forward the “perfect” framework for looking at these three components and bringing them together in an intuitive and practical way  (possibly, that doesn’t exist). What the working party has done is given considerable thought to a number of worked examples that act as  “corner cases” and challenge people to  think qualitatively about those different components of risk, what investment and funding strategies should result and  key metrics to track to best monitor.

With some of the current high profile cases in the news, the examples seemed very pertinent.  It’s clear that thinking in some of these cases has to come back to the member perspective, and what gets the best outcome for members benefits, and this inevitably includes thinking about the possible interaction with the PPF, something that clearly has to be handled carefully.

While this remains a work in progress I think the thinking that went into the worked examples, including legal opinion on certain points, is a welcome contribution to this important area. The suggested further reading (including  the Blake / Harrison paper  The Greatest Good for the Greatest Number looks interesting and I look for ward to reading further)

The session ended with a challenge to the audience – someone needs to take the initiative and bring these strands together. The working party proposed that actuaries are well placed, I would argue that the investment consultant is, too. The WP expect to communicate more toward the end of the year, I look forward to continuing the conversation.

2. British Steel

Not surprisingly this seemed to be one of the major talking points of the conference over many a coffee and beer. It’s putting the issue of pensions on the front pages of mainstream papers and into the general public discourse in a way that I haven’t really seen during the rest of my career (possibly you need to go back to the 1990’s and Maxwell to find a similar time). The panel discussion organised to address the consultation and the profession’s possible response was remarkable both for the number of people filling the room, and the strength of views expressed. This session was explicitly under “Chatham house rules” so I won’t share too many details, but fair to say there was a lot of questioning of the need to make this scheme a special case, and the danger of making up legislation and precedent “on the hoof”. A couple of interesting prior cases were mentioned including British Midland/Lufthansa which I need to read up on.

The discussion of the individual consultation options was interesting, albeit somewhat drowned out by the wider question of whether creating a special case is the right thing. What was clear is that there is an increasing acknowledgement that the ability (or not) of any scheme to move benefits from RPI to CPI may essentially be an accident of drafting and in effect is a lottery. I strongly suspect that whatever happens to British Steel we haven’t heard the last of the CPI/RPI debate which looks set to continue rumbling on.

3. Endgame & Self sufficiency management

Paul Sweeting of LGIM wrote a recent paper on a blueprint for self sufficiency management, and this got quite a few mentions. It’s a welcome contribution to the area and ties in with a lot of our thinking. The use of contractual cashflow, and alternative metrics for risk, with a nod to the practice and mindsets of the insurance industry makes a lot of sense.

the key findings of the paper are:

  • Focusing on the probability of paying all benefits is a helpful metric for end game schemes (more so than VaR or volatility);
  • For schemes that are well funded enough, corporate bonds are the best asset to hold to maximise this metric;
  • Allowing for defaults, downgrades and being dynamic with decision to reflect changes in spread level is important;
  • Conventional mark-to-market volatility becomes less of a consideration;
  • Where liability present values are needed, the corporate bond spread  can be embedded into the discount rate, to create matching of assets and liabilities.

While probably not that many schemes are in the end game yet, as we noted in this blog it is important to set out with this destination in mind, even if it is some way off. And as we know from survey data many schemes have self-sufficiency in mind as a goal. A useful first step is to make sure they have advisors and fund managers in place with a clear and sensible plan to construct and implement a strategy to deliver the scheme’s self-sufficiency goal.

Ri_End_Game_Infographic

Roboadvisor Europe 2016 – The Future?

The future of asset and wealth management?

A thoroughly excellent event was organised by Level39 in London on 25 May 2015, featuring speakers from all the key players and analysts in the fairly nascent European roboadvisor scene and around 250 delegates.

My five top takeaways are below, or you can read my storify story here.

1. Mind the 2016 inflection point

Rohit Krishnan of Mckinsey made this point, but it was echoed by others. The growth rates of the two longest stablished US roboadvisors (Betterment and Wealthfront) have stalled somewhat, possibly co-inciding with the robo launches of two large incumbents Vangard and Charles Schwab. With significantly lower AUM than is probably needed to justify costs and valuations, 2016 could be an inflection point, which way will things tip?

2. Customer acquisition cost is key

It’s a closely guarded secret when it comes to individal firms, but surveys and other data in the public domain suggest that customer acquistion costs can be in the region of $300 or higher, but lifetime value of an average client may only be $250. If that’s true, then it would seem to pose a challenge to the business model.

3. But Europe is a bit different

Several of the European robos made the point that the European market is a bit different to the US. With less competition on fees in the traditional advised space, European robos charge in the region of 40-70bps rather than 20bps in the US. This means the breakeven point in AUM might be somewhat lower, 0.5-1bn was suggested.

4. It’s all about the api

There was a fascinating panel covering the tech aspects. The main takeaway being that we have entered a new era of openness, and what’s important is opennes with regard to architecture & api , to enable other components “plug in” to create an ecosystem.

5. Scale & brand are hard

These two comments stood out to me from all the points made by the startups. Firstly, building the right scale to reach 1m+ customers is difficult (more difficult than we thought said Shaun Port of Nutmeg). Secondly, several panellists commented that building the wider brand and customer awareness was key, no-one had really done it yet, and many firms were in a race to try and do so.

that’s it! plenty more I could say (and check out the storify for more).