Review Article :
Estimated Stock Price Based on Company Communications: Mind Genomics and Cognitive Economics as knowledge-Creation Tools for Behavioral Finance
Moskowitz H, Baum H, Rappaport S and Gere A
Abstract
Respondents
estimated the price of a share of stock for a company, based upon a set of
short vignettes, one estimate for each vignette. The vignettes comprised 2-4
elements-statements selected from four groups: WHO presents the information,
the companys VALUES, how the presenter shows ALIGNMENT with company values, and
how CUSTOMERS respond. The linkage between expected dollar value of the stock
and message was highest for the element talking about positive customer
reviews. The respondents divide into two groups or mind-sets, based upon their
patterns of response to the elements. Mind-Set 1 estimates stock price based on
messages communicating good governance. Mind-Set 2 estimates stock price based
on messages communicating customer intimacy and excitement.
Full-Text
Introduction-The
Importance of Corporate Valuation as a Topic in Finance
An excursion through the economics and
finance literatures shows a deep focus on the valuation of a company. The
company is usually valued along a series of economic indicators, involving the
company and the market. The soft, human factor is also considered, such as the
perception of how the company treats its employees, how the company pays
attention to the environment, and so forth [1-3]. One need only listen and look
at the daily news to see the clear evidence of companies trying to optimize
their price by appealing both to the rational economics of their business and
to represent themselves as guardians who uphold the publics values [4-6].
The notions of corporate reputation and
HR (Human Resources) have become increasingly intertwined in the last decade.
Corporate reputation is a function of corporate performance, as well as image
of the corporation both in the press and in the social media, respectively.
With the increasing prevalence and power of social media, and the rapidity with
which negative information can be spread, it becomes increasingly important to
understand the way the ordinary person converts messaging about the company to
estimated price of a companys shares on the stock market. In recent years,
researchers have moved from direct questionnaires to more direct measures. One
of these is a simulated auction, where respondents are asked to invest in a new
product [7]. Rather than instructing the respondent
to rate how she or he likes or would purchase a product, the respondent is
given some money, and asked either to bid or to invest. The foundational notion
is that homo economicus
is a more realistic judge of a product or company than is homo emotionalis. That
is, when people are instructed to put up money, whether their own or
hypothetical money, they tend to act more realistically than when they are
instructed to rate their feelings. A lot of this effort can be found in the
developing fields of Behavioral Economics and Behavioral Finance, which look at
the softer, human side of what was once considered simply as a rational set of
behaviors and decisions [8-10]. In previous studies, author Moskowitz presented
an approach called Mind Genomics [11,12].
Mind Genomics is an emerging
psychological science whose goal is to understand how people make decisions
under uncertainty. Mind Genomics works by simple experiments, presenting the
respondent with combinations of elements or ideas about a situation, e.g., a
company, with these combinations created according to a systematic plan known
as an experimental design. The respondent evaluates a set of these, which the
messages repeating, but in different combinations. The experimental design
ensures that the different elements are statistically independent of each
other. The data generated by the respondent are analyzed by regression analysis
to reveal how each message drives the rating. When Mind Genomics combines with
economic aspects, such as price willing to pay, we enter a new world of what
might be called Cognitive
Economics. Rather than instructing the respondent to evaluate the test
stimuli, the combinations or vignettes, in terms of emotion, we instruct the
respondent to act as a more rational measuring instrument. The respondent is
instructed to read a test vignette, i.e., a concept presenting information
about the company, and then rate the company on an economic scale, in this
study specifically the expected dollar price of a share. Observations from
unpublished data suggest that when respondents rate vignettes on economic
dimensions, specifically price related, they appear to be conservative. People
may be interested in an idea and willing use extremes to express their
feelings, i.e., that they love or hate the idea. When the rating becomes dollars,
an economic variable, respondents are not loose and free with their
economics-based evaluation.
