Michael O. AdairManaging Director, Senior Investment Consultant | 2018

Cognitive Errors: Heuristics & Biases

Cognitive errors are defined as basic statistical, information processing, or memory errors that cause a person’s decision to deviate from the rationality assumed in traditional finance.

Cognitive errors are defined as basic statistical, information processing, or memory errors that cause a person’s decision to deviate from the rationality assumed in traditional finance. These errors fall into two sub-categories: belief preservation errors (the tendency to cling to one’s initial belief even after receiving new information that contradicts it) and information processing errors (mental shortcuts).

The three major cognitive shortcuts that laid the groundwork of prospect theory are representativeness (belief preservation), anchoring (information processing), and availability (information processing). These heuristics influence our judgments, typically subconsciously, and can certainly bias investment decisions.



Representativeness, the first of the “big three” heuristics, is a cognitive shortcut that replaces a question of probability with one of similarity. In other words, rather than considering the objective chances of a scenario happening, individuals find it easier and faster to assess how closely it corresponds to a similar question. The representativeness bias further supports the notion that people fail to properly calculate and utilize probability in their decisions. Investors can fail to notice trends or extrapolate data erroneously because they interpret it as fitting their preconceived notions.

The most common mistake to arise from this heuristic is the conjunction error. This refers to when the probability of A&B happening is judged to be higher than the probability of A. For instance, after reading a brief character description of someone lacking imagination but being very analytical, individuals deemed such a character more likely to both be an investor and play jazz than just play jazz. They failed to realize that an investor who plays jazz is nested within the category of anyone playing jazz.19 In the markets, investors can encounter the conjunction fallacy when interpreting key indicators. Pointing this error out does not preclude people from falling prey to it again. Although they understand the basic calculating error, people are prone to making the mistake time and time again.20 What is even more concerning is that experts making high-stakes decisions make the conjunction error too. The failure to recognize nested scenarios affected nearly all economists, analysts, and professional statisticians—illustrating how difficult it can be to avoid this mistake.21

What follows are some additional examples of belief preservation errors.


Conservatism refers to the tendency to insufficiently revise one’s belief when presented with new evidence. In other words, it occurs when a person overweighs their prior view and underweights new information. The original information is considered to be more meaningful and important than the new information, even when there is no rational reason for this belief.22

In finance, conservatism can lead investors to under-react to corporate events such as earnings announcements, dividends, and stock splits.23

Confirmation Bias

One’s tendency to search for, interpret, favor, and recall evidence as confirmation of one’s existing beliefs is referred to as confirmation bias. For example, people tend to gather or remember information selectively, or to interpret ambiguous evidence in a manner that supports their existing position. Confirmation bias also manifests when people tend to actively seek out and assign more weight to evidence that confirms what they already think, and to ignore or underweight evidence that could disconfirm it.24

In finance, confirmation bias can lead investors to ignore evidence that indicates their strategies may lose money, causing them to behave to overconfidently.25

Hindsight Bias

Hindsight bias refers to when past events appear to be more prominent than they actually were, leading an individual to believe that said events were predictable, even if there was no objective basis for predicting them. Essentially, this bias occurs when, after witnessing the outcome of an unpredictable event, one believes they “knew it all along.”

Illusion of Control

The illusion of control occurs when people overestimate their ability to control events or influence outcomes, including random ones, even when there is no objective basis for such a belief. In finance, this bias may lead investors to underestimate risks and have greater difficulty adjusting to negative events.



The second of the “big three” heuristics, and one of the hardest to mediate, is anchoring, which occurs when people consider a seemingly arbitrary value before estimating a quantity. Merely repeatedly saying a number, or having it drawn at random, can influence the estimate of an unfamiliar value. Before answering mathematical survey questions, participants had to write down the last four digits of their phone number. When analyzing the results, researchers found a correlation between those who reported high numerical estimates and those who had “high” phone numbers and, vice versa, a correlation of low estimates and “low” phone numbers.26 A completely rational investor would easily discount the extraneous information, yet research indicates that these seemingly irrelevant factors play a role in our judgments.

