MANAGERIAL DETERMINANTS OF BANK RISK TAKING

MANAGERIAL DETERMINANTS OF BANK RISK TAKING. THE INFLUENCE OF CEO
AFFECTIVE TRAITS
JUAN MANUEL DE LA FUENTE SABATÉ
Catedrático de Universidad
Universidad de Burgos
Facultad de CC.EE. y Empresariales
C/ Los Parralillos, s/n
09001 Burgos
SPAIN
Tel: +34 947 25 89 73
Fax: +34 947 25 89 60
e-mail: [email protected]
JUAN BAUTISTA DELGADO GARCÍA
Profesor Ayudante Doctor
Universidad de Burgos
Facultad de CC.EE. y Empresariales
C/ Los Parralillos, s/n
09001 Burgos
SPAIN
Tel: +34 947 25 90 32
Fax: +34 947 25 89 60
e-mail: [email protected]
ESTHER DE QUEVEDO PUENTE
Profesor Titular de Escuela Universitaria
Universidad de Burgos
Facultad de CC.EE. y Empresariales
C/ Los Parralillos, s/n
09001 Burgos
SPAIN
Tel: +34 947 25 90 33
Fax: +34 947 25 89 60
e-mail: [email protected]
Área temática: C) Direccción y Organización
Palabras clave: discrecionalidad directiva; afectos; upper echelons; risk taking bancario.
1
RASGOS DIRECTIVOS DETERMINANTES DEL RISK TAKING BANCARIO. LA INFLUENCIA
DE LOS RASGOS AFECTIVOS DEL CEO
RESUMEN
La investigación en banca ha desarrollado numerosas explicaciones de los factores
determinantes del risk taking bancario. Sin embargo, esta línea de investigación ha ignorado
el papel de las características demográficas y de los rasgos psicológicos de los directivos en
sus decisiones estratégicas. Este artículo analiza la influencia de los rasgos afectivos de los
CEOs en el risk taking bancario. Las hipótesis se han testado en una muestra de bancos y
cajas de ahorros españoles. Nuestros resultados muestran que los rasgos afectivos
negativos de los CEOs están relacionados con un menor nivel de riesgo, mientras que los
afectos positivos no parecen influir en el nivel de riesgo.
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MANAGERIAL DETERMINANTS OF BANK RISK TAKING. THE INFLUENCE OF CEO
AFFECTIVE TRAITS
ABSTRACT
Research in banking has generated numerous explanations of determinants of bank risk
taking. However, this line of research has traditionally ignored the role of managers’
background characteristics and psychological traits in shaping their strategic choices. This
article analyzes the influence of the emotional traits of CEOs on bank risk taking. The
hypotheses are tested on a sample of Spanish banks and savings banks. Our results show
that managers’ negative affective traits are related to lower risk taking. Positive affective traits
do not seem to influence on the level of risk.
3
INTRODUCCIÓN
El análisis de los determinantes del riesgo ha sido una de las preocupaciones más frecuentes
en la literatura bancaria. Dejando a un lado las condiciones relacionadas con la estructura de
los mercados 1 o con las diferencias regulatorias 2 entre países, la investigación empírica se ha
centrado en múltiples determinantes organizativos e individuales. A partir del trabajo de Merton
(1977), un gran número de trabajos se ha basado en la influencia de la existencia de un sistema
de fondo de garantía de depósitos de prima fija sobre los incentivos de los accionistas de los
bancos para incrementar el nivel de riesgo bancario, lo que algunos autores han llamado el
problema del riesgo moral. En este sentido, el fondo de garantía de depósitos se puede
considerar como un subsidio para los accionistas con forma de opción put, cuyo valor aumenta
con el riesgo asumido por el banco, lo que a su vez incrementa el incentivo de los accionistas
para incrementar el nivel de riesgo bancario (Merton, 1977; 1978; Dothau y Williams, 1980).
Otros autores han considerado el efecto de la forma organizativa y de la estructura de
capital en el risk taking bancario. Esty (1997a and b) encontró que las entidades financieras
constituidas como sociedades anónimas (stock thrifts) mantenían un mayor nivel de riesgo que
las entidades financieras constituidas como mutuas (mutual thrifts), y que la transformación de
estas mutuas en sociedades anónimas conllevaba un aumento en sus niveles de riesgo. Barth,
Hudson y Jahera (1995) y García Marco y Robles Fernández (2002) han obtenido unos
resultados similares en muestras de cajas de ahorros tejanas y de bancos y cajas de ahorros
españoles. Schrand y Unal (1998) también encontraron que las entidades financieras con forma
de mutuas que se convertían en sociedades anónimas aumentaban el nivel riesgo después de
esta transformación. La estructura de propiedad y la naturaleza del principal accionista también
se han mostrado determinantes de los niveles de risk taking. El trabajo de Laeven (2002)
mostraba que el nivel de riesgo era superior en bancos de propiedad concentrada. Además
cuando el principal accionista era una compañía u otra institución financiera el nivel de riesgo
era superior que cuando la entidad era de propiedad estatal o familiar.
