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New Year resolutions for investors
AS we start the New Year, I thought I would set out a list of New Year's resolutions for investors. They will highlight issues within the framework of behavioural finance and encourage good investing "habits".
The notion of anchoring in behavioural finance is illustrated by the idea that if, for example, a stock was US$100 and falls 50 per cent that somehow it is now cheap based upon that US$100 "benchmark", leading to so-called value traps.
At the macro level, an obvious example is oil. Because it was over US$100, but is now below US$40, the bias is to believe that it should be above US$50, perhaps even as high as US$70.
We might doubt the US$20 forecasts (not least because they come from the same people that gave us US$200 forecasts a few years ago), but an average between where we are now and where we are "anchored" makes no sense either.
Another example is China's GDP (gross domestic product), where anchoring suggests that anything under 6.5 per cent is a disaster because we are anchored on a period of double-digit growth.
This ignores the law of big numbers; 5 per cent growth today adds as much economic activity as 10 per cent did even five years ago.
Moreover, with 11 per cent retail sales, 6 per cent industrial output growth and only modest credit growth, in my view, any country would welcome the problems that China has.
Avoid confirmation and hindsight bias
Confirmation bias is the tendency to accept the data you want to receive and to ignore anything that contradicts this.
At the stock level, this usually means investing in exciting "stories" and concepts while ignoring the red flags - usually the lack of profitability.
This was one of the issues I discussed at the end of last year, the number of "unicorns " threatening a repeat of the dotcom era, where investors buy the dream, and in this case the venture capitals' (VC) declared valuations, while overlooking the fact that there are no profits.
At the macro level this can as easily work in reverse, focusing on the negative and ignoring the positive.
Here again, we have the example of China, where the manufacturing Purchasing Manager's Index (PMI) data is obsessed over as "bad" while the service-sector PMI data, which is good and is in fact the whole story we are trying to invest in, is ignored.
One additional resolution that should apply to macro commentators - and their editors - is to learn how diffusion indices work and understand that a reading below 50 is not a sign of shrinking, but a deceleration.
Chinese manufacturing is not shrinking, it is just not growing as fast as it used to.
Confirmation bias also appears to support a particular narrative, either bullish or bearish where the contrary view is ignored, until when it becomes the new consensus view and then everything reverses.
At the stock and sector level, this is important when it comes to some of the competitive threats we discussed at the end of last year.
The impact of the sharing economy for assets such as Uber, and Airbnb and peer-to-peer for services should not be ignored even if the "experts" say not to worry.
Remember that a lot of "experts" are operating in a very narrow group and often think with the existing corporates.
Electric vehicles (EVs) are a classic example of this; the auto industry was extremely dismissive of EVs even a year ago, now they are rushing to join in, while still protecting their assets by claiming it won't really happen.
Similarly, disruptive biotech aimed at curing people rather than simply providing drugs to maintain their condition is a huge threat to "big pharma".
It works both ways of course, new technology companies are equally biased in their own direction, but investors need to think like consumers, not producers.
Hindsight bias meanwhile is the tendency to believe, after the fact, that everything that happened was obvious. In fact, sometimes confirmation is the reason that we only recognise things in hindsight as we swing from being overly bullish to overly bearish and vice versa.
There is also the danger that the explanation is either over-simplified or indeed incorrect as investors then focus on the so-called warning signs defined ex-post.
A recent example of this was those claiming to have predicted the global financial crisis (GFC) based on their views of the US housing market, when in fact it were the overleveraged credit default swap (CDS) structures built on the back of the mortgage debt that were the real problem.
This led to an obsession with the US housing market for a while with little or no attention on the misaligned risk management that allowed highly leveraged products to be rated as low risk on account of their low volatility.
So much so that the response to the GFC has been to allow a remarkably similar set of leveraged products to appear in the place of the CDS market. Hindsight bias also extends to an exaggerated faith in econometric modelling, the idea that because A and B were correlated in the past, not only did A cause B but will do so again, even if there are multiple other variables involved (and indeed that maybe B caused A instead).
