Biases Flashcards
Preference for securities listed on the exchanges of one’s home
country over that for international securities.
Home Bias
However, concentrating portfolio
exposure in home country securities may result in a less diversified, less efficient
portfolio
The behavioral biases most relevant in asset allocation (Module 5) include:
- Loss aversion (emotional bias)
- Illusion of control (cognitive bias)
- Mental accounting (information processing bias)
- Representative/Recency bias (cognitive bias)
- Framing (information processing bias)
- Availability bias- home bias & familiarity bias (information processing bias)
- the tendency to overestimate one’s ability to control events
- may result in extreme tactial asset allocation, excessive trading, use of leverage, elminiating or shorting an asset class
- It can be exacerbated by overconfidence bias & hindsight bias
- mitigate by: Global market portfolio as the starting point in developing the SAA
Illusion of Control
Cognitive bias
- If investors believe they have more or better information than what is reflected in the market, they have (excessive) confidence in their ability to generate better outcomes.
- past mistakes are ignored, accuracy of forecasts is overestimated
Overconfidence Bias
Emotional Bias (Illusion of Control)
Ex:
* Investor is convinced of a return of 12.53%
* excessively precise forecast
* overly confident in ability to provide accurate forecast
- the tendency to perceive past investment outcomes as having been predictable
- exacerbates the illusion of control
Hindsight bias
(Illusion of Control) Cognitive Bias
When a bad event occurs, the belief that you “knew it all along,” when it is very likely that the event was unpredictable
- People treat one sum of money differently from another sum based solely on the mental account the money is assigned to
- mitigate by: use Goals based investing
Mental accounting
Information-processing bias
Ex:
* Client is considering her $3 million tax-deferred retirement account, her $500,000 account for the girls’ education, and the $400,000 emergency account separately, rather than seeing them all as a combined investable total.
* In doing this, she sets herself up for the possibility of less than optimal allocation
- is the tendency to overweight the importance of the most recent observations and information relative to a longer-dated or more comprehensive set of long-term observations and information
- Results of bias: return chasing/overweighting asset classes with strong recent performance
- mitigate by: objective SAA process & strong governance
Representativeness/Recency bias
Cognitive Bias
Ex:
* Client is somewhat reluctant to take money out of stocks to rebalance (keeping stocks overweight)
* Client justifies this by expressing confidence that strong investment returns will continue
* Representative bias results in overweighting asset classes with strong recent performance
an information-processing bias in which a person may answer a question differently based solely on the way in which it is asked
choice of asset allocation may be influenced by how risk/return tradeoff is presented
mitigate by: present risk in multiple ways
Framing bias
- when people take a mental shortcut when estimating the probability of an outcome based on how easily the outcome comes to mind
- what is easiest to remember is overweighted
- overly influenced by events that left a strong impression
- where investors who personally experience an adverse event are likely to assign a higher probability to such an event occurring again
- mitigate by: using global market portfolio as the starting point for asset allocation, and use objective evidence and analytical procedures
Availability bias (includes familiarity & home bias)
Information-Processing bias
Ex:
* Client refuses the addition of EM to portfolio because they are convinced it is too risky
* This belief of the client is justified by referring to significant losses the family trust suffered during the recent economic crisis
* Client is showing strong preferance for avoiding the asset classes due to a personal adverse event
* Therefore assigning a higher probability of a negative outcome again in the future
Ex:
* Investor “does not want to miss another market low and recommends a large increase to equities”
* Investor is strongly influenced by the past experience of missing a buying opportunity during a market low
stems from availability bias: People tend to favor the familiar over the new or different because of the ease of recalling the familiar.
Familiarity bias
the first information received is overweighted
Ex: anchoring expectations on the performance of the respective domestic markets
anchoring bias
- Predictions are highly influenced by the recent past
- tendency for forecasts to perpetuate recent observations
- Searching for support to support the decision to overallocate to outperforming assets
- Ex: increasing allocation to the US based on recent outperformance
- Results in: managers avoid making changes
- avoid by: being discplined
Status Quo Bias
Emotional Bias
- only information supporting the existing belief is considered, and may be actively sought while other evidence is ignored
- searching for evidence to support a favored view
Confirmation Bias
Cognitive Bias
- Forecasts are overly conservative to avoid the regret of making extreme forecasts that could end up being incorrect
- mitigated by considering a range of potential outcomes
Prudence Bias
Cognitive Bias
Ex:
* manager has good record of projecting the correct direction of relative performance among markets, but has not translated that into reallocations large enough to add meaningful value
Ex:
* Manager recommends increase to equities. Client fears that this allocation could cause the portfolio to underperform peers significantly
* Concern that recommendation could appear extreme
- Repeatedly searching for the information you want, until a statistically significant pattern emerges
- creating random, significant relationships
- Results in: unreliable models
- Avoid by: out of sample test, review econmoic basis for the variables selected
Data Mining
(bias in methodology)