Estas investigaciones, entre otras, muestran interesantes explicaciones de los
determinantes del risk taking bancario, pero han centrado básicamente sus argumentos en el
interés de los accionistas. Sin embargo, es la dirección la encargada de adoptar las decisiones
que condicionan el nivel de riesgo de las entidades financieras. Siguiendo este argumento, un
segundo grupo de trabajos (Saunders, Strock y Travlos, 1990; Gorton y Rosen, 1995; Brewer y
Saidenberg, 1996; Chen, Steiner y White, 1998; Cebenoyan, Cooperman y Register, 1999;
1
Véanse Rhoades y Rutz (1982); Keeley (1990); Shy y Stenbacka (2004) y Brewer y Jackson (2006), entre otros.
Véanse Dothau y Williams, 1980; Suárez (1998); Matutes y Vives (2000); Hendrickson y Nichols (2001); Barth,
Caprio y Levine (2004) y González (2005), entre otros.
2
4
Anderson y Fraser, 2000) han analizado el papel de la propiedad de los directivos. Los
resultados de estas investigaciones posteriores han mostrado relaciones diversas,3 pero en
todas ellas se observa una influencia significativa de la propiedad de la dirección en los niveles
de risk taking. Estos resultados reflejan la influencia de las decisiones de la dirección en los
niveles de riesgo bancario. Sin embargo, esta literatura no contempla la existencia de
diferencias idiosincrásicas entre los directivos que también puede influir en el risk taking
bancario.
En las últimas décadas son numerosos los investigadores en dirección estratégica que
se han preocupado por comprender en modo en que las características de los ejecutivos
pueden condicionar sus decisiones estratégicas, lo que se reflejaría en el comportamiento de la
organización. Esta línea de investigación, frecuentemente denominada perspectiva “Upper
Echelons”, se ha centrado en el papel de ciertas características demográficas de los CEOs (por
ejemplo, edad, experiencia, experiencia funcional y nivel y tipo de educación; Thomas et al.,
1991; Hitt y Tyler, 1991; Jensen y Zajac, 2004) y de sus rasgos psicológicos (por ejemplo,
aversión al riesgo, alcance de control, flexibilidad y necesidad de logro; Gupta y Govindarajan,
1984; Miller et al., 1986) en la explicación de determinados comportamientos empresariales
como la asunción de riesgos. Nuestro estudio sigue esta línea de investigación e incorpora un
nuevo rasgo psicológico en la investigación “Upper Echelons”: el papel de los rasgos afectivos
del CEO.
La literatura en emociones emplea diferentes términos como afecto, estado de ánimo o
emoción que son en ocasiones difíciles de distinguir. En nuestra investigación seguimos a
Forgas (1991) y otros autores que utilizan “afecto” como un término general que engloba tanto
emociones como estados de ánimo. Los “rasgos afectivos” hacen referencia a diferencias
estables en la tendencia a experimentar afecto positivos—e.g., excitación, entusiasmo—o
negativos—e.g., culpa, irritación— (Rusting, 1998; Watson y Clark, 1984; Watson, Clark y
Tellegen, 1988).
El trabajo presenta la siguiente estructura. El apartado siguiente proporciona una
revisión de la investigación que ha mostrado la influencia de las características del directivo en
la propensión a asumir riesgos. A continuación se introducen las teorías psicológicas y la
evidencia empírica sobre la influencia de los afectos individuales en el proceso de toma de
3
Los estudios han mostrado relaciones positivas (Saunders et al. 1990; Knopf y Teall, 1996), convexas (Gorton y
Rosen, 1995), cóncavas (Brewer y Saidenberg, 1996; Cebenoyan, Cooperman y Register, 1999) y negativas (Chen et
al., 1998) entre la propiedad de los directivos y el risk taking. No obstante, estas relaciones contradictorias pueden
ser el resultado de los diferentes períodos temporales y de las diferentes medidas de riesgo empleadas (Anderson y
Fraser, 2000; Sullivan y Spong, 2005). En este sentido, Cebenoyan, Cooperman y Register (1995) y Anderson y
Fraser (2000) encontraron diferentes asociaciones dependiendo del entorno regulatorio.
5
decisiones y en la asunción de riesgos. Estos planteamientos nos permiten desarrollar las
hipótesis relativas a la influencia de los rasgos afectivos del CEO en el risk taking bancario. El
cuarto apartado describe la muestra, las variables, y la metodología, el quinto muestra los
resultados. El trabajo se cierra con la discusión de las principales conclusiones e implicaciones
de nuestro estudio.
