August 2018
Information: Hard and Soft*
José María Liberti
Kellogg School of Management
Northwestern University
and DePaul University
and
Mitchell A. Petersen
Kellogg School of Management
Northwestern University
and NBER
June 2018
Abstract
Information is a fundamental component of all financial transactions and markets, but it
can arrive in multiple forms. We define what is meant by hard and soft information and describe
the relative advantages of each. Hard information is quantitative, easy to store and transmit in
impersonal ways, and its information content is independent of its collection. As technology
changes the way we collect, process, and communicate information, it changes the structure of
markets, design of financial intermediaries, and the incentives to use or misuse information. We
survey the literature to understand how these concepts influence the continued evolution of
financial markets and institutions.
Keywords: soft information, hard information, hardening soft information, boundaries of firm,
organizational design, lending, distance, transmission of information, FinTech
JEL codes: G20, G21, G30
* This is a significantly revised version of the working paper previously circulated as “Information: Hard and Soft”
(2004) by Mitchell A. Petersen. The authors thank the editors Efraim Benmelech and Paolo Fulghieri for their patience
and guidance in bringing this paper to fruition. We also thank Sumit Agarwal, Alan Berger, Richard Cantor, Bruce
Carruthers, Beverly Clingan, Barry Cohen, Kent Daniel, Diane Del Guercio, Bob DeYoung, Joey Engelberg, Scott
Frame, Andreas Fuster, Jon Garfinkel, Michael Faulkender, Andrew Karolyi, Juhani Linnainmaa, Tamim Majid,
David Matsa, Gregor Matvos, Amit Seru, Philip Strahan, and conference participants at the University of Oregon, the
Midwest Finance Conference, the University of South Carolina, and SFS Cavalcade for their suggestions and advice.
The research assistances of Sang Kim, Austin Magee, and Mark Scovic is greatly appreciated.
1
I) Introduction.
Information is an essential component in all financial transactions and markets. A major
purpose of financial markets and institutions is to collect, process, and transmit information. Given
the importance of information and its transmission to the study of finance, as technology changes
the way information is communicated, it also fundamentally changes financial markets, securities,
and institutions, especially financial intermediaries. However, new technologies (i.e., those
developed in the past fifty years) are more adept at transmitting and potentially processing
information that is easily reduced to numbers. We call this hard information. Information that is
difficult to completely summarize in a numeric score, that requires a knowledge of its context to
fully understand, and that becomes less useful when separated from the environment in which it
was collected is what we call soft information. Building upon the extensive literature on “soft” and
“hard” information, we examine the definitions of these terms and their role in understanding
financial markets and institutions.
The distinction between soft and hard information arose in the finance literature as a way
to understand the evolving organization of lenders, although the theoretical ideas reach back much
further. Banks have historically been a repository of information about borrowers’
creditworthiness and the kinds of projects available to them. This information was collected over
time through frequent and personal contacts between the borrower and the loan officer. Over time
the banks built up a more complete picture of the borrower than was available from public records.
This private information, most of it soft information, was valuable to the bank. The value arose
not only from its ability to inform the bank’s lending decisions but also due to the difficulty of
replicating and transmitting the information outside the bank.
2
The growth in the amount of numerical data available about borrowers, and the subsequent
ability to automate the credit decision, transformed banking from an exclusively local and personal
market to a national, competitive, and in some cases impersonal market. Some functions and
decisions which had resided inside the bank have been moved outside the bank due to a greater
reliance on hard versus soft information. Information type is an important characteristic of the
lending environment and helps explain how the design of lending markets and institutions in which
they operate has evolved.
Although the study of hard and soft information began in the banking literature, as
technology progressed, the role of soft or hard information in financial markets and institutions
outside of banking and even outside of finance has grown. Not only have researchers used these
concepts to examine a variety of financial markets and institutions (e.g., public equity markets,
venture capital, municipal bonds, and real estate), but they have examined how the type of
information available to an institution helped determine which organizational structures are
feasible and most efficient. Organizational constraints fed back into the kinds of information an
institution could effectively use.
The purpose of this paper is to survey the literature on soft and hard information in order
to provide a review of what we know but also identify which questions remain unanswered. We
describe what we believe to be the fundamental characteristics that define hard versus soft
information in Section II. This provides a framework of how these terms have been used in the
literature and which can be used to inform future work. We also discuss two historical examples
of the hardening of soft information: the origin of credit ratings and the creation of the Center for
Research in Security Prices. An institution’s or market’s decision to rely on hard or soft
information is driven by what is available but also by the relative advantages of each. In Section
3
III, we describe the main advantages of each type of information using the literature to provide
examples and intuition. In Section IV we return to the roots of the soft and hard information
literature. We start with a discussion of its foundation in the theoretical banking and organizational
design literature, and then we turn to efforts by the empirical literature to measure information
type, directly and indirectly (e.g., by using geographic or organizational distance). This leads us to
a discussion of the empirical challenge of designing incentives as a function of the type of
information an institution uses. In the next section, we examine applications of soft and hard
information outside of the banking literature. Specifically, we examine the lessons learned from
the financial crisis as seen through the lens of information type. We also discuss the emerging
work on FinTech, which in many ways is the newest attempt by markets and firms to replace soft
information with hard. This section provides a guide to the future evolution of this literature, as
financial innovation and financial crisis are reoccurring themes in finance. Section VI concludes.
II) Defining Soft and Hard Information.
An initial challenge of using soft and hard information as useful constructs has been
creating precise definitions. As the literature has expanded, the problem has not gotten easier. Thus,
we will start with a brief description of the attributes of information that make it soft or hard. This
description should be both consistent with much of the literature and also useful in framing
research questions. Like many labels in finance (e.g., debt versus equity), there is no clear
dichotomy. Rather than two distinct classifications, we should think of a continuum along which
information can be classified. Our interest is what characteristics of information, its collection, and
its use make it classifiable as hard or soft, and how these characteristics influence the structure of
financial markets and institutions.
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A) Characteristics of Soft and Hard Information.
1) Numbers versus text.
Hard information is almost always recorded as numbers. In finance we think of financial
statements, the history of payments which were made on time, stock returns, and the quantity of
output as being hard information. Soft information is often communicated as text.
1
It includes
opinions, ideas, rumors, economic projections, statements of management’s future plans, and
market commentary. The fact that hard information is quantitative means that it can easily be
collected, stored, and transmitted electronically. This is why the advent of computers, large
database programs, and networking has generated such growth in the use of production
technologies that rely on hard information (e.g., quantitative lending, quantitative trading, and
FinTech more generally).
2) The unimportance of context.
One can always create a numerical score from soft information, for example by creating an
index of how honest a potential borrower is. This in and of itself doesn’t make the information
hard. Your interpretation of a 3 must be the same as mine. Thus, a second dimension of hard
information is the unimportance of the context under which the information is collected. One can
collect and code information and then transmit it to someone else. The meaning of the information
depends only upon the information that is sent. It does not depend upon dimensions of the
environment under which it was collected but which are not encoded in the data (Ijiri 1975). Thus,
the receiver of the data knows all that the sender knows (or at least all that is relevant). With soft
1
Text files can obviously be translated into numbers; this is how they are stored and transmitted. Can’t text files be
processed electronically? Again, the answer has to be yes, conditional on what one means by processed. The ability
of computer algorithms to process and generate speech (text) has improved dramatically since we first discussed soft
and hard information. Whether it can be interpreted and coded into a numeric score (or scores) is a more difficult
question. A numeric score can always be created, the question is how much valuable information is lost in the process.
We call this process the hardening of information and we will discuss it below.
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information, the context under which the information is collected and the collector of the
information are part of the information. It is not possible to separate the two. This constrains the
environments in which the data is collected and used. The environment has to be well-defined and
predictable. In some cases, prior to entering the environments and collecting the data (e.g., talking
to a potential borrower), the decision maker such as a lender will know what variables they need
to collect, what possible values those variables can take (i.e., a signal will be either good or bad;
one, two, or three), and precisely how they will be used in the specific decision (values above 2
lead to a loan approval).
2
In other cases, prior to collecting the information, the lender may be
unsure what they might find or why it may be valuable until after they have collected the data.
3
Think of this knowledge as arising out of training and experience (Berger and Udell 2006). Later,
when confronted with a decision, the lender can recall the information collection process (e.g., the
experience), and only then will it become apparent how the information is useful. This
interdependence between collection and use is another characteristic of soft information. If at the
time of collection it is not known what the information will be used for, or which parts of the
information are relevant or useful, it will be difficult to code and catalog it for future use.
The importance of context for soft information is related to the distinction in the contracting
literature of whether a signal that is observable by outsiders is also verifiable by outsiders (Hart
2
A firm’s sales revenue or their stock return are examples of hard information. There is wide agreement as to what it
means for a firm to have had sales of $10 million last year or the firm’s stock price to have risen by 10%. However,
if we say the owner of the firm is trustworthy, there is less agreement about what this means and why it is important.
Our definition of trustworthy may be different from yours and the context under which we evaluate their
trustworthiness may be relevant.
3
This distinction is reminiscent of the difference between the approach we take when we teach first-year graduate
econometrics and the way empirical research is done in practice. In Econometrics 101, we assume we know the
dependent variable, the independent variables, and the functional form. The only unknown is the precise value of the
coefficients. In an actual research project, we have priors about the relationships between important economic concepts,
but we don’t know how to precisely measure the concept behind the dependent and independent variables nor the
functional form. Only after collecting the data, and examining the preliminary results, do we understand how the
variables are related. This leads us to modify our hypothesis and often requires the collection of additional data or a
change in our interpretation of the data. The research process helps us see and understand the missing context.
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1995; Aghion and Tirole 1997; Baker, Gibbons, and Murphy 2002). For a signal to be verifiable,
the interpretation of the signal by the two contracting parties—and any third party who may be
required to enforce the contract—must be the same. This is a characteristic of hard information.
By contrast, soft information is private and not verifiable as it involves a personal assessment and
depends upon its context, neither of which can be easily captured and communicated. Previous
lenders can produce records showing that a borrower has paid their bills on time (hard information),
but they cannot fully document for an unknown third party that a borrower is honest as this relies
on multi-dimensional observations and on each party’s personal assessment and standards.
