How
Non-Scientific Granulation Can Improve Scientific Accountics
Bob
Jensen at Trinity University
Accountics is the mathematical science of values.
Charles Sprague [1887] as quoted by McMillan [1998, p. 1]
http://www.trinity.edu/rjensen/395wpTAR/Web/TAR395wp.htm
For me, she says, "this really showed the
beauty of science, that you can have this personal experience that isn't
reflected in big data."
Jennifer Jacquet as quoted
by Robin Wilson, Inside Higher Ed,
October 22, 2012 ---
http://chronicle.com/article/The-Hard-Numbers-Behind/135236/?cid=at&utm_source=at&utm_medium=en
In quantitative finance and accountics science, we
call important factors not reflected in big data, or otherwise that cannot be
scientifically quantified, "black swans" or "causal
factors."
The purpose of this paper is to show how
non-scientific research cannot only add value to quantitative studies, non-scientific research can find granules of causation
that cannot be discovered in most quantitative studies.
Example 1: An Accountics Science Illustration
Suggested Interview
Research Granulation Searches for Causality
Example 2: granular Non-Science Research and Database
Variables
A simple operating definition of accountics science
research is any accounting research focused primarily upon analysis with
mathematical equations and/or statistical inference tables. Since the 1980s the
leading academic accounting research journals are almost exclusively accountics
science journals that seldom publish non-accountics articles or even
commentaries on published research.
“An
Analysis of the Evolution of Research Contributions by The Accounting
Review: 1926-2005,” (with Jean Heck), Accounting Historians Journal,
Volume 34, No. 2, December 2007, pp. 109-142.
http://www.trinity.edu/rjensen/395wpTAR/Web/TAR395wp.htm
Nearly all of the
empirical accountics science articles fall into two types:
1.
Multivariate models (especially
regression) models using purchased databases such as Compustat, Audit
Analytics, CRSP and other very large commercial databases.
2.
Behavioral
experiments that usually use students as surrogates for real-world decision
makers.
As a result, accountics scientists seldom leave the
campus to obtain databases used in multivariate models and statistical
inference analysis.
Typical Absence of Causal Analysis
in Accountics Science
Accountics scientists rarely do causation analysis. Their multivariate regression
and other data mining outcomes rely upon correlation and/or tenuous causation
inferences rather than direct search for causation. Findings dependent upon
experiments student behavior must generally be extrapolated tenuously to
untested hypotheses concerning real world decision making and risk-taking
behavior. There are of course some exceptions, but causal discoveries are
generally not found in accountics science. The old saying that correlation is
not causation is nagging limitation of so many, many accountics science
findings.
Typical Presence of Causal Analysis
in Granular Non-Science (protocol analysis, interviews, surveys, cases,
anecdotes, and field studies)
Whereas
accountics scientists have to indirectly infer/assume causality, granular
studies focus more directly upon uncovering causes of outcomes. It’s obvious
that it’s possible to add a great deal to confirm or disconfirm accountics
science findings with granular non-science findings. But therein lies the
problem, because granular findings are often subjective, anecdotal, and
possibly cherry picked. This creates doubt about the reliability and robustness
`of their findings. More importantly, granular studies may be so expensive that
only small samples are practical, including samples of just one company or one
executive.
This does not mean that, when doing accountics
science research on large samples, the researchers did not do granular research
supplements such as preliminary field research or small sample (e.g., case)
studies. However, due to the limitations of the non-scientific nature of
granular research, accounting research journal referees may require deleting any
mention of the granular research outcomes that can be very misleading due to
their non-scientific nature and potential biases.
How Statistics Can Mislead
"MOOC Students Who Got Offline Help Scored Higher, Study Finds," by
Steve Kolowich, Chronicle of Higher Education, June 7, 2013 ---
http://chronicle.com/blogs/wiredcampus/mooc-students-who-got-offline-help-scored-higher-study-finds/44111
Jensen Comment
Although I like this article, it is yet another example of the many times
statistics are used to mislead readers. At the roots this is really a rehash of
the issue of causation versus correlation.
This extrapolates to the granulation problem that I've previously mentioned with respect to how often (most always) accountics science researchers really cannot say anything about causality. See below.
