Behavioural Finance Jan 2013

“Behavioural Finance & Investment”

 MSc in Investment Management, Heriot-Watt University

22 January 2013

Presentation by Professor Colin McLean

I am going to focus on some practical ways in which behavioural finance can be applied day-to-day, and draw some conclusions about what we can do about psychology.  I am not going to tell you to try to eliminate emotion – I do not think that can be done.  Indeed, some is helpful to speed decision-making.  But, I think we can attempt to constrain it by techniques and rules.  My talk will look first of all at some of the areas where emotion is clearest – bubbles and crashes – and then at particular problems that apply to experts, confidence and hindsight.  I think that research does point to ways in which we can improve search processes and decision making.

Behavioural finance offers greater insight into bubbles and crashes, than conventional analysis or CAPM.  The Efficient Markets Hypothesis certainly suggests that bubbles should not happen.  However, they have been frequent in history, and were well documented by Charles Mackay in 1841 in Extraordinary Popular Delusions and the Madness of Crowds; this is still in print.  He describes bubbles and crashes as panics of buying and selling.  Asset prices appear decoupled from economic fundamentals.South Sea Company

Certainly, we can see what happened in some of the outstanding historic bubbles; South Sea Company and Dutch tulip bulbs.  The South Sea bubble of 1720 was effectively a leveraged buy-out of the national debt of Britain, a parallel with the Mississippi bubble of the same era which attempted to privatise French government debt.  We can even see documented here some of the elements we will see in more recent bubbles.  The South Sea experience is illuminating.

Isaac NewtonFirst, as Newton is recognised as being one of the smartest people alive at the time, it shows that it is not simple intelligence or deep thought that will stop us being drawn into bubbles.  Secondly, we can recognise quite a lot of the behaviour patterns that are common today, almost 300 years later.  The earlier tulip episode also reminds us that investors were not trying to be irrational.  The value of a tulip bulb was meant to represent an option on cornering the market in all future bulbs of that type, and the large sums involved represented the net present value of many generations of bulb progeny.  We can see some of the same arguments that emerged again when internet territory was being staked out in the dot.com bubble or even possibly in Apple.  I do not think it is helpful to think of these periods as collective irrationality.

Anatomy of a bubble We can see that bubbles can also take many years to build up, even although unwinding is typically faster.  This is shown with the Wall Street crash of 1929.

Dow Jones

Nikkei

The bubble in Japanese stocks in more recent decades has shown much the same pattern.  We can also see some other features.  There is often a lot of volatility before the collapse, and the price gains from trough to peak can be ten-fold to twenty five-fold.  Bubbles usually take at least 20 years to recover, and often 30 years, as with silver and gold from their 1981 peaks.  The Nikkei is still below is 1989 level of 40,000 even after 23 years.  The tulip and South Sea bubbles never recovered.  In almost every case the bubbles retraced their entire price move as they fell.

 I think an objective definition today of a bubble would be a period when a price index for an asset class trades more than two standard deviations outside its historic trend.  This is one that has been used by Jeremy Grantham of GMO and seems to work well, not just for equities, but applies to commodities, residential houses, real estate, tulips and many other things.  If returns are normally distributed, these extremes should happen only 5% of the time.  For some stockmarkets and certain asset classes these extremes of valuation represent more than 10%.

US house pricesUK House Prices

Bubbles and Crashes

In bubbles and crashes, prices differ markedly from trend value, and also from intrinsic value if that can be measured.  Intrinsic value may not always be clear, as a bubble sometimes involves different valuation metrics – such as the optionality on dot.com stocks – or for that matter, tulips – on all future growth from that franchise.  That is, investors may attempt a rational discounting of future returns, but simply not reflect the risks involved, such as fading of margins as competition is attracted.  Investors may be attempting to be rational. I think the trend value differences, shown as standard deviations, are more relevant than intrinsic value which will be debated at the time.  In these anomalies, price changes are typically the main component of returns.

Crashes are typically more rapid than bubbles, which develop more slowly.  Different behavioural factors are involved in these.  A crash might involve a fall of 30% or more in asset prices over several months.  It is important to recognise that not all of this may be irrational.  Some bubbles and crashes reflect unanticipated rapid changes in economic prospects.  Think of the global oil price shock of the 1970s and the Japanese asset price bubble of the late 1980s.   Investors might view their trading as a rational response to changes in the environment, such as a liquidity squeeze or easing of monetary conditions.