Following the organizational economics literature, hard and soft information can also be referred
to as objective or subjective information.
3) Separation of information collection and decision-making.
The unimportance of context for hard information means it is possible to separate the
collection of hard information from the decision-making based on that information. Adam Smith
observed that the division of labor and specialization can create value in manufacturing; the same
principal applies to the collection and processing of hard information. Knowing what information
to look for and why it is valuable (i.e., what will it be used for) is essential if information collection
is to be delegated. The collection of hard information does not even need to be personal. Hard
information can be entered into a form without the assistance of or significant guidance from a
human data collector (home mortgage mobile apps are an example). The data collector does not
need to understand what decisions the data will be applied to. Soft information must be collected
in person, and the information collector and the decision maker are often the same person.
4
This
4
A typical example is the relationship-based loan officer. The loan officer has a history with the borrower and, based
on a multitude of personal contacts, has built up an impression of the borrower’s honesty, creditworthiness, and
likelihood of defaulting. Based on this view of the borrower and the loan officer’s experience, the loan is approved or
7
is the intuition behind Stein’s (2002) argument that smaller, less hierarchical firms are better able
to use soft information in their decisions. This is also why relationship lending is built upon soft
information (Berger and Udell 1995).
B) History of Soft and Hard Information
Historically, most information collection and decision-making was local and between
individuals who were familiar with each other, and thus the distinction between hard and soft
information was not relevant. As technology made it feasible for financial transactions to occur
between more distant and less familiar participants, the distinctions that we have been discussing
started to arise. We have implicitly been assuming that information type is static. Information is
either hard or soft; it is not malleable. This simplification allows us to focus on the definition and
advantages of each type of information. How discrete and immutable is information type? That is
an empirical question. We can think of hard information as a numeric index, but soft information
can and is converted into an index, though not without a loss of information or context. Markets
and individuals are constantly taking in soft (and hard) information and condensing (hardening) it
into binary decisions: whether to fund a project, sell a stock, or make a loan. This does not create
a meaningful loss of information if the decision is the final step and does not feed into later
decisions.
5
Before moving on to the relative advantages of hard and soft information, we will discuss
two historic examples of the hardening of information and the ways in which the process changed
denied. Uzzi and Lancaster (2003) provide detailed descriptions of such interaction between borrowers and loan
officers.
5
In Bikhchandani, Hirshleifer, and Welch’s (1992) study of informational cascades, they model sequential decisions
where agents see the (binary) decisions of prior agents but not the information upon which the decision is made. This
reduction (hardening) of information leads to agents ignoring their own (soft) information and following the crowd.
8
the markets or institutions that rely on this information. The examples are: the origin of credit
rating agencies and the creation of the Center for Research in Security Prices (CRSP).
1) The Origin of Credit Ratings.
Credit ratings originated in the United States during the nineteenth century. Prior to this
time, most trade among merchants was local. The extension of trade credit was common, and
merchants traditionally relied on soft information accumulated over time and through repeated
personal interactions to make their credit decisions (Carruthers and Cohen 2010a, Carruthers and
Cohen 2010b).
6
The development of communication and transportation technologies gradually
made it possible to sell one’s goods to a geographically much larger market. These were new
customers with whom merchants had no prior personal experience, and thus their traditional
approach to trade credit lending was not possible (Carruthers and Cohen 2010b). These
technological shocks created demand for new sources of information about creditworthiness that
did not rely on direct personal connections, i.e., hard information. This led to the formation of
firms which collected information about remote customers starting in the latter half of the 19
th
century.
7
These firms promised precise, standardized ratings that would allow merchants to avoid
extending credit to distant customers who were not creditworthy.
The credit rating bureaus established local offices in major cities and relied on local
merchants, lawyers, or bankers as the sources of their information. The input to the process was
the same soft information that had previously been the basis of credit decisions.
8
The credit
6
The authors’ description of trade credit markets during this period is strikingly similar to Nocera’s (2013) description
of the US consumer lending market of the 1950s.
7
The precursor to Dun and Bradstreet, the Mercantile Agency, was founded in 1841 (Carruthers and Cohen 2010b).
The precursor to Standard and Poor’s, the History of Railroads and Canals in the United States by Henry Poor, was
founded in 1860.
8
“…what went into credit evaluations was a variable and unsystematic collection of facts, judgments and rumors
about a firm, its owner’s personality, business dealings, family and history. …what came out was a formalized,
systematic and comparable rating of creditworthiness…” (Cohen 1998, Carruthers and Cohen 2010b)
9
agencies used this information to create two credit scores which were sold to merchants: pecuniary
strength (essentially net worth) and general credit (ability and willingness to repay, Carruthers and
Cohen, 2010b). In this way, the agencies were able to take the soft information based on personal
contacts and available to local merchants and provide it in a form that was useful to distant
merchants. Merchants could make lending decisions based on this number, even though they had
no contact with the potential customers or the data collectors. The standardization of this
information in the form of credit score resulted in a very early form of hard information, which
allowed the geographic reach of trade credit lenders to expand. This is an example of how soft
information can be hardened.
2) Creation of Center for Research in Security Prices (CRSP).
The second historical example comes from the equity markets. The Center for Research in
Securities Prices began as a database of monthly and then daily returns on all NYSE stocks in the
early sixties: stereotypic hard information. There is rarely disagreement about what a return of four
percent means. Prior to the construction of the CRSP databases, however, there was limited
knowledge about what the returns on equities actually were, let alone what the determinants of
equity returns were.
9
The existence of a comprehensive database containing the returns on all
stocks unleashed a torrent of research into the determinants of both expected returns (e.g., factor
9
CRSP began with a question from Louis Engel, a vice president at Merrill Lynch, Pierce, Fenner and Smith. He
wanted to know what the long-run return on equities was. He turned to Professor Jim Lorie at the University of Chicago,
who didn’t know either but was willing to find out for them (for a $50,000 grant). The process of finding out led to
the creation of the CRSP stock return database. The fact that neither investment professionals nor academic finance
knew the answer to this question illustrates how far we have come in depending upon hard information such as stock
returns. Professor Lorie described the state of research prior to CRSP in his 1965 Philadelphia address: “Until recently
almost all of this work was by persons who knew a great deal about the stock market and very little about statistics.
While this combination of knowledge and ignorance is not so likely to be sterile as the reverse—that is, statistical
sophistication coupled with ignorance of the field of application—it nevertheless failed to produce much of value.” In
addition to CRSP, he talks about another new dataset: Compustat (sold by the Standard Statistics Corporation) which
had 60 variables from the firm’s income statement and balance sheet.
http://www.crsp.com/research/james-lorie-recognized-importance-crsp-future-research
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models) and realized returns (e.g., event studies). It was now possible to carefully document what
announcements or events influence stock prices (MacKinlay 1997). The dependent variable is a
unidimensional index of value: the stock price (or changes in the stock price). The independent
variables in this work are also coded into numeric values. Initially the coding was rudimentary:
dividends increased, decreased, or did not change. Over time, the independent variables used to
explain stock returns in event studies became more elaborate. However, they were always
quantitative simplifications of the underlying events.
Although the event studies often found important determinants of stock prices, even when
they focused on the individual days when seemingly large announcements were made, the fraction
of cross-sectional variability that the models were able to explain was small (Roll 1984; Roll 1988).
This omission could be due to daily movements driven by the trading process (market micro-
structure effects) or by the inadequacy of the explanatory variables. There are many forces that
move stock price (e.g., rumors, news accounts, or different interpretations of public releases) that
are not easily and accurately converted to a numeric score. Although market participants capture
this soft information and impound it into the hard information of stock prices, the academic models
have had difficulty replicating the process.
III) Advantages and Disadvantages of Hard Information.
The choice between hard and soft information is driven by its availability and, more
importantly, by the relative costs of each. In this section, we describe the relative advantages of
hard or soft information. The objective is both to explain why one kind of information is preferable
in a given context, but also to more fully understand the definition of each.
A) Lower Costs of Production and Market Competition.
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One of the major advantages of using hard information is the lower transactions costs
(Frame, Srinivasan, and Woosley 2001). These savings come from several sources. First, by its
nature, production technologies (such as loan originations) that depend upon hard information are
easier to automate. The job of collection, and in some cases processing of information, can be
delegated to lower-skilled workers or computers. Expensive labor can be replaced by cheaper labor
or cheaper capital.
Hard information is more standardized. By construction it arrives in the same format and
is processed in the same way for each application or transaction. The expertise to make the decision
given the possible inputs is embedded into the decision rules or the computer code. This
standardization introduces savings into the production process due to economies of scale. Once
the computer system is designed and built to retrieve credit scores from the credit bureau and make
an approval decision, adding additional applications to the system has a small incremental cost.
This is one reason why lending based on hard information (e.g., credit cards) has come to be
dominated by large lenders much more so than traditional relationship lending (Cole, Goldberg,
and White 2004; Berger, Miller, Petersen, Rajan, and Stein 2005; Berger and Black 2011). These
potential cost savings have created economic pressure to find ways to automate small loans to
firms or individuals, since a large fixed cost can make these loans prohibitively expensive.
10
Greater reliance on hard information may also increase the competitiveness of financial
markets.
11
First, the standardization of information and the resulting lower transactions costs can
expand the size of the market by increasing the number of suppliers who can profitably offer such
10
For small business loans, the size of the fees is independent of the size of the loan. Thus, the percentage fee declines
with loan size (Petersen and Rajan 1994). The lowering of transactions costs, especially through digital delivery and
automation can be particularly important in microfinance lending where the loan amounts are very small (Karlan et
al. 2016)
11
The causation can also run in the opposite direction. Greater competition, which can arise from deregulation for
example, increases the pressure to lower costs and thus to transform the production process to depend more on hard
information.
12
loans or services. In addition to expanding the number of suppliers in a given market, a reliance
on hard information can also increase the geographic reach and competitive impact on existing
suppliers. The evolution of the mortgage and signature loan (now called the credit card) market is
an example.