Constrained, and there are fewer swings
in the magnitude of the rating, even among those who are segmented by the
patterns of what they like (so-called mind-sets). That is, people may love or
hate what is represented in a vignette, but their ratings of the monetary value
will be much narrower, and more logical. The Mind Genomics process and its application
to behavior economics/behavioral finance. The origin of this study was the
desire to understand how people respond to news stories about corporate
governance and the behavior of employees. The study applies Mind Genomics to assess
the finance-oriented response to messages about a corporation. The study shows
the estimated dollar value of a share of company stoke that can be linked back
to each of 16 different aspects of the messages, ranging from the role of the
employee giving the information to the way the company demonstrates its values.
Mind
Genomics combines Socratic methods with consumer research. The objective is to
understand how people weight the different sources of information when choosing
among alternatives. Rather than evaluating the stimulus in splendid isolation,
Mind Genomics exposed people to combinations of messages about a topic and
deconstructs the responses to these combinations into the contributions of the
individual messages. The paradigm simulates the natural experience of
individuals, first by showing combinations, and second by presenting these
combinations quickly, obtaining the responses quickly, thus forestalling
intellectualization of the problem. In the language of Nobel Laurate Daniel
Kahneman, when writing about decision making, Mind Genomics employs System 1
thinking, the almost automatic, seemingly unthinking responses to the stimuli
of everyday [12,13]. The steps below trace the implementation of the Mind
Genomics experiments from problem definition to raw material, test stimulus,
analysis, discovery of mind-types, and finally the application of the findings
to understanding the larger population beyond the confines of the foundational
study.
Step 1: Define
the topic:
The topic for Mind Genomics must be limited to something that can be explored
in a simple study. The easiest version of Mind Genomics is afforded by the
do-it-yourself program, BimiLeap (www.BimiLeap.com). BimiLeap works
with 16 different elements, or statements/messages, related to a single topic,
with each message painting a word picture appropriate to a topic. The topic is how
different messages about a company drive the price a person would pay for a
single share of stock. In simple terms, when a company talks about itself,
either directly or through its employees, or through advertising and PR, what
are the messages which might drive up the value of the companys shares, when
the respondent is selected to an ordinary individual with moderate or higher
annual income (> $ 50,000)?.
Step 2: Define
four questions about the topic which tell a story: With the
Socratic Method, the understanding comes from the pattern of answers to
questions. The second step requires that the research ask four questions which
revolve around the topic chosen in Step 1. Most researchers are accustomed to
asking questions, but not accustomed to the discipline of a defined sequence of
questions to elucidate a topic. Mind Genomics requires
this laser-focus. Mind Genomics creates a library of integrated knowledge about
the topic from the point of view of the human being. It is only by asking a set
of related questions about a topic that one can move from the utterly general
or the utterly granular to a structure which is almost systematic, archival,
and integrated. Table 1 presents the
four questions, followed by four phrases or answer for each question. It is
clear from the questions that the study is dealing with the description of the
individual, the companys behavior, and the customers behavior. These are the
aspects which together tell one facet of the story of a company, as narrated by
an individual working in the company.
Table 1: The four
questions and the four answers to each question.
Step 3: Provide
four answers to each question or a total of 16 answers: It will be the
answers which, in combination, constitute the test stimuli. Table 1 also shows
the 16 answers. One of the most frequently asked questions revolves around the
desire that there be right and wrong answers. The reality is that in these Mind
Genomics explorations there are no right or wrong answers, but only different
factoids to which a person responds. It is from the pattern of responses to
these elements or answers, responses obtained in short, inexpensive, iterative
experiment, that one understands the mind of a person. The questions and the
answers in Table 1 are simply first approximations. They will suggest patterns,
but they can always be improved, and re-tested, with some questions and answers
eliminated new questions and answer inserted, and the process repeated. At some
point the relevant patterns will emerge clearly.