Behavioural finance does not yet provide a full explanation for such market behaviour, but a number of specific cognitive biases and emotional biases prevalent during such periods can be identified.  Rational investors may expect a future crash but not know its exact timing. There may not be effective arbitrage.  Investment managers incentivised on, or accountable for, short term performance may even rationalise their participation in the bubble in terms of commercial or career risk.  We heard more of that in 2008.

The extent to which investors may rationalise their behaviour during bubbles is evident in the way some managers describe their actions.  For example, in the technology bubble of 1998-2000, one hedge fund manager stated he recognised that technology stocks were overvalued in December 1999, but thought there was one more leg to the market.  He did not contemplate the NASDAQ Index dropping by one-third in 15 days.  That was an example of staying in too long, and possibly not recognising the risk of a move to the exit becoming a stampede.  Another manager at the same time, stood back from the market completely, given the perceived over-valuation.  Missing out on this boom meant his fund folded in 2000, with his returns having failed to keep up with technology stocks, and his investors disappointed.

This pattern has been repeated in many bubbles and crashes.  Misunderstanding risks can show the illusion of control bias.  In the case of the first fund, the manager was wrong to believe he could sell near the top of a bubble.  The other manager appeared to have underestimated the potential scale of the bubble and how clients would react to that.  Again, participants may believe they are acting rationally at the time.

NokkiaI will briefly run through some other graphs that will help us to recognise how difficult it is to spot irrationality at the time.  There was a time when Nokia dominated the cellphone market and it was thought that part of the locking-in of customers was their familiarity with Nokia’s technology and layout.  Now we know what actually happened.  Much the same happened with RIM’s Blackberry.  And today, there are strong arguments that Apple is a value stock, an income stock and a growth one.  Yet, history tells us that such high margins do not persist and apparent moats like Apple’s are not unassailable.

AppleRIMAre there still bubbles

Are there still bubbles today, or unjustifiably low prices caused by a crash?  Prem Watsa is a recognised long term value investor at Fairfax Financial Holdings, a Canadian financial services holding company that includes fund management and insurance.  He profited from the crisis of 1987 and 2008.  Asked about current bubbles he cites some soft commodities, precious metals and base metals, as well as Chinese residential property.  He doesn’t just rely on the parabolic price performance, but on what appears to be unusual behaviour in not hedging and locking-in a super-normal profit.

Commodities Gold Monetary easing has made it easy to borrow money to finance speculation in commodities.  And Exchange Traded Funds have greatly increased demand and supply.  He highlights the psychological difficulty of exiting a bubble into still-rising prices.  The early trades look wrong, and until the bubble is unwound there is no perspective on this.

 The picture I am painting of bubbles is that many participants are trying to be rational, but over-estimate their control.  They may be sticking to what they have always done in terms of rational analysis, but data is increasingly flawed, such as the wrong discount rates.  Risks can be under-estimated.  Information coming in from brokers and companies may all be coloured by excess optimism in the case of bubbles and the crowd will include some smart money that is already swimming against the tide.  Other incentives may cause conflicts.  And, as we have seen, some of the smart money was Key pointsnot the people who were best placed to get signals, but ordinary savers and investors who do not understand the business models of some building societies and banks, or how certain products could ever be profitable. Certainly, looking at the extensive feedback on sites like the BBC blogs of Robert Peston, showed that some of the public as well as some of the investment banks had got wind of the problems ahead.

 At the peak of a bubble, there may not necessarily be a crescendo of trading volumes – this may come later.  But, there is typically more price volatility, and rebounds in prices are slower.  Investors buying into the bubble may be looking more at confirming signals.  They often have faulty learning models, leading to over-trading.  Trading appears to generate gains, but they are a feature of the rising market. Contradictory information may be rejected, and instead the confirmation bias means tending to focus on what is supportive.  Individual behaviour can also be impacted by self-attribution bias, and ultimately many investors behave like the first hedge fund manager with an illusion of control.  There may be scepticism about market levels or the valuation of a specific stock, but everyone believes they can get out first.  The speed of a decline, and the lack of liquidity during it, is often not foreseen.  The market can gap down.

