How psychological framing affects economic market prices in the lab and field
Most people like to think of themselves as rational, making reasoned decisions based on the best available information. But all humans are susceptible to a range of mental biases. For example, members of the “born digital” generation often think that multitasking means greater efficiency, even though research shows that heavy multitasking can exact a penalty. Professional basketball teams sometimes discount how collegiate players perform during the “March Madness” tournament. And people often prefer misinformation when it’s in line with their pre-existing attitudes — even when mistakes are corrected.
A fundamental human challenge is calculating risk — probability, in essence. We all know that a flipped coin has a 50-50 chance of coming up heads or tails, but what about the chance of getting two heads with two flips? Many of us can quickly figure out that it’s one in four (25%), but that wouldn’t stop us from believing in the possibility of, say, a “hot hand” in a game of craps — or an octopus that can predict the outcome of World Cup matches.
Our ability to quickly and relatively accurately calculate risk affects far more than coin tosses and soccer games, of course. Traditional economics is based on the idea of rational decision makers, but decisions are almost always informed by available information and how it’s presented. An initial price for a house can be set high — something known as “anchoring” — and any subsequent bids naturally play off that price, however realistic it may or may not be. A related effect is “partition dependence,” which refers to the human tendency to assign probability based on the way possible outcomes are grouped — will the stock market as a whole rise or fall? How about just tech stocks? And what about Apple versus Samsung? With each shift in the framing, our perceptions can change.
A 2013 study published in Proceedings of the National Academy of Sciences (PNAS), “How Psychological Framing Affects Economic Market Prices in the Lab and Field,” examines this subject in greater depth. The researchers, Colin Camerer of Caltech, Ulrich Sonnemann and Thomas Langer at the University of Münster, Germany, and Craig Fox at UCLA, sought to better understand how partition dependence (PD) can affect our judgment in “prediction markets” — those requiring the calculation of future probabilities.
Four datasets were examined. The first came from an experiment with 192 subjects asked to make bets about the future value of financial, sports and weather events. Trading was in two rounds over 20 minutes and subjects were told to submit either bids to buy or asks to sell, or to accept current bids and asks to make a trade. To see how partition dependence affected prices, “the full possible numerical range for each event value was divided into four intervals. For each of two separate trading groups, either the two lowest-valued intervals or the two highest valued intervals were combined into a single interval.” The second dataset was based on a multiweek game by 456 players, who bet on the number of victories scored by four basketball teams in the NBA playoffs and the total number of goals by four soccer teams in the World Cup. Here too, the partitioning of the assets (in this case, teams) differed: For some study participants teams were broken into separate groups and for others they were combined.
The third dataset came from 153 economic prediction markets on four macroeconomic indicators, while the fourth examined pari-mutuel betting, in which bets are pooled. In horse racing, “many studies show that these long shots are overbet relative to their actual chances of winning (and favorites with high probability and low odds are relatively underbet). This tendency is called the ‘favorite–long-shot bias’ and is well established across many countries and decades.”
The study’s results include:
- Grouping potential outcomes together lowered perceived probability, while separating them in distinct groups increased perceived probability. For example, when participants were asked if the weather in Muenster, Germany, would be between 20 and 23.9 Celsius, the average of the last three trade prices was 0.354; when asked if it would be above 24 Celcius, the average was 0.496. However, when participants were asked the probability of its being above 20 Centigrade (encompassing both earlier ranges), the average price was only 0.707 — 0.143 less than the sum of the smaller ranges, 0.85. In other words, when participants were asked to make bets, they were influenced by the grouping of the numbers in front of them.
- “Overall, the difference between the average of the last three prices of unpacked assets and the associated packed asset was 0.267, 0.229, and 0.149 for finance, sports and weather events, respectively.”
- In the second experiment, player bets also showed partition dependence: “Individual belief judgments summed across unpacked intervals are a median of 0.20 higher in NBA and 0.15 higher in World Cup markets than in comparable packed intervals.”
