Understanding behavioural biases in financial markets

Few would dispute that a grounding in economics is a vital weapon in any professional investor’s arsenal, but evidence increasingly suggests an understanding of psychology could be just as or even more important. Recent financial events reveal repeated patterns of irrationality, inconsistency and incompetence in human decision-making.

James Maltin, Investment Director, Rathbones

Rarely has the validity of behavioural finance been more evident than in today’s capital markets. For many years, business school academics espoused the Efficient Market Hypothesis, under which investors always act rationally and asset prices reflect all available information while following a ‘random walk’, meaning they react instantly to new 'news' and cannot be predicted. Latterly this has been challenged and it appears there might be more to economic decisions than rational reasoning.

Consider the yield on Swiss government bonds, which for the first time in history is less than zero, meaning people are paying the Swiss National Bank to give them back less than they deposit; or the shares of Twitter, the social media company, which prior to suffering a fall of more than 70%, changed hands for more than 100 times the company’s earnings. From such extraordinary phenomena, it is clear that we do not live in a precise, rational world. Psychology holds the answer. This article uses recent examples of financial events to reveal repeated patterns of irrationailty, inconsistency and bias in human decision-making.

Lies, damned lies, and statistics lead to much misunderstanding in financial markets. To make sense of the world, we seek precision using mathematics. This works much of the time but it can lead to excessive confidence, with devastating consequences. Mathematical models are just that: they help explain reality and provide examples of potential future realities. However they are not reality and to consider them as such can lead to catastrophe.

Long Term Capital Management (LTCM) was the pinnacle of mathematical modelling. Founded in 1994 by John Meriwether, a highly successful trader, LTCM was an investment fund managed by the best in the business. Six of its partners had PhDs from the Massachusetts Institute of Technology while two, Robert Merton and Myron Scholes, were professors of finance at Harvard and Stanford universities respectively. They got off to a flying start, generating returns after fees of 21% in the first year, 43% in the second, 41% in the third and 27% in 1997, the year Merton received the Nobel Memorial Prize in Economic Sciences for his earlier work on how to price derivatives.

As LTCM’s confidence grew, so did its bets. Forgetting that human behaviour is neither entirely consistent nor predictable, and therefore unreliably modelled, the partners at LTCM were flabbergasted when in 1998 the fund lost $4.6 billion in less than four months. The Federal Reserve had to intervene to avoid a collapse of the financial system, ultimately leading to the dissolution of the firm. There is no better example of the limitations of mathematical modelling.

Within a decade investors had forgotten these limitations. They started to rely again on the forecasts of financial models, this time to make big bets on the US housing market. The fundamental problem with mathematical models is the concept of a ‘normal’ environment. Under assumptions of normality embedded in modern portfolio theory, it was assumed that over the course of a career one would observe at most a single one-day market fall in excess of ‘four standard deviations’ or ‘four sigma’ — a statistical term which describes the degree of variation in a set of values. Statistically the frequency of a four sigma event was once every 63 years, a five sigma event once every 7,000 years, six sigma once every two million years, and a seven sigma event once every 1.5 billion years.

All well in theory. In 1998 it took four months to obliterate LTCM. In 2008, four months were all it took to rewrite the history of stock market volatility: from the end of August to the beginning of December, the stock market experienced 10 one-day market falls exceeding four standard deviations, including one six sigma and two seven sigma events. We now seem to experience ‘once-in-a-lifetime’ crises every three or four years. Black swans have been breeding.

We all fall into the trap of looking at something in a particular way. Then something happens and we view the same thing very differently, and may struggle to see it how we saw it before. This is the reason why financial markets function: if everyone thought alike there would be no transactions, for no one would sell the shares one wants or buy those one chooses to sell. Greed, fear, and confidence play their part, and this is where the inefficiencies lie. To quote Warren Buffett, “I’d be a bum on the street with a tin cup if the markets were always efficient.”

Generally we give far too much weight to previous experience and extrapolate recent trends that are at odds with long-run averages. We tend to become more optimistic when the market goes up and may grow despondent when it goes down. We often see order where it does not exist and attribute accidental success to personal skill. We are overconfident in our abilities. We all like to think we can beat the market just as most of us consider ourselves better than the average driver. Statistically this is simply not possible. People tend to remember their successes but ignore their failures, leading to unjustifiable increases in confidence. When buying a share you have to believe you are smarter than the person selling it and vice versa.

Cognitive illusions, like optical illusions, lead to false conclusions. We use representativeness to identify people and situations based on similarity to prior experiences when in reality they may be quite different. We have a misconception of randomness, instinctively seeking patterns in information where none may exist. We rely too heavily on the first piece of information when making decisions, which anchors our interpretation of future data. And we succumb to a herd mentality. Once the majority of market participants share the same view, and therefore sit on the same side of a trade, the price will often move in the opposite direction, simply due to the law of supply and demand: if there are no more buyers, what will drive the price higher?

Fear is a major psychological characteristic of the short-term trading herd and when it takes over, investors make the most irrational decisions. This is Prospect Theory in action: because we are more distressed by prospective losses than we are happy about equivalent gains, we take more risk to avoid losses than we do to realise profits. Such behavioural biases are the prime cause of speculative investment bubbles. Investors follow the crowd and conventional wisdom to avoid the possibility of regrettable decision, for it is much easier to buy a popular stock that falls in price than to be wrong and alone. Psychology moves markets and it is risky to be a contrarian. As Keynes wrote, “worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally”.

The job of the professional investor is to strike a balance between being nervous over losing money and being anxious about missing opportunities. While a degree in economics may be helpful, an understanding of investor psychology is essential. We need to know how to take the temperature of the market, which as Benjamin Graham explained in The Intelligent Investor (1949), is not a fundamental analyst but a barometer of investor sentiment.

Market participants may have limited insight into fundamentals and any intelligence that could be behind their buy and sell decisions is clouded by emotions. The rate of change in economic fundamentals does not equal the pace of change in market prices. This is why the market can move 5% in a single trading day. This is indicative not of scientific evaluation, but of selective perception and skewed interpretation. Sometimes investors take note of only positive news, ignoring the negative headlines, while at other times the opposite is true. Rarely are our collective perceptions and interpretations balanced and neutral.

The world today may seem more uncertain than ever. In such an environment, one must remain cognisant of emotions and their role in creating biases in financial markets, aware that notions of market efficiency — the idea that assets are correctly priced — are based on investor rationality and objectivity, traits rarely observed in real life.

This article first appeared in Rathbones Review Summer 2016

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