Step 4: Mix the
answers (elements) into small, easy to read combinations, so-called vignettes: Each respondent
evaluates a unique set of 24 vignettes. Each vignette comprises 2-4 answers, at
most one answer from each question, but for a some vignettes the structure
deliberately creates incomplete vignettes, with some questions not providing
answers for the vignette. This incomplete design structure allows for the
model, the equation, to generate coefficients which have absolute, i.e.,
ratio-scale properties, with a 2 being twice as much as a 1 and half as much as
a 4. The rationale for the individual-level experimental design is that in the
analysis it will be important to create individual models or equations relating
the rating to the presence/absence of the 16 elements. When the combinations
are created by experimental design at the respondent level one can be sure of
being able to create these individual equations. The rationale for the
uniqueness of combinations for each respondent is the desire to cover the space
of possible combinations. In most studies using experiment design, the
researcher presents the same set of combinations to many respondents, so that
the noise is averaged out. Mind Genomics goes in a different direction,
assessing many different combinations or vignettes, as many as 1200 for 50
respondents. The noise or extraneous variation is cancelled out, not so much by
noise-cancelling repetition, but rather because the pattern emerges more
clearly when the test stimuli cover most of the design space.
Metaphorically, Mind Genomics can be likened to the MRI of the mind, which
creates an image by combining pictures taken from different angles, i.e., from
vignettes created from many different combinations.
Step 5: Create an
orientation paragraph to the topic, and an appropriate rating scale: The orientation
paragraph should be as general as possible in order not to bias the respondent.
Here are the orientation paragraph and the rating scale. Both are minimal in
terms of text, yet have a time frame, and a sense of economic reality. Please
read the entire screen. It describes a company going into the stock market in 6
months.
Please tell us what you think the price will be. 1=$ 1/share…9=$ 100/share.
Rate the price per share 1=1 $/share… 9=$ 100/share.
Mechanics-setting
up the Mind Genomics study
The
Mind Genomics process is straightforward to set up, and is aided by a
user-friend app, BimiLeap (www.BimiLeap.com). The app guides
the researcher towards creating the questions and the answers, as shown in Figure 1, on the left and middle
panels, respectively.
Figure 1: The set up for
the study. The figure shows the requirement to ask four questions (left panel),
the requirement to provide four answers to each question (middle panel), and an
example of a vignette combining three answers or elements (right panel)
BimiLeap
creates 24 combinations of vignettes for each respondent. Each vignette or combination
comprises 2-4 elements or answers, at most one answer from each question. The
basic experimental design ensures that all 16 elements are statistically
independent of each other. Each respondent evaluates an individual permuted experimental
design, with the property that the vignettes or combinations evaluated by
each respondent are generally different from the vignettes evaluated by every
other respondent [14]. The structure of the design remains the same, but the
combinations change. Figure 1 (right panel) shows an example of a vignette as
the respondent would see the vignette on a smartphone. The respondent receives
an email link from the on-line panel provider (Luc.id, Inc.), with the panel
provider specializing in on-line surveys and experiments. The respondents,
already members of the 20+ million panels from Luc.id, open the invitation, and
if interested, begin the study.
The
typical study lasts approximately 3-5 minutes and is usually done with the
respondent paying modest attention to the task. The fact that the respondent is
not fully engaged in the study, but rather just does it, is important to the
success of Mind Genomics. It means that the respondent is using system 1
thinking, which is automatic, and represents the typical way people think about
every day, unimportant issue. Considered opinions, system 2 thinking, may force
the respondent into thinking in a rational, considered way, atypical for
everyday life [13]. The respondent begins by answer three questions about
gender, age, and a third question dealing with interest in the news stories
about corporations. The objective of self-profiling is to understand how the
respondent describes himself or herself, and she or he approaches the topic of
news stories about corporations. The deeper information will come from the
pattern of responses, not from the classification.