David Johnstone asked me to write a paper on the following:
"A Scrapbook on What's Wrong with the Past, Present and Future of Accountics
Science"
Bob Jensen
February 19, 2014
SSRN Download:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2398296
Abstract
For operational convenience I define accountics science as research that features equations and/or statistical inference. Historically, there was a heated debate in the 1920s as to whether the main research journal of academic accounting, The Accounting Review (TAR) that commenced in 1926, should be an accountics journal with articles that mostly featured equations. Practitioners and teachers of college accounting won that debate.
TAR articles and accountancy doctoral dissertations prior to the 1970s seldom had equations. For reasons summarized below, doctoral programs and TAR evolved to where in the 1990s there where having equations became virtually a necessary condition for a doctoral dissertation and acceptance of a TAR article. Qualitative normative and case method methodologies disappeared from doctoral programs.
What’s really meant by “featured equations” in doctoral programs is merely symbolic of the fact that North American accounting doctoral programs pushed out most of the accounting to make way for econometrics and statistics that are now keys to the kingdom for promotion and tenure in accounting schools ---
The purpose of this paper is to make a case that the accountics science monopoly of our doctoral programs and published research is seriously flawed, especially its lack of concern about replication and focus on simplified artificial worlds that differ too much from reality to creatively discover findings of greater relevance to teachers of accounting and practitioners of accounting. Accountics scientists themselves became a Cargo Cult.
http://www.trinity.edu/rjensen/Theory01.htm#DoctoralPrograms
Example 1: An Accountics Science Illustration
Granularity
--- http://en.wikipedia.org/wiki/Granularity
Granularity is the extent to which a system is
broken down into small parts, either the system itself or its description or
observation. It is the extent to which a larger entity is subdivided. For
example, a yard broken into inches has finer granularity than a yard broken
into feet.
Coarse-grained systems consist of fewer, larger components than fine-grained
systems; a coarse-grained description of a system regards large
subcomponents while a fine-grained description regards smaller
components of which the larger ones are composed.
The terms granularity,
coarse, and fine are relative, used when comparing systems or
descriptions of systems. An example of increasingly fine granularity: a list of
nations in the United
Nations, a list of all
states/provinces in those nations, a list of all counties in those states, etc.
The terms fine
and coarse are used consistently across fields, but the term granularity
itself is not. For example, in investing, more
granularity refers to more positions of smaller size, while photographic film that is more granular has fewer and larger
chemical "grains".
For example, in the granular protocol analysis the researcher might record the
verbalized thoughts of a single real-world decision maker while making a
real-world decision such as how many equity shares of a company to add to a
client’s portfolio or a loan officer’s decision as to the maximum amount of
credit to extend to a borrower. Interviews and case studies typically do not
entail protocol recordings of actual decision making thought processes, but
interviews, surveys, and case studies often rely upon self-reporting by
decision makers on how decisions are reached ---
http://www.trinity.edu/rjensen/000aaa/thetools.htm#Cases
Purchased
databases such as Compustat do not contain the level of granular detail found
in protocol and case analyses. At the same time, protocol and case studies do
not contain the sample sizes of purchased databases of much coarser variables.
This is why case studies are sometimes called non-scientific “small-sample
studies.” There is no basis for scientific inference from small sample studies
except in the rare instance where even one anomaly will destroy a hypothesis.
A recent accountics science study suggests
that audit firm scandal with respect to someone else's audit may be a reason
for changing auditors.
"Audit Quality and Auditor Reputation: Evidence from Japan," by
Douglas J. Skinner and Suraj Srinivasan, The Accounting Review,
September 2012, Vol. 87, No. 5, pp. 1737-1765.
We study events surrounding
ChuoAoyama's failed audit of Kanebo, a large Japanese cosmetics company whose
management engaged in a massive accounting fraud. ChuoAoyama was PwC's Japanese
affiliate and one of Japan's largest audit firms. In May 2006, the Japanese
Financial Services Agency (FSA) suspended ChuoAoyama for two months for its
role in the Kanebo fraud. This unprecedented action followed a series of events
that seriously damaged ChuoAoyama's reputation. We use these events to provide
evidence on the importance of auditors' reputation for quality in a setting
where litigation plays essentially no role. Around one quarter of ChuoAoyama's
clients defected from the firm after its suspension, consistent with the
importance of reputation. Larger firms and those with greater growth options
were more likely to leave, also consistent with the reputation argument.
