How do diagnostic expectations work?
Tversky and Kahneman (1973) first coined the term “availability heuristic” to indicate instances when people consider a hypothesis more likely to occur if it is easier to recall a similar event. In other words, beliefs depend on recalled personal experiences, such as stock market crashes, and not just on statistical information. Diagnostic expectations (DE) capture the insight that agents overrepresent states of the world for which they have some memory.
Bordalo et al.in this paper use DE to explain why people overestimate the future prospects of individual firms and the aggregate market after periods of rapid earnings growth leading to long-run return reversals, but underestimate the impact of other news, such as earnings surprises, leading to price drifts and momentum. Their model of memory-based probability judgments shows that:
This notion that probability estimates are shaped by content (as captured by feature similarity) and not just by objective frequency accounts for and reconciles a wide range of seemingly inconsistent experimental and field evidence, including availability and representativeness heuristics proposed by Kahneman and Tversky (1974), overestimation of the probabilities of unlikely hypotheses, conjunction and disjunction fallacies in experimental data, as well as under- and over-reaction to information. We tested several novel predictions of the model using an experimental design in which we control both the memory database and the cues subjects receive, and found strong supportive evidence.
Memory and Probability
Authors: Pedro Bordalo, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon, Andrei Shleifer
From: University of Oxford, Harvard University, Bocconi University
DE and asset prices
Kwon and Tang in this paper use DE to explain financial markets’ systematic over- and under-reaction to corporate news. Their hypothesis is that more extreme types of news are associated with greater overreaction, as investors are relatively likelier to focus on the tail events . Their model introduces DE agents into an asset pricing model: extreme corporate news trigger idiosyncratic signals and as a result investors overreact and asset prices shoot-up. These idiosyncratic signals imply a greater disagreement among investors which leads to higher trading volume despite the fact that fundamentals have basically remained the same. They test their hypothesis on a large dataset of corporate announcements categorized according to their event types and confirm that corporate event types with more extreme distributions of fundamentals are associated with more overreaction and disagreement-driven trading volume.
While in our framework people react to news by recalling past news of the same type, measuring precisely which events come to mind is an important area for future work. For example, can we systematically identify which events are referenced by investors when evaluating new information? Why are some events referenced and not others? Understanding which events are recalled in a given context is an important next step to understanding how investors react to news and ultimately how information is incorporated into asset prices.
Extreme Events and Overreaction to News
Authors: Spencer Yongwook Kwon, Johnny Tang
From: Harvard University