12
In the 1950s, the market was local and based on soft information obtained through
personal contact. It is now national and based on hard information often obtained through
impersonal contact. This has led to a wider availability of and arguably cheaper capital for the
middle class (Nocera 2013).
The nature of the information may also increase the competitiveness of the markets. Once
information is systematized and easy to communicate (hard), it also becomes more difficult to
contain. In the early years of the credit reporting agencies (e.g., J. M. Bradstreet & Son or R. G.
Dun), only a summary of the information the agencies had on borrowers was published in their
quarterly books. This disclosed information was quantitative and easy to compare and
communicate. For an additional fee, subscribers could visit the office of the agencies to view a
detailed report on a potential customer. The credit rating bureaus were either unable or unwilling
to quantify and include all of the soft information they held into their reported credit scores.
Interestingly, information in these private reports was better at predicting bad outcomes (business
failures) than the published credit ratings (Carruthers and Cohen 2010b). By keeping the
information difficult to replicate and transmit, by maintaining its softness, the credit reporting
agencies hoped to maintain their control over the information and thus extract greater rents from
the information they had collected. Once information is hard, providers have difficulty preventing
one customer from passing it to additional customers who can then capture the information’s full
value. Information that is hard can be understood independent of the collector and the context
12
Subprime mortgage loans are less standardized and more informationally sensitive than normal mortgages because
sometimes borrowers are not able to provide full disclosure of their income (Mayer, Pence, and Sherlund 2009).
13
under which it was collected. If the collector is not necessary once the user has the data, this makes
charging high rates for the information more difficult.
B) Durability of Information.
The durability of information is also greater when it is hard. The fact that it is easily stored
means that the cost of maintaining it for future decisions is low. The fact that the information can
be interpreted without context means that it is possible to pass it along to individuals in different
parts of an organization (Stein 2002). Individuals or even firms no longer need to be part of the
data collection process to be part of the decision-making process. This ease of interpretation is
especially important if the people involved in data collection are not expected to be around in the
future. It effectively frees the decision process from constraints of space (distance) and time. Given
the increased turnover in many finance professions (loan officers or investment bankers), the
movement toward hard information seems inevitable.
13
As described in Crane and Eccles (1988),
junior investment bankers used to rise through the bank at the same time as junior employees of
their clients were rising through the ranks at their own firms. By the time junior bankers became
senior bankers, they had developed a relationship with the people who were now in senior
management positions at the client firms. There is no need to rely on formal records (hard
information) in the presence of these long-term relationships. However, if bankers turn over more
frequently, new bankers must rely on the records left behind by the previous bankers (Morrison
and Wilhelm 2007). This creates a greater reliance on hard information.
C) Lost Information.
13
Karolyi (2017) finds that the relationship lies with the individuals, not the firms. After exogenous changes in
leadership (the death or retirement of a CEO), firms are significantly more likely to switch to lenders with whom the
new CEO has a relationship (see also Degryse, Liberti, Mosk, and Ongena 2013). This is one reason why firms that
rely on soft information in securing debt capital care about the fragility of the banks from which they borrow (Schwert
2017).
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Part of the reason that hard information is less costly to communicate is that there is less
of it. The replacement of soft with hard information inevitably results in a loss of information (as
when an analog signal is converted to a digital signal). This is why it is possible to use a smaller
bandwidth to transmit the information. As an example, compare two methods of making a loan
approval decision. First is the stereotypic credit scoring decision, in which a finite number of
quantitative variables are weighted and summed to obtain a credit score. The loan is approved if
the value of the score is above a critical value. Now compare this to the traditional relationship
approach to lending. After spending several hours discussing the borrower’s investment plans and
using the loan officer’s years of experience with the borrower and knowledge of the local business
environment, a decision is rendered. Both decision-making methods lead to a binary approval or
rejection decision, but the first requires less information as inputs into the decision.
The reduction of information is never good, as long as processing costs are zero. However,
decision makers (e.g., the loan approval committee of a bank) have limited time and attention to
devote to each decision.
14
To prevent information overload, decision makers need the information
to be boiled down to what is important.
15
The larger the organization, and the higher one goes in
the organization, the more the information needs to be concentrated or the decision-making
authority needs to be delegated. The question then becomes, not whether information will be lost,
but how important the lost information is. The concern about small firms’ and individuals’ access
to capital in the presence of bank consolidation and the growing use of credit scoring type lending
14
Using a randomized control trial, Paravisini and Schoar (2015) evaluate the adoption of credit scores in a small
business lending setting. They find that using credit scores improves the productivity of credit committees (e.g., less
time is spent on each file).
15
Friedman (1990) argues that this is one advantage of a market versus a planned economy. He argues that all of the
information that is relevant to a consumer or producer about the relative supply of a good is contained in the price.
Thus, it is not necessary for a supplier to know whether the price has risen because demand has risen or supply has
fallen. The supplier only needs to know that the price has risen, and this will dictate her decision of how much to
increase production. Friedman’s description of a market economy depicts a classic hard information environment.
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decisions is driven by this question (O’Neil 2016). If there are borrowers that are really good credit
risks, but they look bad on paper (i.e., when only hard information is considered), then such
borrowers would be incorrectly denied credit. How often are such mistakes happening? The
empirical evidence thus far is mixed. It is clear that some small borrowers are dislocated by their
banks when the banks merge, but there is also evidence that existing and new small banks may fill
the gap (Berger, Miller, Petersen, Rajan, and Stein 2005; Berger, Goulding, and Rice 2014;
DeYoung, Gron, Torna, and Winton 2015; Berger, Bouwman, and Kim 2017).
D) Gaming the System.
Accounting numbers, such as a firm’s income statement and balance sheet, are a classic
example of hard information. The information is all quantitative, it is easy to store and transmit
electronically, and there is relatively uniform agreement about what numbers like revenues and
costs mean. Quantitative decisions from asset allocation to credit approval all rely on these
numbers because of those characteristics. At the same time, newspaper accounts of accounting
manipulation and the size of the credit rating manuals make it clear that these decisions are not
simply a function of the numbers the firms disclose. This ambiguity raises another cost of using
hard information (e.g., automated or delegated decisions methods): a loss of certainty regarding
who controls the information which is fed into the decision-making process.
The discussion thus far has focused on the decision maker (e.g., the loan officer making a
loan decision), not the target of the decisions (e.g., a loan applicant). By choosing to use hard
versus soft information, the (ultimate) decision maker is trading off the lower cost of collecting
and processing the information with potential loss in accuracy of the information upon which they
are basing their decisions. The way a decision is made, including the type of information upon
which the decision depends, will also influence the actions of the target of the decision.
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The behavioral response of borrowers (or other targets of the decision) places restrictions
on how decisions based on hard information can be made. Having a decision depend entirely upon
the numbers and a transparent decision rule can work, but only if the cost of manipulating the
numbers is sufficiently high relative to the benefit of the preferred outcome.
16
If a firm can raise
its reported assets or sales by a small amount for a small cost, and this will raise its credit rating
and lower its cost of capital sufficiently, it has an incentive to inflate its reported assets or sales.
17
The rules cannot be a direct and transparent function of the hard numbers if the hard numbers are
under the discretionary control of the target of the decision. In this case, the decision maker has an
incentive to make the decision a fuzzy and opaque function of the inputs. The line between an AA
and an A rating can be kept secret or additional sources of soft information can be included.
18
In
16
In the financial crisis of 2008, a large number of investment grade securities defaulted. The magnitude of the defaults
suggested there was a problem with the rating process (see Benmelech and Dlugosz 2009a; Benmelech and Dlugosz
2009b). Observers in industry, academics, and government suggested possible sources of the problem and potential
solutions. What is intriguing is the defaults experience was very different in the corporate bond market (debt of
operating companies) compared to the structured finance market (e.g., RMBS). Defaults in the corporate bond market
spiked in 2009, but the peak is not drastically different than the peak in prior recessions (see Vazza and Kraemer 2016,
Chart 1). The peak in defaults in the structured finance in 2009 is dramatically larger (see South and Gurwitz 2015,
Chart 1). Although the collapse of the housing market hit the structured finance securities harder, this suggests that a
part of the problem with the rating process resides uniquely in the structured finance segment of the market. For an
operating company, a low cost of capital is an advantage but not its only or predominant source of competitive
advantage. For a securitization structure, a lower cost of capital is one of its few source of “competitive advantage.”
Thus, a bank might change which mortgages are placed into a securitization if this change would increase the faction
of the securitization rated AAA and thus lower the cost of capital. An auto-manufacturing firm is unlikely to close
plants or close down a division solely to get a higher credit rating. The costs of altering the business to improve a
credit score are higher and the benefits are (relatively) lower for an operating firm. This may be why we saw relatively
fewer defaults in the corporate bond sector relative to the securitized sector. This issue prompted the credit rating
agencies to consider different rating scales for structured finance versus corporate debt (Kimball and Cantor 2008).
17
Hu, Huang, and Simonov (2017) see the same behavior in the market for individual loans. The theoretical
importance of nonlinearities in the mapping of inputs (hard information) to outputs (decisions) is discussed in Jensen
(2003). In his examples, the incentives to misstate one’s information are smaller if the payoff function is linear. Small
changes in the reported information have only small changes in the manager’s payoff.
18
There may also be strategic reasons to avoid a transparent mapping between the numbers and the credit rating. The
business model of credit rating agencies relies on market participants being unable to replicate the ratings at lower
cost than the agency. If the mapping were a direct function of easily accessible inputs (e.g., the income statement and
balance sheet) and nothing else, some clever assistant finance or accounting professor would figure out the function.
This is one reason that the early credit reporting agencies released only a fraction of their information publicly in the
form of a credit score. For additional fees, users could review a more complete report (Carruthers and Cohen 2010a,
Cohen and Carruthers 2014).
17
practice, ratings models that try to explain the ratings as a function of the firm’s financial numbers
have R
2
appreciably below 100 percent.
E) The Role of Discretion.
Hard information reduces the information that is used, but equally importantly it delegates
the decision-making authority. The individual collecting the data does not make the decision. This
role has been delegated to a higher up or to an algorithm (whose author is divorced from the target
of the decision). The separation of these two roles should eliminate discretion, and this can be a
positive or a negative. Relationships are useful as a way to elicit information that is not available
in the numbers. Relationships have additional, non-informational, dimensions as well.