 To look at how signals are treated and crowds behave, I would like to turn to some different examples.  I think it is important that crowds are not dismissed as dumb, or involving some mutually agreed collective stupidity.  Some of the features of bubbles arise through individual failings rather than needing a social explanation.

For example, as trading becomes more entertaining whilst profits are being made, individuals may pay more attention to confirming signals.  Also, in a general uptrend on stocks, an individual trader may associate his or her increased trading with making profits rather than recognising that simply passively holding the stocks might have done as well or better.  This is a faulty learning mechanism, and encourages someone to trade more and ascribe it to their own intelligence or edge.

Understanding crowd behaviour is an important aspect of behavioural finance.  And, light has been shed on this from a surprising source.  Studies of social animals such as honey bees, fish shoals and ants highlight the conditions needed for smart crowds, rather than a herd mentality.  In effective crowds, individuals collect information independently, but are quick to pick up when another has better signals.

 FishWe can think of the little fish on the left as being the smart investment bank that has been first to spot the danger in asset backed securities, leverage and credit ratings, whilst others like RBS, were still moving into risk.  As a bubble matures, the charts, some astute commentators, and some social media may point to the fact that some have changed direction.  In 2007 and 2008, the sell-off started well before the panic; there were signals in terms of rotation between asset classes and also volumes. Defensive stocks were at relative lows in mid 2007, as they were at the peak of the dot.com bubble.  There can be a misunderstanding that there needs to be a crescendo of volume at a market peak, but that may not be the best signal. Where there is consensus, as in a momentum driven market, there may be little need for trade – simply no difference of opinion.

 Following the crowd can be rational behaviour, particularly when new threats are emersmart crowdsging. Research has shown that investors do tend to place more importance on crowd wisdom than their own private judgement when the environment is moving fast.  This mirrors behaviour in the animal kingdom. Swarms fleeing danger should follow the crowd 60% of the time, but spend the other 40% searching out their own escape routes.  It shows that we should pay some attention to what others are doing, even if we think we are smarter.  For any individual, or even organisation, there is a chance that the usual information sources are not picking up the key signals.  In a turbulent environment, we should give greater weight to signals from others.

 We should not be so quick to dismiss “herd mentality” in a sell-off.  New information might be driving the adjustment to share prices.  And it would be wrong to think of this as “inside” information.

  

Submerged public information – crowd sourcing and technical analysis

 Interesting research is being done in the area of crowd wisdom and crowd sourcing.  This is the potential for the wider public to capture and relay information which is otherwise submerged.  It is useful to think about the wisdom that might lie within a crowd, and its power or ability to relay that.  That is, gather information by assuming that the facts are somewhere public, and then wondering how that public expression might be captured.

 I think there are some indicators of bubbles and crashes, although these warning signs may be subtle.  First, how could the credit bubble be missed?  2007 and 2008 showed just how policymakers, central banks and bank chief executives – despite their unique access to information – completely missed the big picture.   The big picture was clearly the credit bubble and the many signs that risks in banks and some other financial institutions had moved off the scale.  Yet, even although the first clear signs of trouble in AAA bonds came in January 2007, few of these leaders interpreted the information in front of them correctly.

 I think the “canary in the mine” was in the credit market.  15 years’ ago, there were very few genuine AAA credit ratings around. ABX Indices The rating was reserved for top quality securities like the US Government or General Electric.  For those, there was absolutely no question of default, and the indices always traded very close to par.  But, as AAA ratings were created structurally, by slicing and dicing pooled credits, there were thousands with the top rating.

 I will summarise some of the biases involved.  In bubbles, investors can be overconfident, which involves under-estimation of risks, over-trading, insufficient diversification and rejecting contradictory information.  Volatility can also increase in a market where traders are confident, and may happen at the same time as some individuals lose real conviction on underlying value.  Key biases are confirmation bias and self-attribution bias.  Selling for a gain appears to validate an original decision to buy, and may confer pride in locking-in a profit.  This can generate overconfidence.  Investors who feel they are missing out on profit opportunities during a bubble, may be encouraged to participate through regret aversion.  This can also eventually impact investors who believe they have sold too early.  Faulty learning models, matching profits and losses to the wrong decisions and activities are also encouraged by the plethora of signals and market noise amidst this volatility, and also relate to hindsight bias.