Analysis
Mind
Genomics studies follow a simple structure for analysis. The experimental
design enables the creation of models relating the presence/absence of the 16
elements to the dollar value that the respondent thinks that a single share
should command. To estimate the contributions of each element to the dollar
value of a share, and thus to the perceived value of the element, we use the
method of OLS, ordinary least-squares
regression, also known as curve fitting. The analysis uses the full set of data
for a key subgroup, e.g., gender, to estimate the coefficients of the following
equation: Estimated Dollar Value=k1 (A1) + k2 (A2) … k16 (D4). The equation
states simply that the estimated dollar value of one share of stock in the
corporation, as estimated by the respondent, is a weighted sum of the dollar
values of each element, respectively. The
higher the coefficient, the great the dollar contribution. When we look at the
equation above, we see immediately that the equation lacks an additive constant
(intercept), k0, which corresponds to the dollar value of a share in the
absence of elements. The additive constant is meaningful when we talk about
feelings, such as the likelihood of buying the stock. In that case, the
additive constant tells us the basic interest in the stock, based upon the
copy. When we deal with the actual price that would be paid, the additive
constant has less meaning. To establish the statistical significance of the
coefficient, we follow a two-step procedure, based on estimating the equations
both with an additive constant, and without an additive constant, respectively.
The two steps allow us to calibrate the coefficients and estimate statistical
significance. The first step involves estimating the coefficients with an
additive constant and without an additive constant, respectively, for the same
data set, here the 1200 observations from the total panel.
Table 2 shows the data
for the 16 coefficients. The additive constant is irrelevant here. We focus
only on one the coefficients emerging from the two methods of estimating, with
an additive constant (intercept), and without an additive constant (through the
origin). The two sets of 16 coefficients virtually perfectly co-vary. Figure 2 (left panel) shows for these
data how the T statistic varies with the value of the coefficient. We look for
a T statistic of 2.0 to define a significant level. The left panel suggests a
coefficient of +5 for the equation or model with an additive constant.
In
turn, the right panel of Figure 2 shows that a coefficient of +5 for an
equation with an additive constant (our threshold of significance), corresponds
to a coefficient of approximately +20. Thus, we assume that the coefficient of
+20 or higher for an equation or model without an additive constant, i.e.,
without an intercept, a model which goes through the origin. Table 3 shows the
coefficient for six groups, based upon who the respondents say they are. The
groups are Total, Gender, and Age. To make the extraction of key messages
easier, Table 3 shows the strong coefficients
in bold, and in shaded cells. Strong is defined as a dollar value for the
element of $ 21 or higher.
Table 2: Statistical
parameters of the equations relating the presence/absence of the 16 elements to
the ratings (dollars would pay). The pairs of columns show the statistical
parameter of the equation for each element, first for the model through the
origin (no intercept), and then with an intercept (additive constant). The 16
elements are sorted in order of magnitude.
Figure 2: Scatterplot
showing the relation between the coefficient and the T-statistical (left panel,
model with an additive constant, i.e., intercept), and a scatterplot showing
the relation between the two sets of coefficients estimated for the total panel
data of this study (right panel).
Although
the respondents did not estimate the dollar value of each message as a
contributor to likely stock price, the regression model enables the estimation
of the dollar value. The use of inferential statistics to determine significance
is less relevant than a holistic view of the patterns separately strong
performing elements driving high dollar values from weak performing elements
driving low dollar values. There is very little in terms of general population.
It may well be that no elements really drive estimated price willing to pay,
or, as other observations suggested, there are differences in price traceable
to how people think of themselves, not traceable to who they are suggests the
importance of one element only across groups: Customers: give high reviews and
ratings.
How the individual
giving the information drives the estimated stock price
The
Mind Genomics experimental design permutes the different designs to create 50
different sets of 24 vignettes, most of which differ from each other. It is
thus possible to isolate vignettes which the same presenter, defined as
the person doing the talking. These are the vignettes whose first element
begins with the words MY JOB.
Table 3: Models relating
the presence/absence of elements to the dollar value of the stock. The models
are based on WHO the respondent says she/he IS. The table shows strong
performing elements ($21 or higher), in shaded cells, and in bold
font.
By
sorting the vignettes into the five groups, one group for each of the five
answers to Question A, one comes up with a set of homogeneous vignettes in
terms of who is presenting the information. The presenter may be either no
person (A0), a person who makes the products (A1), a person who sells the
product (A2), a person who trains (A3), or a person who manages project
timelines (A4), respectively. When we build a model for each of the five
strata, the question immediately becomes whether the presenter of the
information can drive the price of the other elements or messages. All other
factors held constant, does the who does the saying affect the estimated price
for a share of stock?