. . .
To test whether the F2006 auditor
switches away from ChuoAoyama are unusually frequent, we estimate a logit model
of factors that explain auditor changes. The control variables are drawn from
previous research on auditor switches and include firm size (log of total
assets), growth (percentage change in total assets), leverage, change in
leverage, profitability (ROA), a loss dummy, U.S. listing, keiretsu
inclination, auditor industry expertise, earnings quality as measured by
accruals, whether the firm completed an M&A transaction in the preceding
two years, and industry fixed effects.22 We provide details of data sources and
variable definitions in Appendix B. The keiretsu inclination variable measures
whether and to what extent these firms are part of the large corporate groups
common in Japan (e.g., Aoki et al. 1994; Hoshi and Kashyap 2001).
We include dummy variables for
whether the client is a ChuoAoyama client (CA), for fiscal year 2006 (F2006),
and for the interaction of these two dummies (CA_F2006). The interaction
variable is our primary interest because it measures the extent to which client
firms switch away from ChuoAoyama in fiscal 2006, the period in which we argue
that auditor reputation drives switching.
. . .
Our results are
largely consistent with the importance of reputation effects. We find evidence
that a relatively large number of ChuoAoyama's clients left the firm for other
auditors as the seriousness of ChuoAoyama's quality problems became evident.
The rate of client turnover at ChuoAoyama in fiscal year 2006, before it became
apparent that the firm would be shut down but after audit-quality questions had
been raised, was substantially higher than would otherwise be expected,
consistent with clients leaving once the firm's reputation for quality was
seriously diminished. Moreover, we find that the likelihood of switching is
higher for larger clients and clients with higher market-to-book ratios,
characteristics associated with a demand for higher-audit quality, and lower
for firms with greater managerial ownership, indicating a lower demand for
audit quality in such firms. Clients that moved to Aarata were also larger,
with higher market-to-book ratios, a greater extent of cross-listing, and
higher foreign ownership. These switches are not the result of clients
following their audit teams to new auditors. Our event study results weakly
support the auditor-quality argument, but are likely to lack power because
questions about ChuoAoyama's audit quality were revealed over an extended
period.
Our conclusions are subject to two caveats. First, we find that clients
switched away from ChuoAoyama in large numbers in Spring 2006, just after
Japanese regulators announced the two-month suspension and PwC formed Aarata.
While we interpret these events as being a clear and undeniable signal of
audit-quality problems at ChuoAoyama, we
cannot know for sure what drove these switches (emphasis
added). It is possible that the suspension caused firms to switch
auditors for reasons unrelated to audit quality. Second, our analysis presumes
that audit quality is important to Japanese companies. While we believe this to
be the case, especially over the past two decades as Japanese capital markets
have evolved to be more like their Western counterparts, it is possible that audit quality is, in general, less important in
Japan (emphasis
added) .
These are very honest admissions that extend to the entire history of most accountings science studies. The Skinner and Srinivasan inference that the audit firm’s loss of reputation caused a third of the clients to switch is very tenuous and superficial since two thirds of the clients remained loyal and did not switch. This suggests at a minimum that reasons for switching are far more complicated than assumed by Skinner and Srinivasan.
In other words, like most accountics science papers causality that is inferred could be slightly off base or largely off base. There’s no way of knowing because the accountics models cannot see the granules of causation. This is where non-science granular research might be of some help.
Non-science protocol analysis
is not of much use as a follow up to the Skinner and Srinivasan study since changing auditor
decisions in this study are one-time past historical events for the PwC-affiliated
ChuoAoyama auditor client switching and are not frequently repeated observable decision
events such as portfolio decisions of a trust investor or a bank’s decision to
set a credit limits of borrowers.
Non-science mail survey research where the clients
of the ChuoAoyama audit firm at the time are surveyed are not likely to be of
much use since there’s no incentive for those clients to respond at all, and if
some of them respond the results will be questionable since the respondents
quite likely to provide answers they think the researchers and public want to
hear.