 Herding describes the tendency of a group of investors to trade in the same way – something that has been evident for example over the past year in buying defensives.  It reflects a low dispersion of opinion amongst investors about the interpretation of information.  This may be a response to cognitive dissonance insofar as it may give reassurance and comfort to investors to align themselves with the consensus opinion.

 There may also be the illusion of knowledge where excess of information adds to noise and unnecessary trading.  Investors would be better off not trading on all the news, discarding irrelevant noise.  And remember that search processes can be flawed, with investors ultimately focusing primarily on identifying additional confirmatory information rather than looking for that which might disconfirm.  As Odean & Barber suggest, investors can have a bias to buy stocks that attract their attention; the emotional effects of entertainment and pride can be involved as the market is rising.

 Bubbles may not unwind dramatically initially, with anchoring on prior prices causing an under-reaction.  Investors may be slow to update their beliefs sufficiently with this anchoring.  A later capitulation and over-reaction can be emotionally driven.  But, emotion is just part of what is involved.

 In summary, there are some clear misunderstandings of bubbles and crashes.  They are a mixture of cognitive errors and emotional biases, both individual and social.  But fewer are social than many think, just as an attempt to be rational drives much of what goes on.  I do think that behavioural finance offers better explanations than the Efficient Market Hypothesis.

 The overconfidence of experts, and our readiness to believe spurious detail, is evident in the illusion of precision.

Illusion of precisionPrecision Cartoon

We can also see the signs of excessive complexity in the European bank stress tests in July 2010.  Dexia required a bail-out within just three months of the calculations, yet was assessed by this complicated mechanism as being Stress testsstronger than banks like HSBC, even taking into account risks right through to the end of 2012.  The almost 7% short interest in Dexia – retained by investors even after short selling restrictions were brought in – might have been a simpler and better predictor of problems.

 And, in our own industry it is interesting to see how Pablo Fernandez’s (IESE Business School) research has highlighted how many views there can be on something as simple as beta.

betas

As we can see the Chairman of the Fed is not given to any lack of confidence, and can even precisely calibrate this.

Precisely confident

Fukishima

More recently, I noted an expert view on the Japanese nuclear accident, from our chief scientist.  The transcript also shows how easily experts can go beyond what information is known at the time.

 Investors can try too hard to ignore the crowd.  The recency of the previous year’s experience could be influencing strategists’ expectations.  Humans latch on to patterns, and this sort of repeat is the easiest to forecast. Some strategists have even said they were not changing as a matter of policy, lest it be seen as a “reactive mindset”.  But, there are times when it is right to respond to what others are doing.  Indeed, in turbulent conditions, where the environment is changing rapidly, a crowd may get to the underlying truth first.

Why are expertsExperts

Signals of new trends can come from surprising areas.  It could be easy for an individual, or even a single investment organisation, to miss these sources.  The challenge is to balance what one knows or believes against the possibility that the market might actually be capturing useful new information. Not all rapid market falls represent information cascades, where the wrong signals are amplified.  Investors need to recognise that their trusted information sources can, at times, simply be behind the curve.Analysts

Optimistic, but largely meaningless, language does lead analysts astray, as evidenced by the next slide.  This shows that analysts over the past 25 years, using US data, have been perpetually over optimistic.  Each year, growth estimates have typically ranged from 10-12%, but with the actual earnings outcome being growth of just 6%.  Only twice, during the earnings recovery following a recession, have forecasts underestimated actual earnings growth.

Nor is this specious language confined to analysts researching companies.  The next slide shows some of the phrases that appear regularly on managers’ reports to clients and trustees.  These are all phrases I have seen in recent reports.  The emotional reassurance of hearing words like “quality” makes it clientshard for us to move on to the next stage of rational analysis in trying to ascertain if there is any meaning in the statement.  Generally these phrases are involved in managers explaining disappointing performance, which does sound much better if it is being achieved with conviction and a selective approach to quality stocks.

Essentially, this expectation that outperformance can be achieved with better quality companies involves a behavioural error.  Representativeness means we tend to believe that good companies must be good investments.  But, expecting higher returns from better companies inherently involves believing risk and return are negatively correlated.  This sort of communication to clients is not rational or helpful.