6
INTRODUCTION
The analysis of determinants of banks’ risk taking behaviour has been one of the frequent
concerns of banking literature. Setting aside market structure conditions4 or regulatory
differences 5, empirical research has focused on several organizational and individual
determinants of risk taking. Following Merton (1977) much of this research has based on the
influence of fixed-rate deposit insurance systems on incentives of banks’ shareholders to
increase the levels of risk taking- the moral hazard problem-. Deposit insurance can be
considered as a put option like subsidy to shareholders, the value of which increases with bank
risks, raising the incentives of shareholders to increase the levels of banks’ risk taking (Merton,
1977; Sharpe, 1978; Dothau and Williams, 1980).
Other authors have considered the effect of organizational form and ownership structure
on risk taking. Esty (1997a and b) found that stock thrifts exhibit greater risk than mutual thrifts
and that converting from mutual to stock also increases risk taking. Barth, Hudson and Jahera
(1995) and García Marco and Robles Fernandez (2002) obtained similar results for a sample of
Texas savings and loans and Spanish banks and savings banks respectively. Schrand and Unal
(1998) also found that mutual thrifts which convert to stock institutions increase total risk
following conversion. Regarding the effect of ownership concentration, research by Laeven
(2002) has show that risk is higher for banks with concentrated private ownership, especially
those owned by a company or another financial institution and to a lesser extent in the case of
those state or family owned.
These and other related research show interesting explanations of determinants of risk
taking but they have mainly focused on shareholder interests. Yet, it is management who takes
the decisions that condition the risk structure of a bank. Following this argument, a second group
of papers (Saunders, Strock and Travlos, 1990; Gorton and Rosen, 1995; Brewer and
Saidenberg, 1996; Chen, Steiner and White, 1998; Cebenoyan, Cooperman and Register, 1999;
Anderson and Fraser, 2000) have analyzed the role of managerial shareholdings on bank risk
taking. These studies have obtained diverse relationships6, but all have found a significant
4
See Rhoades and Rutz (1982); Keeley (1990); Shy and Stenbacka (2004) and Brewer and Jackson (2006), among
others.
5
See Dothau and Williams, 1980; Suárez (1998); Matutes and Vives (2000); Hendrickson and Nichols (2001);
Barth, Caprio and Levine (2004) and González (2005), among others.
6
Studies have found positive (Saunders et al. 1990; Knopf and Teall, 1996), inverted U shaped (Gorton and Rosen,
1995), U shaped (Brewer and Saidenberg, 1996; Cebenoyan, Cooperman and Register, 1999) and negative (Chen et
al., 1998) relationships between managerial shareholdings and risk taking. However, these conflicting relationships
can be the results of differing time periods and measures of risk (Anderson and Fraser, 2000; Sullivan and Spong,
2005). In this sense, Cebenoyan, Cooperman and Register (1995) and Anderson and Fraser (2000) found different
associations depending on the regulatory environment.
7
influence of managerial ownership on risk taking. These results show the influence of
managerial decisions on the levels of bank risk taking. However, they have not focused on
idiosyncratic differences across managers that can also influence on risk taking.
During the last decades, several researchers on strategic management have been
concerned with understanding how executive characteristics can condition their strategic
choices, which in turn are reflected in organizational outcomes. This line of research, frequently
termed as the “Upper Echelons” perspective, has focused on the role of certain demographic
characteristics of CEOs (e.g., age, tenure, functional background, and formal education; Thomas
et al., 1991; Hitt and Tyler, 1991; Jensen and Zajac, 2004) and psychological traits (e.g., risk
aversion, locus of control, flexibility, and need for achievement; Gupta and Govindarajan, 1984;
Miller et al., 1986) to explain organizational outcomes, such as risk-taking. Our study follows this
line of research and incorporates a new psychological trait in “Upper Echelons” research: the
role of CEO affective traits.
The literature on emotions employs different terms, such as affect, mood, or emotion that
are sometimes difficult to distinguish. We follow Forgas (1991) and other theorists who use
“affect” as a general and inclusive label that refers to both emotion and mood. “Affective traits”
refers to stable individual differences in the long-term tendencies to experience positive—e.g.,
excited, enthusiastic—or negative—e.g., guilty, irritable—affects (Rusting, 1998; Watson and
Clark, 1984; Watson, Clark, and Tellegen, 1988).
The remainder of our paper is structured as follows. The next section provides an
overview of research that has shown how managerial characteristics do influence risk taking.
Next we introduce the psychological theories and empirical evidence on the influence of affects
on individuals’ decision making and risk taking, which allow us to develop hypotheses regarding
the influence of CEO affective traits on bank risk taking. The third section describes the sample,
the variables, and the methodology; the fourth shows the results. We close with a discussion of
the main conclusions and implications of our study.
DO CEO CHARACTERISTICS INFLUENCE ORGANIZATIONAL RISK TAKING?