A secondary troubling finding regarding the anchoring bias is how difficult it is to control. Even when people were told about the anchoring effect, they were influenced by it despite reporting that they had consciously disregarded it.27 Anchoring further defies standard economic theory because high monetary incentives do little to mitigate its effect. Even large cash rewards for accurate estimates were not enough to make individuals more careful with their value judgments.28

For investors, the anchor can even be the price of the stock at the time of purchase. Future investment decisions can be associated with that value. For example, if a stock price drops, an investor may wait to break even to sell despite other indicators suggesting that a rebound in price is unlikely.29 Regardless of how the anchor manifests itself, whether it’s the buy-price or the 52-week high, investors should remain objective in their strategies and allocations.


The availability heuristic demonstrates how ease of recall can make a phenomenon seem more likely to occur. Additionally, an easier to imagine scenario is perceived to have a higher chance of happening than one that is harder to imagine. As a result, individual differences arise and can lead to vastly disparate perceptions. If an investor saw their property value plummet after the housing market crash, that experience will influence their decision in future real estate investments. Although adjustment is possible if people are made aware of the bias, it is not a foolproof method.30

The availability heuristic can help explain speculative bubbles. As interest rises for a particular asset, the media reports on it more frequently, more conversations revolve around the subject, and speculation increases. This creates a self-fulfilling prophecy in which investors bolster their own expectations thanks to the exuberance surrounding the asset or commodity. The ease of recall fuels such speculation and consequently a downturn is perceived to be unlikely.

What follows are additional examples of information processing errors.


A framing bias occurs when people view or react to information differently depending on the context in which it was framed. For instance, whether something is viewed as a loss or a gain may depend upon the description of the scenario. When information is presented in a positive manner, people tend to avoid risk. However, when the same information is presented in a negative manner, they tend to seek risk. This is because, according to prospect theory, a loss is more significant than an equivalent gain, and a certain gain is considered preferable to a likely gain. Meanwhile, a likely loss is preferred over a certain loss.31

In investing, framing bias can lead to a lack of understanding about the risk of short-term market movements since headlines tend to focus on the negative, leading investors to fail to adequately process the positives that remain in place.

Mental Accounting

Individuals tend to take a bucket approach to forming portfolios, mentally segregating their assets in order to simplify them. For example, they may separate their safe investment portfolio from their speculative portfolio to prevent the negative returns that speculative investments may have from affecting the entire portfolio. However, despite the effort of separating the portfolio, the investors’ net wealth will be no different than if they had held one larger portfolio.32


The aforementioned heuristics can all be applied to FAANG (namely Facebook, Apple, Amazon, Netflix, and Alphabet’s Google) stocks.33 The repetitive and popular coverage of these assets can give rise to the availability bias. Their past performance notwithstanding, the ease with which investors can recall the fundamentals of FAANG stocks compared to lesser known ones can bias asset allocations. The representativeness bias, on the other hand, can influence the generation and perception of benchmarks. When evaluating certain equities, investors may compare them to FAANG stocks and look for any similarities. In fact, many headlines on news sites already make these comparisons—judging a tech company based on how it measures up to Amazon.34 Since objective probability is hard to judge, the easier question of similarity takes its place. Although nearly every page of disclosures mentions that past performance does not predict future results, many investment decisions can be swayed by precedents and retrospection. Anchoring also mitigates the effects of objective evaluations because irrelevant values can impact decision-making. Therefore, understanding fundamentals and ensuring diligent research can help immensely with making better decisions. However, it is crucial to be cognizant of the effect extraneous information can have on behavior because expertise does not eliminate these biases entirely.33

Not unlike other shortcuts, heuristics can be advantageous in many situations. They are so pervasive because of how effective they tend to be. Unfortunately, occasional errors can occur, and in the world of finance and wealth management, those can be disastrous.

Cognitive errors are defined as basic statistical, information processing, or memory errors that cause a person’s decision to deviate from the rationality assumed in traditional finance.


