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1051 days ago

Alphabet was the most commonly held stock by ESG funds

Funds that invest sustainably usually like tech companies, and Alphabet particularly. The information technology sector of the S&P 500 accounts for the largest allocation in most funds, with holdings going between 3.5.% and 37%. Out of the 20 largest funds that invest in ESG studied by MSCI, 12 held Alphabet, making it the most widely held stock with an average weight of 1.9%.  The next stocks to be most commonly held were Ecolab, Thermo Fisher Scientific and Microsoft. 

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Alphabet was the most commonly held stock by ESG funds

1052 days ago

Hedge Funds see biggest shorting of tech stocks in 5 years

Information technology was net sold for a 4th straight week and saw the largest net selling in more than 5 years, driven by short sales. Despite large short positions as seen with Burry earlier today, the FAAMG sector has moved higher in the past 3 days amid a reassessment of reflation concerns with the transitory camp currently winning. When will this one-sided short position push max levered funds over the edge, and lead to a squeeze higher in the tech sector.

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1052 days ago

Gaming could present a buy the dip opportunity

Gaming subsector is down about 24%. DKNG and PENN are 40% off their recent highs (must like ARKK). Online gaming multiples have contacted about 50% from peak levels. Online gaming companies still command a median 5.4X EV/Sales ratio with DKNG seeing a 2.5X premium on this.

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1053 days ago

Who are the top 50 social media influencers?

An infographic of the top 50 influencers on Facebook and Instagram combined - Ronaldo is No.1!

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Who are the top 50 social media influencers?

1053 days ago

Despite some false positive flaws, the US face recognition market could reach $3B by 2027.

The Chinese are seeing even more traction on this front being market leaders and having much more lax legislation around the use of face ID tech for public surveillance. San Francisco has banned the use, London has not. Current flaws are with regards to skin color false positives. The reason for this is not necessarily a bias against non-whites, but the nature of machine learning. If the AI sees more white features, it will produce white feature biased results (because that's what it ''knows''). Engineers will need to feed machines more non-white data to solve this problem which could take some time.

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