During the last decades, several researchers on strategic management have focused on
understanding what conditions executives’ strategic choices, which in turn are reflected in
organizational outcomes. “Upper-Echelons” research, developed by Hambrick and Mason
(1984), has addressed the relationship between idiosyncratic differences of managers and risk
taking in the strategic management literature. This perspective analyzes the relationships among
managers’ demographic and background characteristics, their strategic choices, and
8
organizational outcomes. Specifically, it emphasizes how individual experiences shape
executives’ cognition and values, which in turn affect their strategic choices (Hambrick and
Mason, 1984; Wiersema and Bantel, 1992). Studies, mostly based on firms in non financial
industries, have found significant effects on the adoption of risky strategic choices for observable
characteristics such as age, tenure, functional background and formal education.
Regarding age and tenure, research has followed Vroom and Pahl’s (1971) argument
that managerial age is negatively related to risk-taking. Results obtained have shown that age
and tenure are negatively associated to strategic choices that imply high levels of risk.
Finkelstein and Hambrick (1990) found that top management team tenure was positively related
to strategic persistence and conformity in strategy and performance. Wiersema and Bantel
(1992) found that top executive tenure in the organization and age were negatively related to
corporate strategic change. Similarly, research by Grimm and Smith (1991) showed that tenure
in the industry and age of top executives in the railroad industry were inversely related to the
degree that their firms changed strategies after deregulation. Finally, drawing on the typology of
Miles and Snow (1978), Thomas, Litschert and Ramaswamy (1991) also found that executives
of companies following defender strategies were older and had longer tenures in the company
and in the position than those following prospector ones.
“Upper-echelons” research has also related formal education and functional background
experience to strategic choices involving risk taking. Research by Thomas et al. (1991) shows
that executives of companies following prospector strategies had higher levels of educations
than managers of defender ones. Geletkanycz and Black (2001) found that functional
background diversity was positively related to commitment to the status quo. In summary, these
results show that the adoption of strategies involving different levels of risk taking is also a
consequence of differences in CEO characteristics. However, this line of research has
traditionally ignored one factor in which individual decisions are commonly rooted: affects.
The importance of affective factors in strategic decision-making has been recently
asserted by authors such as Daniels (1998; 1999; 2003), Hodgkinson and Sparrow (2002), and
Langley et al. (1995). Empirical research, although still scarce (Baldwin and Bengtsson, 2004;
Daniels, 1998; Elsbach and Barr, 1999; Kisfalvi and Pitcher, 2003; Mittal and Ross, 1998; Staw
and Barsade, 1993), has shown the influence of managers’ affects on their decisions and
reveals the need to consider this topic both theoretically and empirically.
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HYPOTHESES: IMPACT OF CEO AFFECTIVE TRAITS ON RISK TAKING
Amongst the literature on the relationship between cognition and emotions, several theories
have tackled the impact of emotions on decision making. One hypothesis common to these
approaches is that of affective congruency (Rusting, 1998), which predicts that individuals better
process information that is consistent with their affective state and their affective traits. The
Affect as Information Model (Clore and Parrot, 1991; Schwarz and Bless, 1991; Schwarz and
Clore, 1988) and the Network Theory of Affect (Bower, 1981, 1991) have addressed the
influence of affective valence on selective perception, selective attention, learning and recall,
and interpretations and associations.
On the basis of the effects of affects on cognition researchers have analyzed the
influence of affects on risk taking. In line with the congruency hypothesis, positive affect would
lead to better recalling, attending, perceiving and interpreting positive information (Bower, 1981;
Forgas, 1995). Therefore, when faced by any situation of a neutral, positive or even ambiguous
nature, its evaluation by the individual with this affective valence will be positive (Isen and
Shalker, 1982; Isen, Niedenthal and Cantor, 1992). In fact, research has shown that positive
affect generates positive expectations (Isen, Shalker, Clark and Karp, 1978; Isen and Shalker,
1982), leads to overestimate the probabilities of positive events (Wright and Bower, 1992;
Nygren et al, 1996) and decreases the estimated frequency of risks and other negative events
(Johnson and Tversky, 1983; Wright and Bower, 1992; Nygren et al., 1996). Following these
arguments, research has also shown that positive affects motivate decisions that are liable to be
more risky, at least when facing a hypothetical situation (Isen and Patrick, 1983) or the chances
of a meaningful loss are low for the decision maker (Arkes et al., 1988; Isen and Geva, 1987).
Negative affects have been shown to increase estimates of the frequency of risks and other non
desirable events, and to underestimate probabilities of positive events (Johnson and Tversky,
1983; Wright and Bower, 1992). As a consequence of these effects, Mano (1994) found that
negative affectivity of subjects was related to more risk averse decisions. We therefore expected
that positive affects of CEOs will be related to risk taking strategic choices, whereas negative
affects will favour less risky decisions.