Looking back at 2015, the "obvious" events would have been China and emerging markets (EMs), based on valuations and economic fundamentals, yet both were in fact examples of market mechanics and capitulation - both actually the next two aspects of behavioural finance, herd behaviour and prospect theory.
Avoid herd behaviour
Herding is one of the biggest problems at the market and sector level and is, to a large part, a function of one of the issues discussed last year - the failure to properly measure investment risk and more importantly the incentives for professional investors to herd as a result.
Part of the problem in the dotcom era was the fact that fund managers were penalised for moving away from the herd and the bubble was in fact an indexation bubble.
Cisco was on an absurd valuation, but it was a large part of the benchmark so investors "reduced risk" by owning it. Indeed, they were advised to have five times as much in Cisco as in Berkshire Hathaway Inc.
Similarly a decade earlier, they had been told to reduce risk by putting over half of their investment into Japan, with the same net result.
The official measures of risk, particularly benchmark and volatility, continue to encourage dangerous herding into inappropriate investments, especially when other measures of risk, such as leverage, liquidity and credit risk, are taken properly into account.
Herding as represented by indexation means that markets overshoot due to forced buyers at the top and distressed sellers at the bottom.
Institutional investors may not be able to totally avoid this, but recognising when it is happening is important.
Prospect theory, don't take more risk to avoid loss than to achieve gain
One of the most intriguing and insightful studies conducted on behavioural finance was the one looking at the asymmetry of investment risk - where people would take a bigger risk to avoid a loss than to secure a gain.
The most recent example of this has been with emerging markets, where five years of under-performance finally led to a capitulation.
Investors had been reluctant to move first on selling out of EMs, even though the relative performance had been terrible. They were hoping somehow to reverse their losses, or more importantly not to be the ones that cut at the bottom.
However, when the losses became absolute and funds began to see redemptions, the desire to minimise losses kicked in and capitulation ensued.
Moreover, the (relatively disinterested) asset allocators then stepped in and divested further while the herd behaviour mechanistically led to further index selling as the prospect theory and herd behaviour work in reverse.
As with herding, the institutional pressures may make it hard to avoid, but this is a key behaviour to examine in trying to understand what markets are "doing" at any time.
Avoid over-reaction and availability bias
Such herding and prospect theory leading to capitulation also leads to over-shooting and over-reaction which forms the basis of a lot of value investment.
Studies have shown that "bad stocks" tend to outperform "good stocks" over time because the markets tend to over-react to the news in both directions.
This is a good reason to listen to contrarian views, though not a justification to always follow their advice.
Being aware of distressed selling is not the same as betting when it will end.
Meanwhile, overreaction can also be extended to over-trading on noise, which is a great way to lose money. Investors should recognise that in certain areas they are operating in markets dominated by "noise traders", who are highly leveraged and for whom small moves are highly significant.
In the real world, even the first decimal place on a currency cross rate is rarely significant and yet FX (foreign exchange) traders regard the second decimal place as "a big figure move".
It might be for them, but it isn't for you.
This can lead to a distorted view of the macro economy, a 3 per cent drop in the renminbi (RMB) is not a "massive devaluation" unless you are 20 times leveraged, a 20 per cent move in the Swiss franc-euro rate is much more significant.
Similarly, in my view, anybody other than a leveraged fixed-income trader spending much time on high frequency macro data such as the non-farm payrolls is misallocating their resources. Just because the data is widely available doesn't mean it is relevant or needs to be acted upon.
Be careful of overconfidence, of yourself and others
Several studies have shown that all investors consider themselves to be average or above (!) despite the obvious logical impossibility of that statement.
Such "overconfidence" is a factor in either being too quick to be a contrarian (nobody has seen what I have seen and they are all herding) or in dogmatic anchoring such that a value investment becomes a value trap (nobody but me understands how cheap this stock is). The best fund managers I know are remarkably humble people.
At a more general level, investors should question the overconfidence of others, particularly the so called "experts", which often leads to herding.
A question I often put to people is "when you know about something in detail and you recognise that the received wisdom is very often wrong, why do you automatically believe the received wisdom about everything else is correct?".
Recognise that every think tank and expert report has an agenda, most usually to generate more income for the writers.