Table 4 shows the
analysis of the data, comprising five equations, one for each of the five
groups defined by defined by WHO presents the information. There are only 12
coefficients, one for each of the remaining elements. The four elements
corresponding to WHO present the information are no longer part of the model
because they are held constant for each model. For these vignettes we raise the
criterion of an important element to a slightly higher dollar value of $26 or
more for one share to simplify the table. Depending upon who does the
presenting, the other elements show variation in the dollar values they
command, suggesting that the presenter acts as a director or anchor.
No
Defined Presenter
Company:
acts legally with its self-interest in mind
Company:
takes advantage of social media
Note:
Providing news without identifying who said it maintains estimated share value.
Providing an employee as source of information decreases estimated share value.
Those who MAKE
the products
Customers:
give high reviews and ratings
Me:
talk honestly about my company in a positive way
Those who SELL
the products
Customers:
give high reviews and ratings
Customers:
love to contact customer service with feedback whether it is positive or
negative
Customers:
after purchase become evangelists for the products/services
Those who TRAIN
within the company
Customers:
give high reviews and ratings
Customers:
love to contact customer service with feedback whether it is positive or
negative
Customers:
after purchase become evangelists for the products/services
Those who MANAGE
timelines
Me:
dutifully follow company policy
Table 4: Dollar value of
elements, with the vignettes stratified by WHO presents the information. Those
dollar values of 26$ or higher are shown in shaded cells with bold font.
The dollar
value based upon the interest in stories about companies and their
employees/customers
At
the start of the experiment, the respondent was asked to select one of three
descriptions about interest in how companies treat their customers and how
their employees feel. There were four answers, collapsed to three by combining
those who selected not interested with those who selected not applicable. Table
5 presents the dollar values for the total panel and for the three groups
defined by how they feel about these kinds of stories. The key results from are
that there only five relevant elements which drive a contribution to the
estimated stock price, with the one element extraordinarily important for the
stock price, Customers:
give high reviews and ratings Table 5. The
other elements add to the value of the stock, but not in a consistent way
across all respondents. The key here is reviews and ratings from customers
· Me: dutifully
follow company policy
· Customers: after
purchase become evangelists for the products/services
· Company: acts
legally with its self-interest in mind
· Me: talk honestly
about my company in a positive way
It
is important to note that in the Mind Genomics experiment, the test vignettes
are presented and respondent to so quickly that the strong performing elements
emerge almost automatically, without much conscious processing.
Mind-Sets,
people who think alike with respect to the messages which drive value
One
of the major tenets of Mind Genomics is that in every topic, there are
different groups of ideas which flow together. These are ideas, not people, but
it is only by studying the response of actual people to these ideas is it
possible to discover these different groups of ideas, or so-called, metaphorically,
mind-genomes.
Table 5: Models relating
the presence/absence of elements to the dollar value of the stock. The models
are based on what type of story interests the respondent. The table shows
strong performing elements (dollar value 21 or higher) in shaded cells, and in
bold font.
In
simple terms, one can think of a mind-genome as a set of ideas, the combination
of which make sense and gives us a feeling that there is a coherent theme. Moving
further into the metaphor, the notion is that there are probably a limited
number of mind-genomes for any topic, that these genomes representing different
mental primaries of a topic, and that the mind-genomes may be uncovered through
the analysis of a persons pattern of responses to a set of relevant stimuli. The
mind-genomes are uncovered empirically. They may be hypothesized to exist,
based upon ones experience, but the real test is whether these coherent groups
of ideas can be demonstrated. The
approach to discover the mind-sets uses the statistical method of clustering.
The clustering is done on the pattern of coefficients generated by each
respondent in order to discover groups or clusters of individuals showing
similar patterns of coefficients. The name of the cluster (renamed mind-set)
comes from the commonality of the highest scoring coefficients in the cluster.
