Blockchain é uma mentira

 With the value of Bitcoin having fallen by around 70% since its peak late last year, the mother of all bubbles has now gone bust. More generally, cryptocurrencies have entered a not-so-cryptic apocalypse. The value of leading coins such as Ether, EOS, Litecoin, and XRP have all fallen by over 80%, thousands of other digital currencies have plummeted by 90-99%, and the rest have been exposed as outright frauds. No one should be surprised by this: four out of five initial coin offerings (ICOs) were scams to begin with.1

INTO THE BREXIT LABYRINTH

Oct 12, 2018 JACEK ROSTOWSKIshows that there is no viable path to a politically and economically acceptable withdrawal from the EU.

20Add to Bookmarks

Faced with the public spectacle of a market bloodbath, boosters have fled to the last refuge of the crypto scoundrel: a defense of “blockchain,” the distributed-ledger software underpinning all cryptocurrencies. Blockchain has been heralded as a potential panacea for everything from poverty and famine to cancer. In fact, it is the most overhyped – and least useful – technology in human history.

Fonte: aqui

Argumentos ruins

Nestes tempos onde a racionalidade não funciona, que tal lembrar algo sobre argumentos ruins:

Adaptado daqui

Rir é o melhor remédio

Efeito do Uber no trânsito

A entrada dos serviços de compartilhamento em uma cidade deveria reduzir o trafego e o número de mortes. Entretanto, uma pesquisa (via aqui) realizada em uma grande cidade mostra que ocorreu o oposto. Com a entrada do Uber, o número de veículos aumentou, assim como o congestionamento.

Isto parece contra a intuição: se você bebe, em lugar de usar seu automóvel, você usa o Uber. Menos perigo nas ruas e menores as chances de um acidente. Entretanto, o que ocorreu, segundo os dados coletados na pesquisa, foi o contrário.

A explicação parece ser a seguinte: algumas pessoas que usavam o transporte público passaram a usar o Uber. Quem tinha automóvel, continua com seu veículo.

Rir é o melhor remédio

Preço é igual a qualidade? Uma fotógrafa profissional resolveu contratar o serviço de um editor profissional. O mercado é concorrido e ela achou quem cobrasse de 0,25 US$ a 10 dólares. Ela testou três serviços: US $ 0,25, US $ 5 e US $ 10. E pediu “Eu gostaria que você tornasse esta imagem muito vibrante, quente. Limpe a pele, olhos brilhantes e faça o cabelo dela parecer mais vermelho (...) confio em seu julgamento em fazer essa imagem parecer linda ”. Eis o resultado:

Na esquerda, a foto original. Na direita, o trabalho do profissional. Qual foi o trabalho que cobrou US$0,25?

Finanças Corporativas Comportamentais

Resumo:

Behavioral Corporate Finance provides new and testable explanations for long-standing corporate-finance puzzles by applying insights from psychology to the behavior of investors, managers, and third parties (e. g., analysts or bankers). This chapter gives an overview of the three leading streams of research and quantifies publication output and trends in the field. It emphasizes how Behavioral Corporate Finance has contributed to the broader field of Behavioral Economics. One contribution arises from the identification of biased behavior (also) in successful professionals, such as CEOs, entrepreneurs, or analysts. This evidence constitutes a significant departure from the prior focus on individual investors and consumers, where biases could be interpreted as `low ability,' and it implies much broader applicability and implications of behavioral biases. A related contribution is the emphasis on individual heterogeneity, i. e., the careful consideration of the type of biases that are plausible for which type of individual and situation


Behavioral Corporate FinanceUlrike MalmendierNBER Working Paper No. 25162

Algoritmos de machine learning fracassam no mercado

Machine learning is enabling investors to tap huge data sets such as social media postings in ways that no mere human could. Yet, despite the enormous potential, its record remains mixed. The Eurekahedge AI Hedge Fund Index, which tracks the returns of 13 hedge funds that use machine learning, has gained only 7 percent a year for the past five years, while the S&P 500 returned 13 percent annually. This year the Eurekahedge benchmark dropped 5 percent through September.

One of the potential pitfalls for machine learning strategies is the extremely low signal-to-noise ratio in financial markets, says Marcos López de Prado, who joined AQR Capital Management as head of machine learning in September and is the author of the 2018 book Advances in Financial Machine Learning. “Machine learning algorithms will always identify a pattern, even if there is none,” he says. In other words, the algorithms can view flukes as patterns and hence are likely to identify false strategies. “It takes a deep knowledge of the markets to apply machine learning successfully to financial series,” López de Prado says.

Nigol Koulajian echoes that view. The founder and chief investment officer at Quest Partners, a New York-based systematic macro hedge fund that manages $1.7 billion, says that quants coming out of finance programs and high-tech companies often expect to create optimizations at a much higher level of precision than is warranted in finance. “They’re coming with a mindset that we’re going to conquer the world with big data,” Koulajian says. In finance, though, the market regime is not static, and markets aren’t closed systems like a chess game. “You can have one little pin drop that can basically make you lose over 20 years of returns,” he says.

[...]

Fonte: aqui