This is not to be cynical, rather to be sceptical. I find investors are often extremely analytical about companies and sceptical about the motives of management to the point of being extremely contrarian and yet will accept at face value the statements made by politicians, macro economists and scientists on the basis that that they can't disagree with the consensus. It is always helpful to ask, cui bono (who benefits)?
Beware of the gambler's fallacy
The gambler's fallacy is the concept of the "hot hand", where a random event is assumed to have some predictive ability.
This tends to lead to momentum style investing - most evident in China A shares this year.
China A shares can of course turn extremely quickly and this is a reminder to Western investors who tend largely to have an intrinsic mean reversion bias that many investors in Asia, especially retail, tend to be much more focused on momentum. Even in the West, the landscape is changing however.
One recent technological development, high frequency trading (HFT), has made this far worse in my opinion as now the machines are programmed to operate as momentum traders, while the traditional market-making role of trading desks has collapsed under macro-prudential regulation, itself a classic example of misguided hindsight bias.
We thus have a greater bias to momentum and a smaller one to value. This will undoubtedly encourage over-reaction.
Another example is the way that bottom-up earnings momentum models have been adopted by macro traders.
In the former, there is serial correlation in earnings announcements, one upgrade tends to lead to another as management guide towards a level of earnings and thus an investor will pay more for a stock with positive earnings momentum.
In the case of the macro data however, it is far less clear. GDP statistics do not demonstrate serial correlation and yet they are often interpreted as doing so. Far more often they are mean reverting around an underlying trend. This is an example of availability bias, the data is there and is thus assumed to have insight.
Examples from 2015 and a look into 2016
Obviously many of the events of last year were a combination of several of these behaviours.
In Europe, the biggest example was probably the Greek bond market, which was clearly a problem (hindsight) early in the year and yet confirmation bias meant investors refused to see it. There was then some evidence of overconfidence on the basis of several hedge funds being contrarian in areas such as Greek banks.
Post the Greek election, the perception flipped from being no problem to being a huge problem, producing over-reaction as well as some prospect theory based reluctance to accept losses.
In Asia, the Chinese stock market was another example. Confirmation bias meant investors ignored the departure from fundamentals in Q1 and herding encouraged the final bubble in Q2 as speculators crowded in on the basis that MSCI benchmarks would force Western investors to buy the benchmark.
That didn't happen of course and there was then over-reaction in Q3, this time on the downside, encouraged by the noise markets who saw the opportunity for reverse confirmation bias (everything good was now ignored and everything bad emphasised) in order to trigger capitulation in EM currency and asset markets.
This in turn reflected prospect theory, which had kept EM investors in relative value losing trades for several years, but became absolute losses and too much to take hence capitulation.
In turn, this has probably produced over-reaction in these markets, but rather than demonstrate too much overconfidence and being contrarian, or anchoring a perception of value at the 2011 peak, it probably makes sense to move towards a neutral position.
Looking into 2016, I continue to believe that the negative confirmation bias on China will begin to unwind while the positive confirmation bias on the new dotcom unicorns will unravel as the equity markets refuse to bail out the VCs at the crazy valuations they have awarded themselves.
Markets will continue to over-react to high frequency data, not least until the HFT problem is solved, but the lesson will continue to be to fade the hype.
Avoiding the poor risk management embedded in a lot of herding will be prudent, smart beta means avoiding large-cap bias in equity and the inherent imprudence of putting most money into the biggest borrowers in credit markets for example.
At the stock sector and market level, focusing on credit risk, liquidity risk and leverage are far more important than benchmark or volatility.
It is probably sensible to continue to resist anchoring in EMs and the commodity complex when trying to assess "fair value" and recognise that the former is prone to a continued unwind of previous herding and crowded trades while the latter has much more to do with supply than demand.
Finally, the US election and the prospect of Brexit are two of the more widely discussed risk factors out there along with instability in the Middle East, all of which tend to favour the US dollar (but this would involve getting into forecasting again!).
Investors should be careful however, not to confuse their own politics with markets, which are agnostic. Equally, they should not confuse the emotions of commentators, who are often in a small political bubble, with what is good or bad for the economy.
- The author is head of AXA Framlington Equities Asia
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