H.1. Positive affective traits of CEOs will be positively related to risk taking.
H.2. Negative affective traits of CEOs will be negatively related to risk taking.
However, some empirical evidence has found opposite results (Isen and Patrick, 1983;
Isen and Geva, 1987; Dunegan et al., 1992; Mittal and Ross, 1998). The explanation provided
10
by Isen and Patrick (1983), Isen and Geva (1987), Isen, Nygren and Ashby (1988) and Isen
(2000) is based on the idea of affect maintenance. According to this idea, those subjects in a
positive affect are motivated to maintain this affective state, for which reason they would not be
ready to take risks. Similarly, it has been argued that, under negative affects, subjects would be
willing to take greater risks in the hope that potential gains would alter their negative affects
(Mittal and Ross, 1998). Under both arguments, Isen and colleagues consider two components
that can be distinguished when analyzing risk preferences, which go in opposite direction:
expected probability and utility. On the one hand, positive affect should increase the subjective
positive probability, so that the CEO should categorize and interpret ambiguous information as
an opportunity rather than as a threat (Dutton and Jackson, 1987; Jackson and Dutton, 1988;
Mittal and Ross, 1998). On the other hand, however the negative utility for a potential loss
should increase.
However, the relevance of this second component can be questioned in the case of
managers’ strategic choices (Mittal and Ross, 1998). It can be argued that the separation
between ownership and control (Berle and Means, 1932; Jensen and Meckling, 1976) assures
that potential losses for banks may not influence them personally, so that the motivational
argument to maintain a positive affect may not exist. Nevertheless, these arguments lead us to
treat the expected sign of the hypotheses with some caution.
METHOD
The hypotheses were tested in the Spanish banking industry. We sent out a survey in January
2004 to the CEOs of all Spanish banks and savings banks (70 banks and 46 savings banks),
which contained a set of questions related to affective traits and demographic characteristics.
The survey included a letter asking the CEOs to complete the questionnaire and promising
anonymity. The questionnaire was pilot tested using two savings banks. Fifty-six questionnaires
were returned, for a response rate of 48.3%, which we consider acceptable. From this sample,
five questionnaires were discarded because the financial information needed for the analysis
was not available. Two additional questionnaires were discarded since there was a CEO
succession in the period of analysis. The final sample was composed entirely of men; average
tenure was 25 years in the industry, and 10 as CEO. We examined and found no performance
differences between institutions included in the sample and those excluded (p>0.10). Nonresponse bias related to CEO traits was investigated with the widely used method suggested by
Armstrong and Overton (1977), which involves comparing early and al te respondents. This
method is based on the finding that subjects who respond less readily are more like non-
11
respondents. Two sets of late respondents were defined corresponding to those who responded
after receiving a follow-up call and the last 25 percent of the returned questionnaires. We found
no differences between early and late respondents.
To acquire financial data, we used the quarterly reports of banks and savings banks
provided by the Asociación Española de Banca (Spanish Banking Association) and the
Confederación Española de Cajas de Ahorros (Spanish Confederation of Savings Banks) for the
period March 2003 December 20057.
Independent variables
CEOs’ affective traits were measured by a widely used scale, the Positive and Negative Affect
Schedule (PANAS), as developed by Watson et al. (1988), and adapted into Spanish by Sandín
et al. (1999). The scale is composed of 20 items—10 items related to positive affects and the
other half related to negative ones—and can be used for different time frames, from the present
moment, which rates affective states, to a general option that rates affective traits; we employed
the latter.
Following Watson et al. (1988), we identified the categories of affect through a principal
component analysis. Results showed a two-component solution, via complementary criteria:
eigenvalue, the scree plot, and interpretability (Kim and Mueller, 1978). Component loadings for
the varimax rotation are shown in Table 1. For the first component (labeled negative affect), nine
negative affect items loaded; the remaining item—distressed—showed a loading of 0.292. The
second component comprised positive affect items—interested, excited, strong, enthusiastic,
proud, alert, inspired, determined, attentive, active—. Internal consistency reliabilities
(Cronbach’s alpha) were 0.853 for negative affective traits and 0.820 for positive affective traits,
above the 0.7 cut-off (Nunnally, 1978).
INSERT TABLE 1 ABOUT HERE
Control variables
We included control variables related to CEO background and financial institutions’
characteristics. The background characteristics included in the questionnaire were those
analyzed most frequently in “Upper-Echelons” research: tenure in the position, formal education,
7
Since all the CEOs of our sample had been in their position at least since 2002, we decided to begin our database in
2003. The survey measures demographic characteristics and affective traits -i.e., stable individual differences in the
tendencies to experience positive and negative affects (Watson and Clark, 1984; Watson, Clark and Tellegen, 1988;
Rusting, 1998)-, so that reverse causality was not expected to be a problem (Daniels, 1998; 1999). From our initial
sample two institutions changed their CEOs during the period of analysis, so that our final sample included 49 CEOs.