There are two steps requiring judgment. The first is interpretability-does the
pattern make sense? The second is parsimony is it
possible to develop only a limited number of patterns which cover the range
well, albeit not necessarily perfectly? Mind Genomics provides the appropriate
data for clustering.
The
inputs are the 50 rows of data, one row for each of the 50 respondents. The
columns are the 16 coefficients, each column being the dollar value of the
specific element for the specific respondent. It is now, in the clustering
phase, that we see the power of the individual-level modeling, made
straightforward by the experimental design. Each respondents ratings can be
immediately analyzed by OLS regression to estimate the 16 dollar-values for one
share of stock, a dollar value for each of the 16 elements. Statisticians have
provided many methods for clustering. The clusters, groups of like-objects are
not fixed in stone, but rather are constructed according to objective
mathematical criteria.
The
clusters themselves are simply a collection of objects which fulfill the
clustering requirements but may not have meaning when interpreted. It is left
to the researcher to decide the clustering method to use and within that method
to choose the number of clusters to create. In the end, the action of
clustering is more of a heuristic to identify patterns in the data rather than
the specification of tight, non-overlapping groups [15]. For the price data,
the clustering method, so-called k-means, defines a distance between every pair
of respondents based on a definition of distance in quantitative terms.
Distance for this study is defined by the dissimilarity of two patterns, one
per respondent, based upon the dollar values of the 16 elements. The actual
measure of distance is (1-R), where R is the Pearson correlation between the
two respondents, using the 16 dollar-values as cases.
The
Pearson R reaches a high of +1 when the two patterns are perfectly related to
each. In that case, with R=1, the distance is 0 (1-1=0). The Pearson R reaches
a low of -1 when the two patterns are perfectly, but inversely related to each
other, so that increases in the dollar value of Respondent A is counterbalanced
by the same relative magnitude of decrease for Respondent B. In that case, the
distance is 0 (1 - -1 = 2). When the clustering process finishes, it emerges
with two, three, or, when necessary, even more groups. These groups are quite
different in the patterns. The important thing for Mind Genomics is that these
clusters or Mind-Sets are parsimonious (few) and interpretable (tell a story). Table 6 shows the dollar value of the
different elements, by total, and then by two mind-sets.
The
mind-sets make sense when we look at the dollar values. The clusters or
mind-sets are not dramatic. Often, a different rating scale calling into play
emotion instead of price generates more dramatically different mind-sets. The
one overwhelmingly strong element is D1 (Customers: give high reviews and
ratings.) That strong performing element may reflect the increasing use of
customer feedback mechanism as a business tool, both for improving the business
and for communicating the business to those who buy of products and services.
Beyond the strongest element there are two clearly different mind-sets.
Mind-Set 1 (MS1) assigns high share values to messages about good corporate
governance. Mind-Set 2 (MS2) assigns high share values to messages about
customer intimacy and involvement.
Table 6: Dollar values of
the elements by total and two emergent mind-sets.
Finding these
respondents in the population
A
recurrent issue in Mind Genomics is the realization that although the mind-sets
are clearly different from each other, the reality is that they are spread
through the population. Table 7
shows the distribution of mind-sets across gender, age, and ones own attitude
about stories dealing with a business. There is no simple pattern. The
temptation is always to abandon the Mind Genomics segments, the mind-sets, in
favor of easier groups to reach, such as clusters of individuals based upon who
they ARE, based upon what they SAY about general topics such as business or
society, or based upon what they DO. There is also the temptation to give these
individuals an extensive and onerous self-profiling questionnaire. The effort
to find some way to figure out the patterns of membership in a mind-set
collapses under the heavy weight of the questionnaire. The inevitable
consequence is the resort to very large-scale segmentations of a general, a
segmentation which has little relevance to the delimited, specific, and rather
defined topics which need the segmentation the most.
Table 7: Distribution of
the two mind-sets by the self-profiling classification, Numbers are the base
sizes.