There are other indications of this optimism bias.  A report by the Wall Street Journal MarketWatch, based on S&P equity research looked at the 1485 stocks that make up the S&P 1500 and found none had a sell rating.  Just five were ranked as a weak hold. By contrast, 73% were buy or buy/hold.  Of 19,868 Wall Street research reports they reviewed, there were just 0.1% at sells and 4.2% at weak hold.

Search Processes

Search processes have generally been overlooked, even despite the big role they can play.  Hasearch processesving a structured search process and a clear way of incorporating evidence sequentially, either as decision rules or in a Bayesian way, can encourage much faster adaptation of forecasts.  As experts, we should recognise that complex reasoning can be bettered by a series of simpler rules or probabilities, even though we may be uncomfortable with the idea.  The danger is that without structure in search, we can simply collect more confirmatory evidence whilst overlooking anything that is contradictory.

An interesting research study that emerged in 2010 also points to difficulty in repetitive tasks.  This study by Danziger, examined eight judges in Israel reviewing each day many parole decisions; over 10 months, a total of 1112 hearings. As we can see, the proportion of favourable decisions, granting parole, fell off sharply after each break.  For some, this move was very marked – one, in particular, moving from most lenient to the toughest, as quickly as their blood sugar ran down.  The judges had an average of 22 years of experience.   We suffer from choice overload, and when our mental resources are drained, we start opting for the easiest choice. The smarter the individual, the greater the risk.

repetitive taskWhat might be done about some of the behavioural issues?  First, in teams, I think it is important that there is a strong process and focus on evidence.  The challenge is the social interactions between team members and the hierarchy of the group.  It is tempting not to emphasise disconfirming evidence, and indeed not to objectively record a decision or expectation and the assumptions that underlie it.

addressing teamsThese dangers can be greater with individuals.  As I mentioned earlier, w hen confronted with disconfirming evidence, our conviction tends to be reinforced.  In the battle between emotion and evidence that is cognitive dissonance, emotion often wins.  However, I think there is a good solution for managers and traders – recording decisions and activity in a daily journal.  This allows a re-appraisal, and being in black & white, is much more convincing.  From this, individuals should aim to build their own checklists for investing, that essentially comprise previous errors and what has been learned from that.  I find rules helpful.  They will vary from individual to individual.  For example, when faced with a decision that must be made, but with uncertainty over timing or process, I often close one-third or one-half of the position.  This immediately reduces emotion, but also starts the process of recognising that confidence should be reduced.  In private equity, some would exit a position completely.  It can often allow for a much better re-entry level.

There are a number of ways of dealing with analysts’ biases in forecasting.addressing forecasting

I think that making clear forecasts that can be subsequently appraised is important, and maintaining a record of analyses and decisions helps this a great deal.  I also believe that underlying base rates – the prevalence in a population – need to be recognised.  The base rate might be GDP growth, or even recognising that in the long term most companies fail, and that discounting growth far into the future has great risks.

We have seen from the initial slides just how complex strategies can fail.  One issue is that base rates – the underlying prevalence – are often stacked against investment professionals.  Using Bayes, the starting assumptions or background prevalence matter less given a sequence of useful evidence.  However, in investment we often have a much clearer idea of the base rate, but tend not to have such discriminating evidence.  For example, if stocks within a sector have high correlation then an analytical effort that is typically 60% right, may not be the basis for a useful discrimination between the two.  In situations where the base rates are strong, I believe we are better to build a strategy around that base rate, or to make sure we use a series of simple rules.   For example, we might set a rule restricting the frequency of same sector switches.

What can be done about all of this in investing institutions?  Behavioural finance asks individuals to recognise and deal with cognitive illusions, but are there ways in which organisations can be structured, or their decision making controlled, to counteract some of the adverse impact?  Based on emerging research in this area, I will attempt some suggestions.

We do tend to ignore information that is of no apparent causal significance, and underweight it.  Sampling in an unbiased way can be more helpful.  For example, Google Reader and Google Alerts can assist unbiased sampling.

One of the biggest issues is overconfidence, clearly a bigger issue for experts who may not be helped by additional information.  One solution is to put portfolios into a structure, much as is done with focus funds, where managers are discouraged from making much bigger bets on some stocks based on perceived confidence.