12
and functional background diversity. Executives’ tenure has been negatively related to the
willingness to take risk (Finkelstein and Hambrick, 1990). This relation has been evidenced in
several firm outcomes. Executive tenure has been positively related to the commitment to the
status quo (Hambrick, Geletkanycz and Fredrickson, 1993) and to performance conformity to the
central tendencies of the industry (Finkelstein and Hambrick, 1990), and negatively related to
strategic change (Grimm and Smith, 1991). Thomas et al. (1991) also found that executives of
companies following defender strategies had longer tenures than prospector ones. Hambrick
and Mason (1984) and Finkelstein and Hambrick (1996) have argued that education in
management is negatively related to risk-taking and extreme performance. Our variable
measured the level of formal education in management and economics, and ranged between 0 if
CEOs had no formal education in the field and 4 if they had a PhD. Finally, we considered
diversity in functional experience. Functional diversity can broaden managers’ belief structures
(Walsh, 1988) and give them a wider array of strategic approaches, so that they perceive lower
risks in alternatives that differ from central tendencies of the industry (Geletkanycz and Black,
2001). In order to measure functional experience diversity we followed Walsh (1988), who used
Herfindahl's index formulation Y =
∑( X
i
2
/ X ) , in which Xi is the number of years of work in a
given functional area i, and X is the number of years of work experience. Therefore, these
measures show functional experience concentration.
We used four characteristics of financial institutions as control variables. Size, measured
as the log of total assets, had also been used in the analyses of Esty (1997a); Anderson and
Fraser (2000), Konishi and Yasuda (2004) or Sullivan and Spong (2005) among others. The
predicted effect on risk was negative since larger banks are more likely to have more diversified
asset portfolios. Age, measured as the number of years since the financial institution was
founded, had also been related to less risk taking (Esty, 1997a). The third control variable
distinguished between banks and savings banks, by a dummy in which banks were rated as 1.
Previous research by Esty (1997a and b), Barth et al. (1995); Schrand and Unal (1998), and
García Marco and Robles Fernandez (2002) found that banks take more risks than savings
banks. Location is a dummy variable which takes the value of 1 when the financial institution
operates in a single province of Spain and 0 otherwise. This variable measures the possibilities
of the institution to geographically diversify the risk (Saurina, 1998).
Measures of bank risk
To asses the level of risk of banks and savings banks we used two measures. The first measure
is the variance in return on assets. Although a market based risk measure such as the variance
13
in stock returns is preferable, savings banks do not have traded equity. Nevertheless, it is a
common measure of risk in finance and strategy research, (e.g. Cool, Dierickx and Jemison,
1989; Sinkey and Nash, 1993; Esty, 1997a; Palmer and Wiseman, 1999). The measure
considered is the standard deviation of the return on assets over 12 quarters from 2003 to 2005.
Following Nash and Sinkey (1997), we also considered the min/max difference of the 12
quarters to compute each bank’s and savings bank’s variability in ROA.
Our second measure of risk is based on the “Z-score” developed by Hannan and
Hanweck (1988). This measure has also been frequently employed in research on bank risk
taking (Brewer, 1989; Boyd, Graham and Hewitt, 1993; Konishi and Yasuda, 2004). The z score
is a measure of bank stability and indicates the distance from insolvency (Beck and Laeven,
2006). The empirical form of the “Z score” is:
Z= [ROA+CAP]/s
Were ROA = return on assets, CAP = is the capitalization ratio (equity capital to asset
ratio), and s is the standard deviation of ROA. As stressed by Nash and Sinkey (1997), this
measure of risk is appealing since it includes a widely used measure of bank’s performance
(ROA), a common measure of risk in finance and strategy (variability of ROA), and the
capitalization ratio. Thus Z is the number of standard deviations below the mean by which ROA
must fall in order to eliminate equity.
From this score, and using the Chebychev’s inequality, Hannan and Hanweck (1988)
derive upper-bound probability of book-value insolvency:
PU p=1/2(Z) 2
This measure of probability of insolvency has been employed as a measure of risk by
Eisenbeis and Kwast (1991); Sinkey and Nash (1993); Garcia Marco and Robles Fernández
(2002) and Blasko and Sinkey (2006), among others. Following our first measure of risk, the
variability of ROA was computed both by the standard deviation and the min/max difference of
ROA over the 12 quarters from 2003 to 2005.
RESULTS
Table 2 presents the means, standard deviations, and correlations of the variables. To test the
hypotheses we used regression analyses. White’s (1980) test showed no problems of
heteroskedasticity in all models. Values for the Variance Inflation Factor (VIF) indicated no
problems of collinearity.
