Como as mentiras do CEO podem aumentar as classificações das ações e enganar até mesmo analistas financeiros respeitados
O colapso multibilionário da FTX – a importante bolsa de criptomoedas cujo fundador aguarda agora julgamento por acusações de fraude – serve como um duro lembrete dos perigos do engano no mundo financeiro.
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)
Domínio público
O colapso multibilionário da FTX – a importante bolsa de criptomoedas cujo fundador aguarda agora julgamento por acusações de fraude – serve como um duro lembrete dos perigos do engano no mundo financeiro.
As mentiras do fundador da FTX, Sam Bankman-Fried, remontam ao início da empresa , dizem os promotores . Alega-se que ele mentiu tanto para clientes como para investidores, como parte daquilo que o procurador dos EUA, Damian Williams, chamou de "uma das maiores fraudes financeiras da história americana".
Como tantas pessoas foram aparentemente enganadas?
Um novo estudo publicado no Strategic Management Journal lança alguma luz sobre o assunto. Nele, os meus colegas e eu descobrimos que mesmo os analistas financeiros profissionais caem nas mentiras dos CEO – e que os analistas mais respeitados podem ser os mais crédulos.
Os analistas financeiros fornecem conselhos especializados para ajudar empresas e investidores a ganhar dinheiro. Eles preveem quanto uma empresa ganhará e sugerem se deve comprar ou vender suas ações. Ao orientar o dinheiro para bons investimentos, ajudam não apenas as empresas individuais, mas toda a economia a crescer.
Mas embora os analistas financeiros sejam pagos pelos seus conselhos, eles não são oráculos. Como professor de administração , me perguntei com que frequência eles são enganados por executivos mentirosos – então, meus colegas e eu usamos o aprendizado de máquina para descobrir. Desenvolvemos um algoritmo, treinado em transcrições de teleconferências de lucros do S&P 1500 de 2008 a 2016, que pode detectar fraudes com segurança 84% das vezes. Especificamente, o algoritmo identifica padrões linguísticos distintos que ocorrem quando um indivíduo mente.
Nossos resultados foram impressionantes. Descobrimos que os analistas eram muito mais propensos a dar recomendações de “compra” ou “compra forte” depois de ouvirem CEOs enganadores – em quase 28 pontos percentuais, em média – do que os seus homólogos mais honestos.
Descobrimos também que analistas altamente conceituados caíram nas mentiras dos CEO com mais frequência do que os seus homólogos menos conhecidos. Na verdade, os analistas considerados “estrelas” pela editora comercial Institutional Investor tinham 5,3 pontos percentuais mais probabilidade de promover CEOs habitualmente desonestos do que os seus homólogos menos célebres.
Embora tenhamos aplicado esta tecnologia para obter informações sobre esta área das finanças para um estudo académico, a sua utilização mais ampla levanta uma série de questões éticas desafiantes em torno da utilização da IA para medir construções psicológicas.
Preconceituoso em acreditar
Parece contra-intuitivo: por que é que os profissionais de aconselhamento financeiro se apaixonariam consistentemente por executivos mentirosos? E por que os conselheiros mais respeitáveis parecem ter os piores resultados?
Estas descobertas reflectem a tendência humana natural de assumir que os outros estão a ser honestos – o que é conhecido como “ preconceito da verdade ”. Graças a esse hábito mental, os analistas são tão suscetíveis a mentiras quanto qualquer outra pessoa.
Além do mais, descobrimos que o status elevado promove um viés de verdade mais forte. Primeiro, os analistas “estrelas” muitas vezes ganham uma sensação de excesso de confiança e de direito à medida que aumentam de prestígio. Eles começam a acreditar que têm menos probabilidade de serem enganados, o que os leva a considerar os CEOs pelo valor nominal. Em segundo lugar, estes analistas tendem a ter relações mais próximas com os CEO, o que estudos mostram que pode aumentar o enviesamento da verdade . Isso os torna ainda mais propensos ao engano.
Dada esta vulnerabilidade, as empresas podem querer reavaliar a credibilidade das designações “all-star”. A nossa investigação também sublinha a importância da responsabilização na governação e a necessidade de sistemas institucionais fortes para combater os preconceitos individuais.
Um ‘detector de mentiras’ de IA?
A ferramenta que desenvolvemos para este estudo pode ter aplicações muito além do mundo dos negócios. Validamos o algoritmo usando transcrições fraudulentas, artigos retratados em revistas médicas e vídeos enganosos do YouTube. Poderia ser facilmente implantado em diferentes contextos.
É importante observar que a ferramenta não mede diretamente o engano; identifica padrões de linguagem associados à mentira . Isto significa que, embora seja altamente preciso, é suscetível tanto a falsos positivos como a negativos – e falsas alegações de desonestidade, em particular, podem ter consequências devastadoras.
Além do mais, ferramentas como esta lutam para distinguir “mentiras inocentes” socialmente benéficas – que promovem um sentido de comunidade e bem-estar emocional – de mentiras mais sérias. Sinalizar indiscriminadamente todos os enganos poderia perturbar dinâmicas sociais complexas, levando a consequências indesejadas.
Essas questões precisariam ser abordadas antes que esse tipo de tecnologia fosse amplamente adotado. Mas esse futuro está mais próximo do que muitos imaginam: empresas em áreas como investimentos, segurança e seguros já estão começando a utilizá-lo .
Grandes questões permanecem
A utilização generalizada da IA para detectar mentiras teria profundas implicações sociais – sobretudo, ao tornar mais difícil para os poderosos mentirem sem consequências.
Isso pode soar como uma coisa inequivocamente boa. Mas embora a tecnologia ofereça vantagens inegáveis, como a detecção precoce de ameaças ou fraudes, também poderá inaugurar uma perigosa cultura de transparência . Num mundo assim, os pensamentos e as emoções poderiam ficar sujeitos a medição e julgamento, corroendo o santuário da privacidade mental.
Este estudo também levanta questões éticas sobre o uso da IA para medir características psicológicas, particularmente no que diz respeito à privacidade e ao consentimento. Ao contrário da investigação tradicional sobre fraude, que depende de seres humanos que consentem em ser estudados, este modelo de IA opera secretamente, detectando padrões linguísticos diferenciados sem o conhecimento do falante.
As implicações são surpreendentes. Por exemplo, neste estudo, desenvolvemos um segundo modelo de aprendizado de máquina para avaliar o nível de suspeita no tom de um locutor. Imagine um mundo onde os cientistas sociais possam criar ferramentas para avaliar qualquer faceta da sua psicologia, aplicando-as sem o seu consentimento. Não é muito atraente, não é?
À medida que entramos numa nova era da IA, as ferramentas psicométricas avançadas oferecem tanto promessas como perigos. Essas tecnologias poderiam revolucionar os negócios, fornecendo insights sem precedentes sobre a psicologia humana. Podem também violar os direitos das pessoas e desestabilizar a sociedade de formas surpreendentes e perturbadoras. As decisões que tomamos hoje – sobre ética, supervisão e uso responsável – definirão o rumo nos próximos anos.
Mais informações: Steven J. Hyde et al, As teias emaranhadas que tecemos: Examinando os efeitos do engano do CEO nas recomendações dos analistas, Strategic Management Journal (2023). DOI: 10.1002/smj.3546
Informações da revista: Revista Gestão Estratégica