Unfolding Financial Markets
Taught by Dr. Daniel Ennis
June 17, 2021 | 2pm ET
Imagine a small child, playing with his or her toys in their backyard. In a world of instant accessibility and limitless possibility, this child has a lot of toys. Although older toys are familiar and offer a sense of comfort, this inquisitive child also likes (and often prefers) to explore the new and unfamiliar. An unexpected experience with a new toy might bring the child’s attention back to the familiar toys, while sudden environmental changes such as a startling sound or the onset of a storm might cause the child to abandon the ensemble altogether and head inside.
Treating a market as an organism responding to positive and negative experiences allows us to adopt a behavioral model of utility to better grasp market behavior. According to a model due to Coombs called unfolding, a person’s hedonic response to an item (the degree of utility placed on it) depends on the similarity of the item to an individual’s ideal. The basis for the similarity of interest may depend on several underlying drivers that are not identified in advance and are treated as latent variables. The word unfolding refers to the result of estimating the parameters associated with items’ latent variables, or coordinates, in a multidimensional space. Coombs conceived the items and ideals as deterministic (fixed) points and this led to degeneracies which, despite sometimes fitting the data well, are uninterpretable solutions. This problem was solved by assuming that the items are probabilistic rather than deterministic. In 2001, The Institute for Perception introduced a novel method called Landscape Segmentation Analysis® (LSA) that solved the degeneracy problem. LSA has been applied to numerous types of hedonic data in many product categories and has also been applied to complex sensory variables. In this webinar, we consider an application of LSA to financial markets as a tool in behavioral economics to add to its already extensive range of applications.
This webinar is intended for a general audience of industrial and academic sensory professionals and graduate students, stock market analysts, and financial specialists.
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