14
INSERT TABLE 2 ABOUT HERE
Results of the regression analyses taking as a dependent variable the probability of
insolvency variable are shown in Table 3. Coefficients in this model show a significant
association between CEOs’ affective traits and bank risk taking. Particularly, results show that
negative affective traits are negatively related to the probability of insolvency (p<0.05). These
results support hypothesis H2. In the case of positive affective traits, results show a no
significant coefficients, so that hypothesis H1 is not supported.
Regarding control variables, the positive and significant (p<0.05 and p<0.10) coefficients
for management education suggest that CEOs with formal education in management are more
likely to take more risky strategic choices for their institutions. The rest of the demographic
variables show non-significant coefficients. Results also show a negative and significant sign for
the size variable (p<0.05). This result is consistent with previous research (e.g., Esty, 1997a;
Anderson and Fraser, 2000; Konishi and Yasuda, 2004; Sullivan and Spong, 2005) suggesting
that larger financial institutions are more likely to have more diversified asset portfolios (Esty,
1997a). Finally, a financial institution’s age is negatively related to the probability of insolvency
(p<0.05). The sign of the coefficient is consistent with that obtained by Esty (1997a), indicating
that the younger the financial institution the more risk taking. The rest of the variables show nonsignificant coefficients.
INSERT TABLE 3 ABOUT HERE
Results of the regression analyses taking as a dependent variable the variability in ROA
are shown in Table 4. Results obtained are very similar to models of probability of insolvency.
The negative and significant (p<0.10) coefficients for negative affect suggests again that CEOs’
negative affects are related to less risk taking strategic choices, supporting H2. Coefficients for
the positive affect variable were non-significant (p>0.10) not supporting H1. Several control
variables are also found to be important determinants of risk taking. Specifically, results show a
positive and significant coefficient for the management education variable in both models
(p<0.05). In line with the analysis of probability of insolvency, models also show negative and
significant coefficients for size (p<0.01) and age (p<0.10).
INSERT TABLE 4 ABOUT HERE
15
DISCUSSION
The aim of our study was to extend the research on determinants of bank risk taking by
analyzing the impact of CEO affects. Much research has addressed the effect of financial
institutions’ specific characteristics on their levels of risk. “Upper echelons” research has
considered the influence of CEO background characteristics on the levels of risk taking of the
firms they manage in non financial industries. Psychological literature has shown that affective
valence influences on risk preference of people. Research by Mittal and Ross (1998) and
Dunegan et al. (1992) have tested these findings in the context of organizational decisions. Our
objective was to apply the arguments of this line of research to the banking literature. Our results
reveal that CEOs’ affective traits do influence their strategic choices, which are reflected in risk
taking.
In particular, we found that negative affective traits condition less risk taking strategies,
while positive affective traits have no effects on the levels of risk. These findings seem to
support arguments by researchers on cognition and emotion that associate negative affects to
increases in the estimates of the frequency of risks and other non desirable events (Johnson
and Tversky, 1983; Daniels, 1998; Wright and Bower, 1992). Specifically, our results suggest
that negative affects provide lower probabilities of positive events and overestimate probabilities
of negative ones. In this sense, our results are very similar to those obtained by Mano (1994) in
experiments based on students of a business school, who found that higher negative affectivity
led to higher willingness to pay for insurance and lower negative affectivity led to greater
willingness take risks. As stressed by Mano (1992; 1994) this result suggest negative affectivity
prompts CEOs to frame the situation in more negative light and to protect themselves from
further deterioration of their emotional state.
Our findings also show effects of CEOs’ background on banks risk taking. CEOs with
formal education in management and economics generated more risky strategic choices. This
result contradicts the suppositions of Finkelstein and Hambrick (1996) and Hambrick and Mason
(1984), who stressed that business school education teaches risk avoiding techniques. On the
contrary, our findings are similar to those of Grimm and Smith (1991), who found that firms that
changed their strategies were more likely to have MBAs among their Top Management Teams
than were those that did not change them. Our results can also be related to research that has
found positive associations between executive education levels and organization innovation
(e.g., Bantel and Jackson, 1989; Thomas et al., 1991; Wiersema and Bantel, 1992). Since
16
innovation involves taking risk, they support our findings that those CEOs with higher
management education tend to adopt more risk taking strategic choices.
Finally, our results also suggest that differences in size and age also affect risk taking.
These results are consistent with previous analyses suggesting that larger financial institutions
are more likely to have more diversified asset portfolios (e.g., Esty, 1997a; Anderson and Fraser,
2000; Konishi and Yasuda, 2004; Sullivan and Spong, 2005) and that younger financial
institutions take more risks (Esty, 1997a).
