Importance of understanding decision-making:
Our choices shape the world, within which financial and economic systems play a
crucial role. Yet, the significance of decision-making is often dismissed as ‘common
sense,’ a simplification that traditional economic and financial theories once
overlooked.
Predicting the decisions of others to anticipate investment trends requires a nuanced
understanding that transcends surface-level models like traditional game theory or
historical stock performance analysis. Even an innocuous compliment can tap into
an investor’s mood - a key factor in risk perception. Lowenstein et al., 2001 showed
that positive moods can reduce risk aversion, potentially leading to more aggressive
investment behaviour, highlighting how subconscious biases and emotional states
interplay with rational strategies, shaping the market in ways that established
theories often fail to capture.
However, in the real world our emotional state does not work in isolation. It is a mix
of factors, where the way information is presented is equally pivotal. Framing the
same financial opportunity in terms of gains rather than losses has a significant
potential of exploiting our brain’s reliance on heuristics. While the heuristic
mechanism has evolved to help us make quick decisions and simplify our lives, it
now has a different effect on the contemporary financial market environment and
consumer behaviour. For example, in consumer markets, the anchoring heuristic
distorts judgment by concentrating focus on an initial reference point, even when the
framing of the outcome is changed. This can lead to an overestimation of benefits,
such as when consumers prefer discounts framed as a percentage (e.g., 50% off)
rather than a flat price reduction (e.g., $50 off), because the relative value of the
discount appears larger when expressed as a percentage, even though the
monetary value is identical.
Prospect theory vs Expected Utility Theory:
Prospect theory explains framing bias, suggesting that people value gains and
losses differently, leading to decisions driven more by the risk of loss than the
potential for gain. According to this theory, people are more likely to take risks to
avoid losses than to achieve gains, which directly contradicts the basic rational
assumptions of the underlying economic theories. For example, The Expected Utility
Theory assumes that utility is quantifiable (if A>B and B>C, then A>C) and decisions
are based on the consideration of probability outcomes; suggesting that individuals
prefer a guaranteed outcome over a probabilistic one with a higher expected utility. It
thereby suggests that introduction of alternative choices will have no effect on
decisions, which does not accurately reflect the actual outcomes. This set of
independence axiom assumptions has been increasingly criticised by the pioneers of
behavioural economics. Simon, 1955 pointed out the common choice inconsistence
in the real-world scenarios, raising a question about the transitive preferences
suggested by the Expected Utility theory.
The groundbreaking findings of 2 Israeli psychologists Tversky and Kahneman in
1981, have led to the Prospect Theory creation and opened the doors into the filed of
behavioural science, promoting more emphasis on cognitive biases. In their
research, participants had to choose between two treatments for 600 people afflicted
with a fatal disease. The first treatment would result in 400 deaths, while the second
had a 66% chance of everyone dying and a 33% chance of no one dying. Of the
participants, 72% chose the first option when it was positively framed, but only 22%
chose it when it was negatively framed. This research has clearly showed the effect
of positive framing on decisions, even in life-or-death situations.
Behavioural Challenges to traditional financial models:
The Capital Asset Pricing Model (CAPM) is a widely used framework in the financial
sector that provides insights into how an asset’s expected return can be calculated
based on its beta coefficient, which measures its sensitivity to overall market
movements. CAPM assumes that investors are only concerned with systematic risk,
the portion of risk that cannot be diversified away. According to this model, the
Efficient Market Hypothesis (EMH) posits that all available information is already
reflected in stock prices, implying that markets are efficient, and investors cannot
consistently achieve returns greater than the market average.
However, these assumptions have been increasingly criticised, particularly when
considering the role of psychological and emotional factors in decision-making.
Framing bias is one such factor that can significantly alter investor behaviour,
causing deviations from the rational decisions predicted by CAPM. For instance,
when risks are framed as losses rather than gains, investors tend to exhibit greater
risk aversion, even if the underlying probabilities remain unchanged. In asset
markets, such biases can lead to overreactions or underreactions to new information
and market events, contrasting with the rational actor assumption of the EMH.