Most investment consultants have views on how investment committees should work, and there is much received wisdom on the value of diversity, leadership etc. Some of the conclusions from behavioural finance focus on other issues.  In particular it is important to draw out information that might be retained privately, possibly soliciting it in advance.  Identifying biases in advance is also important.  Warren Buffett has recommended that all takeovers involving issue of paper should have an adviser against the deal.  Committees should have ways of ensuring challenge.  Often challenges can be recognised, but insufficient information gathered, to give contra views a real chance.  It is also important to critically appraise past decisions, looking for biases or other patterns of error.  Pre-mortems are also a useful tool, allowing criticism to be offered without directly undermining the decision or social context.  It is giving explicit permission to criticise and demanding that all do equally.  And, reframing questions can be simple but powerful.

Charlie Munger and Mohnish Pabrai, extremely successful investors, believe that checklists are essential – largely consisting of a list of previous mistakes.  I think that documentation of decisions once they are made is important for investment professionals; the pain of regret is a powerful learning tool, and mistakes need to be evidenced and understood.  International chess grandmaster, Yassir Seirawan says that when he started out he made many mistakes, but then always repeated the errors no matter how painful, so he was in no doubt about what went wrong and could recognise the position.  He viewed maintaining a written record of every game as essential.  Yet professionals and committees often will not admit error, even to themselves.  Records of trading decisions are a key tool.

I do not think that investor psychology is just a limited aspect of investment, relevant only when things go wrong.  Instead, it is deeply interwoven into the fabric of what we do each day, and how we react to the information we get.  I think that understanding the findings from behavioural finance, and attempting to integrate them into our daily work, can make us all better investors.

Investment managers should use journals, documenting their key decisions at the time they are made.  There are good reasons for doing this, rooted in the findings of behavioural finance.  We need to learn as observers of our own performance. And, I believe it can make all of us better investors.

There are a number of other psychological failings also at work.  Behavioural finance is often accused of lacking conclusion or application.  Identifying errors and biases is all very well, but what specifically can be done about it? This is one area where psychology and research has delivered a workable solution.   I think that working with a contemporaneous record is a simple practical way of addressing a number of the problems, and making better decisions as a result.

memoryOur memory is designed for learning, to help us in the future, but not necessarily with the right understanding of complex events.  “Memory is the mother of all wisdom” (Aeschylus), but it fails us.  Put simply, “hindsight does not equal foresight” (Fischoff). Salient information and more emotionally significant events distort our memory, often blinding us to other factors.  And, we tend to edit our memories to favour our own particular personal narrative.  This is not always obvious to us, though it may be to others.

But, the key issue is the tendency to reframe the past with the knowledge we have today.  In investment, this typically means we know whether a decision ended up with a good or bad result.  This knowledge of the outcome can strip bare all the nuances and factors involved, till all we see is a stark binary choice. We have a tendency to view events as being more predictable than they really are – hindsight bias, often referred to as “I knew it all along”.  Oversimplifying causality – seeing history as being more determinant and the outcome as inevitable – is a coping mechanism, helping us to come to terms with adverse outcomes.

As Kahneman puts it; “a general limitation of the human mind is its imperfect ability to reconstruct past states of knowledge, or beliefs that have changed”.  Once you adopt a new view of the world, you immediately lose much of your ability to recall what you used to believe before your mind changed.   And, we tend to attribute our mistakes to circumstance rather than bad calculation.  In contrast, the mistakes we make in judging others, tend to be the opposite, where we place too much emphasis on their personal characteristics rather than on context and the environment; fundamental attribution error. In essence, we judge others by their actions, but we wish that we instead are judged by our intentions.

And, self-serving bias means that we tend to want to claim personal credit for success but blame failure on external factors.  Preserving self-esteem and our personal narrative gets in the way of any new contradictory evidence.  So, for the same reason that we do not usually let pupils mark their own homework, we are not the best judge of our own decisions, even in retrospect.

reviewingOf course, we could ask others to review this, and it may be in team work this sort of cross-appraisal goes on.  But we often do not trust it and therefore do not learn the right lessons.  Many investment managers have sophisticated performance and attribution analysis systems, putting on their screen detailed analysis of their trading history.  But, this too can be reshaped to suit our perspective.  There are so many numbers, that it is easy to focus on the ones, or the specific timescale, that looks best.  And, these systems typically capture numbers rather than context.  Typically we want to blame the context for bad decisions, but do not actually document it.