Relationships generate a sense of mutual obligation (reciprocity). You help me out and I want to
help you out (Uzzi 1999). Thus, when a loan officer is evaluating a potential loan from a long-
term borrower, they can use their discretion to more accurately evaluate the borrower’s current
credit quality as well as any changes in the likelihood of repayment. A borrower in these cases
would not be defaulting on an obligation to an unknown faceless financial institution, but to
someone with whom they have worked for years.
19
These examples illustrate the positive side of
the relationship that can be lost with decisions based on hard information.
On the negative side, loan officers can also use their discretion to put a thumb on the scale
and influence a loan decision for their own benefit. A number of academic papers have documented
that loan officers do use their discretion, and in the documented cases, the discretion does not
improve the quality of the decision.
20
The challenge is one of incentives. The loan officers are not
19
Guiso, Sapienza and Zingales (2013) find that borrowers feel less obligated to repay an underwater mortgage if the
mortgage has been sold in the marketplace.
20
Brown, Schaller, Westerfeld, and Heusler (2012) find that loan officers use discretion to smooth credit, but there is
limited information in discretionary changes. Degryse, Liberti, Mosk, and Ongena (2013) provide evidence that soft
information helps predict defaults over public information (e.g., financial statements), but discretionary actions do not
predict default. Gropp, Gruendl, and Guettler (2012) show that the use of discretion by loan officers does not affect
the performance of the bank portfolio. Puri, Rocholl, and Steffen (2011) document the widespread use of discretion
18
lending their own capital, but the bank’s. The bank manager or shareholder must trade-off the
value of the loan officer using their soft information (better quality decision and lower transactions
costs) against the misaligned incentives between the loan officer and the bank. The advantage of
hard information is that it can remove the loan officer’s discretion. The relevant variables and the
mapping from the variables to the decision is beyond the control of the loan officer in these cases.
21
IV) Traditional Banking and the Organizational Design of Lending.
The evolution of financial markets over the past forty years has been in part a replacement
of soft information with hard information as the basis for financial transactions. The full
ramifications of this transformation are not yet fully apparent, and as we discussed above, there
are both advantages and disadvantages of this transformation. In this section, we describe the
evolution of soft information since its theoretical origins, the application of the concept of
information type in the traditional banking literature, and its implications for the organizational
design of lending by financial intermediaries.
A) Beginnings: Theoretical Literature.
The finance literature has been exploring the distinction between soft and hard information
for several decades now, and our understanding has evolved since the early years. The distinction
was not always explicitly stated, and even when it has been, the definition was not complete,
inside a German savings bank but find no evidence that loans approved based on discretion perform differently than
those that do not use discretion. Cerqueiro, Degryse, and Ongena (2011) find that discretion seems to be important in
the pricing of loans, but plays only a minor role in the decision to lend.
21
This turns out to be an imperfect solution when the loan officer has an incentive and the ability to manipulate the
inputs, just as the borrower might. The loan officers in Berg, Puri, and Rocholl (2016) work for a bank that uses an
internal credit score to evaluate loans. They show that loan officers repeatedly enter new values of the variables into
the system until a loan is approved. Not only are they able to get loans approved that were originally rejected, but they
also learn what the model’s cut offs are and thus what is required to get a loan approved. These results suggest that
even hard information decision-making algorithms which are transparent and depend upon data subject to the control
of either participant (local decision maker or the target of the decision) are subject to the Lucas critique (see the
Gaming the System discussion above).
19
formally treated, or consistent across applications. One origin of soft and hard information traces
back to the theoretical financial intermediation literature and the distinction it drew between the
role of banks (or other private lenders) versus the public bond markets. A key distinction was the
superior ability of banks to collect and process information (Diamond 1984; Diamond 1991;
Ramakrishnan and Thakor 1984; Allen, Carletti, and Gu 2015). This explained why many opaque
firms relied exclusively on banks. The public debt markets, however, with the help of rating
agencies, have the same job description: to evaluate the credit quality of firms (Ederington and
Goh 1998). The difference is the type of information each specializes in collecting and processing.
The public bond markets and the rating agencies collect financial disclosures, accounting
reports, and default histories. These are sources of hard information. They can all be reduced to a
series of numbers. Banks, on the other hand, especially as described by the lending relationship
literature, collect information that is neither initially available in hard numbers (the ability of the
managers, their honesty, the way they react under pressure), nor easily or accurately reducible to
a numerical score. Even if reduced to a numerical score, the interpretation of the information may
be judgmental and include a discretionary component (Cole, Goldberg, and White 2004; Hertzberg,
Liberti, and Paravisini 2010). Once the relationship is established, even then this information is
not hard. The firm is still unable to communicate this information to the broader lending markets
and thus negotiate a lower loan rate from its bank (Petersen and Rajan 1994).
Originally, finance scholars borrowed the concept of soft information from organizational
economics and the theoretical literature on decision making in organizations. One feature of those
initial models was that the interests of the parties were imperfectly aligned. This misalignment
created incentives for individuals to distort the information that was collected and transmitted in a
20
way that influenced decisions to their advantage (Milgrom and Roberts 1988).
22
The fact that
information needs to be transmitted to a superior who ultimately has final authority in the decision-
making process—in our case, a loan officer who transmits soft information to their supervisor—
led the traditional banking literature to analyze the role that organizational form may play in the
lending process.
B) The Role of Organizational Form in Lending.
In many industries, both large and small firms coexist. One might think that a dominant
production technology would lead to a uniform firm size. However, if the information collection,
processing, and communication is fundamentally important to the production process (e.g.,
banking, drug research, or film production, Goetzmann, Ravid, and Sverdlove 2013), then firms
may specialize in different sectors of the market depending upon the type of information (hard or
soft) that is used in their production process. Some firms may specialize in production processes
based on soft information, and others in a production process based on hard information. Stein
(2002) argues that larger, more hierarchical firms, where the decision maker is further from the
information collector, are more likely to use production technologies that rely on hard information
(Brickley, Linck, and Smith 2003). These organizational diseconomies suggest that large banks
are expected to be less efficient at making relationship loans—that is, loans that depend upon soft
information. In a large bank, information may be collected by one individual or group, and a
decision made by another. These decisions require information that can efficiently be transmitted
22
There are a variety of possible costs embedded in the transmission of information in an organization. Theories of
costly communication, where soft information may be more costly to communicate across hierarchies (Becker and
Murphy 1992; Radner 1993; and Bolton and Dewatripont 1994); theories of loss of incentives to collect, process and
use soft information as in Aghion and Tirole (1997) due to the anticipation of being overruled by one’s superior; and
strategic manipulation of information as in Crawford and Sobel (1982) and Dessein (2002) offer three different but
related explanations. In all of these theories, those who send the information will make it noisier and less verifiable if
their preferences are not aligned with those who are receiving it and, ultimately, have the final authority to make the
decision.
21
across physical or organizational distances. The information must also have a uniform
interpretation that does not depend upon the context under which it was collected. Large banks are
more likely to have multiple layers of management. They are hierarchical or centralized as opposed
to flat or decentralized organizations. Thus, the oversight of loans in this context implies that larger
banks rely relatively more on hard information (Berger, Miller, Petersen, Rajan and Stein 2005;
Qian, Strahan, and Yang 2015; Liberti 2017).
The literature on organizational form in financial institutions has exploited the distinction
between hard and soft information to help explain the scope of the firm, but these ideas appear in
the economics literature much earlier. As noted above in Section II-A-3, a key feature that made
scholars interested in exploring issues of hard information in traditional banking is that the process
of collection and decision-making are separated; thus, transmission becomes especially important.
Since Coase (1937), the idea of allocating control and decision-making within organizations has
been a core principal of the theory of the firm. The allocation of control shapes the incentives of
agents working in the organizations. In seminal papers, Grossman and Hart (1986), Hart and
Moore (1990), and Hart (1995) define allocation of control as arising from the ownership of a
tangible asset. In the case of financial institutions (or other firms whose production process
depends upon intellectual property or information that is not easy to transmit), the critical resource
or asset is intangible in nature: the access to information, especially soft information that needs to
be communicated to the decision maker.
23
As financial institutions have become larger, more globalized, and more complex, they
face a tradeoff between benefits arising from economies of scale and costs of inferior
23
Rajan and Zingales (1998) argue that ownership is not the only way to allocate power in an organization. Another
and in some cases a better way is through access. Access is the ability to work with or use a critical resource, not
necessarily a physical resource that can be owned. In financial institutions (and increasingly in other firms), this
resource is often information.
22
organizational designs. This has led to a debate in the banking literature over whether a
decentralized organizational structure is better or worse than a centralized one in terms of
providing the right incentives to loan officers to collect, process, transmit, and use soft information.
The discussion has centered on how the informational distance between the decision maker and
the loan officers shapes the nature of information acquisition and, therefore, the types of activities
performed inside or outside the financial institution. As innovations in communication technology
have reduced the cost of accessing information at a distance, they have also changed the
competitive landscape of banking (see also the discussion of Fintech in Section V-C). For example,
Liberti, Seru and Vig (2017) examine the introduction of a credit registry in Argentina. They find
that it led to an improvement in the allocation of credit to borrowers for whom there was now more
public hard information available, but it also changed the internal organization of the bank. We
next turn to the empirical literature that has explored these issues.
C) From Geographical to Hierarchical Distance.
Based on the theoretical predictions regarding the challenge of transmitting some types of
information (i.e., soft information), the empirical literature has attempted to document the
importance of distance. An important branch of the traditional banking literature shows that
geographical distance affects lending decisions (Petersen and Rajan 2002; Degryse and Ongena
2005; Mian 2006; DeYoung, Glennon, and Nigro 2008; Agarwal and Hauswald 2010).
24
The
24
Although these papers all examine geographical distance, they are different in nature. Petersen and Rajan (2002)
document that distance between lenders and borrowers increased due to improvements in information technology.