Consequently, the possibility of “beating the market” becomes more plausible than
the EMH suggests when cognitive biases are taken into account, challenging the
assumption that investors always act to maximise utility.
To address these shortcomings, the Behavioural Asset Pricing Model (BAPM) has
been developed. Unlike traditional models, BAPM acknowledges that investors often
make decisions that deviate from pure rationality. It incorporates psychological
factors like emotions, overconfidence, and heuristics such as anchoring bias,
providing a more nuanced and realistic understanding of investor behaviour and
asset pricing in real-world financial markets.
Effect of Framing Bias on Public Opinions:
Brexit referendum is one of the clearest real-life examples of the effect of
framing bias on political decisions, public opinions, and consequently
economic state of the UK. The proponents of leaving the EU often employed
framing techniques to emphasise the potential losses associated with
remaining, rather than focusing on gains. The use of loss aversion was their
dominant strategy, involving the emphasis on what the UK stood to lose - such
as sovereignty, control over borders, or financial contributions to the EU
despite there being strong economic ties especially in trade and investments,
that have contributed to the UK’s stability in the aftermath of the 2008 financial
crisis. Phrases like “taking back control” or “protecting British jobs” tapped into
voters’ emotional reactions to the perceived loss of national autonomy,
exploiting their fear of loss. This heightened sensitivity to modern ‘threats’ is
believed to have evolved as a survival mechanism, making individuals more
motivated to avoid harm than to pursue equivalent benefits. Brexit proponents
successfully triggered these evolutionary responses, leading voters to
prioritise avoiding the perceived losses of EU membership over the ‘uncertain’
benefits of remaining.
Role of Framing Bias in Real-life Events:
In times of uncertainty, framing effect is even more pronounced, influencing asset
allocation. While equities and bonds typically offer higher returns, many investors
shift toward safe-harbour assets: government bonds, gold, or money market funds
during periods of financial instability. These investments, which are seen as low risk,
provide stability despite lower returns. This shift is often driven by framing the current
market conditions as “risky” or “uncertain,” which pushes investors to prioritise
security over returns, even when potential gains may outweigh the risk. This same
mechanism can also fuel speculative bubbles, which the Dot-Com Bubbles of the
late 1990s reflects. In this case, investors were overconfident, driven by the fear of
missing out and excitement over latest technology, and influenced by herd mentality
where investors mirrored the actions of others, pushing prices up further. During this
bubble framing has significantly contributed to positive wealth effect, which increased
investor optimism leading to over investment. The media was also a crucial factor in
framing tech stocks as revolutionary and essential for the future. This led to investors
building up those stocks, but when the reality did not meet expectations, the bubble
burst, causing major market declines.
Another more recent example is the Cryptocurrency Bubble of 2017, driven by the
rapid rise of Bitcoin specifically, as it was framed as revolutionary blockchain
technology, fuelling speculative investment. Over time, market corrections occurred
due to both natural market forces, such as the rapid decline in the value of Bitcoin
and Ethereum which brought prices back to sustainable levels; and government
regulations, such as licensing requirements and trading restrictions with a
discouragement purpose.
Another striking example of how framing bias can exacerbate economic
instability is the Northern Rock bank run of 2007.The media intensified the
bank’s liquidity issues by using sensationalist language, triggering widespread
panic. This framing led to a rush of depositors withdrawing their funds, fearing
the loss of their savings. The ensuing panic led to the bank’s collapse, which,
in turn, contributed significantly to the broader financial crisis, severely
undermining economic stability.
Importance of acknowledging framing bias:
Framing bias is far from a subtle influence - it is a powerful force shaping
decision-making across the variety of fields. Whether it is influencing the
investment choices of seasoned traders or swaying the political beliefs of
electorate majority, framing bias impacts how we perceive risk, reward from
insights provided by data. From Brexit to the Dot-Com and Cryptocurrency
Bubbles, framing bias has proven time and again to be a powerful tool for
those who understand its influence. By acknowledging its effect on everything
from asset pricing models to consumer behaviour, we not only enhance our
ability to predict market trends but also make more rational choices in the
increasingly unpredictable world.
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