Suggested
Interview Research Granulation Searches for Causality
So
what could be the reasons for switching away from the ChuoAoyama audit firm
other than the firm’s auditing scandal and resulting loss of reputation?
What comes to mind are
those clients who may have used the audit firm scandal as an excuse rather than
a reason to switch to another audit firm.
The ChuoAoyama audit firm is a Japan Big Three audit
firm (PwC) that is likely to be at or near the top in terms of Japan’s audit
fees. Perhaps clients initially chose this audit firm affiliated with PwC to
enhance their own appeal to investors in Japan’s fledgling equity markets.
After the fact, it’s very difficult to change such an audit firm without
possibly having a huge negative impact on the client’s stock price and credit
standing.
But some
clients in retrospect may be very unhappy about the high audit fees relative to
what it views as the quality of the audit and the importance of a ChuoAoyama
audit for equity prices relative to less expensive audit firm alternatives.
Thus we cannot rule out that some proportion of the
clients that changed audit firms did so because the highly publicized scandal
concerting the ChuoAoyama audit firm gave them an excuse to switch with
positive publicity rather than negative publicity.
Switching audit firms as an excuse
rather than a reason is entirely consistent with the accountics science
findings of the Skinner and Srinivasan article published in The Accounting Review.
So how could non-scientific
granulation studies provide added value to the Skinner and Srinivasan
scientific findings?
I
would look toward personal face-to-face interviews on causality. Case research
could also be used, but the number of clients that can reasonably be expected
to participate is probably larger for focused interviews vis-à-vis more
extensive cases. Interviews and case research has some advantages over mail survey
research if the interview/case researchers meet on-site and face to face with
respondents. There’s a better chance of getting at the real causes, although
there still might be reluctance to have those real causes publicized.
Interviewers should probably assure respondents that their responses will
remain anonymous.
Ideally the clients included in the interview
studies would be randomly picked if the entire population is not interviewed.
One reason this type of interview research is non-scientific is that there’s no
accounting for clients that absolutely refuse to participate.
The interviewers ideally should be highly respected
in the Japanese business community and be fluent in the Japanese language. This
is why they should probably be current or former Japanese citizens. However,
there might be exceptions such for well known case researchers like Robin
Cooper who is highly respected in Japan for his case research and writing
focused on Japanese companies.
Interview research experts should decide how best to
phrase the key questions and where to couch them in the entire interview. There
are many nuances in interview research to be considered when trying to get
potentially sensitive answers. This is where promises of anonymity may be
extremely important.
There are the usual scientific arguments against interview
and case research, including the possibility of cherry picking the clients to
be studied. The clients might not be entirely truthful about sensitive causal
factors. And the number of clients studied is miniscule relative to the number
of clients included in the accountics science study. However, this may be less
of a problem in the Skinner and Srinivasan since there is a relatively small population
of clients who switched audit firms.
Possible
Answer 1
Skinner and Srinivasan suggest (but could not conclude) that nearly all the
clients that changed audit firms did so because of the possible adverse effect
keeping the scandalous audit firm would have on cost of capital increases for
clients who used a scandal-ridden audit firm. But this suggestion is weak
because it cannot explain why a majority of the ChuoAoyama audit firm’s clients did not switch
auditors.
Possible Answer 2
Skinner and Srinivasan did not consider the possibility that some clients
switched auditors because the scandal gave them an excuse to dump an expensive
and possibly over-priced auditor while at the same time appearing to be more
noble when switching from a scandal-ridden auditor For example, the client may
strongly suspect the audit firm is padding the work hours for no good reason. If at least one interview found that the
scandal was an excuse rather than the reason for switching auditors we have
slightly more evidence of causality than we had with just the accountics science
study that can say zero about causality.
Possible Answer 3
Skinner and Srinivasan did not consider the possibility that some clients
switched auditors because the scandal gave them an excuse to change to
an auditor having a local office nearby that promised better service due to
response times and at lower cost due to such things as lower travel expense
billings. Auditors having nearby offices also improve relationship building at
civic meetings, golf outings, etc. This may not be ideal from the standpoint of
independence considerations, but clients are generally less concerned about
independence than investors.