Degryse and Ongena (2005) study the relationship between the competitiveness of the lending market and the distance
between the borrower, their lender, and other potential competitors (banks). Mian (2006) suggests that greater distance
not only decreases the incentives of a loan officer to collect soft information, but also makes it more costly to produce
and communicate soft information. DeYoung, Glennon, and Nigro (2008) document the relationship between the use
of hard information using credit scoring technologies and an increase in borrower-lender distances. Finally, Agarwal
and Hauswald (2010) study the effects of distance on the acquisition and use of private information in informationally
opaque credit markets. They show that borrower proximity facilitates the collection of soft information, which is
reflected in the bank’s internal credit assessment.
23
literature has interpreted this finding largely in terms of the difficulty of transmitting soft
information. Despite its prominence, this interpretation is largely based on the observed correlation
between loan characteristics and distance.
A classic example of a first generation paper using geographic distance is Berger, Miller,
Petersen, Rajan, and Stein (2005). Consistent with Stein (2002), they find that larger banks are
more likely to lend to more distant customers (a greater physical distance between a firm and its
bank) and communicate with borrowers more impersonally (by mail or phone rather than face to
face). They also find that relationships between a firm and its banks are less durable and less
exclusive when the banks are larger. Most importantly, they find that firms that are forced to
choose a larger bank than they would prefer (i.e., informationally opaque firms) are credit rationed.
When informationally opaque firms have the choice of which size bank to borrow from, they
choose to borrow from smaller banks. The correct matching alleviates much of the credit rationing.
Second generation papers focused on the organizational structure of financial
intermediaries. The lower costs of producing hard information can depend on more than just the
nature of the information. It may also depend upon the organizational design of financial
institutions. Lenders who are larger in size and hierarchically organized benefit from economies
of scale in using hard information but can find it more costly to transmit soft information. This
will cause them to place a greater reliance on hard information. Nevertheless, large banks may try
to mimic the organizational structure of small banks in order to be more efficient in collecting soft
information and, therefore, to improve their ability to compete against smaller banks. For example,
Liberti and Mian (2009) use hierarchical distance between loan officers and their superiors to study
the causal impact of hierarchical structures on the relative importance of hard versus soft
information in the credit approval decisions inside a large financial institution. The authors find
24
that greater hierarchical distance is associated with less reliance on soft information and more on
hard information. They also find that personal interaction between loan officers and the superiors
approving the loans helps mitigate the effects of hierarchical distance on information use and
minimizes the loss of soft information (see also Qian, Strahan, and Yang 2015).
One natural question is why the delegation of decision rights would be a solution to the
problem of transmission of soft information. There are several potential explanations. An
incomplete list includes increased capacity in a limited-resources environment, expertise,
communication costs, and ex-ante incentives. Assigning a task to another person adds capacity to
carry out the task by relying on the comparative advantage of that specific individual. The
collection and processing of soft information is a time-consuming activity, and a bank manager,
possessing limited time, may decide to delegate certain tasks. There may be efficiency gains from
using a loan officer with the ability and skill set to collect soft information (Geanakoplos and
Milgrom 1991).
Delegation of decisions may also arise because of the difficulties of communicating
specific information to the superior, making communication costs high, as described in Jensen and
Meckling (1992). A key tradeoff in the organizational design of lending occurs between efficient
communication and the cost of collecting soft information. Garicano (2000) explores the
acquisition of knowledge by the creation of knowledge-based hierarchies in a more general setting.
In these hierarchies, certain individuals solve the easiest problems, and more difficult problems
are solved by specialized supervisors. In our banking setting, loan officers may have the easier
task of collecting information while supervisors use the information to make the final decisions.
There is another strand of the literature that focuses on ex-ante incentives (Aghion and
Tirole 1997, Crawford and Sobel 1982, Dessein 2002). For example, Aghion and Tirole (1997)
25
and Stein (2002) argue that large hierarchical organizational structures inhibit the ex-ante
incentives to collect and use soft information. This decrease in incentives occurs because those in
charge of collecting soft information cannot act on it, and instead have to send the information to
their superiors. The nature of soft information means it may be overruled or ignored, thus
dampening the incentives for loan officers to collect it. Liberti (2017) provides support for this
view by showing that loan officers who receive relatively more authority put more effort into
collecting and using soft information.
D) Implications of Relying on Soft Information
Consolidation of financial institutions may have a negative impact on small business
lending due to the potential loss of soft information and of the incentives to collect it going
forward.
25
Small banks are superior at relationship lending using soft information because their
fewer layers of management make it easier to communicate and use such information. Large banks
can simulate the managerial environments of small banks, in order to minimize the negative impact
of losing information (Berger, Miller, Petersen, Rajan, and Stein 2005). The results in Liberti (2017)
also highlight how a large bank may be able to replicate the organizational structure of a small
bank by delegating decision-making authority to the lower layers of the organizations.
Along these lines, Agarwal and Hauswald (2016) provide direct evidence that the findings
on distance and loan characteristics in the existing literature are really due to the difficulty of
transmitting soft information. In other words, they provide evidence that a bank endogenously
25
Starting in the early eighties the number of banks in the US has declined by over fifty percent, with most of the fall
occurring in the first decade (Petersen and Rajan 2002, Figure 4; Berger and Bouwman 2016, Figure 8.1). The decline
in the total number of banks is completely driven by the decline of small banks defined by those with gross total assets
less than $1B. The number of large banks has grown. The decline in small banks is driven in part by the technology
and the shift to hard information, but also by deregulation (Strahan and Kroszner 1999). The growing reliance on hard
information and automated decision-making, and the associated cost savings, created pressure to reduce regulations
on bank expansion. In turn, as regulatory restrictions diminished, this raised the value of capturing cost savings by
shifting to production processes which rely on hard information and enabled greater economies of scale.
26
responds to information transmission problems by effectively delegating more authority to loan
officers. Skrastins and Vig (2018) also find evidence that increasing the hierarchical structure of a
branch decreases the ability of the loan officers to produce soft information, leading to an increased
standardization of the information collected for each loan.
The empirical literature has found that firms’ access to capital depends upon how
informationally transparent the firms are or how much hard information the financial markets have
about the firms. We expect small firms to face greater credit rationing because of the limited hard
information available about them. This is why they are more reliant on banks that are better at
extracting and using soft information. However, when we look only at small firms, we still find
that a firm’s access to credit is a function of how much information is available to the financial
markets, not just to the bank. Firms that are more informationally transparent, for example those
that maintain formalized records, have a higher probability of their loans being approved (Petersen
and Rajan 2002).
For publicly traded firms, the amount of hard information available about the firm is much
greater. However, even for publicly traded firms, the existence of information that is easy to access
and evaluate on their likelihood of default—such as a credit rating—appears to increase their
access to debt capital (Petersen and Faulkender 2006). Controlling for traditional determinants of
capital structure (e.g., taxes, asset tangibility, and growth opportunities), firms with a debt rating
have 35 percent more debt than otherwise identical firms.
V) Applications of Soft and Hard Information beyond Traditional Banking.
Although much of the initial research on the implications of information type focused on
the banking and lending market, the literature has expanded beyond that. In this section we discuss
27
three additional areas where researchers have applied these concepts: distance research outside of
banking, the financial crisis, and the emerging financial technology sector (FinTech).
A) Distance Research Outside of Banking.
Based on the research in the banking literature, distance is related to information type. Hard
information can be transmitted across distance without loss of content; soft information cannot.
This raises the question as to which financial markets must be geographically close and which do
not need to be. This analysis helps us understand what kind of information undergirds each market.
The finance literature has studied distance in a variety of other economic settings and
markets including: the public equity markets (Grinblatt and Keloharju 2001; Hong, Kubick, and
Stein 2005; Malloy 2005), the municipal bond market (Butler 2008, Cornaggia,Cornaggia, and
Israelsen 2018), the venture capital market (Lerner 1995), the real estate market (Garmaise and
Moskowitz 2004; Granja, Matvos, and Seru 2017), the allocation of capital between divisions
within a firm (Landier, Nair, and Wulf 2009), and the impact of the organization design of
conglomerates on their productivity (Seru 2014).
26
In part due to credit ratings, the corporate bond
market is national or international. Even though municipal bonds (tax-exempt bonds issued by
state and local government entities) are also rated, Butler (2008) finds that the underwriting market
is highly local (80% of municipal bonds are underwritten by investment banks with a local office).
Unlike small business loans, the underwriters of municipal bonds do not hold the securities and so
do not have incentives to monitor borrowers post issuance. The local underwriters have been able
to credibly communicate to investors that their soft information is valuable and certify the bond’s
26
Even in markets that we think are dominated by hard information, and thus where we would expect distance not to
be relevant, research has sometimes found a preference for local investments. Mutual fund managers tend to hold a
higher concentration in shares of local firms, since access to soft information of local firms is cheaper (Coval and
Moskowitz 1999; Coval and Moskowitz 2001). The effect is strongest in small and highly levered firms.
28
quality. Local underwriters are able to sell municipal bonds for higher prices, and these results are
strongest when the ratings signal is weakest (i.e., bonds with low ratings and unrated bonds).
27
One of the most opaque financial sectors is the equity of new and private firms. There is
little to no hard information available about such investments, in part because they have no track
record and in part because they are often in emergent industries. Due to their “detailed knowledge
of the firm they finance, (venture capitalists) can provide financing to young businesses that
otherwise would not receive external financing” (Lerner 1995). The venture capitalist acquires soft
information by serving on the firm’s boards, making frequent visits to the firm, and meeting with
customers and suppliers. Since this is costly in terms of time, distance matters. A venture capitalist
is twice as likely to sit on the board if their office is within five miles of the firm compared to 500
miles (Lerner 1995).
The discussion thus far has focused on the role of information type in explaining external
distances. Distances inside a firm may be relevant as well. Landier, Nair, and Wulf (2009) explore
how the distance between divisions and headquarters within the same conglomerate may have an
impact on corporate decision-making. Managers are more likely to lay off employees or divest
divisions that are more distant from headquarters. Although the authors argue that this could be
driven by a greater affinity for the people that the management interacts with most often, they find
that the effect is strongest in environments that rely on soft information.