In other areas of work, such as experimental science, the concept of contemporaneous records is well established.  As a profession we aspire to being scientific, yet typically do not take the same rigorous approach.  Certainly, many will write up an investment recommendation or meeting and some will make occasional jottings of useful thoughts.  But, I do think it is important that a record can be organised, allowing search, and refined.  The key is that it must be a trusted tool that can be used alongside the other information sources for your work.

The perspective put on tools and cognition by philosophy lecturer and author, Professor Andy Clark of Edinburgh University, is a useful one.  In his paper with David Chalmers, The Extended Mind, he argues that humans integrate their tools into their decision-making.  If someone asks us if we know a friend’s phone number, we might answer yes before getting out our phone contact list to see exactly what it is.  In terms of what goes into a decision, the boundaries between what lies in our existing memory and what is readily available in tools we trust, is blurred.  Nearby objects in the environment can function as part of the mind. The key is to maintain a record that we do trust as a guide to what has happened in the past.  That will make it more likely we will integrate it into what we do in future.

what to documentAs many of us will already have analysis systems, I think the key additional contribution that a notebook can make is in documenting other factors not usually captured.  Without documentation, it is hard to recall accurately the influence of the circumstances at the time and context of a decision.  We need also to record at the time the outcome we expected. What was the exit strategy? Traditionally, analysing habits involves looking at time, place, emotional state, presence of others and preceding action/catalysts.  Some of this may be relevant.  For example, I have found decisions taken outside the office are generally poor and are usually reactive with a negative catalyst.  In terms of time of decisions, some may conclude that a rush to make decisions before market opening or in reaction to early trading, might be poor.

For investment decisions, I think it is good to capture some of the contrary information at the time and an impression of where the decision stands against the consensus.  Whether a manager feels under pressure at the time, or what the background of overall performance is might also be worth noting.

And, I think one of the key factors to capture is what were the alternatives at the time?  Even that a decision might have been delayed to capture further information is worth noting and can allow a pattern to be later identified.  We are more willing to accept our decisions might be mistimed rather than plain wrong.

Through this, we should be able to identify the patterns that are supportive of good decision-making against poor.  We should not, however, always allow the fact that history gives us the luxury of perfect knowledge of the result, to always give a binary verdict of right or wrong on past decisions. This is a failure of many accident enquiries, which tend to focus on a simplistic category of human error rather than trying to understand why it made sense at the time for people to do what they did.  It is important we do not over-simplify past choices, but understand the context.

What might come out of record keeping?  Well, I think we can derive our own personal rules and checklists.  We are inclined to regret, but it can reinforce learning and a notebook is the way to learn the right things, particularly when emotion is part of judgement.

I do not believe that emotion need always mean poor decisions.  Emotion may capture subtle understandings of past events and decisions and therefore be an inevitable part of our decision-making, sometimes helpful.  We just need a good tool to appraise this mix of emotion and progression in our past decision-making.

The key constituents of hindsight bias are the impression of the inevitability of outcomes, exacerbated if we can pin things on a salient cause.  And, the less surprised we are by the outcome, the more we view the result as foreseeable.  And, our memory is distorted as to timing and the actual events themselves.

Helpfully, research points to the value of spending more time in studying the record.  Recall typically improves as we gather facts, and the notebook can present those.  The process of making and reviewing the record addresses each of these issues in hindsight.  And, for those who can continue to make the effort to learn, there is an additional benefit.  The process of gaining new knowledge tends to make us revise down our belief in our past wisdom.  So there are practical conclusions from behavioural finance.

PLEASE NOTE:

Colin McLean may have an investment in any of the companies mentioned in this presentation.

This presentation is for informational purposes only, and to the extent that it is passed on, care must be taken to ensure that it is in a form which accurately presents the information presented here.  The information and author’s opinions presented in this presentation have been obtained from sources believed by the author to be reliable, however, the author makes no representation as to their accuracy or completeness and accept no liability for loss arising from the use of the material.

Past performance is not necessarily a guide to future performance.  Stockmarkets and currency movements may cause the value of an investment and the income from it to fall as well as rise and investors may not get back the amount originally invested.

One Response to “Behavioural Finance Jan 2013”

  1. Hi, thanks for the feedback and your kind comments. It’s great to hear that my writing is appreciated. Thank you for reading the blog and for taking the time to comment.regards, Colin

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