Possible Answer 4
Skinner and Srinivasan did not consider the possibility that some clients
switched auditors because the scandal gave them an excuse to change from
an audit firm that communicated poorly with some clients. Reasons in general
that companies give for changing auditors are that their auditors communicated
poorly with management and audit committees.
Possible Answer 5
Skinner and Srinivasan did not consider the possibility that some clients
switched auditors because the scandal gave them an excuse to change from
an audit firm that was inefficient and superficial in the audit. For example,
the audit teams might be comprised of novice auditors having little or no
experience with the industry and/or the types of accounts being audited. For
example, auditors being assigned to audit interest rate swaps might keep asking
naïve questions about derivative instruments contracts and hedging.
Possible Answer 6
Skinner and Srinivasan did not consider the possibility that some clients
switched auditors because the scandal gave them an excuse to change from
a newly assigned partner in charge that the client really disliked relative to
previous partners in charge. Audit firms change partners in charge of audits
for various reasons, and client experiences with a new partner and charge may
greatly sweeten or sour the audit experience.
There
are of course many other possible
reasons for switching and/or retaining audit firms. We won’t really know until
we ask.
Conclusion
The point here is that non-scientific research methods have chances of finding
granules of causation that are impossible to find when the granules of
causation cannot possibly be uncovered in the accountics science studies that
do not drill down to granules of causation.
Example
2: Granular Non-Science Research and
Database Variables
On occasion, databases have granularity that’s
ignored in scientific study because the granularity is not easily placed in
mathematical models. Sometimes there’s just too much granularity. And the
granularity data may be too subjective and/or immeasurable. An example of bank
stress testing is shown below.
Banks must also submit much more granular
information, including dozens of details about individual loans.
"Stress for Banks, as Tests
Loom," by Victoria McGrane and Dan Fitzpatrick, The Wall
Street Journal, October 8, 2012 ---
http://professional.wsj.com/article/SB10000872396390444024204578044591482524484.html?mod=WSJ_hp_LEFTWhatsNewsCollection.
U.S.
banks and the Federal Reserve are battling over a new round of "stress
tests" even before the annual exams get going later this fall.
The
clash centers on the math regulators are using to produce the results. Bankers
want more detail on how the calculations are made, and the Fed thus far has
resisted disclosing more than it has already.
A
senior Fed supervision official, Timothy Clark, irked some bankers last month
when he said at a private conference they wouldn't get additional information about
the methodology, according to people who attended the event in Boston. Wells Fargo WFC -0.78%
& Co. Treasurer Paul Ackerman said at the same conference that he still
doesn't understand why the Fed's estimates are so different from Wells's. His
remarks drew applause from bankers in the audience, said the people who
attended.
The
annual examinations in their fourth year have become a cornerstone of the
revamped regulatory rule book—and a continuing source of tension between the
nation's biggest banks and their overseers.
Smaller
banks will soon have to grapple with similar requirements. On Tuesday, the
three U.S. banking regulators—the Fed, the Comptroller of the Currency and the
Federal Deposit Insurance Corp.—plan to complete rules requiring smaller banks
with more than $10 billion in assets to also run an internal stress test each
year. That would widen the pool of test participants beyond the Fed's current
requirement of $50 billion in assets, a group comprised of 30 banks.
The
stress tests, which started in 2009 as a way to convince investors that the
largest banks could survive the financial crisis, now are an annual rite of
passage that determines banks' ability to return cash to shareholders.
The
financial crisis taught regulators that they need to be able "to look
around the corner more often than in the past," said Sabeth Siddique, a
director at consulting firm Deloitte & Touche, who was part of the Fed team
that ran the inaugural stress test in 2009.
The
Fed asks the big banks to submit reams of data and then publishes each bank's
potential loan losses and how much capital each institution would need to
absorb them. Banks also submit plans of how they would deploy capital,
including any plans to raise dividends or buy back stock.
After
several institutions failed last year's tests and had their capital plans
denied, executives at many of the big banks began challenging the Fed to
explain why there were such large gaps between their numbers and the Fed's,
according to people close to the banks.
Fed
officials say they have worked hard to help bankers better understand the math,
convening the Boston symposium and multiple conference calls. But they don't
want to hand over their models to the banks, in part because they don't want
the banks to game the numbers, officials say.