28
The problem with
interpreting this empirical result is that the choice of locations is likely to be endogenous, making
it difficult to establish causality. Giroud (2013) has a clever way of solving the endogeneity
27
If the local underwriters have soft information that non-local underwriters do not have and they can thus sell the
bonds at higher prices, they should be able to extract larger fees. Oddly, they do not. Local underwriters charge lower
fees relative to non-local underwriting, suggesting that local competition is limiting their pricing power.
28
They use the measure of distance between banks and borrowers from Petersen and Rajan (2002) to classify whether
industries are hard- or soft-information intensive. Industries where distance between borrowers and lenders is larger
are classified as hard information environments.
29
problem. He studies the impact on plant-level investment and productivity when the headquarters
is either close to or far from the plants, arguing that travel time is a better proxy for monitoring
than geographical distance, especially when the inputs to the management’s decision are soft
information.
29
He exploits the introduction of new airline routes as a source of exogenous variation
to proximity and measures the causal impact of distance on plant-level investment and productivity.
This makes sense if the information the managers need to acquire is soft and thus can be obtained
only by visiting the plants more frequently.
B) The Financial Crisis: The Role of Information Type and Incentives.
A reliance on hard information was an essential factor in enabling the growth of
securitization and the ensuing expansion of mortgage lending. If lenders have information that
they could not pass along (e.g., soft information) and they use this information to determine which
loans to sell, the securitization market can break down (Stiglitz and Weiss 1981; DeMarzo 2005).
The reliance on hard information in the mortgage market reduced transactions costs, expanded
access to capital, and diversified regional risk (Ranieri 1997; Demsetz and Strahan 1997; Allen
and Carletti 2006; Loutskina and Strahan 2011). The reliance on hard information also had a dark
side, and the financial crisis reminded us of these costs. We next turn to the growing academic
literature that has documented the causes and implications of these problems.
As securitization increased the distance between the originator and the ultimate investor
that bears the default risk, the performance of credit models deteriorated. The interest rate on loans
became a worse predictor of default. Statistical models estimated in a low securitization period
broke down during the subsequent period of high securitization (Rajan, Seru, and Vig 2015). The
literature has documented a number of explanations for the declining accuracy. First, the history
29
A plant may be located far away in terms of geographical distance, but monitoring may be easier when there are
direct flights between the cities where the headquarters and plants are located.
30
upon which the models were built was short. It is a common theme that new default models are
often built during times of calm in the credit markets, only to fail when the calm passes (Frame,
Gerardi, and Willen 2015). It is not just that the historical record was short. When loans are
securitized using only information that can be passed on to investors (i.e., hard information), there
is little incentive for the lenders to collect soft information (Rajan, Seru, and Vig 2010;
Purnanandam 2011; Saengchote 2013; Furfine 2014).
The problem is not just a loss of (soft) information. The historical data is not useful if the
behavior of market participants changes in response to the model’s introduction (Lucas 1976).
30
An example is the evolving distribution of FICO scores among loan applicants. The underlying
distribution of FICO scores in the population is continuous. However, as securitization grew, the
number of low documentation loans just above the 620 cutoff jumps relative to the loans just below
620 (Keys, Mukherjee, Seru, and Vig 2010, see Figures I and II). FICO scores above 620 are much
easier to securitize and the authors document that they are more likely to be securitized. Loans
with a FICO score just above 620 should be of slightly higher quality than loans with FICO scores
just below 620, but these loans actually default at appreciably higher rates. This implies that
lenders respond to securitization (the sale of loans) by more carefully screening borrower’s credit
risk using soft information when they are more likely to hold the mortgage (Arentsen, Mauer,
Rosenlund, Zhang, and Zhao 2015). As we discussed with bond ratings (Section III-D), when the
decision rule is transparent, borrowers have an incentive to alter the numeric inputs to the credit
model. Borrowers “…whose income is more variable and easier to overstate are more likely to end
up in the 620+, low-documentation subprime loan pool” (Keys, Seru, and Vig, 2012). Ben-David
30
Analogously, firms’ attempted to alter the financial information they reported in response to the introduction of
credit ratings in an effort to increase their access to credit in the late nineteenth century (Carruthers and Cohen 2010b,
footnote 36).
31
(2011) documents another example in the mortgage market. He shows that borrowers (and home
sellers) were able to inflate the stated value of the house and thus lower the reported loan-to-value
(LTV) ratio.
Both hard and soft information are inputs into the regulation of financial institutions as
well. The ability of the regulators to see inside the credit institution will dictate the tools they have
to regulate financial institutions and eliminate inconsistencies of internal ratings across banks,
which is crucial under Basel II and III (Firestone and Rezende 2016; Plosser and Santos 2016).
Just as outside investors cannot observe and use soft information generated inside a lender,
regulators may have the same problem. This means regulators will need to pay special attention to
regulatory policies that depend excessively on default models based on hard information (Keys,
Mukherjee, Seru, and Vig 2009). These models ignore the strategic behavior of lenders when it
comes to reporting, due to the potential loss of rents through the acquisition of private soft
information (Hauswald and Marquez 2006; Giannetti, Liberti, and Sturgess 2017).
The lessons from the financial crisis suggest a number of avenues for exploring the benefits
and costs of hard relative to soft information. An obvious lesson is that models based only on hard
information are potentially subject to manipulation by market participants when the models are
transparent and the benefits of small changes in the inputs are sufficiently inexpensive and valuable.
The challenge is to see this problem the next time. Exploring these issues may help us understand
what kinds of loans (or other financial securities or decisions) and what kind of market
environments best restrict this behavior. It also raises the question of how and when experience or
memory can help or hurt based on the incentives it creates (Chernenko, Hanson, and Sunderam
2016; Diamond, Hu and Rajan 2018).
C) New Financial Markets: FinTech.
32
The number of financial firms and markets which are labeled as FinTech has increased
significantly. The financial problems which these new firms and markets are trying to solve are
not new (e.g., evaluating credit quality, allocating investor’s assets, raising equity capital for new
firms, and increasing the efficiency of payments, among others), but the application of new
technology allows for potentially new solutions as well as old problems. Since many of these
business models depend upon substituting numerical data and automated decisions based on hard
information for decisions made by individuals, they are often built on the concept of substituting
hard information for soft. Thus the ideas we discussed above are relevant in understanding why
these new solutions may work and the challenges they will face.
1) Numbers versus text revisited.
Some of the logic which underlies FinTech can be traced to a set of academic papers which
expanded the data used by academics. A typical firm’s 10K filing can run into hundreds of pages.
Its financial statements (e.g., its income statement and balance sheet) take up half a dozen pages
at most. However, a large fraction of the vast studies that try to explain the changes in equity values
with firm data relied only on these accounting numbers and macroeconomic data. This changed
when academics started including textual information in regressions by coding the text into
numerical scores.
A very early attempt was Das and Chen (2007). They examined the effect of message board
postings on the stock prices of Amazon and Yahoo. Although the algorithm was crude, it showed
a potential way to incorporate the vast amount of textual data into empirical research. With the
digital availability of text and gains in automated methods of analyzing text, there has been an
increase in this kind of research. The next iteration, and arguably the paper that kicked off the
revolution, was Tetlock (2007), who “…quantitatively measure(d) the interactions between media
33
and the stock market using daily content from a Wall Street Journal column.” As the datasets have
grown and finance researchers have become more adept at coding the text into numbers in
meaningful ways, the literature has grown significantly (Li 2008; Tetlock 2010; Da, Engelberg,
and Gao 2011; Dougal, Engelberg, Garcia, and Parsons 2012; Huang, Zang, and Zheng 2014;
Hoberg and Phillips 2016; Gentzkow, Kelley, and Taddy 2017; Gianni, Irvine and Shu 2017). The
literature has expanded our understanding of how information reported in the financial disclosures
and the media (news stories, opinion columns, internet searches and social media) is impounded
into stock prices and financial decisions. In each of these papers, the text is condensed into
numerical indexes, which capture relevant information (given the empirical results) but likely
capture only a portion of what a human interpreter could glean from the original text.
31
This is a
fundamental challenge of hardening soft information. This extraction may undoubtedly lead to a
loss of information.
32
Since the nature of information is not an exogenously fixed quantity, using
text analysis or coding soft information into numeric scores may change or degrade the information.
Whether the ability to harden soft information is useful in predicting default models or explaining
stock returns, for example, is an empirical question.
31
The literature began by simply counting positive and negative words, which proved to be more complicated than
one would have initially guessed. The language of finance is not as simple as we think (Longhran and McDonald
2011). For example, the sentence “The Dell Company has 100 million shares outstanding” would have been classified
as an extremely positive sentence by the early dictionaries, since “company”, “share”, and “outstanding” are coded as
positive words (Engelberg 2008). Hoberg and Phillips (2010) is similar in method but they are interested in a very
different question. They use text-based analysis of firms’ 10-Ks to measure the similarities of firms involved in
mergers and thus predict the success of the mergers. Mayew and Venkatachalam (2012) took this idea one step further
and examined the information embedded in the voice tone of managers during earning calls.
32
Loss of information is not only due to the effect of hardening the information. A change in the compensation
structure of agents may also affect the use of information. In a controlled experiment, Agarwal and Ben-David (2018)
study the impact that changing the incentive structure of loan officers to prospect new applications has on the volume
of approved loans and default rates. They find that after the change, loan officers start relying more on favorable hard
information and ignoring unfavorable soft information. The results highlight how incentives dictate not just what
information is collected but what role it plays in the decision. Another form of loss of information is due to the
portability of soft information. For example, Drexler and Schoar (2016) show that when loan officers leave they
generate a cost to the bank, since it impacts the borrower-lender relationship. As the departing loan officers have no
incentives to voluntarily transfer the soft information, borrowers are less likely to receive new loans from the bank in
their absence.
34
For example, in Appendix A, we show the assessment criteria of subjective information
measures from an international bank with operations around the globe. This rating is subjective in
nature since the criteria are completed by loan officers for each of their clients. This soft rating is
part of a final credit rating composed of this soft input plus a hard information score. It is unclear
whether this information enhances the information environment of the international bank even
compared with a bank’s internal rating. Is this information a substitute or a complement of the
bank’s hard information?