1Appelbaum, B. (2017, October 9). Nobel in Economics Is Awarded to Richard Thaler. The New York Times. Retrieved from https://www.nytimes.com/2017/10/09/business/nobel-economics-richard-thaler.html
2 Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving Decisions About Health, Wealth, and Happiness. New York: Penguin Books.
3 Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
4 Tversky, A., & Kahneman, D. (1983). Extensional Versus Intuitive Reasoning: The Conjunction Fallacy In Probability Judgment. Psychological Review, 90 (4), 293-315.
5 Northcraft, G.B. and M.A. Neale, 1987, “Experts, Amateurs, and Real Estate: An Anchoring-and-Adjustment Perspective on Property Pricing Decisions,” Organizational Behavior and Human Decision Processes 39, 228–241.
6 Englich, B., T. Mussweiler, and F. Strack, 2006, “Playing Dice with Criminal Sentences: The Influence of Irrelevant Anchors on Experts’ Judicial Decision Making,” Personality and Social Psychology Bulletin 32, 188–200.
7 Kahneman, D. & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
8 Tversky, A., & Kahneman, D. (1975). Judgment Under Uncertainty: Heuristics and Biases. Utility, probability, and human decision making (pp. 141-162). Springer.
9 Kahneman, D. (2015). Thinking, fast and slow. New York: Farrar, Straus and Giroux.
10 Odean, T. (1998). Are Investors Reluctant To Realize Their Losses?. The Journal Of Finance, 53 (5), 1775-1798.
11 Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
12 Gonzalez, R., & Wu, G. (1999). On The Shape Of The Probability Weighting Function. Cognitive Psychology, 38 (1), 129-166.
13 Barberis, N., & Huang, M. (2008). Stocks As Lotteries: The Implications Of Probability Weighting For Security Prices. The American Economic Review, 98 (5), 2066-2100.
14 Barberis, N., Huang, M., & Santos, T. (2001). Prospect Theory and Asset Prices. The Quarterly Journal of Economics, 116(1), 1-53.
15 Thaler, R. H., & Johnson, E. J. (1990). Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice. Management Science, 36(6), 643-660.
16 Kahneman, D. (2003). Maps Of Bounded Rationality: Psychology For Behavioral Economics. The American Economic Review, 93 (5), 1449-1475.
17 Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
18 Barberis, N. C. (2013). Thirty Years Of Prospect Theory In Economics: A Review And Assessment. The Journal of Economic Perspectives, 27(1), 173-195.
19 Yates, J. F., & Carlson, B. W. (1986). Conjunction Errors: Evidence for Multiple Judgment Procedures, Including “Signed Summation”. Organizational Behavior and Human Decision Processes, 37(2), 230-253.
20 Wanke, M., Schwarz, N., & Bless, H. (1995). The Availability Heuristic Revisited: Experienced Ease Of Retrieval In Mundane Frequency Estimates. Acta Psychologica, 89 (1), 83-90.
21 Tversky, A., & Kahneman, D. (1983). Extensional Versus Intuitive Reasoning: The Conjunction Fallacy In Probability Judgment. Psychological Review, 90 (4), 293-315.
22 Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under Uncertainty: Heuristics and Biases. New York: Cambridge University Press.
23 Kadiyala, Padmaja; Rau, P. Raghavendra (2004). “Investor Reaction to Corporate Event Announcements: Under-reaction or Over-reaction?”. The Journal of Business. 77 (4): 357–386. doi:10.1086/381273. JSTOR 10.1086/381273.. Earlier version at doi:10.2139/ssrn.249979
24 Bollen, K., Cacioppo, J., Kaplan, R., Krosnick, J., Olds, J., & Dean, H. (2015). Social, Behavioral, and Economic Sciences Perspectives on Robust and Reliable Science: Report of the Subcommittee on Replicability in Science Advisory Committee to the National Science Foundation Directorate for Social, Behavioral, and Economic Sciences. Retrieved from National Science Foundation website: https://www.nsf.gov/sbe/AC_Materials/SBE_Robust_and_Reliable_Research_Report.pdf.
25 Zweig, J. (2009, November 19). How to Ignore the Yes-Man in Your Head When Investing. The Wall Street Journal. Retrieved from https://www.wsj.com/articles/SB10001424052748703811604574533680037778184
26 Wilson, T. D., Houston, C. E., Etling, K. M., & Brekke, N. (1996). A New Look At Anchoring Effects: Basic Anchoring And Its Antecedents. Journal of Experimental Psychology: General, 125 (4), 387.
27 Wilson, T. D., Houston, C. E., Etling, K. M., & Brekke, N. (1996). A New Look At Anchoring Effects: Basic Anchoring And Its Antecedents. Journal of Experimental Psychology: General, 125 (4), 387.
28 Quattrone, G.A., Lawrence, C.P., Finkel, S.E., & Andrus, D.C. (1981). Explorations In Anchoring: The Effects Of Prior Range, Anchor Extremity, And Suggestive Hints. In Gilovich, T., Griffin, D., & Kahneman, D. (2002). Heuristics And Biases: The Psychology of Intuitive Judgment. Cambridge University Press.
29 Campbell, S. D., & Sharpe, S. A. (2009). Anchoring Bias in Consensus Forecasts and Its Effect on Market Prices. Journal of Financial and Quantitative Analysis, 44(2), 369-390.
30 Tversky, A., & Kahneman, D. (1973). Availability: A Heuristic for Judging Frequency and Probability. Cognitive Psychology, 5 (2), 207-232.
31 Tversky, A. & Kahneman, D. (1981). The Framing of Decisions and the Psychology of Choice. Science. 211 (4481): 453–58.
32 Phung, A. Behavioral Finance: Key Concepts. Investopedia. Retreived from https://www.investopedia.com/university/behavioral_finance.
33 FAANG is an acronym for the five most popular and best performing tech stocks in the market, namely Facebook, Apple, Amazon, Netflix, and Alphabet’s Google. Source: FAANG Stocks https://www.investopedia.com/terms/f/faang-stocks.asp#ixzz502sfDgO1.
34 Russolillo, Steven (2017, November 21). Tencent’s Market Cap Surges Past Facebook; Next Up, Amazon. The Wall Street Journal. Retrieved from https://blogs.wsj.com/moneybeat/2017/11/21/tencents-market-cap-surges-past-facebook%e2%80%8b-next-up-amazon. Hickins, M. (2011, October 27). How Does Amazon’s Jeff Bezos Compare to Steve Jobs, Other Silicon Valley Icons?. The Wall Street Journal. Retrieved from https://blogs.wsj.com/digits/2011/10/27/how-does-amazons-jeff-bezos-compare-to-steve-jobs-other-silicon-valley-icons. Kam, K. (2017, September 28). 3 Stocks Like Apple Was 10 Years Ago: Tesla, Nvidia And Alibaba. Forbes. Retrieved from https://www.forbes.com/sites/kenkam/2017/09/28/3-stocks-like-apple-was-10-years-ago-tesla-nvidia-and-alibaba/#38df566b2f0f. Muoio, D. (2017, July 28). Tesla’s model 3 Launch Could Be as Big as the Introduction of the iPhone. Business Insider. Retrieved from http://www.businessinsider.com/gene-munster-tesla-model-3-launch-compares-iphone-2017-7.
35 Kaustia, M., Alho, E. & Puttonen, V. (2008), How Much Does Expertise Reduce Behavioral Biases? The Case of Anchoring Effects in Stock Return Estimates. Financial Management, 37: 391–412.
36 Morewedge, C & Giblin, C. (2015). “Explanations of the Endowment Effect: An Integrative Review”. Trends in Cognitive Sciences. 19 (6): 339–348.
37Kahneman, D. & Tversky, A. (1992). “Advances in prospect theory: Cumulative representation of uncertainty”. Journal of Risk and Uncertainty. 5 (4): 297–323.
38 Gilbert, D., Morewedge, C., Risen, J., & Wilson, T. (2004). “Looking Forward to Looking Backward The Misprediction of Regret”. Psychological Science. 15 (5): 346–350.
39 Pompian, M. (2006) Self Control Bias. In Behavioral Finance & Wealth Management. New York, NY: John Wiley & Sons.

