The instinctive biases investors must beware – Part 2: Cognitive errors
The many behavioural biases hardwired into the human brain that investors need to beware of split roughly into ‘emotional biases’ and ‘cognitive errors’. Here we run through the principal examples of the latter
Behavioural science – and its financial offshoot, behavioural economics – have over the years proved fertile ground for articles, here on The Value Perspective.
As such, we thought it would be useful to corral the key behavioural biases investors need to be alert against into one handy checklist – albeit one handy checklist divided neatly into two halves.
That is because investors’ inherent biases can be split roughly into two types – ‘cognitive errors’ and ‘emotional biases’.
We deal with emotional biases here but, in essence, cognitive errors are the inability to analyse information, which could for example be down to faulty reasoning, a lack of understanding around any statistics involved or not making use of all the available information.
Lying at the heart of cognitive errors is the fact that, while investors will be doing their best to make rational decisions, they lack the capacity or information to do so.
Cognitive errors – which are arguably the easier behavioural biases to address, and can often be corrected with improved information or schooling – can be further subdivided into ‘belief perseverance’ and ‘processing errors’:
* Conservatism bias: Investors use rational decision-making to form an initial opinion but then fail to change this view when new information comes to light. Symptoms: where new information is difficult to process , there could be too little turnover of stocks. Conversely, since stock changes are easy to make, investors may find themselves spending too much time overtrading as a way of procrastinating on more difficult decisions.
* Confirmation bias: Investors specifically seek out information that supports their current view on stocks and ignore everything else. Symptoms: under-diversified portfolios.
* Illusion of control: Investors start to believe they can control outcomes which, realistically, they simply cannot. Symptoms: too much portfolio turnover; not diversifying a portfolio properly.
* Hindsight bias: Investors display selective memory of the past – or of what it was possible to know at various points in the past. Symptoms: overestimating success in predicting outcomes in the past, or being very critical of others and their ability to do so. This bias is far easier to control if investors keep records of their forecasts, including the data and reasoning they used to reach decisions.
* Representativeness bias: Investors believe what has happened in the past will continue indefinitely into the future – and thus that new information can be classified based on their past experience or on classifications they have always used. Although using experiences to classify information can make an investment process more efficient, it can also lead to misclassifications if information superficially resembles properties investors associate with a certain past experience or classification to the extent they use ‘rules of thumb’ rather than analysing it deeply. Symptoms: using lots of ‘rules of thumb’ without properly analysing information; putting too much weight on new information that has not been properly analysed, which can lead to excessive turnover.
Representativeness bias can be further split into two errors. ‘Base rate neglect’ is where the original base case classification an investor uses is wrong, but they take it as 100% correct without reconsidering or changing it. ‘Sample-size neglect’ is where the initial classification is set using a sample set that is too small and/or unrealistic.
* Framing: Investors let the way a question or data is presented affect how they digest it. Symptoms: failing properly to assess the risk of an investment – for example, as a result of companies’ reports and accounts tending to be overly positive and present good information first. This can lead to a sub-optimal and overly risky – or, in some cases, overly risk-averse – portfolio, and to investors becoming overly concerned with short-term price movements that further lead to excessively high turnover.
* Availability bias: Investors give disproportionate weight to information or experiences that are easy to find or remember. A classic example of this behavioural sin – also known as ‘recency bias’ – is where a driver immediately slows down after being surprised by a speed camera only to accelerate a few miles further down the road. Symptoms: not seeking out information that is hard to find and settling instead for easy-to-recall or easy-to-locate information.
* Mental accounting: Investors treat investments differently, depending on how they categorise them. This can ignore correlation between different categorisations – or layers – often seeing an overemphasis on income-generating assets, which can lead to a sub-optimal portfolio with lower total return. Symptoms: structuring a portfolio into different layers with different goals. Often these layers are arbitrary categories.
* Anchoring and adjustment: Instead of revisiting and reformulating an investment decision when new information comes to light, investors’ decisions are influenced by the value they originally attached to something – for example, the price at which they bought a stock. Essentially they are basing or ‘anchoring’ their perceptions on the current environment rather than recognising that something entirely different could happen in the future. Additionally, they may start using heuristics-based trial-and-error ‘rules’. Symptoms: overweighting an original opinion on a stock and using new information as an independent adjustment of that opinion, rather than reforming a view of the stock as a whole. Often this leads to inadequate changes as a result of new information and, when combined with framing (see above), can lead to overly positive forecasts.
Research Analyst, Equity Value
I am an investment analyst for the Global Value Team, having joined Schroders in 2016 as part of the graduate programme. After spending a year as an investment analyst for the Quantitative Equity Products team, I realised my affinity for the deep value investment mindset and joined the Global Value Team in 2017. Prior to working for Schroders I studied mathematics at Oxford University.
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