It
isn't clear if smaller banks will have to start running their tests
immediately, as regulators have issued guidance indicating that midsize banks
will have at least another year until they have to run the tests.
One
new frustration for big banks is that the information requested by the Fed is
changing. This year the Fed began requiring banks to submit data on a monthly
and quarterly basis, in addition to the annual submission. Banks must also submit much more granular information, including
dozens of details about individual loans.
Fed
officials say the new data gives them the information they need to build their
stress-test models and to see banks' risk-taking over time. Banks say the Fed
has asked them for too much, too fast. Some bankers, for instance, have
complained the Fed now is demanding they include the physical address of
properties backing loans on their books, not just the billing address for the
borrower. Not all banks, it turns out, have that information readily available.
Daryl
Bible, the chief risk officer at
BB&T Corp., BBT -0.77% a Winston-Salem,
N.C.-based bank with $179 billion in assets, challenged the Fed's need for all
of the data it is collecting, saying in a Sept. 4 comment letter to the
regulator that "the reporting requirements appear to have advanced beyond
the linkage of risk to capital and an organization's viability," burdening
banks without adding any value to the stress test exercise. BB&T declined
further comment.
The
Fed has backed off some of its original requests after banks protested. For
example, the Fed announced Sept. 28 that it wouldn't require chief financial
officers to attest to the accuracy of the data submitted after banks and their trade
groups argued that the still-evolving process was too fresh and confusing for
any CFO to be able to be sure his bank had gotten it right.
Banks
needed more time to build up the systems and controls to report data reliably,
the Fed said. But the regulator also warned that it may require CFO sign-off in
the future.
Accountics
scientists and financial analysts typically ignore the granular data when they
build mathematical models of bankruptcy risk of a bank. For example, typical
mathematical models are the Value at Risk (VaR) model and the Altman Z-Score model. Neither model analyzes the
granular detail of a bank’s loans to specific
individuals.
Much more subjectivity in valuation
becomes necessary for "granular factors" that take uniqueness of each
loan into consideration. The typical valuation model is discounted cash flow
(DCF economic value) adjusted by granular factors. In 1932, Bill Paton (in his Accountants
Handbook), Bill Paton outlines granular "appraisal factors" in
the following categories:
1. Length of time the account has run.
2. Customer's practice with respect to discounts.
3. General character of dealings with the
customer.
4. Credit ratings and similar data.
5. Special investigations and reports.
This
highlights how ostensibly scientific databases can contain non-scientific
elements of data. For example, when the Fed’s stress test database contains a
data element for working capital, there’s little concern over the accuracy and
interpretation of this data point. But
when the database contains a description of the creditor’s general character
there’s a much more subjective aspect to this data even if the data is a single
point on a Likert Scale.
Whereas
bank managers and bank auditors may examine Paton’s granular detail on some
type of sampling basis, the VaR and Altman Z-Score scientists do
not build such granular detail into their models even on a sampled basis.
Qualitative Research
--- http://en.wikipedia.org/wiki/Qualitative_research
Qualitative
research is a method of inquiry
employed in many different academic disciplines, traditionally in the social sciences, but also in market research and further contexts.[1]
Qualitative researchers aim to gather an in-depth understanding of human behavior and the reasons that govern such
behavior. The qualitative method investigates the why and how of decision making, not just what, where, when.
Hence, smaller but focused samples are more often needed than large samples.
In the conventional
view, qualitative methods produce information only on the particular cases
studied, and any more general conclusions are only propositions (informed
assertions). Quantitative
methods can then be used to seek
empirical support for such research hypotheses. This view has been disputed by
Oxford University professor Bent Flyvbjerg,
who argues that qualitative methods and case study research may be used both for hypotheses-testing and for
generalizing beyond the particular cases studied
"How to qualitatively assess
indefinite-lived intangibles for impairment," Ernst & Young,
October 18, 2012 --- Click Here
http://www.ey.com/Publication/vwLUAssetsAL/TechnicalLine_BB2420_Intangibles_18October2012/$FILE/TechnicalLine_BB2420_Intangibles_18October2012.pdf
What
you need to know
• Companies that use the optional qualitative assessment and
achieve a positive result can avoid the cost and effort of determining an
indefinite-lived intangible asset’s fair value.
