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Nota fiscal e software

Grupo indiano é acusado de fraude contábil

Notícia do Estadão

As ações do conglomerado indiano Adani Enterprises, que pertence ao homem mais rico da Ásia, Gautam Adani, registraram queda de 15% nesta sexta-feira, 27, o que provocou a suspensão da cotação na Bolsa de Mumbai, poucos dias depois do grupo ser acusado de fraude por uma empresa de investimentos norte-americana. (...)

A empresa Hindenburg Research alegou esta semana que o grupo Adani teria desenvolvido um “sistema de fraude na contabilidade durante décadas” e manipulado os lucros “para manter a aparência de boa saúde financeira e de solvência” de suas filiais cotadas em Bolsa. O conglomerado rebateu as acusações, que descreveu como “mal-intencionadas”.

Em termos absolutos, a queda nas ações representam 50 bilhões de dólares no valor de mercado do grupo. A reação da equipe jurídica do grupo foi afirmar que está pensando em uma ação legal contra a Hindenburg. O momento foi crítico, já que uma das empresas do grupo estava iniciando um processo de venda de ações. 

(Foto com o controlador do Grupo)

Rir é o melhor remédio

 

e isto também

IA escrevendo uma letra de Taylor Swift sobre volatilidade

 

Eis o resultado

Rir é o melhor remédio

 No fim de ano é costume "deixar" trabalho para o próximo ano. E quando chega janeiro ... 

Raça, filiação e gênero: experimento com Twitter

This paper assesses the results of an experiment designed to identify discrimination in users’ following behavior on Twitter. Specifically, we created fictitious bot accounts that resembled humans and claimed to be PhD students in economics. The accounts differed in three characteristics: gender (male or female), race (Black or White), and university affiliation (top- or lower-ranked). The bot accounts randomly followed Twitter users who form part of the #EconTwitter academic community. We measured how many follow-backs each account obtained after a given period. Twitter users from this community were 12% more likely to follow accounts of White students compared to those of Black students; 21% more likely to follow accounts of students from top-ranked, prestigious universities compared to accounts of lower-ranked institutions; and 25% more likely to follow female compared to male students. The racial gap persisted even among students from top-ranked institutions, suggesting that Twitter users racially discriminate even in the presence of a signal that could be interpreted as indicative of high academic potential. Notably, we find that Black male students from top-ranked universities receive no more follow-backs than White male students from relatively lower-ranked institutions.

Fonte: aqui

A cor das palavras

Our paper relies on stock price reactions to colour words, in order to provide new dictionaries of positive and negative words in a finance context. We extend the machine learning algorithm of Taddy (2013), adding a cross-validation layer to avoid over-fitting. In head-to-head comparisons, our dictionaries outperform the standard bag-of-words approach (Loughran and McDonald, 2011) when predicting stock price movements out-of-sample. By comparing their composition, word-by-word, our method refines and expands the sentiment dictionaries in the literature. The breadth of our dictionaries and their ability to disambiguate words using bigrams both help to colour finance discourse better.

Aqui

O primeiro parágrafo é bem esclarecedor:

Since Tetlock (2007), the literature in Finance and Accounting studying different types of textual data has flourished.1 The current state of the art to measure sentiment is to use a “bag-of-words” approach, counting words in dictionaries that are specialized to Finance and Accounting jargon, namely those developed by Loughran and McDonald (2011) (LM dictionaries). This approach has been criticized as potentially having low power in comparison to more sophisticated machine learning techniques (Gentzkow et al., 2019). Our paper contributes to this debate by constructing new dictionaries using techniques from the natural language processing literature (NLP) in Computer Science, explicitly comparing their composition and predictive power relative to the LM dictionaries.

Foto: Sharon Pittaway