Authors
Gere with Moskowitz have developed an algorithm to classify individuals as
members of one of two mind-sets, or one of three mind-sets, respectively. The
algorithm is called the PVI, the Personal Viewpoint Identifier. The algorithm
works with the coefficients of the model, identifying six elements, turned into
questions that are answered with NO/YES, or with some other 2-point scale. The
patterns of 64 patterns of answers (26=64) are mapped to mind-sets. The
underlying data from the experiment are perturbed by noise 20,000 times, to see
which patterns best assign hypothetical people to the correct mind-set. Figures 3A and Figure 3B shows the PVI. Acquires the personal information of the
respondent, but maintains privacy [16].
Figure 3A: First part of
the PVI (Personal Viewpoint Identifier), which captures information about the
respondent. The link is: https://www.pvi360.com/TypingToolPage.aspx?projectid=100&userid=2018.
Figure 3B: The actual PVI,
The top part comprises three specialty questions about the topic, the bottom
comprises six questions, and each answered on a 2-point rating scale.
Presents
the actual set of six questions, randomized for each respondent. The PVI return
with the assignment of the individual to the mind-set, along with additional
information. The PVI provides a rapid device to understand the mind of the
individual, in terms of whether the individual belongs to Mind-Set 1 focusing
on good corporate governance or belongs to Mind-Set 2 focusing on customer
intimacy and customer delight.
Beyond
price to engagement-response time
An
evolving topic of consumer research is so-called objective measures
which assess physiological parameters, rather than direct rating. An
increasingly popular, but unproven, belief holds that these objective measures,
generally physiological in nature, represent the true response of a person when
control with a stimulus. Perhaps the oldest of these objective measures is
response time or reaction time, the time between the appearance of the stimulus
and the response. BimiLeap, measures the time from the appearance of the
vignette to the response, and then deconstructs this response time to the
estimated response times of the individual elements in the vignette. The longer
times are assumed to reflect engagement and/or difficulty in comprehension. The
analysis to create the models for response time follows the same path as
before, OLS regression but without first transforming the dependent variable.
The independent variables are the 16 elements which take on the value 1 when
present in the vignette, or 0 when absent from the vignette. The
dependent variable is the number of seconds, measured to the nearest tenth of
second. Any response longer than 9 seconds is brought to 9 seconds, under the
assumption that it never takes more than seven or eight seconds to read a short
vignette, and so the respondent must be otherwise occupied, perhaps
multi-tasking. Just because an element takes a while to process, generating a
long response time, does not mean it will be interesting, or lead to an
increase or decrease in estimated share price Rather, at this stage of
development in the study of messaging we must simply report it, and uncover
patterns which are apparent. As noted above, the elements themselves are cognitively
rich messages. The pattern should emerge from both the meaning of the elements,
and from the morphology,
e.g., the length of the element in words and letters. Table 8 shows the estimated response time attributed to each
element. We have operationally classified the elements into two groups, the
first being those which engage and are defined as response times of 1.5 seconds
or longer, except for the response times of the older respondents, age 50+, who
generally respond to all the stimuli more slowly.
The
other groups are those elements which fail to engage. These non-engaging
elements are operationally defined as generating response times of shorter than
1.5 seconds. It is important to keep in mind that with the developing science
of Mind Genomics we are only beginning to create a database of response times
for different types of messages. From studies already run but not reported
here, the first generalization which is emerging is that serious messages,
those pertaining to a persons health, insurance, or immediate well-being, are
characterized by longer response times, often 2.0 seconds or longer. The second
generalization which is emerging is that those elements dealing with the
everyday, products and services which are promoted by advertisements tend to
have much shorter response times. Our data for the corporation is somewhere in
the middle, being neither daily and trivial, or serious. Table 8 reveals that,
in general, males engage with the messages longer than do females.
There
are notable differences in response times, with males looking much longer at
element B1 (Company: clearly transparent, trustworthy and approachable) and
element C4 (Me: talk honestly about my company in a positive way). Both
elements are expressions of openness, which appears to engage the male reader,
but not the female reader. Our third grouping of the respondent, by emergent
mind-set based upon the pattern of dollar values, suggested two minds, MS1
valuing governance, and MS2 valuing customer centricity. Table 10 shows the
response times and reveals the dramatic difference between the two mind-sets.
