The father of value investing and renowned economist, professor and investor Benjamin Graham once famously stated, “The investor’s chief problem – and even his worst enemy – is likely to be himself.” Behavioural finance argues that a wide range of psychological biases exist and play a pivotal role in undermining an investor’s ability to make well-informed, accurate and unbiased investment decisions.
Market analysts are often subject to two psychological biases when performing comprehensive financial market analysis: data-mining bias and time-period bias. Data-mining bias is the cognitive act of consistently searching within a dataset to identify a statistically significant association, relationship, or pattern. Many market analysts fall victim to data-mining bias and attempt to find a meaningful relationship among variables when, in fact, none actually exists. The lack of an explicit and concrete economic or financial justification is often a red flag when mitigating against data-mining bias and ensuring only the most prevalent and significant relationships are investigated. The phrase “correlation does not imply causation” is often used when explaining data-mining bias. Suppose a financial market analyst is investigating whether any significant relationship exists between returns to the S&P 500 Index and returns to the S&P 500 Value Index. Even though returns to the two indices may be somewhat correlated, this does not mean that one index explains the returns to the other index. Analysts often need to correct correlations when identifying a significant relationship between variables. When a variable X is identified as being significantly correlated with another variable Y, there are several possible reasons for the correlation: (1) Variable X predicts variable Y, (2) Variable Y predicts variable X, (3) an independent, third variable Z, predicts both variable A and variable B, or (4) the correlation is random and spurious. Just because the returns of two assets showcase some correlation, this does not mean a significant relationship exists between the assets. To mitigate against this form of data-mining bias, financial analysts should be cautious when investigating correlation relationships, only including those with vital statistical significance. On the other hand, it is also possible for two variables to exhibit a weak linear correlation but be strongly associated with one another. Analysts should be mindful that the lack of a strong correlation does not mean that no relationship exists – the relationship may be non-linear, for example.
Time-period bias is a psychological bias that relates to results and findings that are time-sensitive and period specific. Specific conclusions and relationships may often be sensitive to the particular time period in which the findings were identified. Time-period bias can be explained using the following illustration: A prominent phenomenon within the investment industry is the small firm effect, a theory that argues that small-cap stocks and small firms with a smaller market capitalization will generally outperform large-cap stocks and large firms with a larger market capitalization. Evidence supporting the small-firm effect ranges from highly concrete and supportive to relatively weak, insufficient, and inadequate, dependent on the specific time period from which the relevant data was gathered. For example, from 1926 to 1974, US small-cap stocks marginally outperformed large-cap stocks by a mere 0.43% per annum. However, if one considers the period from 1975 through 2016, US small-cap stocks outperformed large-cap stocks by a respectable 3.46% per annum. Therefore, an analyst is investigating the relevance of the small firm effect. The analyst can justify the small impact of the firm and its existence far more effectively by considering the period 1975 to 2016. In contrast, he may need help identifying supporting data from 1926 to 1974.
Traders and investors alike are also prone to fall victim to psychological biases such as anchoring bias, status quo bias, confirmation bias, overconfidence bias, and loss aversion.
Anchoring bias refers to the cognitive tendency of placing undue weight on the first piece of information received, investigated, or ideated. The information may be subsequently adjusted, but such adjustment is often insufficient. Referring back to the small-firm effect, suppose the average investor or trader stumbles across a dataset from 1926 to 1974 and wishes to identify how small-cap stock returns fare against large-cap stock returns. Given the period under analysis, the investor or trader may anchor his expectations going forwards based on the findings identified in the relevant dataset spanning from 1926 to 1974. On that notion, the investor or trader may avoid taking excessively large, long positions in small-cap securities since he has anchored his expectations on the belief that small-cap stocks will only marginally outperform large-cap stocks but with significantly greater risk exposure. The investor or trader has thus fallen victim to anchoring bias and may have adopted a very different investment, or trading strategy had he investigated the dataset spanning from 1975 to 2016. As much as he has fallen victim to time-period bias, he has also cognitively anchored his beliefs on the first set of information investigated, thus being overcome with anchoring bias.
Status quo bias refers to the tendency for forecasts and expectations to perpetuate recent observations. In other words, the trader or investor who falls victim to status quo bias cognitively avoids making drastic changes, thus preserving the status quo. This psychological bias is derived from behavioural finance literature in that most market participants reflect greater pain from errors of commission, that is, making a change or adjusting expectations, than from errors of omission, that is, doing nothing and preserving the status quo. Status quo bias can be best explained by using a simple example: Suppose the average day-to-day trader has predominantly taken overweight US securities and underweight ex-US securities. The trader would have been exceptionally satisfied with his returns over the ten years ending September 2018, seeing that US equities significantly outperformed the MSCI World ex-US Index by approximately 4% per annum over the period. The trader would fall victim to status quo bias if he continued to overweight US securities in his trades as he is preserving the status quo because US equities predominantly outperform ex-US equities.
Confirmation bias refers to the tendency of traders and investors to identify information and evidence that confirms a prior belief whilst also discounting any such evidence that disproves that same belief. The most effective method to mitigate confirmation bias is to remain entirely objective and unbiased when making an informed investment decision or trade. Searching for evidence to support a view or belief, such as continued strength or outperformance of US equities, is a classic symptom of confirmation bias.
Overconfidence bias is the psychological tendency to place disproportionate weight on one’s intellectual capacity, knowledge, or ability. This cognitive bias may lead investors or traders to overestimate their ability to generate superior risk-adjusted returns. For example, a trader may believe he has exceptional stock-picking skills due to recent sector outperformance and may overestimate his ability and skill in the future.
Many traders, investors, and general market participants are especially prone to the behavioural finance concept of loss aversion. Loss aversion refers to the cognitive tendency of refusing to sell anything at a loss due to the hope of potentially breaking even on a specific investment or trade. This psychological tendency to which many traders and investors succumb is also referred to as get-events, maintaining losing positions solely to break even, recover, or turn the loss into a profitable position. According to behavioural finance studies, the psychological pain of experiencing a loss is approximately two and a half times greater than the pleasure of experiencing a gain of similar magnitude. Loss aversion can exacerbate losses and be detrimental to traders and investors alike. Loss aversion creates the potential for losses to grow. It is associated with the opportunity cost of foregoing another potentially profitable position to break even on a losing position.
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