• Using the new qualitative assessment will require significant
judgment.
• Companies that use the qualitative assessment will have to
consider positive and negative evidence that could affect the significant
inputs used to determine fair value.
• Companies that have indefinite-lived intangible assets with
fair values that recently exceeded their carrying amounts by significant
margins are likely to benefit from the qualitative assessment.
• Using the qualitative assessment does not affect the timing or
measurement of impairments.
Overview
The Financial Accounting Standards Board (FASB or Board)
introduced an optional qualitative assessment for testing indefinite-lived
intangible assets for impairment that may allow companies to avoid calculating
the assets’ fair value each year.
Accounting Standards Update (ASU) 2012-021 allows companies to
use a qualitative assessment similar to the optional assessment introduced last
year for testing goodwill for impairment.2 The goal of both standards is to
reduce the cost and complexity of performing the annual impairment test.
ASC 3503 requires companies to test indefinite-lived intangible
assets for impairment annually, and more frequently if indicators of impairment
exist. Before ASU 2012-02, the impairment test required a company to determine
the fair value of
Continued in article
Bob Jensen's threads on intangibles and contingencies ---
http://www.trinity.edu/rjensen/theory01.htm#TheoryDisputes
The purpose of this paper was to show how non-scientific
qualitative research cannot only add value to quantitative studies, qualitative
research can find granules of causation that cannot be discovered in most
quantitative studies.
Bob
Jensen’s threads on case method research ---
http://www.trinity.edu/rjensen/000aaa/thetools.htm#Cases
"The Rise of Big Data: How It's Changing the Way We Think About the
World," by Kenneth Neil Cukier and Viktor Mayer-Schoenberger, Foreign
Affairs, May/June 2013 ---
http://www.foreignaffairs.com/articles/139104/kenneth-neil-cukier-and-viktor-mayer-schoenberger/the-rise-of-big-data
Big Data, we’re told, will change everything. So what will remain of intuition and serendipity in our brave new hyperquantified world?...
"Mechanical Turk and the Limits of Big Data: The Internet is
transforming how researchers perform experiments across the social sciences,"
by Walter Frick, MIT's Technology Review, November 1, 2012 ---
Click Here
http://www.technologyreview.com/view/506731/mechanical-turk-and-the-limits-of-big-data/?utm_campaign=newsletters&utm_source=newsletter-daily-all&utm_medium=email&utm_content=20121102
It’s telling that the most interesting presenter during MIT Technology Review’s EmTech session on big data last week was not really about big data at all. It was about Amazon’s Mechanical Turk, and the experiments it makes possible.
Like many other researchers, sociologist and Microsoft researcher Duncan Watts performs experiments using Mechanical Turk, an online marketplace that allows users to pay others to complete tasks. Used largely to fill in gaps in applications where human intelligence is required, social scientists are increasingly turning to the platform to test their hypotheses.
The point Watts made at EmTech was that, from his perspective, the data revolution has less to do with the amount of data available and more to do with the newly lowered cost of running online experiments.Compare that to Facebook data scientists Eytan Bakshy and Andrew Fiore, who presented right before Watts. Facebook, of course, generates a massive amount of data, and the two spoke of the experiments they perform to inform the design of its products.
But what might have looked like two competing visions for the future of data and hypothesis testing are really two sides of the big data coin. That’s because data on its own isn’t enough. Even the kind of experiment Bakshy and Fiore discussed—essentially an elaborate A/B test—has its limits.
This is a point political forecaster and author Nate Silver discusses in his recent book The Signal and the Noise. After discussing economic forecasters who simply gather as much data as possible and then make inferences without respect for theory, he writes:This kind of statement is becoming more common in the age of Big Data. Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics, where the data is so noisy. Statistical inferences are much stronger when backed up by theory or at least some deeper thinking about their root causes.
Bakshy and Fiore no doubt understand this, as they cited plenty of theory in their presentation. But Silver’s point is an important one. Data on its own won’t spit out answers; theory needs to progress as well. That’s where Watts’s work comes in.
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