Measuring executive personality using machine-learning algorithms: A new approach and audit fee-based validation tests

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11230%2F20%3A10410967" target="_blank" >RIV/00216208:11230/20:10410967 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=UPd9kWfSEs" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=UPd9kWfSEs</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/jbfa.12406" target="_blank" >10.1111/jbfa.12406</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Measuring executive personality using machine-learning algorithms: A new approach and audit fee-based validation tests

Popis výsledku v původním jazyce

We present a novel approach for measuring executive personality traits. Relying on recent developments in machine learning and artificial intelligence, we utilize the IBM Watson Personality Insights service to measure executive personalities based on CEOs&apos; and CFOs&apos; responses to questions raised by analysts during conference calls. We obtain the Big Five personality traits - openness, conscientiousness, extraversion, agreeableness and neuroticism - based on which we estimate risk tolerance. To validate these traits, we first demonstrate that our risk-tolerance measure varies with existing inherent and behavioural-based measures (gender, age, sensitivity of executive compensation to stock return volatility, and executive unexercised-vested options) in predictable ways. Second, we show that variation in firm-year level personality trait measures, including risk tolerance, is largely explained by manager characteristics, as opposed to firm characteristics and firm performance. Finally, we find that executive inherent risk tolerance helps explain the positive relationship between client risk and audit fees documented in the prior literature. Specifically, the effect of CEO risk-tolerance - as an innate personality trait - on audit fees is incremental to the effect of increased risk appetite from equity risk-taking incentives (Vega). Measuring executive personality using machine-learning algorithms will thus allow researchers to pursue studies that were previously difficult to conduct.

Název v anglickém jazyce

Measuring executive personality using machine-learning algorithms: A new approach and audit fee-based validation tests

Popis výsledku anglicky

We present a novel approach for measuring executive personality traits. Relying on recent developments in machine learning and artificial intelligence, we utilize the IBM Watson Personality Insights service to measure executive personalities based on CEOs&apos; and CFOs&apos; responses to questions raised by analysts during conference calls. We obtain the Big Five personality traits - openness, conscientiousness, extraversion, agreeableness and neuroticism - based on which we estimate risk tolerance. To validate these traits, we first demonstrate that our risk-tolerance measure varies with existing inherent and behavioural-based measures (gender, age, sensitivity of executive compensation to stock return volatility, and executive unexercised-vested options) in predictable ways. Second, we show that variation in firm-year level personality trait measures, including risk tolerance, is largely explained by manager characteristics, as opposed to firm characteristics and firm performance. Finally, we find that executive inherent risk tolerance helps explain the positive relationship between client risk and audit fees documented in the prior literature. Specifically, the effect of CEO risk-tolerance - as an innate personality trait - on audit fees is incremental to the effect of increased risk appetite from equity risk-taking incentives (Vega). Measuring executive personality using machine-learning algorithms will thus allow researchers to pursue studies that were previously difficult to conduct.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50201 - Economic Theory

Projekt

<a href="/cs/project/GA15-13040S" target="_blank" >GA15-13040S: Kvalita účetních údajů, ziskovost obchodování manažerů a informační obsah ceny akcií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Business Finance and Accounting

ISSN

0306-686X

e-ISSN

—

Svazek periodika

47

Číslo periodika v rámci svazku

3-4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

26

Strana od-do

519-544

Kód UT WoS článku

000530550000008

EID výsledku v databázi Scopus

2-s2.0-85073987772