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python src/main.py

À prova de balas

São setores perenes, com produtos ou serviços tão essenciais que, independentemente do cenário, recuperam mais rápido, ou sofrem menos durante uma crise.

  • Louise Barsi
  • bancos; (ex: Bradesco (BBDC4) e Banco do Brasil (BBAS3))
  • seguros; (ex: Sul América (SULA11) e BB Seguridade (BBSE3))
  • telecomunicações; (ex: Telefônica (VIVT4) e Telebrás (TELB4))
  • energia; (ex: Copel (CPLE3) e CPFL energia (CPFE3))
  • saneamento. (ex: sanepar (SAPR3) e sabesp (SBSP3))

À prova de Barsi

"É só olhar a montanha russa, como sofreram as empresas de commodities, construção civil e aviação, que estão no olho do furacão nesta crise na pandemia"

  • Louise Barsi.
  • varejo; (ex: Lojas Americanas (LAME4) e Via Varejo (VVAR3))
  • frigoríficos; (ex: Brasil Foods (BRFS3) e JBS (JBSS3))
  • aviação e turismo; (ex: Gol (GOLL4) e CVC (CVCB3))
  • construção civil. (ex: Cyrela (CYRE3) e Direcional Engenharia (DIRR3))

We simulated both wallets and here is the outcome: (spoiler: Barsi is right!) Simulating Barsi's wallets

Other infos:

S&P500 correlation matrix

notes:
Often enough competing companies will showcase a negative correlation around the time of an important announcement. That said, the previous matrix represents ~20 years of data, so we are not going to see this very narrow-timed correlations.
Also, there are no negative numbers closer to -1 than to 0 because we are biased by S&P 500, meaning that given two stocks with that correlation, only one would be in the S&P 500, because the other would have had a dip in price and would be removed from S&P 500 long before getting close to -1.

Tesla (NASDAQ: TSLA) Stock price (US Dollars) 2010-2020

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