2) FinTech lending.
The use of technology (hard information, automated data collection, and automated
decision-making) is not new in lending (Einav, Jenkins, and Levin, 2013), but a number of new
lending models have arisen under the label FinTech. Peer-to-peer lending (P2P) is one example.
In its earliest incarnation it involved individuals lending to other individuals through online
platforms. The electronic interface combined with borrowers and lenders who often had no prior
relationship suggested that credit decisions would depend solely on hard information. Like the
research on equity markets discussed in the prior section, lenders expanded the information upon
which their lending decisions were made beyond the traditional credit metrics.
Several papers have examined the expanded information used by P2P lenders and have
tried to determine what value it has. After controlling for credit score, credit history, income,
employment status, and homeownership, personal characteristics, such a physical attractiveness,
increase the likelihood of getting a loan and reduce the interest rate (Ravina 2018).
33
Borrower
narratives which claim the borrower is trustworthy and successful increase the probability of the
33
Appearance also played a role in the early credit reports collected by the Mercantile Agency. The agency’s
instructions to their agents stated “…give us your impressions about them, judging from appearances; as to their
probable success, amount of stock, habits, application to business, whether they are young and energetic or the
reverse…” (Carruthers and Cohen 2010b).
35
loan being funded as well as lowering the interest rate (Herzenstein, Sonenshein and Dholakia,
2011). This is despite the fact that these factors (physical appearance and claims of trustworthiness)
are not correlated with lower probabilities of default. Other researchers present a more positive
picture of P2P lender’s abilities. Iyer, Khwaja, Luttmer, and Shue (2015) find the interest rate set
by the market is a better predictor of default than the borrower’s credit score is. Lenders are able
to use additional information such as the borrower’s appearance (picture) or their description of
the reasons for borrowing. Duarte, Siegel, and Young (2012) find that borrowers who, based on
their pictures, are perceived to be more trustworthy are more likely to get funded and to receive a
lower interest rate, but unlike in other studies, these borrowers were found to have lower default
rates.
Not all participants in online lending platforms are strangers. In cases where participants
are connected off line, these relationships may influence credit outcomes. As with relationship
lending, these connections can either provide or signal information, as well as create social
pressure to repay loans (Uzzi 1999; Guiso, Sapienza, and Zingales 2013). Having more friends on
the lending platform increases the probability that a loan is funded, lowers the interest rate, and is
associated with lower default rates (Lin, Prabhala, and Viswanathan, 2013). When a borrower’s
friend defaults, the borrower is more likely to default (Lu, Gu, Ye, and Sheng 2012). Groups of
lenders experience lower default rates when lending to an individual personally connected to a
group member (Everett 2015). Connections or friendships should matter, but only if they are a
credible and sufficiently costly signal. Returns on loans are higher if an investor who is a friend
endorses the loan and bids on it, but returns are lower if a friend endorses but does not bid on the
loan (Freedman and Jin 2011). The importance of context is one of the distinctions between hard
36
and soft information that we discussed in Section II. How good a friend you consider this person
to be is open to interpretation; the fact that you invested your money in the project is not.
These results describe a market that combines characteristics of both relationship lending
and arms-length lending, but intermediated through an online platform. It is also a market that is
evolving from individuals lending to individuals to an increased reliance on institutional funding
of the loans. Eighty percent of the capital on the two major US platforms is now provided by
institutional investors (e.g., pension funds and hedge funds, Ford 2014; Morse 2015). These
investors are arguably more sophisticated (Vallee and Zeng 2018). How the market develops will
determine the potential cost reductions and the type of information that can credibly be
communicated. What is clear is that the set of data upon which lenders make credit decisions has
expanded.
There are a few possible directions in which these markets may evolve. They can depend
upon social connections and soft information, but at the cost of limiting the size of the market and
the cost savings of scale and automation. The long-term predictability of some of the expanded
source of information is not clear. As we discussed in Sections III-D and V-B, borrowers have an
incentive to alter the inputs to the credit decision when the benefits exceed the costs. These results
raise the question of why borrowers do not learn how to alter their pictures, the search engine they
use, and the narrative of why they need to borrow to generate more favorable terms (Duarte, Siegel,
and Lang, 2012; Morse 2015; Berg, Burg, Gombovic, and Puri 2018). This learning may happen,
in which case these disruptive technologies will be disrupted by a familiar problem (Uzzi 2016).
Alternatively, the market can evolve into using only hard information: numbers which are
verifiable and whose interpretation does not depend upon any context that is not encoded in the
data. This is the direction taken by the FinTech mortgage lenders. By replacing soft information
37
completely with hard information, these lenders have removed data collection or real time
decisions made by humans. The advantages of hard information are apparent in this business model.
The loan processing is faster and less expensive due to automation. This may explain the growth
in their market share from 2% in 2010 to 8% in 2016 (Fuster, Plosser, Schnabl, and Vickery 2018).
Interestingly, defaults are lower than would be predicted from observable credit metrics (FICO
scores and loan-to-value ratios), because lenders can more accurately compare submitted
financials to databases and thus prevent fraud and mistakes (Buchak, Matvos, Piskorski, Seru,
2017; Fuster, Plosser, Schnabl, and Vickery 2018).
3) FinTech in equity markets.
The next two applications of new technology to finance are in their infancy: raising equity
and investing one’s wealth. Both of these applications depend upon the ability to reduce the
dependence upon soft information and relationships, and to introduce new types of transactions
based on hard information and more impersonal automation. Public equities are traded among
strangers and the investment decisions are based heavily on hard information. The question is
whether an analogous market can be developed for the private equity of startups. Equity is more
opaque than debt, and the equity of startups even more so. Since there is little hard information (or
history) available on these firms, most investors rely on personal connections and face to face
meetings, and thus soft information. Traditional sources of capital to these firms, such as venture
capitalist and angel investors, are therefore geographically close and often intimately involved
with the firms before and after funding (Lerner 1995; Sohl 2010; Wong 2002).
38
Although crowdfunding has drawn a lot of interest and raised a significant amount of
capital, its future is unclear.
34
Only a small fraction of the capital raised is in the form of traditional
equity (5%), an exchange of capital for a stake in the future earnings and cash flows of the firm.
Instead, most of the capital is raised in exchange for a reward or as patronage (Mollick 2014).
35
To the extent that crowdfunding draws in small, possibly unsophisticated investors, the
opportunity for fraud is nontrivial.
36
The hope is to develop a set of hard information which can
capture the economic viability of the potential investments. Given the difficulties we have
discussed above in debt markets, which are informationally less sensitive securities, this seems
problematic.
37
The efficient market hypothesis argues that an uninformed investor can purchase
all public equities and earn the market return. This only works because there is a finite set of stocks,
which have been vetted by professionals (through the IPO process), and informed investors can
buy or sell the stocks and thus push their prices toward the correct value. The logic is different
with private firms. There is little to no filtering or vetting of the value of the private firms in this
market. Since there is potentially an infinite number of bad investments, market efficiency
provides little protection. This raises the question of what role there may be for regulation versus
a market solution (Agarwal, Gupta, and Israelsen 2016). We already see the emergence of
intermediaries that can potentially take on the task of collecting and processing the soft information
necessary to evaluate such investments (Catalini and Hui 2018). It is an open research question
34
Mollick (2014) defines crowdfunding as “…the efforts by entrepreneurial individuals and groups… to fund their
ventures by drawing on relatively small contributions from a relatively large number of individuals using the internet,
without standard financial intermediaries.”
35
Participants contribute capital in exchange for a product or so that they may participate in supporting an event or
creative endeavor. The first is a form of trade credit (pre-paying for a product) and in most examples is more akin to
market research than equity funding, since the existence and quality of the product are often uncertain.
36
Newman (2011) has raised the concern that “...crowdfunding could become an efficient, online means for
defrauding the investing public...”
37
Investors “…rely on highly visible (but imperfect) proxies for quality such as accumulated capital, formal education,
affiliation with top accelerator programs, advisors, etc.” (Catalini and Hui 2018).
39
whether and how the incentive problems documented in the lending market will reoccur in this
market.
Another question that deserves attention is how the automation and the elimination of
subjective decision-making in choosing the “right” investment by crowdfunding investors might
affect other market participants. The market for small business lending is partially split between
lenders that focus on soft versus hard information. If equity crowdfunding continues to grow, this
may affect the strategy or returns of other angel investors and venture capitalists, who must
compete with new, more automated selection methods in the early stages and choose how to select
and invest among the firms that successfully navigate this process.
4) Robo-advising.
When personal investing expanded beyond the wealthy, many retail investors relied on
brokers for advice and information. This model was similar to the relationship lending model of
banking, relying on soft information and long-term relationships. The rise of mutual funds and
discount brokers not only brought in an expanded set of investors but also provided direct access
to investing not directly intermediated by brokers (Nocera 2013). FinTech is beginning to make
inroads into the retail investment advising business, changing the information available to
investors, how it is delivered, and how it is converted into investing decisions. Consistent with the
themes of this paper, the information is hard or a hardened version of soft information. This
characteristic allows for automation of the investment process with associated ability to scale the
platforms, reduce costs, lose information, and create perverse incentives.
The FinTech data aggregators have the potential to let investors quickly and cheaply sift
through a broader wealth of data on the web. Investors that visit a FinTech (aggregator) web site,
however, are significantly less likely to visit the original content site (Grennan and Michaely 2018).
40
Since the recommendations are often a concentrated extract of the original information, some
information is lost (see Section III-C). Investors actually prefer recommendations opposed to the
underlying data and reasoning (Grennan and Michaely 2018).
38
There is another potential cost.
Collecting and processing soft information is costly for the analysts (Bradshaw, Wang, and Zhou,
2017). As we saw with lending, if investors are relying on the recommendations extracted by
finance blogs, and thus don’t value the original content, the analysts who produce the underlying
information (data and reasoning) may have less incentive to invest in and process soft information,
and this reduced incentive can lead to less accurate and more biased forecasts (Grennan and
Michaely 2018).