The main implication of our research is that bank risk taking can be also conditioned by
CEO affective traits and other background characteristics. In this sense our findings suggest
opportunities to examine connections between background characteristics and psychological
traits of CEOs and various other bank outcomes. It would also be interesting to extend the
analysis to Top Management Teams, in line with research by Bantel and Jackson (1989) that
has evidenced the relationship between the characteristics of Top Management Team members
and innovation adoption in banks.
We cannot conclude our paper without considering its main limitations. One of these is
sample size. However, we consider that the response rate is appropriate for research of this
nature. The sample analyzed represents 42 percent of the population, and we found no
response bias related to CEO and financial intermediaries characteristics. Second, our analyses
are based on one country in order to avoid the problem of allowing for different institutional
constraints and regulations, but this choice limits at the same time the possibilities of
generalizing our conclusions. We therefore think it would be especially interesting to extend the
analysis to other countries to obtain new evidence of the effects of CEO affective traits.
17
Table 1. Factor loadings of the affect scale
Factor 1
Negative affect
Factor 2
Positive affect
Interested
Distressed
Excited
Upset
Strong
Guilty
Scared
Hostile
Enthusiastic
Proud
Irritable
Alert
Ashamed
Inspired
Nervous
Determined
Attentive
Jittery
Active
Afraid
0.033
0.292
0.206
0.793
0.017
0.672
0.730
0.756
0.257
0.347
0.637
-0.084
0.514
-0.160
0.609
-0.072
-0.133
0.677
-0.122
0.769
0.499
0.486
0.609
0.010
0.700
-0.202
-0.160
0.144
0.624
0.534
0.173
0.737
-0.032
0.515
0.089
0.688
0.619
0.170
0.700
-0.092
Eigenvalues
Cumulative % of variance
4.660
23.302
4.340
45.004
18
Table 2. Descriptive statistics and bivariate correlations
Means
S.D.
1.
Negative affect
0
1.000
1.
2.
3.
4.
2.
Positive affect
0
1.000
0.000
3.
Tenure
9.796
8.456
-0.315*
4.
Management education
2.571
1.137
-0.095
0.035
0.346*
5.
Functional background concentration
0.472
0.686
-0.074
-0.105
0.153
0.199
5.
6.
7.
8.
9.
10.
11.
12.
-0.048
6.
Bank
0.469
0.504
0.052
-0.236
-0.089
-0.078
0.187
7.
Size
15.392
1.686
-0.164
-0.014
0.189
0.255
-0.336*
-0.189
8.
Age
94.898
49.312
-0.123
0.168
0.227
0.313*
-0.233
-0.151
9.
Location
0.530**
0.837
0.373
0.001
-0.234
0.200
0.028
0.113
0.194
10. Probability of insolvency with standard deviation ROA
0.000
0.001
-0.208
-0.087
0.022
0.086
0.410**
0.293*
11. Probability of insolvency with min/max ROA
0.003
0.007
-0.223
-0.094
0.026
0.087
0.466**
0.287*
-0.465** -0.428** 0.115
0.973**
12. Standard deviation ROA
0.001
0.003
-0.128
-0.073
0.051
0.077
0.215
0.248
-0.482** -0.394** 0.098
0.808**
0.665**
13. Min/max ROA
0.004
0.008
-0.138
-0.080
0.052
0.082
0.273
0.269
-0.517** -0.407** 0.102
0.848**
0.725**
. *p<0.10; **p<0.05.
19
0.185 0.056
-0.485** -0.461** 0.116
0.993**
Table 3. Regression analyses for probability of insolvency
Model 1
Model 2
Probability of insolvency
with standard deviation
ROA
Probability of insolvency
with min/max ROA
-0.306**
0.040
-0.309**
0.028
CEO background
Tenure
Management education
Functional background concentration
-0.050
0.268**
0.087
-0.065
0.230*
0.180
Financial intermediaries characteristics
Bank
Age
Size
Location
0.159
-0.339**
-0.387**
0.177
0.147
-0.287**
-0.351**
0.161
R2
F-ratio
0.4204
4.87***
0.4036
4.61***
Model 3
Model 4
Independent variables
CEO affective traits
Negative affect
Positive affect
N=49. Standardized coefficients . *p<0.10; **p<0.05; ***p<0.01.
Table 4. Regression analyses for ROA variation
Standard deviation ROA
Min/max ROA
Independent variables
CEO affective traits
Negative affect
Positive affect
-0.225*
0.019
-0.236*
0.016
CEO background
Tenure
Management education
Functional background concentration
0.036
0.296**
-0.149
0.028
0.294**
-0.096
0.144
-0.278*
-0.511***
0.186
0.151
-0.264*
-0.535***
0.188
0.2989
3.27***
0.3529
3.91***
Financial intermediaries characteristics
Bank
Age
Size
Location
R2
F-ratio
N=49. Standardized coefficients . *p<0.10; **p<0.05; ***p<0.01.
20
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