- In the third dataset, based on economic indicators, even though the markets’ design “provided adequate predictions of the underlying macroeconomic indicators, the predictions were distorted by a bias toward equal probabilities for all traded events.”
- In the fourth dataset, on horse racing, there was an “orderly pattern in which perceived probabilities of long shots are indeed higher in races with fewer horses, as predicted by the [partition dependence] effect.”
The researchers found consistent evidence of partition dependence in all four datasets. “Results in any one of the studies might be explained by a specialized alternative theory, but no alternative theories can explain the results of all four studies,” they state. “We conclude that psychological biases in individual judgment can affect market prices, and understanding those effects requires combining a variety of methods from psychology and economics.”
A related 2012 study published in Applied Economics, “‘It Ain’t Over Till It’s Over.’ Yogi Berra Bias on Prediction Markets,” looked at market efficiency and trader judgements in sports contests. “While only minor bias can be observed during most of the lifetime of the contracts, the calibration of prices deteriorates very significantly in the last moments of the contracts’ lives,” the researchers found. “Traders tend to overestimate the probability of the losing team to reverse the situation in the last minutes of the game.”
Also of interest is “Plan Format and Participation in 401(k) Plans: The Moderating Role of Investor Knowledge,” which was published in 2012 in the Journal of Public Policy and Marketing. The authors conducted three studies to determine how formatting decisions in 401(k) plans changed participation rates, particularly among people with limited financial knowledge. Among the findings: The larger the number of funds, the lower the plan participation among low-knowledge investors; grouping investments by asset class rather than in alphabetic order increased participation; and star ratings for funds “increased decision satisfaction among low-knowledge investors because of a reduction in perceived task difficulty.”
Keywords: behavioral economics, judgment bias, sports, cognition
Read the study-related Barron's article titled "Investment Size: More Sizzle=Bigger Stakes."
- Reporter's use of the study: Evaluate what the reporter chose to include and exclude from the study. Would the audience have acquired a clear understanding of the study's findings and limits from this article?
- Reporter's use of other material: Assess the material in the article that is not derived from the study. For example: Does the reporter place the study in the context of other research and to what effect? Does the reporter include reactions to the study from other researchers or interested parties (e.g., political groups, business leaders, or community members) and are their credentials or possible biases made clear?
Read the full study titled “How Psychological Framing Affects Economic Market Prices in the Lab and Field."
- What are the study's key technical terms? Which ones need to be put into language a lay audience can understand?
- Do the study’s authors put the research into context and show how they are advancing the state of knowledge about the subject? If so, what did the previous research indicate?
- What is the study’s research method? If there are statistical results, how did the scholars arrive at them?
- Evaluate the study's limitations. (For example, are there weaknesses in the study's data or research design?)
- How could the findings be misreported or misinterpreted by a reporter? In other words, what are the difficulties in conveying the data accurately? Give an example of a faulty headline or story lead.
Newswriting and digital reporting assignments
- Write a lead, headline or nut graph based on the study.
- Spend 60 minutes exploring the issue by accessing sources of information other than the study. Write a lead (or headline or nut graph) based on the study but informed by the new information. Does the new information significantly change what one would write based on the study alone?
- Compose two Twitter messages of 140 characters or fewer accurately conveying the study’s findings to a general audience. Make sure to use appropriate hashtags.
- Choose several key quotations from the study and show how they would be set up and used in a brief blog post.
- Map out the structure for a 60-second video segment about the study. What combination of study findings and visual aids could be used?
- Find pictures and graphics that might run with a story about the study. If appropriate, also find two related videos to embed in an online posting. Be sure to evaluate the credibility and appropriateness of any materials you would aggregate and repurpose.
Class discussion questions
- What is the study’s most important finding?
- Would members of the public intuitively understand the study’s findings? If not, what would be the most effective way to relate them?
- What kinds of knowledgeable sources you would interview to report the study in context?
- How could the study be “localized” and shown to have community implications?
- How might the study be explained through the stories of representative individuals? What kinds of people might a reporter feature to make such a story about the study come alive?
- What sorts of stories might be generated out of secondary information or ideas discussed in the study?