Robo-advising takes the next step and provides investment advice with no direct human
interaction. Thus, by construction, it relies on hard information and automated decision rules. The
information upon which retail investment decisions historically are based is often the hard
information of financial reports and past returns, coupled with their reading of analysts’ reports,
and the advice of brokers who may know them well. When it comes to retail investing, information
about the investments (e.g., stocks) is important but so is information about the investors. What is
their risk tolerance, their level of finance knowledge, and their susceptibility to behavioral biases?
Investment advising, including robo-advising, needs to address both.
39
Empirical examinations of
the effect of robo-advising finds that it increases diversification of investors, but only for the least
diversified investors. It also reduces the “…pervasive behavioral biases such as the disposition
effect (being more likely to realize gains than losses) and trend chasing…” although investors
38
The Mercantile Agency, the precursor to Dun and Bradstreet’s also worried about the tendency of some subscribers,
who had purchased access to their reports, relying too heavily on the ratings, opposed to visiting their offices and
inspecting the underlying data (Carruthers and Cohen 2010b).
39
The evidence that human brokers factor their client’s characteristics into the investment decision is not reassuring.
A retail investor’s asset allocation depends significantly more on who their broker is (e.g., broker fixed effects) than
the investors own characteristics (e.g., risk tolerance, age, financial knowledge, investment horizon, and wealth; see
Foerster, S., J. Linnainmaa, B. Melzer, and A. Previtero. 2017.).
41
appear to trade more often when using these platforms (D'Acunto, Prabhala, and Rossi 2017). The
reduction in behavioral biases could improve performance compared to human brokers who are
known to exhibit these behavioral biases (Linnainmaa. Melzer and Previtero 2018).
40
VI) Concluding Remarks and Reflections.
Production and processing of information lies at the heart of the theory of the firm as well
as the study of financial markets and institutions. Information is the raw material of all financial
decisions. In this paper, we defined the characteristics of what we call soft and hard information
with the objective of creating a guide for researchers. The objective is not to define an absolute
classification of information into one of two categories, hard and soft, but instead to describe the
fundamental characteristics of what we, and the literature, mean by soft and hard information and
thus to frame the discussion of information type. Not only is the classification not discrete, but soft
information can, at least partially, be transformed into hard information (the “hardening” of
information). As financial institutions and markets are designed or have evolved, they use different
types of information in their production processes. As with all of economics, this choice will be
driven by the relative advantages of soft versus hard information. The list of the relative benefits,
and our understanding of those benefits, has expanded since we first considered these issues, and
may become more elaborate as researchers continue to explore the role each plays in financial
decisions.
The terms soft and hard information arose initially in the banking literature (e.g., the
lending relationship literature), although the ideas trace back to work on the theory of the firm and
40
Since the algorithms are written by humans, there is also the possibility that the algorithms may embody the same
behavioral biases that human advisors have (O’Neil 2016, D'Acunto, Prabhala, and Rossi 2017) as well as the biases
of those who design the algorithms or which may be inherent in the data (O’Neil 2016).
42
the contracting and organizational economics literature. Implicit in this discussion was that
information was transmitted from one party (the data collector or agent) to another party (the
decision maker or principal). This transmission was costly and imperfect for a number of reasons.
The literature on information type tried to define these characteristics and measure their impact on
the organization of financial institutions and the structure of markets. As technology allowed
greater and cheaper transmission of hard information, markets became geographically more
disperse, and some institutions became more hierarchical (relying more on hard information) and
others less so.
Although banking was initially the core of research on soft and hard information its
applications and implications have extended far beyond banking. Researchers have applied these
concepts to a variety of markets including: public and private equity, real estate, municipal bonds,
and the allocation of capital within the firm. The value is both in explaining how the use of different
types of information influences the design and outcome of these markets. It also helps us
understand how they will continue to evolve in the future. This is why we used these concepts to
examine the research on the financial crisis and the emergence of FinTech. These are two areas in
which we expect to see future research. Financial crises are a reoccurring feature of financial
markets. Although there are a variety of reasons for each unique crisis, the relative costs of using
soft versus hard information and the incentive problems this choice can create seems to be a
reoccurring factor. We think the same may be true of what is now called Fintech.
Technology and the growth of online data has motivated the drive to convert text and
numbers into quantitative indices. The growth of numeric data combined with a reduction in the
cost of computing have also driven a growth in automated decision-making. This hardening of
information is important in finance, but its application can be seen in contexts far from finance,
43
such as the assessment of teaching quality, college rankings, policing and sentencing, and the
screening of employment applications (O’Neil 2016, Kiviat 2017).
We can also see the implications of information type, not just in our field of study: finance,
but also in what for many of us is a part of our employment responsibilities: teaching. Some
elements of teaching have not changed for a millennium. Professor Peter Norvig and Sebastian
Thurn taught one of the early MOOCS on artificial intelligence in the fall of 2011. In commenting
on what they learned from the experiment, Professor Norvig describes the classroom experience.
It often involves a professor lecturing on a specified body of knowledge and the students taking
notes (Norvig, 2012). Now consider the material and methods we teach as viewed through the lens
of hard and soft information. Some of what we teach is mechanics (e.g., how to amortize a loan,
how to estimate a CAPM regression, and how to calculate present values). Some of what we teach
is intuition, theory, understanding empirical results, or nuance. The first set of material has the
flavor of hard information; the second, of soft information. In financial institutions, the decisions
that depend on hard information have been automated. Expensive labor has been replaced with
cheaper capital. The decisions that depend upon soft information have not been automated. Will
what happened to lending, happen to teaching? To what extent will the teaching of mechanics be
automated and moved out of the classroom? As with financial decisions, this move would allow
for the scarce resource, human time and thought, to be focused on a higher value use: the soft
information of teaching.
Although the core questions of corporate finance: how capital is raised and deployed have
not changed, the answers to these questions have changed over time. Changes in technology, the
growing availability of digital data, and our evolving understanding of how market frictions affect
financial decisions influence the evolution of financial markets and institutions. Since the
44
processing of information is at the core of what financial institutions and markets do, the
dichotomy between hard and soft information will be part of this evolution. We expect the
literature on hard and soft information will continue to influence our understanding of financial
intermediaries and will to find applications in other sectors of finance and beyond.
45
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56
Exhibit A: Assessment Criteria of Subjective Information Measures
BUSINESS RISK ASSESSMENT
1 Industry RR1-RR2 RR3 RR4 RR5 RR6 RR7
Trend in Output Very Strong Growth Strong Growth Growth Stable Uncertain / Declining Declining
Trend in Earnings Very Strong Growth Strong Growth Growth Stable Uncertain / Declining Declining
Cyclicality (Fluctuations) Very Stable Very Limited Small Moderate Large Large &
Unpredictable
External Risks No Risks Few Risks, Non
Cyclical
Few Critical Risks Variuos Critical Risks Numerous Critical
Risks
Widespread Risks
2 Competitive Position RR1-RR2 RR3 RR4 RR5 RR6 RR7
Market Position Over 50% / Clearly
Dominant
Over 20% / Dominant Over 10% / Major
Player or Strong
Niche
Over 5% / Known
Player or Established
Niche
2 to 3% / Minos
Player
Below 2% / Minor
Player; Declining
Share
Product Line Diversity Over 3 Growing Lines Over 3 Lines At least 2 Growing
Lines
At least 2 Stable
Lines
Only 1 Stable Line Only 1 Declining Line
Operating Cost Advantage Global Leader Achieves Low Global
Costs
Has Lowest Local
Costs
Some Cost
Advantages
No Cost Advantages High Cost Producer
Technology Advantage Global Leader in
Many Areas
Global Player in
Some Areas
Leader in Local
Market
Mostly New;
Upgrading Old
Technology Follower Predominantly
Outdated
Key Success Factors Global Capabilities in
All Factors
Global Capabilities in
Most Factors
Strong Locally in All
Factors
Strong Locally in
Some Factors
Strong in Some;
Weak in Others
None
3 Management RR1-RR2 RR3 RR4 RR5 RR6 RR7
Professionalism At all Levels With
Extensive Experience
At all Levels in
Operations &
Mana
g
ement
At all Key Posi- tions
in Operations &
Mana
g
ement
At Most Key
Positions & Most
Levels
At Some Key
Positions
In Few Positions
Systems and Controls Meets Highest Global
Standards
Meets Highest Local
Standards
Very Reliable and
Strong
Acceptable Unreliable Largely Absent
Financial Disclosure Meets Highest Global
Standards
Always Timely and
Accurate
Usually Timely and
Accurate
Satisfactory
Reporting
Delayed, Inaccu-rate
or Incomplete
Unreliable
Ability to Act Decisively Proven to be Very
Strong
Proven to be Strong Good, but Untested Good, but Untested Weak Hopeless
Risk Management
Policies
RR1-RR2 RR3 RR4 RR5 RR6 RR7
Leverage Policy Extremely
Co ns e rvat i ve
Very Conservative Low Tolerance Some Tolerance High Tolerance Unlimited Appetite
Liquidity Policy Extremely Conser-
vative Cushion
Conservative Cushion
& Contingency Plan
Some Cushion &
Sound Contingency
Plan
Maintains Some
Cushion
Low Liquidity
Acceptable
No Policy
Hedging Policy All Risks
Understood; No Open
Positions
Most Risks
Understood; No Open
Positions
Most Risks
Understood; Few
O
p
en Positions
Risks Understood
but Not Always
Covered
Risks Understood
but Most Not Covered
No Hedging Policy /
Speculative Policy
4 Access to Capital RR1-RR2 RR3 RR4 RR5 RR6 RR7
Capital Markets Wide Access;
Domestic &
International
Wide Access;
Domestic &
International
Primarily Domestic;
Some International
Primarily Domes-tic
Banking; Some
Ca
p
ital Markets
Limited Largely to
Domestic Banking
No access to Capital
markets
Banks Established Re-
lationships; Strong
Commitments
Established Re-
lationships; Strong
Commitments
At Least One Bank
Strongly Committed
At Least One Bank
Strongly Committed
No Bank Strongly
Committed or Some
Banks Getting Out
Bank Cutting Lines;
Some Locked-in
Overall Business Rating
(Do not use +/- in the final Business Rating)