MS1 values governance, but reads many of the elements slowly, especially those
focusing on customers. MS2 values customers but reads everything twice as fast.
MS2 are radically different from MS1, both in what they value, and their
engagement with what they read.
Discussion and
conclusions
The
contribution of Mind Genomics to finance, and specifically to the study of Behavioral Finance
comes from the ability of the Mind Genomics research approach to uncover
responses to stimuli, even when the individual is not aware of them. The
ingoing assumption is that the human being, rational or irrational, must
respond in ordinary life to continuing changing arrays of information. It is
obvious from observing transactions between people or between a person and an
information-bearing object that these behaviors appear guided, reproducible,
and for the most part reasonably rational.
Table 8: Models relating
the presence/absence of elements to the response time, the models are based on
WHO the respondent says she/he IS.
Table 9: Models relating
the presence/absence of elements to the response time, the models are based on
what type story interests the respondent.
Table 10: Models relating
the presence/absence of elements to the response time, the models are based on
mind-sets, which is turn are what the respondent values, emerging from the
response patterns.
A
key feature of the paradigm shown here is that the stimuli are cognitively
meaningful. That is, it is the stimuli themselves and thus the information that
they convey, which are the clues to the respondent strategy. It is not
necessary to look very far beyond the strong performing messages to discover what
is common about those messages, and different from the messages which perform
poorly. By putting the respondent in the position of receiving different
combinations of information varied by experimental design it becomes possible
to understand the criteria by which a respondent makes the decision. The
respondent need not explain the criteria to reveal the pattern, although the
respondent may be questions about why her or his behavior followed a certain
pattern, e.g., selecting information featuring people. If
one were to speculate about next steps for Mind Genomics as a contributor to
Behavioral Economics and Behavioral Finance, one path might be selecting the
different steps in finance, specifically those involving choice. The steps
might first be reduced to topics, ranging from the nature of what is being
chosen, the alternative compositions of what is to be chosen, and perhaps even
the human actions involved in what is being chosen. There might be many dozens
of these topics, and perhaps a dozen or more set of variations of the topic in
terms of the questions, and the answers.
The
answers might be emotions (interest in purchasing; emotion experience when
reading the vignettes; price willing to pay; future success, and so forth). The
result would be several hundred to a thousand or more simple studies, each
executed with 50-100 different respondents, and generating an entirely new
corpus of knowledge in Behavioral Economics and Behavioral Finance, a corpus of
information perhaps best called Cognitive Economics or Cognitive Finance. The
words of Pandit and Srivasta and a decade before them, Ritter are worth quoting
in their entirety as the final words to this paper:
· Mergers and Acquisitions is
considered as a financial expert job but it is more a job of market analysts,
of economists and of psychologists. Winning over trust and commitment of target
companys employees is the main driver for post-merger synergy. Pre-merger,
there is a flurry of activities. Post-merger operations go on as per the
culture of the Acquiring Company [17].
· The two building
blocks of behavioral finance are cognitive psychology (how people think) and
the limits to arbitrage (when markets will be inefficient). The growth of
behavioral finance research has been fueled by the inability of the traditional
framework to explain many empirical patterns, including stock market bubbles in
Japan, Taiwan, and the US [18].
Acknowledgements
Attila
Gere thanks the Premium Postdoctoral Researcher Program of the Hungarian
Academy of Sciences.
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*Corresponding author
Howard
Moskowitz,
Mind
Genomics Associates, Inc. White Plains, NY, USA, Email: mjihrm@gmail.com
Citation
Moskowitz
H, Baum H, Rappaport DS and Gere A. Estimated
stock price based on company communications: mind genomics and cognitive
economics as knowledge-creation tools for behavioral finance (2019) Edelweiss
Appli Sci Tech 3: 60-69.
Keywords
General marketing, Computer
technology, Personal viewpoint identifier.