Important Disclosures

Investment management services provided by City National Bank through its wholly owned subsidiary City National Rochdale, LLC, a registered investment advisor.

The information presented does not involve the rendering of personalized investment, financial, legal, or tax advice. This presentation is not an offer to buy or sell, or a solicitation of any offer to buy or sell, any of the securities mentioned herein.

Certain statements contained herein may constitute projections, forecasts, and other forward-looking statements, which do not reflect actual results and are based primarily upon a hypothetical set of assumptions applied to certain historical financial information. Certain information has been provided by third-party sources, and, although believed to be reliable, it has not been independently verified, and its accuracy or completeness cannot be guaranteed.

Any opinions, projections, forecasts, and forward-looking statements presented herein are valid as of the date of this document and are subject to change.

As with any investment strategy, there is no guarantee that investment objectives will be met, and investors may lose money. Returns include the reinvestment of interest and dividends. Investing involves risk, including the loss of principal. Diversification may not protect against market loss or risk.

Past performance is no guarantee of future performance.

Put our insights to work for you.

If you have a client with more than $1 million in investable assets and want to find out about the benefits of our intelligently personalized portfolio management, speak with an investment consultant near you today.

If you’re a high-net-worth client who’s interested in adding an experienced investment manager to your financial team, learn more about working with us here.